trials indian association for statistics in clinical

Indian Association for Statistics in

Clinical Trials (IASCT)

Welcome You

to

“Analysis and Reporting of Adverse Events”

9th July 2010,Bengaluru

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Latika Sharma, MD

Cognizant Technology Solutions

Head, PV Practice - LS BPO

09 July 2010

7/5/2010 Footer Text 1

Overview of Adverse Events and Reactions in

Drug Safety

A Regulatory perspective

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Agenda


Key Concepts

AE/ADR’s

Importance

Classification of ADR

Causality assessment

ADR Reporting

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Back to Basics

Definitions and Concepts

7/5/2010 Footer Text 3 Indian

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Key Concepts


Pharmacovigilance

AE/ADR’s

Pharmacovigilance Datum

Adverse Event (AE)

Adverse Drug Experience (ADE)

Adverse Drug Reaction (ADR)

Side effect

Serious Adverse Event/ Reaction (SAE/SAR)

Treatment Emergent Adverse Event (TEAE)

Unexpected Adverse Drug Reaction/Experience

Suspected Unexpected Serious Adverse Reaction (SUSAR)

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Pharmacovigilance


“The science and activities relating to the detection, assessment,

understanding and prevention of adverse effects or any other drug

related problem”- (WHO 2002)

• Starts from Discovery and continues through out the product Life Cycle

• PV Datum includes: Exposure in pregnancy, lactation

Lack of Efficacy

Suspected Transmission of an infectious agent by a medicinal product

Overdose (Accidental/ Intentional)

Abuse/ misuse

Off label use

Medication error

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Monitoring Drug Safety

Why? Who?


Patient Safety (US rates)

3-7% of hospitalizations

Mortality rates 0.5%

10-20% of ADR’s in hospitalization are

severe

Financial impact

Challenge to diagnose and confirm

Clinical and Epidemiological reasons

Risk-Benefit Analyses

Regulatory Obligations

Drug

Safety

Reg Body

Patient

Sponsor

IRB/EC Investigator

DSMB

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Adverse event (AE): Definition


“Any untoward medical occurrence in a patient or clinical

investigation subject administered a pharmaceutical product and

which does not necessarily have to have a causal relationship with

this treatment ”..(ICH E2A/E2D)

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Adverse Event (AE): Concept


What is an AE? any unfavourable and unintended

sign

an abnormal laboratory finding

symptom

disease

temporally associated with the

use of a medicinal product

whether or not considered

related to the medicinal product

What is not an AE? Medical or surgical procedure (the

indication leading to the

procedure is an AE)

Pre--existing diseases and

condition, which do not change

Death (death is an outcome, the

underlying cause is an event)

The disease being studied (lack of

efficacy)

Any other Protocol Specific

Exemptions

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AE- Scope


Global Definition

Unfavorable event must occur after exposure to the medicinal product

Broader Concept

Unfavorable event associated with any protocol imposed intervention

including events prior to IMP exposure

Also includes “any change from the baseline”

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Adverse Drug Experience (ADE)


“Any AE associated with the use of a drug/biological product in

humans whether or not considered product related, including the

following…AE {21 CFR 310.305(b), 314.80 (a)}

associated with use in professional practice

Occurring from overdose (accidental or intentional)

Occurring from abuse of the product

Occurring from withdrawal of the product

Any failure of expected pharmacological action

Associated:

Post Marketing- occurs during/after exposure to product regardless of causal attribution

IND Regulations (21 CFR 312.32): reasonable possibility that the event was caused by the

product, based on facts, evidence, clinical judgment

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Adverse Drug Reaction (ADR)

Pre-Approval Phase Post- Approval Phase


“All noxious and unintended

responses to a medicinal product

related to any dose should be

considered adverse drug reactions.”

ICH E2A

A response to a drug which is

noxious and unintended and which

occurs at doses normally used

in man for prophylaxis, diagnosis,

or therapy of disease or for

modification of physiological

function

ICH E2A

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ADR: FDA Definition


“An undesirable effect, reasonably associated with the use of the

drug, that may occur as a part of the pharmacological action of

the drug or may be unpredictable in its occurrence” 21 CFR201.57 (g) Post Market Labeling Regulation

For Clinical trials, we report all AE/SAE judged by either the investigator or the

sponsor to have a reasonable causal relationship with the drug.

Evidence/ plausible arguments

Relationship cannot be ruled out

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Side effect


“Any unintended effect of a drug occurring at normal doses, which is

related to the pharmacological properties of the drug”

May be adverse or not

May have therapeutic implications- Minoxidil and hair growth

Not used in regulatory parlance

E.g. bradycardia in beta blockers, antibiotic associated diarrhea

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Serious Adverse Event/Reaction (SAE/SAR)


“Any untoward medical occurrence that at any dose: Results in death

Is life threatening

NOTE: The term "life-threatening" in the definition of "serious" refers to

an event in which the patient was at risk of death at the time of the event;

it does not refer to an event which hypothetically might have caused death

if it were more severe.

Requires in-patient hospitalization or prolongation of existing hospitalization

Results in persistent or significant disability/capacity or

Is a congenital anomaly/ birth defect”

ICH E2A

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Important Medical Event (IME)


“Important medical events that may not be immediately life-threatening or

result in death or hospitalisation but may jeopardise the patient or may

require intervention to prevent one of the other outcomes listed in the

definition above. These should also usually be considered serious.” ICH E2A

intensive treatment in an emergency room or at home for allergic bronchospasm;

blood dyscrasias or convulsions that do not result in hospitalisation;

or development of drug dependency or drug abuse.

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Treatment Emergent Event {TE(A)E}


“An event that emerges during treatment having been absent pre-

treatment, or worsens relative to the pre-treatment state”

ICH E29

Helpful to capture treatment related AE’s in the background of substantial noise of

signs and symptoms

Most appropriate “capture definition” for safety analyses

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Unexpected Adverse Drug Reaction/Experience


An "unexpected" adverse reaction is one, the nature or severity of

which is not consistent with information in the relevant source

document(s)”. ..ICHE2A

Purpose of Expectedness: Make aware of new, important information on

serious reactions; usually qualify for expedited reporting

involve events previously unobserved or undocumented, in the

reference safety information (IB/GIP/SPC/USPI)

not defined on the basis of what might be anticipated from the

pharmacological properties of a medicinal product

Labeled (Local USPI/SPC); Listedness (CCDS/CCSI)

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Suspected Unexpected Serious Adverse

Reaction-SUSAR


“A SUSAR is a serious adverse drug reaction, the nature or severity of

which is not consistent with the applicable product information (e.g.

IB for unauthorized and SmPC for authorized products)”

EU Directive and EC Guidance

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Seriousness vs. Severity

Seriousness Severity


Based on outcome or action criteria

associated with events that pose a

threat to life or functioning

Based on regulatory reporting

obligations

E.g. fatal MI

Describes the intensity of the event

(mild, moderate, severe)

Based on medico-clinical

assessment

Mild diarrhea

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Classification of ADR’s

Severity

Pharmacological

Reaction Time


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7/5/2010

SEVERITY CLASSIFICATION (Tangera et al 1991)

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Severity of

ADR

Mild

Moderate

Severe

Slightly

bothersome

Relieved by

Symptomatic

treatment

Interferes with

activities

May require specific

treatment

Modification of drug

Prevents regular activities

life-threatening

Specific treatment/

Hospitalization

Discontinuation of

suspect drug

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7/5/2010

PHARMACOLOGICAL CLASSIFICATION

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A

B

C

D

Augmented, Exaggerated normal pharmacological

reaction, dose dependent, e.g. bradycardia in beta

blockers

Bizarre, Not Normal Response, unpredictable

SJ Syndrome in Penicillin, Sulphonamides

Chronic, effects of long-term drug therapy e.g.

Benzodiazepine dependence and Analgesic

nephropathy, well known and can be anticipated

Delayed in onset, carcinogenicity, mutagenicity

Type E (end of treatment)-narcotic withdrawal

Type F (failure of treatment)- OC failure due to enzyme inducer

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7/5/2010

REACTION TIME CLASSIFICATION (Hoigne et al 1990)

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Reaction

Time

Acute

Sub-Acute Latent

< 60 mins

4.5% of Reactions

< 24 hrs.

86.5% 1 Day-Several weeks,

3.5%

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Causality Assessment

Why?

How?


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Why Assess Causality?


Enable treating physician to treat the patient and decide on

course of suspect drug.

Regulatory reporting- Update RSI, decide on product

withdrawals, assess Risk benefit analyses

Sponsor perspective: protocol for future trials

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How to Assess Causality?

7/5/2010 30

Global introspection

WHO Causality

Algorithms

Bayesian approach

Causality

Related

Reasonable

possibility that

the drug caused

the AE

Not Related

No causal

relationship

Obvious

alternate cause

exists

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ADR Reporting

Why?

Reporting requirements


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Why Report ADRs?


Patient Safety

Protection of public health

Legal

Ethics

Updating RSI

Information to prescribers and users

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ADR Reporting


*Expedited

7/15 days for Clinical trials

14 days for India

15 days for spontaneous

Periodic

PM- PSUR/NDA PR

ASR/IND PR

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THANK YOU

Correspondence- [email protected]


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Priya Iyer

Oncology Biometrics and Data Management

Novartis

Hyderabad

CTC grades and Coding of AE

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Topics for discussion

Background and Evolution

Rationale and Purpose

Grades and their Interpretation

Examples and Applications

CTC grades - LABs and ECG

Coding of AE’s – MedDRA

SMQ’s

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Take it easy!

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Why a grading system ?Background and Evolution

Recognition and reporting of the adverse effects (AEs) of treatments is very essential in any clinical trial

More than hundreds of distinct AEs ranging in severity from minor to life threatening injuries or death.

In some therapeutic areas, need for more specificity than mild/moderate/severe

The National Cancer Institute (NCI) stresses the requirement of having an accurate and specific documentation and reporting for adverse events for the following reasons

• Ensuring subject safety

• Regulatory perspective

• Facilitates accurate analysis of effects from investigational cancer interventions

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Multiple grading systems have been developed for grading the AE‟s for cancer treatment over the past 30 years.

• The World Health Organization (1979)

- 24 categories , for grading of Acute effects of Chemotherapy

• Common Toxicity Criteria (v 1.0) (1983)

- Developed by Cancer Therapy Evaluation Program (CTEP) of the NCI

- 18 categories, 49 AE‟s categorized upon 4 grades.

• Radiation Therapy Oncology Group/ European Organization for Research and Treatment of Cancer (RTOG/EORTC) (1984)

- 14 categories for grading acute effects of RT and 16 categories for late effects of RT.

Why a grading system ?Background and Evolution

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It is important to revise these guidelines on a timely basis.

The Common Terminology Criteria for Adverse Events (CTCAE v 3.0)

• Currently used in most of studies.

• Introduced in 2002 as the first uniform and comprehensive dictionary of AE grading criteria available for use by all modalities (chemotherapy, radiation therapy and surgery).

• 370 categories, all organs ,focusing on acute and late effects.

• Current version CTCAE v 4.0, 790 AE terms, with an option of „Other, Specify‟, organized based on MedDRA SOC‟s as compared to CATEGORIES.

Common Terminology Criteria for Adverse Events

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CTCAE serves the following purposes:

• To standardize AE reporting within the NCI oncology research community, across groups and modalities

• To facilitate the evaluation of new cancer therapies, treatment modalities, and supportive measures

• To aid in AE recognition and severity grading

• To monitor safety data and for regulatory reporting

• To define oncology research protocol parameters (e.g., eligibility criteria; dose limiting toxicity; maximum tolerated dose; dose modification)

Common Terminology Criteria for Adverse Events:The Purpose

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Grades

Description of the Grades:

• Grade 0 : No Adverse Event

- Signs/ Symptoms within normal limits

• Grade 1 : Mild AE

- Minor, Mild symptoms, No specific medical intervention, Marginal clinical relevance

• Grade 2 : Moderate AE

- Intervention indicated, limited daily activities.

Grade refers to the severity of the AE.

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• Grade 3 : Severe AE

- Medically significant but not life threatening, inpatient or prolongation of hospitalization indicated.

• Grade 4 : Life-threatening or disabling AE

- Life- threatening consequences, urgent operative intervention indicated.

• Grade 5 : Fatal AE

- Death

Not all Grades are appropriate for all AEs. Therefore, some AEs are listed with fewer than five options for Grade selection.

• Eg: Lymph node pain is defined for grades 1,2,3 where as Leukocytosis is graded for 3, 4, and 5

Grades

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Death: As an AE and as Grade 5

Death as an AE term

• NCI requirement, as AE reporting is the primary mechanism for getting death information to the CTEP.

Many companies do not report death as an adverse event; instead report it as an outcome. For e.g, a preferred term of Sudden Death or Myocardial Infarction could have Grade 5 (death) as outcome

Sometimes its decided not to display Grade 5 in the AE tables, instead have a separate table/listing for deaths.

All of the above makes it hard to compare across products from different companies where death due to AEs are concerned

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Severity and Seriousness

“Serious” and “Severe” are not the same.

Serious is based on patient outcome, or action criteria associated with AEs that pose a threat to the patient‟s life or functioning.

Severity is the intensity of the specific AE( as mild headache, severe headache)

CTCAE grading scale describes severity and not seriousness

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Examples in documenting a grade

A patient need not exhibit all elements of the grade description to be admitted to that grade.

Example: A patient having pain OR itching OR erythema can be attributed to grade 1.

When a patient exhibits elements of multiple Grades, the highest grade is to be assigned.

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CTC grades: Applications

Identifying the Maximum Tolerated Dose based on DLT

• Grades 3,4 of specific AE‟s considered as dose limiting

Eligibility Criteria: Some trials define inclusion/ exclusion criteria based on the CTC grades.

• Eg. Exclude patients who have a History of arrhythmia (multifocal premature ventricular contractions (PVCs), bigeminy, trigeminy, ventricular tachycardia, or uncontrolled atrial fibrillation) which is symptomatic or requires treatment (CTCAE grade 3) or asymptomatic sustained ventricular tachycardia.

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CTC grades: Applications

Dose modification: Some decision making thresholds are employed in oncology trials.

• the US label for docetaxel recommends that treatment not be initiated in patients with neutrophil counts <1500 cells/mm3 (CTCAE grade 2); however, decisions about docetaxel dose modification because of neutropenia typically require reductions to <500 cells/mm3 (CTCAE grade 4) for more than 1 week and/or clinically evident febrile neutropenia.

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Analysis of an AE using LAB/ECG results

For some investigations, it is possible to assign CTCAE grading based on lab/ecg values.

• For e.g.

CTCAE V4.0

Term

Grade 1 Grade 2 Grade 3 Grade 4

Anemia Hgb <LLN -10.0 g/dL;

<LLN - 6.2 mmol/L;

<LLN - 100 g/L

Hgb <10.0 - 8.0 g/dL;

<6.2 -4.9 mmol/L;

<100 - 80g/L

Hgb <8.0 - 6.5 g/dL;

<4.9 - 4.0mmol/L;

<80 - 65 g/L;

transfusion indicated

Life-threatening

consequences;

urgent intervention

indicated

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A similar example for ECG‟s

CTCAE V4.0

Term

Grade 1 Grade 2 Grade 3 Grade 4

Electrocardiogram

QT corrected interval

prolonged

QTc 450 - 480 ms QTc 481 - 500 ms QTc >= 501 ms on at

least two separate

ECGs

QTc >= 501 or >60

ms change from

baseline and

Torsade de pointes

or polymorphic

ventricular

tachycardia or

signs/symptoms of

serious arrhythmia


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However, many of these investigations tend to be under-reported as AEs

• It is preferable to look directly at the lab results themselves to get an idea of the adverse effects of the drug.

• Example of lab grading for HGB

Some of the lab parameters are graded in either direction or both.

• Platelet count decrease, hypo/hyper calcemia

Lab

Parameter

Grade 1 Grade 2 Grade3 Grade 4

HGB < LLN - 100 g/L;

< LLN - 6.2 mmol/L;

< LLN - 10 g/dL

80 - < 100 g/L;

4.9 - < 6.2 mmol/L;

8 - < 10 g/dL

65 - < 80 g/L;

4.0 - < 4.9 mmol/L;

6.5 - < 8 g/dL

< 65 g/L;

< 4.0 mmol/L;

< 6.5 g/dL

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CTCAE – Lab data How to assign grades to Lab results

Analyzing Lab data is one of the major challenges faced in clinical trials.

Not only enough to say that a value of a lab parameter is too high or too low, but its also important to report the intensity of the “highness” or “lowness”

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CTCAE – Lab data Challenges

Local and Central labs

• Data capture is a challenge.

Units of measurement/ Rounding with Unit conversion:

• CTCAE is based on SI units

• Grade the parameter after rounding or before?

Missing normal reference ranges/ Overlapping Ranges

• Optimal way to grade in such cases

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Interpretation of data

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Coding of AE‟sMedDRA(Medical Dictionary for Regulatory Activities)

The medical terminology used to classify adverse event information

Allows health authorities and industry to more readily exchange and analyze data related to the safe use of medical products.

Developed by the International Conference on Harmonization (ICH) , owned by the International Federation of Pharmaceutical Manufacturers and Associations (IFPMA) acting as trustee for the ICH steering committee.

MedDRA is used to report adverse event data from clinical trials, as well as post-marketing and pharmacovigilance.

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MedDRA levels

The structural elements of the MedDRA terminology

• System Organ Class (SOC)- Highest level of the terminology, and distinguished by anatomical or physiological system, etiology or purpose

• High Level Group Term (HLGT) – Subordinate to SOC, super ordinate descriptor for one or more HLTs

• High Level Term (HLT) – Subordinate to HLGT, super ordinate descriptor for one or more PTs

• Preferred Term (PT) – Represents a single medical concept

• Low Level Term (LLT) – Lowest level of the terminology, related to a single PT as a synonym, lexical variant, or quasi-synonym (Note: All PTs have an identical LLT).

Revised every 6 months, Current version 12.0

• Consists of minor(11.1, 11.2 etc..)and major ( 11.0, 12.0...) upgrades

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Examples MedDRA hierarchy

23 | MedDRA_SMQ CIS Training-1_Final | presenter | DD MMM YYYY | Business Use Only

SOC Eye Disorders

HLGT Eye Disorders NEC

HLT Ocular Disorders NEC

PT Eye Pain

LLT Pain behind eyes Eye Ache Sore Eyes etc….

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SMQs - Standardized MedDRA Queries

SMQs are created by grouping relevant MedDRA terms to represent a

particular medical condition or area of interest.

• Assists in the formulation of a "case definition" and in data retrieval &

analysis.

• SMQs are groupings of Preferred Term (PT) level terms, which represent

unique medical concepts.

• SMQ include terms for signs, symptoms, diagnoses, syndromes, physical

findings, laboratory and other physiologic data, etc.

The size and complexity of MedDRA terminology carries the risk that

different users may select differing sets of terms while trying to retrieve

cases relative to the same drug safety problem.

Also the preparation of search queries is essential for their subsequent

acceptance of search results

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SMQ Structure – How does it look?

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SMQ Structure (cont….)

Each term below represents one SMQ – There are 4 SMQ hierarchy levels.

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References

References:

• http://ctep.cancer.gov

• http://www.meddramsso.com/NewWeb2003/index.htm

.

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http://www.meddramsso.com/NewWeb2003/index.htm




Questions???

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www.cytel.com ©2010 Cytel 1

Tabular and Graphical Summaries

Of

Adverse Events

www.cytel.com

Aparajita Dey

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We will go through…Table - 1

Adverse Events by System Organ Class and Preferred Term

Primary System

Organ Class 1.2 mg/m2 2.4 mg/m2 4.8 mg/m2

________________________________________________________________________________________________________________________________________________

Preferred Term N=2 N=3 N=3

_________________________________________________________________________________________________________________________________________________

-Any Primary System Organ Class

-Total 2 (100) 3 (100) 3 (100)

Blood and lymphatic system disorders

-Total 0 (0.0) 1 (33.3) 2 (66.7)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 1 (33.3) 0 (0.0)

Thrombocytopenia 0 (0.0) 1 (33.3) 2 (66.7)

Table - 2

Treatment Emergent Adverse Events by Severity in Subjects Who Received 1.2 mg/m2

Treatment emergent adverse events Severity

Primary System Organ Class Mild Moderate Severe____________________________________________________________________________________________________________________________________________________________

Preferred Term _____________________________________________________________________________________________________________________________________________________________

No. of subjects dosed 1 (50.0) 1 (50.0) 0 (0.0)

No. of subjects with an event 0 (0.0) 1 (50.0) 0 (0.0)


-Total 0 (0.0) 1 (50.0) 0 (0.0)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 0 (0.0) 0 (0.0)


1

2Ind

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We will go through…

Table - 14

Summary analysis of incidence of adverse events

Placebo Active1 Active2

_______________________________________________________________

No. of subjects dosed 6 (100) 6 (100) 6 (100)


No. of subjects with a moderate

or severe event 1 (16.7) 2 (33.3) 3 (50.0)

No. of subjects with a severe event 0 (0.0) 0 (0.0) 1 (16.7)

No. of subjects with an unlikely,

likely or definitely related event 2 (66.7) 3 (50.0) 2 (66.7)Table - 16

Summary of Fatigue

1.2 mg/m2 2.4 mg/m2 7.2mg/m2

N=2 N=3 N=7

_______________________________________________________________

No. of patients with incidences of

all grades 0 (0.0) 1 (33.3) 3 (42.9)

No. of patients with grade 2, 3 or 4

Incidences 0 (0.0) 0 (0.0) 1 (14.3)

Median time to first grade 2* event (days) NA NA 18.0

Median time to first grade 3** event (days) NA NA NA

Median duration of grade 2/3/4*** event NA NA 21.0

4

3

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We will go through…Figure - 1

Counts of Adverse Events

Figure - 2

Duration of Nausea

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Figure

ListingTable

Comparing the Outputs…

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We will go through…Table - 1


Primary System


________________________________________________________________________________________________________________________________________________


_________________________________________________________________________________________________________________________________________________


-Total 2 (100) 3 (100) 3 (100)


-Total 0 (0.0) 1 (33.3) 2 (66.7)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 1 (33.3) 0 (0.0)


1

Counts (Percentages) of Adverse Events for all Doses

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Type 1 Table: An Example

Primary System


________________________________________________________________________________________________________________________________________________

Preferred Term N=2 N=3 N=3________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________


-Total 2 (100) 3 (100) 3 (100)


-Total 0 (0.0) 1 (33.3) 2 (66.7)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 1 (33.3) 0 (0.0)


Table - 1


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Primary System


________________________________________________________________________________________________________________________________________________



-Total 2 (100) 3 (100) 3 (100)


-Total 0 (0.0) 1 (33.3) 2 (66.7)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 1 (33.3) 0 (0.0)


Table - 1

Adverse Events by System Organ Class and Preferred Term Treatments

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Primary System


________________________________________________________________________________________________________________________________________________



-Total 2 (100) 3 (100) 3 (100)


-Total 0 (0.0) 1 (33.3) 2 (66.7)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 1 (33.3) 0 (0.0)


Table - 1


Primary System

Organ Class

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Primary System


________________________________________________________________________________________________________________________________________________



-Total 2 (100) 3 (100) 3 (100)


-Total 0 (0.0) 1 (33.3) 2 (66.7)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 1 (33.3) 0 (0.0)


Table - 1


Preferred

Terms

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Primary System


________________________________________________________________________________________________________________________________________________



-Total 2 (100) 3 (100) 3 (100)


-Total 0 (0.0) 1 (33.3) 2 (66.7)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 1 (33.3) 0 (0.0)


Type 1 Table: An ExampleTable - 1


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Type 1 Table: An ExampleType 1 Table: Some Other ExamplesTable - 2

Moderate or Greater Adverse Events by System Organ Class and Preferred Term

Primary System


________________________________________________________________________________________________________________________________________________

Preferred Term N=2 N=3 N=3________________________________________________________________________________________________________________________________________________


-Total 2 (100) 3 (100) 3 (100)

No. of Subjects with a moderate (or greater) event

-Total 1 (50.0) 1 (33.3) 2 (66.7)


-Total 0 (0.0) 1 (33.3) 2 (66.7)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 1 (33.3) 0 (0.0)

Thrombocytopenia 0 (0.0) 1 (33.3) 2 (66.7) Indian

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Primary System


________________________________________________________________________________________________________________________________________________



-Total 2 (100) 3 (100) 3 (100)

No. of Subjects with a moderate (or greater) event

-Total 1 (50.0) 1 (33.3) 2 (66.7)


-Total 0 (0.0) 1 (33.3) 2 (66.7)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 1 (33.3) 0 (0.0)


Type 1 Table: Some Other ExamplesTable - 2

Moderate or Greater Adverse Events by System Organ Class and Preferred Term

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Primary System


________________________________________________________________________________________________________________________________________________



-Total 2 (100) 3 (100) 3 (100)

No. of Subjects with treatment emergent AE

-Total 1 (50.0) 1 (33.3) 1 (33.3)


-Total 1 (50.0) 0 (0.0) 1 (33.3)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 0 (0.0) 0 (0.0)



Treatment Emergent Adverse Events by System Organ Class and Preferred Term

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Primary System


________________________________________________________________________________________________________________________________________________


________________________________________________________________________________________________________________________________________________


-Total 2 (100) 3 (100) 3 (100)

No. of Subjects with an event leading to discontinuation of study drug

-Total 0 (0.0) 0 (0.0) 1 (33.3)


-Total 0 (0.0) 0 (0.0) 1 (33.3)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 0 (0.0) 0 (0.0)



Incidence of treatment-emergent adverse events leading to discontinuation of study drug by system organ class

and preferred term

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Primary System


________________________________________________________________________________________________________________________________________________

Severity N=2 N=3 N=3________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________


-Total 0 (0.0) 1 (33.3) 2 (66.7)

Mild 0 (0.0) 0 (0.0) 1 (33.3)

Moderate 0 (0.0) 1 (33.3) 1 (33.3)

Severe 0 (0.0) 0 (0.0) 0 (0.0)

Gastrointestinal disorders

-Total 1 (50.0) 1 (33.3) 0 (0.0)

Mild 0 (0.0) 0 (0.0) 0 (0.0)

Moderate 1 (50.0) 0 (0.0) 0 (0.0)

Severe 0 (0.0) 1 (33.3) 0 (0.0)

Table - 8

Treatment Emergent Adverse Events by System Organ Class and Severity

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Treatment Emergent Adverse Events by System Organ Class and Severity

Primary System


________________________________________________________________________________________________________________________________________________

Severity N=2 N=3 N=3________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________


-Total 0 (0.0) 1 (33.3) 2 (66.7)

Mild 0 (0.0) 0 (0.0) 1 (33.3)

Moderate 0 (0.0) 1 (33.3) 1 (33.3)

Severe 0 (0.0) 0 (0.0) 0 (0.0)


-Total 1 (50.0) 1 (33.3) 0 (0.0)

Mild 0 (0.0) 0 (0.0) 0 (0.0)

Moderate 1 (50.0) 0 (0.0) 0 (0.0)

Severe 0 (0.0) 1 (33.3) 0 (0.0)

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System Organ Class Double-Blind Period

Preferred Term Placebo Active P-Value________________________________________________________________________________________________________________________________________________

Number Dosed 124 (100) 126 (100)

INVESTIGATIONS 5 ( 4.0) 20 ( 15.9) (0.003)

Alanine aminotransferase increased 3 ( 2.4) 11 ( 8.7) (0.051)

Aspartate aminotransferase increased 1 ( 0.8) 11 ( 8.7) (0.005)

Gamma-glutamyltransferase increased 0 3 ( 2.4) (0.247)

Transaminases increased 0 3 ( 2.4) (0.247)

Blood creatine phosphokinase increased 0 2 ( 1.6) (0.498)

Blood glucose increased 0 1 ( 0.8) (1.000)______________________________________________________________________________________________________________________________________________________________________________________________________________________________________

P-Value from Fisher's Exact test for the treatment difference without adjusting for multiplicity.


Number (%) of Subjects Reporting Study Drug Related Adverse Events

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System Organ Class Double-Blind Period

Preferred Term Placebo Active P-Value________________________________________________________________________________________________________________________________________________

Number Dosed 124 (100) 126 (100)

INVESTIGATIONS 5 ( 4.0) 20 ( 15.9) (0.003)

Alanine aminotransferase increased 3 ( 2.4) 11 ( 8.7) (0.051)

Aspartate aminotransferase increased 1 ( 0.8) 11 ( 8.7) (0.005)

Gamma-glutamyltransferase increased 0 3 ( 2.4) (0.247)

Transaminases increased 0 3 ( 2.4) (0.247)

Blood creatine phosphokinase increased 0 2 ( 1.6) (0.498)

Blood glucose increased 0 1 ( 0.8) (1.000)______________________________________________________________________________________________________________________________________________________________________________________________________________________________________

P-Value from Fisher's Exact test for the treatment difference without adjusting for multiplicity.


Number (%) of Subjects Reporting Study Drug Related Adverse Events Fisher’s exact test P-Value for Treatment

difference

Significant

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Type 1 Table – At a glance…

• Most common layout

• All Adverse events, all treatments

– Overview in a single table

• Can move to any adverse event of interest

– Compare counts between treatments

• Can enrich in many ways

– Taking subset of population

– Adding columns

– Value AdditionInd

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for S

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Clin

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Table - 2

Treatment Emergent Adverse Events by Severityin Subjects Who Received 1.2 mg/m2


Primary System Organ Class Mild Moderate Severe____________________________________________________________________________________________________________________________________________________________

Preferred Term _____________________________________________________________________________________________________________________________________________________________




-Total 0 (0.0) 1 (50.0) 0 (0.0)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 0 (0.0) 0 (0.0)


2

Counts (Percentages) of Different Types of Adverse

Events by Dose

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Primary System Organ Class Mild Moderate Severe________________________________________________________________________________________________________________________________________________

Preferred Term________________________________________________________________________________________________________________________________________________




-Total 0 (0.0) 1 (50.0) 0 (0.0)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 0 (0.0) 0 (0.0)


Table - 10

Treatment Emergent Adverse Events by Severity in Subjects Who Received 1.2 mg/m2 (N=2)

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Preferred Term________________________________________________________________________________________________________________________________________________




-Total 0 (0.0) 1 (50.0) 0 (0.0)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 0 (0.0) 0 (0.0)


Table - 10


By treatment table

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Type 1 Table: An ExampleType 2 Table: An Example



Preferred Term________________________________________________________________________________________________________________________________________________




-Total 0 (0.0) 1 (50.0) 0 (0.0)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 0 (0.0) 0 (0.0)


Table - 10


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Type 1 Table: An ExampleType 2 Table: Some Other Examples

Treatment emergent adverse events Relationship to Study Drug

Primary System Organ Class None Likely Unlikely Definite________________________________________________________________________________________________________________________________________________

Preferred Term________________________________________________________________________________________________________________________________________________

No. of subjects dosed 1 (33.3) 1 (33.3) 1 (33.3) 0(0.0)

No. of subjects with an event 1 (33.3) 1 (33.3) 1 (33.3) 0(0.0)


-Total 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 1 (33.3) 0 (0.0) 0 (0.0)

Thrombocytopenia 1 (33.3) 1 (33.3) 0 (0.0) 0 (0.0)

Table - 11

Treatment Emergent Adverse Events by Relationship to Study drug in Subjects Who Received 2.4 mg/m2 (N=3)

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Treatment emergent

Adverse Events Grade

Primary System Organ Class 1 2 3 4 5________________________________________________________________________________________________________________________________________________

Preferred Term________________________________________________________________________________________________________________________________________________

No. of subjects dosed 1 (33.3) 1 (33.3) 0 (0.0) 1 (33.3) 0(0.0)

No. of subjects with an event 1 (33.3) 1 (33.3) 0 (0.0) 1 (33.3) 0(0.0)


-Total 1 (33.3) 0 (0.0) 0 (0.0) 1 (33.3) 0(0.0)

Anaemia 1 (33.3) 0 (0.0) 0 (0.0) 1 (33.3) 0(0.0)

Leukopenia 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0(0.0)

Thrombocytopenia 0 (0.0) 0 (0.0) 0 (0.0) 1 (33.3) 0(0.0)

Table - 12

Treatment Emergent Adverse Events by CTC Grade in Subjects Who Received 4.8 mg/m2 (N=3)

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Primary System 1.2 mg/m2 2.4 mg/m2 4.8 mg/m2

Organ Class N=2 N=3 N=3________________________________________________________________________________________________________________________________________________

Preferred Term Any Grade Any Grade Any Grade

grade 3/4 grade 3/4 grade 3/4 ________________________________________________________________________________________________________________________________________________


-Total 2 (100) 1 (50.0) 3 (100) 1 (33.3) 3 (100) 2 (66.7)


-Total 0 (0.0) 0 (0.0) 1 (33.3) 0 (0.0) 2 (66.7) 1 (33.3)

Anaemia 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0)

Leukopenia 0 (0.0) 0 (0.0) 1 (33.3) 0 (0.0) 0 (0.0) 0 (0.0)

Thrombocytopenia 0 (0.0) 0 (0.0) 1 (33.3) 0 (0.0) 2 (66.7) 0 (0.0)

Table - 13


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• All adverse events are displayed – similar to type 1

• Types of adverse events are emphasized

– Repeated tables for each treatment

• Pattern of adverse event within each treatment

– All mild, no severe event – may alter the interpretation of the total

count

• Many ways to classify adverse events

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Table - 14


Placebo Active1 Active2

_______________________________________________________________




or severe event 1 (16.7) 2 (33.3) 3 (50.0)



likely or definitely related event 2 (66.7) 3 (50.0) 2 (66.7)

3

Summary Analysis of Adverse Events

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Placebo Active1 Active2_______________________________________________________________




or severe event 1 (16.7) 2 (33.3) 3 (50.0)




Table - 14


All adverse events

are not displayed

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Placebo Active1 Active2_______________________________________________________________




or severe event 1 (16.7) 2 (33.3) 3 (50.0)




Table - 14


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Type 1 Table: An ExampleType 3 Table: Another Example

Active

All Placebo 380 mg

N (%) of subjects (N=31) (N=15) (N=16)_______________________________________________________________

Dosed 31 (100) 15 (100) 16 (100)

With >=1 AE 16 ( 51.6) 10 ( 66.7) 6 ( 37.5)

With >=1 severe AE 1 ( 3.2) 1 ( 6.7) 0

With >=1 drug related AE 10 ( 32.3) 7 ( 46.7) 3 ( 18.8)

With >=1 serious AE 0 0 0

Who discontinued due to an AE 0 0 0

Due to drug related AE 0 0 0

Due to a serious AE 0 0 0

Table - 15

Summary of Adverse Events

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• Each and every adverse event is not displayed

• Numbers reflect the result for all adverse events

combined

– Compare the numbers between treatments

• Not a replacement of previous types

– Goes hand in hand with them

• Gives quick summary of overall situation Ind

ian A

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Table - 16

Summary of Fatigue

1.2 mg/m2 2.4 mg/m2 7.2mg/m2

N=2 N=3 N=7

_______________________________________________________________


all grades 0 (0.0) 1 (33.3) 3 (42.9)


Incidences 0 (0.0) 0 (0.0) 1 (14.3)




4

Summary Table for Any One Adverse Event

(Adverse Event of Special Interest)

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1.2 mg/m2 2.4 mg/m2 7.2 mg/m2

N=2 N=3 N=7_______________________________________________________________


all grades 0 (0.0) 1 (33.3) 3 (42.9)


Incidences 0 (0.0) 0 (0.0) 1 (14.3)




Table - 16

Summary of Fatigue

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Summary of Fatigue

1.2 mg/m2 2.4 mg/m2 7.2 mg/m2

N=2 N=3 N=7_______________________________________________________________


all grades 0 (0.0) 1 (33.3) 3 (42.9)


Incidences 0 (0.0) 0 (0.0) 1 (14.3)




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* among those whose worst incidence was grade 2.

** among those whose worst incidence was grade 3.

1.2 mg/m2 2.4 mg/m2 7.2 mg/m2

N=2 N=3 N=7_______________________________________________________________


all grades 0 (0.0) 1 (33.3) 3 (42.9)


Incidences 0 (0.0) 0 (0.0) 1 (14.3)




First incidence of WORST grade

Table - 16

Summary of Fatigue

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Study

started

Got

First

Dose

First

AE

First

worst

Grade

Time to First

Worst Grade

Time to Event:Understanding of Logic

Time to first worst grade

= Date of first worst grade – dosing date + 1

Dosing Date Date of first

worst grade

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Time to Event:Understanding of Logic – From Raw Data

Subject Grade AE Date Dosing Date Cut-off Date

1 2 09-May-08 05-May-08 12-May-09

1 3 22-May-08 05-May-08 12-May-09

1 2 30-Jun-08 05-May-08 12-May-09

1 1 01-Aug-08 05-May-08 12-May-09

1 2 30-Aug-08 05-May-08 12-May-09

1 3 15-Sep-08 05-May-08 12-May-09

1 3 30-Sep-08 05-May-08 12-May-09

1 2 18-Oct-08 05-May-08 12-May-09

1 1 15-Nov-08 05-May-08 12-May-09

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1 2 09-May-08 05-May-08 12-May-09

1 3 22-May-08 05-May-08 12-May-09

1 2 30-Jun-08 05-May-08 12-May-09

1 1 01-Aug-08 05-May-08 12-May-09

1 2 30-Aug-08 05-May-08 12-May-09

1 3 15-Sep-08 05-May-08 12-May-09

1 3 30-Sep-08 05-May-08 12-May-09

1 2 18-Oct-08 05-May-08 12-May-09

1 1 15-Nov-08 20-Jan-08 12-May-09

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1 2 09-May-08 05-May-08 12-May-09

1 3 22-May-08 05-May-08 12-May-09

1 2 30-Jun-08 05-May-08 12-May-09

1 1 01-Aug-08 05-May-08 12-May-09

1 2 30-Aug-08 05-May-08 12-May-09

1 3 15-Sep-08 05-May-08 12-May-09

1 3 30-Sep-08 05-May-08 12-May-09

1 2 18-Oct-08 05-May-08 12-May-09

1 1 15-Nov-08 05-May-08 12-May-09


Time to first worst grade

= Date of first worst grade – Dosing Date + 1

= 22-May – 05-May +1

i.e. 18 days

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1.2 mg/m2 2.4 mg/m2 7.2 mg/m2

N=2 N=3 N=7_______________________________________________________________


all grades 0 (0.0) 1 (33.3) 3 (42.9)


Incidences 0 (0.0) 0 (0.0) 1 (14.3)




*** worst incidence is the one that contains the worst grade.

If more than one incidence is of the same grade, the longest is taken

Duration of WORST Grade

Table - 16

Summary of Fatigue

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Study

starts

Got

First

Dose

First

AE

First

worst

Grade

Time to First

Worst Grade

Time to Event:Understanding of Logic

Dosing Date

First

time

grade 1

Again

worst

grade

First

time

grade 1

Duration 1 Duration 2

Duration of Worst Grade

= Date when AE grade becomes 1 – Worst grade date + 1

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Duration of Event:Understanding of Logic – From Raw Data


1 2 09-May-08 05-May-08 12-May-09

1 3 22-May-08 05-May-08 12-May-09

1 2 30-Jun-08 05-May-08 12-May-09

1 1 01-Aug-08 05-May-08 12-May-09

1 2 30-Aug-08 05-May-08 12-May-09

1 3 15-Sep-08 05-May-08 12-May-09

1 3 30-Sep-08 05-May-08 12-May-09

1 2 18-Oct-08 05-May-08 12-May-09

1 1 15-Nov-08 05-May-08 12-May-09

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Duration of Event:Understanding of Logic – From Raw Data


1 2 09-May-08 05-May-08 12-May-09

1 3 22-May-08 05-May-08 12-May-09

1 2 30-Jun-08 05-May-08 12-May-09

1 1 01-Aug-08 05-May-08 12-May-09

1 2 30-Aug-08 05-May-08 12-May-09

1 3 15-Sep-08 05-May-08 12-May-09

1 3 30-Sep-08 05-May-08 12-May-09

1 2 18-Oct-08 05-May-08 12-May-09

1 1 15-Nov-08 05-May-08 12-May-09

Two Durations of Worst

Grade:

1. 01-Aug – 22-May + 1

i.e. 71 days

2. 15-Nov – 15-Sep + 1

i.e. 61 days

Longest duration = 71

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10 2 15-Apr-08 28-Jan-08 26-Dec-09

10 1 01-May-08 28-Jan-08 26-Dec-09

10 2 15-Jul-08 28-Jan-08 26-Dec-09

10 2 26-Aug-08 28-Jan-08 26-Dec-09

Duration of Event:Different situation – Open loop

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10 2 15-Apr-08 28-Jan-08 26-Dec-09

10 1 01-May-08 28-Jan-08 26-Dec-09

10 2 15-Jul-08 28-Jan-08 26-Dec-09

10 2 26-Aug-08 28-Jan-08 26-Dec-09


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10 2 15-Apr-08 28-Jan-08 26-Dec-09

10 1 01-May-08 28-Jan-08 26-Dec-09

10 2 15-Jul-08 28-Jan-08 26-Dec-09

10 2 26-Aug-08 28-Jan-08 26-Dec-09

?

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10 2 15-Apr-08 28-Jan-08 26-Dec-09

10 1 01-May-08 28-Jan-08 26-Dec-09

10 2 15-Jul-08 28-Jan-08 26-Dec-09

10 2 26-Aug-08 28-Jan-08 26-Dec-09

Study end date

OR

Patient’s discontinuation date

OR

Patient’s death date

– Whichever comes first

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10 2 15-Apr-08 28-Jan-08 26-Dec-09

10 1 01-May-08 28-Jan-08 26-Dec-09

10 2 15-Jul-08 28-Jan-08 26-Dec-09

10 2 26-Aug-08 28-Jan-08 26-Dec-09

26-Dec-09


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10 2 15-Apr-08 28-Jan-08 26-Dec-09

10 1 01-May-08 28-Jan-08 26-Dec-09

10 2 15-Jul-08 28-Jan-08 26-Dec-09

10 2 26-Aug-08 28-Jan-08 26-Dec-09

26-Dec-09

Two Durations of Worst

Grade:

1. 01-May – 15-Apr + 1

i.e. 16 days

2. 26-Dec – 15-Jul + 1

i.e. 164 days

Longest duration = 164


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• Summary of any ONE adverse event

– Adverse event of special interest: Study specific (e.g. Oncology)

• Summary Statistics may be uncommon

– How soon AE occurs

– How long AE stays

• Detailed information about the adverse event

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We will go through…Figure - 1


Figures of Adverse Events

- Comparison of Adverse Event Plot, Listing and Table

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Figure 1:

Plot of Adverse Events

Figure – 1


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Corresponding Listing:

Subject

Number

Age/

Sex SAE

Adverse Event

(REPORTED/ Preferred/

System organ class) Grade

Start

date/

day

End

date/

day

Dur.

(days)

Relat.

to study

med.

Action

taken

55 45/F No

BACK PAIN/

Back pain/

Musculoskeletal and

connective tissue disorders 2

30APR

2004/

3

30APR

2004/

3 1 Not susp None

Yes EMESIS-INTERMITTENT/

Vomiting/


3 27MAY

2004/

30

Conti

nuing

Not susp 3

99 56/M No

EMESIS/

Vomiting/

Gastrointestinal disorders 2

13APR

2004/

2

15APR

2004/

4 3 Susp None

No

FATIGUE/

Fatigue/

General disorders and

administration site

conditions 2

13APR

2004/

2

15APR

2004/

4 3 Susp None

Relationship to study drug: Not susp=Not suspected, Susp=Suspected

Action taken: 1=Study drug dosage adjusted/temporarily interrupted,

2=Study drug permanently discontinued due to this AE,

3=Concomitant medication taken,

4=Non-drug therapy given,5=Hospitalization/Prolonged hospitalization

Note: Day is relative to the first day of treatment (day 1).

Listing - 1 (Page 1 of 506)

Adverse events by schedule for Drug B

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Corresponding Table:Placebo Drug A Drug B

Preferred term N=100 N=100 N=100

Pruritus 5 (5.0) 16 (16.0) 15 (15.0)

Application Site Pruritus 4 (4.0) 15 (15.0) 14 (14.0)

Erythema 6 (6.0) 9 (9.0) 10 (10.0)

Application Site Erythema 1 (1.0) 10 (10.0) 9 (9.0)

Rash 3 (3.0) 8 (8.0) 8 (8.0)

Application Site Irritation 2 (2.0) 7 (7.0) 8 (8.0)

Application Site Dermatitis 3 (3.0) 5 (5.0) 6 (6.0)

Dizziness 1 (1.0) 8 (8.0) 5 (5.0)

Skin Irritation 2 (2.0) 3 (3.0) 5 (5.0)

Sinus Bradycardia 1 (1.0) 5 (5.0) 4 (4.0)

Diarrhoea 4 (4.0) 2 (2.0) 3 (3.0)

Headache 3 (3.0) 4 (4.0) 2 (2.0)

Nasopharyngitis 2 (2.0) 5 (5.0) 4 (4.0)

Nausea 1 (1.0) 5 (5.0) 2 (2.0)

Cough 2 (2.0) 3 (3.0) 4 (4.0)

Upper Respiratory Tract Infection 5 (5.0) 2 (2.0) 1 (1.0)

Hyperhidrosis 1 (1.0) 3 (3.0) 2 (2.0)

Myocardial Infarction 2 (2.0) 3 (3.0) 2 (2.0)

Vomiting 1 (1.0) 3 (3.0) 2 (2.0)

Application Site Vesicles 1 (1.0) 3 (3.0) 2 (2.0)

Includes all AEs on treatment and up to 28 days after last dose.

A subject with multiple occurrences of an AE is counted only once in the AE category for that treatment.

Table - 17

Adverse Events by Preferred Term

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Corresponding Table:

Placebo Drug A Drug B


Vomiting 1 (1.0) 3 (3.0) 2 (2.0)


A subject with multiple occurrences of an AE is counted only once in the AE category for

that treatment.

Table - 17


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At a glance…

• Plot – graphical presentation of tables

– Gives total overview

• Presentation has better visual impact

– Patterns are visible

• Classic example of validating all the artifacts

– Table

– Listing

– FigureIndian

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Figure - 2

Duration of Nausea

Figure of a Particular Adverse Event

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Figure – 2

Duration of Nausea

Figure 2:

Duration Plot of a Particular Adverse Event

Figure – 2

Duration of Nausea

Treatment

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At a glance…

• Details of a particular

Adverse Event duration

– For each treatment

– For each patient

• Pattern of Adverse Event

between treatments

• Pattern of Adverse Event

within a single treatment

Less Incidents

More Incidents

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Concluding Remarks…

• Adverse Events – crucial for safety aspect of a CT

study

• AE reports - common or study specific

• Cross-checking of TLFs - increases accuracy

• This presentation – insights to AE reporting

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www.cytel.com ©2010 Cytel 66Indian

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Email Address

Aparajita Dey –

[email protected]

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mailto:[email protected]

Incidence rates and their interpretation

Prasanthi Sanjeevi

Senior Manager (Biostatistics)

Quintiles Technologies Pvt Ltd.,

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Agenda Incidence rates (crude, exposure adjusted, non-constant

hazards) and their interpretation

– Introduction: Adverse Events and/or Efficacy measures

– Risk – Epidemiologic measure

– Aspects of Risk

– Crude Incidence rate

– Exposure adjusted Incidence Rate

– Non-constant Hazard

– Ratios – comparison measures

– How complex can it get!

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Introduction: Adverse Events and/or Efficacy measures

– Aim of Clinical Trial – Summarize Safety Data

– Risk / Probability (Chance) of an Adverse Event

– How to measure risk

– Additional data required

• Independent variables – Crucial – Exposure time

• Dependent Variables – Essential – Number of occurrences, length of events

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Risk is a Probability

– Aspects of risk considered

• Occurrence

• Frequency

• Duration of certain event of interest

– Desirable properties of a measure of risk

• Validity

– Never describe an outcome without considering the input side

• Unbiasedness

– Not be systematically over or under-estimated

• Relevance

– We need measures “to convey the concept of risk to non-statisticians

whose use or choice of the drug may depend on this information”

(O’Neill, 1988)

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Aspects of risk converted as Epidemiologic Measures

Occurrence1. Counts; 2.Time

3. Ratios

Rate, Proportion, Percent,

Risk or odds, Prevalence

– Comparison/ Association• Rate Ratio

• Risk Ratio

• Odds Ratio

– Attribution

Ratios R = x/yx and y can be any number,

including ratios

1. Rate r = x/ ∆t

type of a ratio where

numerator is count and

denominator is a time elapsed

2. Proportionp =

a/(a+b)

type of a ratio where

numerator is part of

denominator

3. Percent

p =

a/(a+b) *

100Indian

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Coin-toss (Binomial) models

Risk(0, t ) = #events in (0,t )

Population at risk at time 0

Also called “CRUDE INCIDENCE RATE”

SUITABLE – measure risk of ABSORBING EVENTS

Validity – 1. Equal chance – same exposure time – justified in single-dose studies

2. Confounded with observation time

3. Incomparable if length of trials are different

Bias – 1. Considers all patients enrolled (even if event occurs much later)

2. True risk may be higher

Relevance – 1. Simplicity & intuitive appeal

2. Most common measure of risk

3. If exposure is long & varies among subjects then it becomes meaningless

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Exposure Adjusted Incidence Rate

Constant Hazard based modelsEAIR = # of subjects with specific events

Total Exposure time of all subjects

Events per unit time

Underlying probability

h = # of positive person-time units

# of all person-time units

Death Per Person Year = h * 365

DPPY is the CUMULATIVE HAZARD

Validity – Constant Hazard – Life table estimate

Bias – Incorrect under non-constant hazard

Relevance – Not a probability

Expected number of events – Refers to populations

DPPY & NPPY

NPPY – Same as DPPY but for non-absorbing events

Expected # events/ One patient-yr exposureIndian

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Measures of occurrence – Cancer rates in

an open cohort study, Person-time data

Rate = #cases

Σ person-timei

= 7 cases

85.7 person-years

= 0.08168028

=8.2 cases per 100 py

Period Rate = 125+130+131

361,975+361,401+366,613

=35.4 per 100,00 per year

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Measures of Occurrence -

Non-constant hazard model– Exponential formula method (fixed

intervals)

– R(0,t )=1 – e-Σri hi

• ri = rate in ith time interval

• hi = length of ith time interval,

• where Σhi = t.

– Kaplan-Meier method (time-to-event

data)

– R(t > t j ) = 1 – S(t > t j )

= 1 – Π (nj - dj ) / n j

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Measures of Occurrence -

Odds & Prevalence – Odds is a transformed risk (R) estimate

– Odds(0,t ) = R(0,t ) / (1 – R(0,t )

– Odds from coin-toss (binomial) data

– Odds(0,t ) = a / (a+b)

b / (a+b)

= a / b

Under steady state

– Prevalence odds = Rate × duration

P = r d

1 - P

P ≈ r d

New cases

removed by

recovery,

migration,

or death

– Point prevalence

P = Number of existing cases

Number in total population

, at a point in time

– Period prevalence

P = Number of existing cases

Number in total population

, during a period of time

Prevalence

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Measures of comparison

Rate ratio = = 4cases/31.3 py = 2.32

3cases/54.4 pyr1 / r0 = a/ PT1

b/ PT0

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Relative Risk & Odds Ratio

DISEASE

EXPOSURE Yes No Total

Yes a b N1

No c d N0

Total N

Risk among the

exposed

Risk among the

unexposed Risk ratio

R1= a/N1 R0= b/N0 RR = R1/ R0

Disease odds

among the

exposed

Disease odds

among the

unexposed

Disease odds

ratio

O1 = R1/(1-R1) O0 = R0/(1-R0)

OR = O1/ O0

ad/bcIndian

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Example

Endemic cryptosporidiosis and exposure to municipal tap water in persons with acquired immunodeficiency syndrome (AIDS): a case-control study. BMC Public Health. 2003;3(1):2

Tap Water Exposure Controls Cases Odds Ratio P value

Lowest (ref) 29 2 1.00 --

Intermediate 64 35 7.93 . 0013

Highest 6 12 29.0 0 .000027

Total 99 49

Unmatched, bi-variate analysis

ORI ,L = ad / bc = (29)(35) / (2)(64) = 7.93

ORH ,L= ad / bc = (29)(12) / (2)(6) = 29.0

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How complex can it get?

•Counting Process models allow

us to predict the number of events

in complicated hazard patterns –

the predicted count is just the

cumulated hazard function,

integrated over the risk interval(s)

•Anderson Gill Model – Extension

of Cox PH model when there are

multiple events

•Heterogeneity among subjects is

handled in frailty models

•Markov Models – Estimation of

Prevalence and their confidence

bands

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Summary

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Thank You!

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ICON Clinical ResearchAdverse Events – SAS Programming Perspective

Salai Ezhil Mathi

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Objectives

The main objective is separated into two parts:

AE Summaries:• Review challenges in programming AE summaries

• Consider ideas on efficient program organization

• Learn a couple of useful SQL features

• Use these to try out a new approach to programming AE tables

Adverse Event with Zero-Record dataset:• Present the basic structure and a simple programming technique to

deal with zero-record SAS datasets in the process of creating tables using

SQL in an efficient way.

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Some traits of organized, intuitive, and

efficient code…

• Related operations are grouped (i.e. calculating n, %)

• Repetition is minimized

• Clear relationship between input & output

• Datasets and variable are given meaningful names

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Organized & Intuitive = Simple & Efficient

• Optimum‟ (not necessarily minimum) number of

programming steps

• Minimize „by-products‟ (macro variables and work

datasets)

• Reduce „memory-work‟

• Produces code that is

– Maintainable

– Re-usable

– Easy to debug

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What‟s the problem with AE summaries?

• They actually consist of 3 nested frequency

counts

• Subjects are counted only once at each „level‟

even though they may provide multiple records

• Code tends to involve extensive „set-up‟ that

obscures the connection to the source data

– Record-selection flags (i.e. “where unique_soc=1”)

– Multiple WORK datasets, record inflation

– proc sort nodupkey steps

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Three Nested Frequency Counts:

1. Total subjects with any AE

2. Total subjects with any AE in the given System

Organ Class

3. Total subjects with an AE in the given

SOC/Preferred Term class

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A typical Adverse Event Summary

System Organ Class (SOC) Treatment Placebo Total

Preferred Term (PT) N=63 N=69 N=132

-----------------------------------------------------------------------------------------------------------

TOTAL SUBJECTS WITH AN EVENT 17 ( 27.0) 30 ( 43.5) 47 ( 35.6)

INFECTIONS AND INFESTATIONS 4 ( 6.3) 9 ( 13.0) 13 ( 9.8)

NASOPHARYNGITIS 1 ( 1.6) 2 ( 2.9) 3 ( 2.3)

RESPIRATORY TRACT INFECTION VIRAL 1 ( 1.6) 0 1 ( 0.8)

UPPER RESPIRATORY TRACT INFECTION 1 ( 1.6) 1 ( 1.4) 2 ( 1.5)

URINARY TRACT INFECTION 1 ( 1.6) 1 ( 1.4) 2 ( 1.5)

ACUTE SINUSITIS 0 1 ( 1.4) 1 ( 0.8)

BRONCHITIS 0 2 ( 2.9) 2 ( 1.5)

EAR INFECTION 0 1 ( 1.4) 1 ( 1.5)

LABYRINTHITIS 0 1 ( 1.4) 1 ( 1.5)

OTITIS EXTERNA 0 1 ( 1.4) 1 ( 1.5)

SINUSITIS 0 1 ( 1.4) 1 ( 1.5)

MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS 4 ( 6.3) 8 ( 11.6) 12 ( 9.1)

BACK PAIN 2 ( 3.2) 3 ( 4.3) 5 ( 3.8)

ARTHRALGIA 1 ( 1.6) 1 ( 1.4) 2 ( 1.5)

MUSCLE SPASMS 1 ( 1.6) 3 ( 4.3) 4 ( 3.0)

INTERVERTEBRAL DISC PROTRUSION 0 1 ( 1.4) 1 ( 0.8)

MYALGIA 0 1 ( 1.4) 1 ( 0.8)

PAIN IN EXTREMITY 0 1 ( 1.4) 1 ( 0.8)

SYNOVIAL CYST 0 1 ( 1.4) 1 ( 0.8)

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Why use SQL?

• Helps maintain a clear connection to the source (analysis) dataset: – query the source data, rather than an intermediate work

dataset

– concurrent subsetting, frequency calculation and „ xx ( xx.x)‟ formatting.

• The three nested frequency counts that make up a typical AE table translate very naturally to SQL queries…

1. select count(distinct patient)

2. select count(distinct patient), bodysys

group by bodysys

3. select

count(distinct patient), bodysys, prefterm

group by bodysys, prefterm

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Maintaining a clear connection to the

source data…

/*Select safety-population treatment emergent AEs*/proc sort data=sasdata.ae out=ae;

by usubjid soc pterm;where saf=1 & teae=1;

run;

/*Calculate frequencies*/select count(distinct patient) as n from ae;

…

select count(distinct patient) as n, bodysys from ae group by bodysys;

…

select count(distinct patient) as n, bodysys, prefterm from aegroup by bodysys, prefterm;

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Or alternatively…

select count(distinct patient) as n

from SASDATA.ae where saf=1 & teae=1;

…

select count(distinct patient) as n, bodysys

from SASDATA.ae where saf=1 & teae=1

group by bodysys;

…

select count(distinct patient) as n, bodysys, prefterm

from SASDATA.ae where saf=1 & teae=1

group by bodysys, prefterm;

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Now, how do we handle treatment group?

/*Calculate frequencies*/

select count(distinct patient) as n, TRT from ae

group by TRT;

…

select count(distinct patient) as n, bodysys, TRT from aegroup by bodysys, TRT;

…

select count(distinct patient) as n, bodysys, prefterm, TRTfrom ae group by bodysys, prefterm, TRT;

/*Transpose by bodysys, prefterm, TRT...*/

proc transpose data=...

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Or alternatively…

• Consider this:

Use „%do‟ groups for vertical table divisions (treatment group) and „by‟

groups for horizontal table divisions (SOC term, preferred term).

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Vertical and Horizontal Divisions



-----------------------------------------------------------------------------------------------------------








BRONCHITIS 0 2 ( 2.9) 2 ( 1.5)

EAR INFECTION 0 1 ( 1.4) 1 ( 1.5)

LABYRINTHITIS 0 1 ( 1.4) 1 ( 1.5)

OTITIS EXTERNA 0 1 ( 1.4) 1 ( 1.5)

SINUSITIS 0 1 ( 1.4) 1 ( 1.5)


BACK PAIN 2 ( 3.2) 3 ( 4.3) 5 ( 3.8)

ARTHRALGIA 1 ( 1.6) 1 ( 1.4) 2 ( 1.5)

MUSCLE SPASMS 1 ( 1.6) 3 ( 4.3) 4 ( 3.0)


MYALGIA 0 1 ( 1.4) 1 ( 0.8)


SYNOVIAL CYST 0 1 ( 1.4) 1 ( 0.8)

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%do trt=1 %to 3; /*VERTICAL TABLE DIVISIONS: TREATMENT GROUP*/

proc sql;

create table _&trt as

select * from (

/*AT LEAST 1 TEAE*/

(select <VARS>

from ae where trt=&trt)

union all

/*AT LEAST 1 TEAE WITHIN SOCNAME*/

(select <VARS>

from ae where trt=&trt

group by socname)

union all

/*AT LEAST 1 TEAE WITHIN SOCNAME AND PTNAME*/

(select <VARS>


group by socname, ptname)

)

order by <VARS>;

quit;

%end;

%macro freq;

%mend freq;

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proc sql;


select * from (

/*AT LEAST 1 TEAE*/

(select <VARS>


union all


(select <VARS>


group by socname)

union all


(select <VARS>



)

order by <VARS>;

quit;

%end;

%macro freq;

%mend freq;

Creates datasets_1, _2, and _3

corresponding to treatment groups 1, 2, 3

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proc sql;


select * from (

/*AT LEAST 1 TEAE*/

(select <VARS>


union all


(select <VARS>


group by socname) /*HORIZONTAL DIVISION: SOCNAME*/

union all


(select <VARS>


group by socname, ptname) /*HORIZONTAL DIVISION: SOCNAME,PTNAME*/

)

order by <VARS>;

quit;

%end;

%macro freq;

%mend freq;

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Merge „vertical‟ divisions by „horizontal‟

by-groups…

data final;

merge _1 _2 _3;

by <VARS>;

run;

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Now, what about denominators and percents?

• To create a formatted “ xx ( xx.x)” (count and percent) string:

put(count(distinct patid),3.0)||" ("||

put(count(distinct patid)/<DENOM>*100,5.1)||")“

/*AT LEAST 1 TEAE*/

(select "At Least One TEAE" as socname,

"At Least One TEAE" as ptname,

put(count(distinct patid),3.0)||

" ("||put(count(distinct patid)/<DENOM>*100,5.1)||")"

as _&trt


Creates “ n (%)” variables_1, _2, and _3

corresponding to treatment groups 1, 2, 3

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Alternately…

%let str=%nrstr(

put(count(distinct patid),3.0)||" ("||

put(count(distinct patid)/&&n&trt*100,5.1)||")"

);

select ...

%unquote(&str) as _&trt

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Within a single SQL statement we can:

1. Subset by treatment group

2. (In effect) subset the data to a single record per

subject within levels specified in the „by‟

statement

3. Calculate a frequency and percent

4. Create a formatted „ xx ( xx.x)‟ string

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Calculating Denominators

%macro freq;

%do trt=1 %to 3;

proc sql;

select count(distinct usubjid)

into :n&trt from subjinfo where trt=&trt;


(etc.)

%mend freq;

Creates macro variables&n1, &n2, and &n3

Corresponding to treatment groups 1, 2, 3

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But what about trt=3, the „Total‟ column..?

data subjinfo;

set sasdata.subjinfo(where=(saf=1 & trt in(1,2)));

output;

trt=3;

output;

run;

data ae;

set sasdata.ae(where=(saf=1 & teae=1 & trt in(1,2)));

output;

trt=3;

output;

run;

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Alternately…

data subjinfo;

set sasdata.subjinfo(where=(saf=1 & trt in(1,2)));

run;

%macro freq;

proc sql noprint;

%do trt=1 %to 3;


into :n&trt

from subjinfo where trta=&trt or &trt=3;

(etc.)

%mend freq;

Condition is true in the third iteration, so all subjects are counted in the &n3 denominator

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The complete calculation:

%macro freq;

%do trt=1 %to 3;

proc sql;


into :n&trt from subjinfo where trta=&trt or &trt=3;


select * from (

/*AT LEAST 1 TEAE*/

(select distinct 0 as section, 0 as ord, "At Least One TEAE" as socname,

"At Least One TEAE" as ptname,


from ae where trt=&trt or &trt=3)

union all

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(select distinct 1 as section, 0 as ord, socname, "At Least One TEAE" as ptname,


from ae where trt=&trt or &trt=3

group by socname)

union all


(select distinct 1 as section, 1 as ord, socname, ptname,


from ae where trt=&trt or &trt=3


)

order by section, socname, ord, ptname;

quit;

%end;

%mend freq;

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By-treatment datasets:

• This creates 3 datasets: _1, _2, and _3 (one dataset per treatment group):

section ord socname ptname _1

------------------------------------------------------------------------

0 0 At Least One TEAE At Least One TEAE 50 (100.0)

1 0 SOC Name 1 At Least One TEAE 17 ( 34.0)

1 1 SOC Name 1 PT Name 1 3 ( 6.0)

1 1 SOC Name 1 PT Name 2 2 ( 4.0)

----------------(etc.)---------------------


------------------------------------------------------------------------



1 1 SOC Name 1 PT Name 1 3 ( 6.0)

1 1 SOC Name 1 PT Name 2 2 ( 4.0)

----------------(etc.)---------------------


------------------------------------------------------------------------



1 1 SOC Name 1 PT Name 1 6 ( 6.0)

1 1 SOC Name 1 PT Name 2 4 ( 4.0)

----------------(etc.)---------------------Indian

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Administrative variables

• „Section‟ and „ord‟ are just arbitrary ordering

variables:

– „Section‟ ensures that the „overall‟ line appears first in the table

– Where section=1 and ord=0, ensures that within each SOC, the overall line appears first

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Final, report-ready dataset

• Merge the by-treatment datasets (vertical

divisions) to create the report-ready dataset:

data final;

merge _1 _2 _3;

by section socname ord ptname;

run;

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-----------------------------------------------------------------------------------------------------------








BRONCHITIS 0 2 ( 2.9) 2 ( 1.5)

EAR INFECTION 0 1 ( 1.4) 1 ( 1.5)

LABYRINTHITIS 0 1 ( 1.4) 1 ( 1.5)

OTITIS EXTERNA 0 1 ( 1.4) 1 ( 1.5)

SINUSITIS 0 1 ( 1.4) 1 ( 1.5)


BACK PAIN 2 ( 3.2) 3 ( 4.3) 5 ( 3.8)

ARTHRALGIA 1 ( 1.6) 1 ( 1.4) 2 ( 1.5)

MUSCLE SPASMS 1 ( 1.6) 3 ( 4.3) 4 ( 3.0)


MYALGIA 0 1 ( 1.4) 1 ( 0.8)


SYNOVIAL CYST 0 1 ( 1.4) 1 ( 0.8)

Section=0, ord=0 Section=1, ord=0 Section=1, ord=1

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Adverse Event with Zero-Record dataset:

• Often times, particular tables or listings in a clinical

trial deal with zero-record SAS datasets.

• This occurs primarily in tables/listings that deal with

death or serious adverse events.

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Use Proc SQL procedure-created macro variable

Proc sql noprint;

select count(*) into: obs

from sasdata.ae

where serious = ‟1‟;

quit;

Creates a macro variable which has a value either 0 or greater than 0

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• Once the macro variable is established, a macro is used to execute the

two logical routes.

%macro AE;

**if no SAE happened, then;

%if &obs = 0 %then %do;

data AE;

array var1, var2,...;

run;

proc report data=AE nowindows;

col var1 var2 ...;

define var1;

define var2;

...

break after /skip;

compute after;

line ' ';

...

line @65 'No serious adverse events were reported';

endcomp;

run;

%end;

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** if SAE happened, then show regular tables;

%else %do;

proc report data = AE nowindows;

col var1 var2 ...;

define var1;

define var2;

…

break after /skip;

%end;

%mend;

%AE;

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-----------------------------------------------------------------------------------------------------------

No serious adverse events were reported

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Conclusion

• Think of the three „nested‟ frequency counts as

distinct SQL queries.

• Query the source data directly to maintain a clear

relationship between input & output.

• Subset, calculate frequency, and generate the

formatted „ xx ( xx.x)‟ string all at the same time.

• Experiment with code-organization ideas (“„%do‟

groups for vertical divisions, „by‟ groups for

horizontal”) to eliminate transposition steps.

• Use a simple programming technique to deal with

zero-record SAS datasets.Indian

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1

Meta-Analysis of Safety Data from

Clinical Trials

Debjit Biswas, Ph.D.

Bristol-Myers Squibb Company

IASCT Event, Bengaluru July 2010

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2

Background

Meta-analysis is a methodology for combining the results of severalstudies that address a set of related research hypotheses

Advantages

• More precise estimation of effect size

• Higher statistical power to test hypotheses

• Generalization to a population of studies

• Ability to control for between-study variation

• Better ability to detect safety signals

Disadvantages

• Studies being combined may not really be similar in terms of design,inclusion criteria, or evaluation of outcome

• Prone to selection bias…only studies with ‘favorable’ or promisingresults may be reported and therefore combined

• Results may be influenced by methodological flaws or issues withimplementation in individual studiesInd

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Pooling vs. Meta-analysis

Pooling consists of combining data without being weighted. The analysis is

performed as if the data were derived from a single study.

- Treatment groups are pooled rather than studies

- Ignores characteristics of the subgroups or individual studies being pooled, and

therefore validity of comparisons

- Is subject to Simpson’s paradox

Meta-analysis is a type of stratified analysis, stratified by study

- Effect sizes are combined using fixed-effects or random-effects models

- Under the fixed effect model we assume that there is one true effect size which

is shared by all the included studies. We are estimating the true effect size.

- Under the random effects model we allow that the true effect could vary from

study to study. We are estimating the mean of the effect-size distribution.

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4

Methodology

Studies can be combined at the individual (subject) level or at

the aggregate (summary) level. Individual level data are

generally preferred.

Steps:

1. Formulate purpose

2. Identify relevant studies

3. Establish inclusion and exclusion criteria for studies

4. Data abstraction

5. Data analysis

6. Interpretation

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5

Meta-Analysis of Safety Data

Driven by increasing concerns about emergent safety signals when drugsare used widely in large real-life patient populations

Small but clinically important signals may not be detected in clinicaldevelopment programs, but combining evidence from many studies mayprovide more precise answers

Examples:

Effect of Rosiglitazone on the risk of myocardial infarction and death fromcardiovascular causes

Effect of Muraglitazar on Death and Major Adverse Cardiovascular Eventsin Patients With Type 2 Diabetes Mellitus

Risk of Cardiovascular Events Associated With Selective COX-2 Inhibitors

Risk of Malignancy with Immunosuppressive Drugs in Inflammatory BowelDisease

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6

Refresher – Binary Outcome

Event No Event Total

Treatment a b a+b

Control c d c+d

Total a+c b+d n=a+b+c+d

Risk of Event in Treatment Arm = a/(a+b)

Risk Difference (RD) = a/(a+b) – c/(c+d)

Number Needed to Treat (NNT) = 1/(Risk Difference)

Risk Ratio (RR) = a/(a+b)/c/(c+d) = a(c+d)/c(a+b)

Odds of Event in Treatment Arm = a/b

Odds Ratio (OR) = a/b/c/d = ad/bc

What to pick for meta-analysis…RD, RR or OR?

Consider – Consistency across studies, Mathematical properties,Interpretability

No statistic is uniformly better, but Odds Ratio is often preferred based onthe above considerations (but OR is hardest to interpret)Ind

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Meta-analysis of Odds Ratios

Appropriate choices of statistical method appear to depend on:

- Control group risk

- Size of the treatment effect

- Balance (treatment vs control) in the constituent studies.

Available methods:

- Inverse Variance

- Mantel-Haenszel

- Peto (useful when events are rare)

- DerSimonian and Laird

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8

Reporting of Results

Mention how many studies were combined along with baseline characteristics

Example: 42 studies were combined…, Mean age was …, Baseline HbA1c was…

State how the data were combined (Eg., fixed-effects model) and the method used for pooling odds ratios (Eg., Peto)

Report the odds ratio for event in the treatment group, as compared with the control group, along with the confidence interval

Example: The odds ratio for risk of MI was 1.43 (95% CI, 1.03 to 1.98; P=0.03), and the odds ratio for death from cardiovascular causes was 1.64 (95% CI, 0.98 to 2.74; P=0.06).

Often useful to give a forest-plot.

State conclusions from the meta-analysis exercise in terms of the hypotheses of interest.

Example: The treatment group was associated with a significant increase in the risk of myocardial infarction and with a numerically higher (but not statistically significant increase in) risk of death from cardiovascular causes Ind

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Forest Plot

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Preparation of Annual Safety Reports and Integrated Summaries of Safety

Ajay Yalwar

9th Jul 2010

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Outline

Annual safety report (ASR)

– Why is it required?

– Regulatory guidelines

– Preparing an ASR

Integrated Summary of Safety (ISS)

– Why an ISS required?

– Preparing an ISS

– ISS in a Common Technical Document (CTD)

Conclusion

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A periodic report submitted to regulatory authorities and

the ethics committee, intended for ongoing assessment of

risks to trial subjects and action proposed to address

safety concerns.

Annual Safety Report (ASR)

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Summarize the current understanding of identified and potential risks

Describe new safety issues

– Update Investigator’s brochure

New information in accordance with previous knowledge

– Suspected Serious Adverse Reaction (SSAR)

– Suspected Unexpected Serious Adverse Reaction (SUSAR)

Provide an update on the status of clinical development program

Required by regulatory bodies

Why an ASR required

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Regulatory Guidelines

Should be intended to submit annually as long as the sponsor conducts clinical trial

or as long as appropriate to satisfy local requirements

A sponsor overseeing more than one clinical trial of a single investigational drug

should prepare only one report based on same Development International Birth

Date (DIBD) and a single Data Lock Point (DLP)

5

DLP 1 DLP 2

01JAN2009 01JAN2010 01JAN2011 01JAN2012

01MAR2009 01MAR2010 01MAR2011

01JUL2009 01JUL2010

01JUN2010

Trial 1 (US)

Trial 2 (UK)

Trial 3 (UK)

DIBD

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Guidelines Cont…

Once the drug gets an approval for marketing in one country, the data lock

point is adjusted according to International Birth Date (IBD) of the approval

6

01JUL2009

01MAR2010

Trial 1

Trial 2

01MAR2009

01MAR2011

01AUG2010

01JAN2009 01JAN2010 01JAN2011 01JAN2012

01AUG2011

DLP 1DLP 3Approval

DLP 2

IBD

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Guidelines Cont…

Should be submitted no later than 60 calendar days from the data lock point

If no further data is collected a covering letter may replace the actual report.

The Annual Safety Report should comprise of three parts

Part 1: Analysis of the subjects safety

Part 2: A line listing of all SSAR (including all SUSAR)

Part 3: An aggregate summary tabulation of SSARs.

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Preparing an ASR

Responsibilities:

The sponsor is responsible for the preparation, content & submission

The chief investigator is responsible for approving the prepared ASR

General considerations:

Latest Investigator Boucher (IB) for drug under trial should serve as a

reference safety information, version and date of IB to be mentioned

Same version of MedDRA to be used if the report involves more than one

trial

Data from all the trials on an Investigational product should be integrated to

show aggregated information in line listings and Summary tables8

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Preparing an ASR

When useful and feasible, SARs can be listed by protocol, indication, or

other variables

If a subject has an SAR more than once with different severity such

subject should be shown only once under most severe case

Same subject can be included in line listing more than once when a

subject has different SARs

Line listing and summary table should include number of SARs organized

by SOC and preferred term for each of investigational treatment arms,

comparators and blinded treatment arms

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A concise global analysis of the actual safety profile based on the

experience of all the clinical trials conducted on a common Investigational

product.

Results of all clinical studies performed on an investigational product

are combined into one database (called pooling)

Results are statistically summarized as a whole

Integrated Summary of Safety (ISS)

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Why is an ISS required

Much larger database than the individual study databases

Pooled safety data base allows

Study-by-study comparison of more common events

Pooled estimates of common and rarer events

Pooled analysis of effects in subgroups

Overview of deaths and withdrawals due to adverse events

Better chances of detecting statistically significant differences between the

safety profile of different treatment groups

ISS is required by regulatory authorities when submitting a new drug

application

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Life Cycle Flow - ISS

Once the analysis is completed for a individual study CSR is written

ISS will be prepared including multiple studies of common investigational

drug

ISS summarizes the pooled data from all studies

Pooled summaries give the overall safety profile of drug

Potential safety issues and the measures will be shown on the label of the

drug once it gets the approval

CSRs ISS Summaries Overview Label

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Process flow – ISS

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Location of an ISS in a CTD

A Common Technical Document (CTD) or eCTD is a document

which provides a common organization for the contents of a

marketing or licensing application

Section 2.7.4 of a CTD include the Summary of Clinical Safety

– Contains text with summary of ISS incorporating few tables and

figures

Module 5 of a CTD especially section 5.3.5.3 is an appropriate

location for ISS

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Conclusion

Annual Safety Reports aid in reporting new Serious Adverse Reactions of

Investigational Drug

Annual Safety Reports are prepared and submitted under strict guidance

of regulatory authorities

Integrated Summaries of Safety are an important part of New Drug

Applications

ISS is not just a summary but an analysis of analysis

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References

http://www.fda.gov/

http://www.sas.com/proceedings/sugi26/p129-26.pdf

http://www.clinical-trials-info.com/tag/iss/

http://www.ema.europa.eu/pdfs/human/ich/

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http://www.fda.gov/











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Questions?

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Thank you

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© 2006-2010, Sciformix Corporation. All rights reserved. Company Confidential Material1

&

&

Drug Product Life Cycle

Post Marketing Safety Assessment and Surveillance

Chitra Lele, CSO, Sciformix Corp IASCT – Bangalore – 9 July 2010

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Agenda

Safety Signal Detection

Utility and Limitation of Statistical Methods

Drug Safety – Recent Trends

Statistical Measures of Signals

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Prescription Drugs Withdrawn since 1997

Brand Use Manufacturer Year Approval Year Withdrawal

Raptiva Psoriasis Genentech 2003 2009

Bextra Antiarthritics Pfizer 2002 2005

Vioxx Antiarthritics Merck 1999 2004

Raplon Muscle Relaxant Akzo Noble 1999 2001

Baycol Cholesterol-lowering Bayer 1997 2001

Lotronex IBS Glaxo-Wellcome 2000 2000

Propulsid Heartburn Janssen (J&J) 1993 2000

Rezulin Diabetes Parke-Davis (Pfizer) 1997 2000

Raxar Antibiotic Glaxo-Wellcome 1997 1999

Hismanal Antihistamine Janssen (J&J) 1988 1999

Seldane Antihistamine Aventis 1985 1998

Posicor Hypertension Hoffmann-LaRoche 1997 1998

Duract Analgesic Wyeth-Ayerst 1997 1998

Redux Obesity Wyeth-Ayerst 1996 1997

Pondimin Obesity Wyeth-Ayerst 1973 1997Indian

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Vioxx -2004

On September 30th of 2004, Merck & Co. Inc. withdrew Vioxx due to its increased

cardiovascular risk. The product had worldwide sales of $2.5 billion in 2003, and had

approximately 20 million users in the United States.

On the same day, Merck's shares plunged, erasing $26.8 billion from its market

capitalization. Its shares fell about $12, or 27%. (WSJ 11/01/2004)

Merck’s legal costs could reach $38 billion. (Forbes 12/03/2004)

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Cost of Catastrophic Product Failures

Harm to Patients

Direct financial loss

Loss of sales of the withdrawn product

Expenses of withdrawing the product from market (e.g., inventory write-offs

and administrative costs).

Negative spillover effects on other brands

Potential litigation expenses

Money paid to lawyers

Settlement or liability costs

Damage to the company's goodwill

Affects all existing products of that company

Early Detection of safety issues is of paramount importanceIndian

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• Very small , narrow andspecific test population

• Limited objective• Limited time period• Ethically Considerations• Relatively expensive

400000

350000

300000

250000

200000

150000

100000

50000

0

Phase 1 Phase 2 Phase 3 Post Marketing

Year 1

Post Marketing

Year 2

Post Marketing

Year 5

Post Marketing

Year 10

Post Marketing

Year 15

Where can we get more data for drug safety investigations?

Nu

mb

er o

f Pa

tien

ts

Clinical Trials

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Agenda





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The Pharmacovigilance Process

Case Intake

Literature Search

Patient Registry

Call Center / E -mail / Fax

Triage

Case Processing

Quality Review

Medical Review

Regulatory Reporting

Periodic Safety Reports & Analysis

Data mining

Signal Detection

Data Management Analysis Risk Management

Safety Database

Labeling changes

HCP communication

Restrictions

Withdrawal

Data Collection

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Definition – from FDA GPVP guidance

A concern about an excess of adverse events compared to what would be

expected to be associated with a product's use. Signals can arise from postmarketing data and other sources, such as preclinical data and events associatedwith other products in the same pharmacologic class. It is possible that even asingle well-documented case report can be viewed as a signal, particularly if thereport describes a positive re-challenge or if the event is extremely rare in theabsence of drug use. Signals generally indicate the need for further investigation,which may or may not lead to the conclusion that the product caused the event.After a signal is identified, it should be further assessed to determine whether itrepresents a potential safety risk and whether other action should be taken.

Safety Signal

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Examples of Safety Signals

New unlabeled adverse events, especially if serious

An apparent increase in the severity of a labeled event

More than a small number of serious events thought to be extremely rare

New interactions

Identification of a new at-risk population

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Approaches to Signal Detection

Individual case review unusual events in the target population unusual severity of a specific event

Periodic review of aggregate information frequency tables – plain old counts increased frequency calculation

Case series analysis

Statistical methods for disproportionality assessment Caveat – not intended for hypothesis testing

Statistical

Clinical

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What is Safety Data Mining?

To systematically and objectively analyze records contained in huge drug safety

databases

Goal: to discover hidden ‘interesting’ patterns of adverse drug occurrences

• Detect rare, unpredictable ADRs

• Detect interactions

drug-drug (incl. OTC, herbal, traditional, etc.)

drug-food (incl. supplements etc.)

drug-disease

• Identify high-risk patient groups

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Databases of Spontaneous ADRs

US FDA Spontaneous Report System (SRS/AERS)

• AERS since 1997

• Over 250,000 ADRs annually

US FDA/CDC Vaccine Adverse Events (VAERS)

• Over 12,000 reports per year

World Health Organization VIGIBASE

Other Databases for Medical Devices, etc.

Company database

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Agenda





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Frequentist (Classical) Methods of Data Mining

Proportional Reporting Ratio (PRR)

Reporting Odds Ratio (ROR)

Pearson’s Chi-squared Test (with Yates’ correction)

Yule’s Q

Poisson Probability

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Newer Methods of Data Mining

IC: Information Component (Using BCPNN)

• WHO UMC

EBGM: Empirical Bayes Geometric Mean (Using Multi-gamma Poisson Shrinkage -

MGPS)

US FDA

HBLR: Hierarchical Bayes Logistic Regression

• Evolving

• Not very easy to implement

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Classical Methods

In all classical methods we look at several drug-event combinations, one at

a time

For a particular drug-event combination, construct a 2x2 contingency table

as

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Reporting Odds Ratio (ROR)

The standard error of ln(ROR) and 95 % confidence interval can be calculated by

Classical Methods

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Proportional ADR Reporting Ratio (PRR)

The standard error of ln(PRR) and 95 % confidence interval can be calculated by

Classical Methods

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Chi square (Yates' correction)

For example:

Deciding criteria is p-value

Classical Methods

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Yule’s Q

The standard error of Yule’s Q and the 95% CI is calculated byc

Classical Methods

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Poisson probability (Probability of getting at least a count in (1,1) cell under Poisson assumption)

Where μ is the expected number of reports in (1,1) cell:

Small p implies a misfit

Classical Methods

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Newer Methods

The other approach is to use the whole data on all possible drug-event combinations.

Consider the following representation:

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A measure of disproportionality, called Information Component (IC):

p(x) = Probability of a specific drug ‘x’ being listed on a case report;

p(y) = Probability of a specific ADR ‘y’ being listed on a case report;

p(x,y) = Probability that a specific drug - ADR combination ‘x’ and ’y’, is listed on a case report.

Newer Methods: IC

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Newer Methods: IC

Then the expectation and variance of IC can be derived as

2,1,2,1,1 jiij

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Newer Methods: IC

With asymptotic normality assumption an approximate 95% CI of IC for each drug-event combination is given by

WHO measure of importance = E (IC) – 2 SD

We suspect any combination if

SDICICVICE ijij 2)(2)(

02SDIC

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Newer Methods: EBGM

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Comparison of Signal Detection Methods

Statistical variability due to small N and multiple comparisons

PRR and ROR need a Chi-square test & p-value to indicate level of

uncertainty associated with small N

IC and EBGM address both issues by using Bayesian shrinkage

Sensitivity

Bayesian methods are more specific and less sensitive as compared to

the classical, frequentist methods

They highlight fewer signals than frequentist methods due to the

statistical shrinkage associated with low reporting frequencies

In general, the classical methods work well when the cell frequencies are

large

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Comparison of Signal Detection Methods

Adjustment for Confounders

Presence of confounders can lead to high scores for certain drug-event

combinations

For e.g., if a drug is more commonly prescribed for women over 50

years of age and an event occurs more frequently in women over

50, we may get a high score for this drug-event combination

Mantel-Haenszel stratification

Is used in MGPS (EBGM)

Divides the database into groups, calculates scores for each group

and then an overall score

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Example

We analyzed data from FDA AERS over 15 quarters

Objective was to study the association of the drug Singulair with mood and suicidal

behavior

PTs related to mood & suicidal behavior were studied separately and pooled

No suggestion of increased risk when PTs are pooled

Suggestion of increased risk for Individual PTs of Abnormal behavior, Crying,

Decreased interest, Depressed mood and Suicidal behavior

Our observed agreement between the qualitative results for PRR, IC and EBGM for

individual PTs is mostly in line with what is expected based on cell counts

All disclaimers with respect to medical relevance & interpretation apply!

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Agenda





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Using Signal Scores

Safety Database

Algorithms Score

•Highlight potential safety Issues•Actual safety signal

• The score indicates association between drug and event.• Need professionals to determine the causal relationship between drug and event.

Higher score = stronger statistical association

High Score may also be result of• Reverse Causality• Publicity Effects and High reporting incidence• Causal relationship that is already known and labeled

Stronger association may indicate a causal relationship between drug and the event

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Data Mining Used to Prioritize Investigation

Ranking

• Sort several million potential combinations of drugs and events in a large public databases• Prioritizing reviews to be undertaken first• Prioritization may be based on company goals, medical knowledge and safety evaluation experience• Just one new report of, for e.g., QT prolongation, may result in high priority for investigation

• Straightforward way to prioritize investigations using statistical scores• Investigation may be started with the highest score generated and work downwards• A pre-determined number of top scores (5, 10, 50 etc.) may be selected for further

investigation, irrespective of their magnitude

Threshold

• A threshold score may be chosen and scores exceeding this are considered for investigation• Threshold scores fluctuate over time• Threshold score can be chosen based on resources within the organization and organizationalpriorities

Prioritization

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Data quality issues

Missing information (esp. for spontaneous reports)

Duplicate reports

Report source

• health professional vs. consumer, caregiver, etc.

• consumer reports often trivial, medically unclear

• consumer reports valid for subjective symptoms

direct patient knowledge vs. hearsay

medical information request vs. suspected ADR report

Coding dictionaries & entry conventions

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Interpretation

No external “gold standard”, i.e. real incidence data, to assess sensitivity and specificity

(and consequently, rates of false positives, false negatives, and predictive value) of data

mining

Therefore sensitivity and specificity cannot be optimized (cf. diagnostic tests)

Need to use a higher threshold for data from public safety databases than when using

smaller & more controlled company databases, since there may be biases due to

over/under reporting in the public databases

Need to test if association exists, or if there are alternate explanations (chance, bias, off-

label use, other confounders)

Assess if further evaluation is required

• Clinical studies, pharmaco-epidemiology, registries etc.

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Causality Assessment

Determination of whether there is a reasonable possibility that the product is

etiologically related to the adverse experience. Causality assessment includes, for

example, assessment of temporal relationships, dechallenge/rechallenge information,

association with (or lack of association with) underlying disease, presence (or absence)

of a more likely cause, and physiologic plausibility

- FDA Draft Guidance March 2001

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Benefits & Costs

Benefits

• Signal detection methods are an independent and automated process for assessing all the drug event– event combinations in a safety database containing millions of records

• Signal detection can provide early warning better than the methods that rely on human knowledgeskill and intuition

• Reduce the amount of effort needed to discover signals in safety data• Detection of unknown and unexpected events that happen more frequently in combination with one

drug than with most all other drugs

Costs

• Resource may be spent investigating “false positives”• “False negatives” may mask a drug-event combination that is causally related• The conundrum – investigating few high scores to reduce false positives might increase the number

of false negatives• The need for efficiency has to be balanced against the cost of missing any true signals

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References

FDA Guidance, Good Pharmacovigilance Practices and PharmacoepidemiologicAssessment, Mar 2005http://www.fda.gov/cder/guidance/6359OCC.pdf

EU Guideline, Risk Management Systems For Medicinal Products For Human Use, Nov 2005http://www.emea.europa.eu/pdfs/human/euleg/9626805en.pdf

ICH Harmonised Tripartite Guideline - Pharmacovigilance Planning (E2E), Nov 2004http://www.ich.org/LOB/media/MEDIA1195.pdf

Guideline on the use of Statistical Signal Detection Methods in the Eudravigilance Data Analysis System of the Eudravigilance Working Group (EV-EWG), Doc Ref EMEA/106464/2006

Quantitative Methods in Pharmacovigilance: Focus on Signal Detection, Hauben M, Zhou X, Drug Safety 2003; 26 (3): 159-186

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http://www.fda.gov/cder/guidance/6359OCC.pdf

http://www.ich.org/LOB/media/MEDIA1195.pdf


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trials indian association for statistics in clinical

Documents