clinical epidemiology & analytics – filling the evidence gap woodie m. zachry, iii, phd global...

Clinical Epidemiology & Analytics – filling the evidence gap

Woodie M. Zachry, III, PhD

Global Lead Clinical Epidemiology and Analytics

2 Company Confidential© 2009 Abbott

The Present – Overview of CE&A activities

Establishing the disease profile

– Natural history of the disease

– Issues in special populations

– Incidence/prevalence of the disease

– Risk factors of disease

Identifying drug safety issues in collaboration with Pharmacovigilance

– Safety issues of Abbott products and other current therapies

– Subpopulations at higher risk?

– Drug-drug interactions?

Providing clinical trial support and instrumentation

– Identifying biomarkers/surrogate endpoints and its relationship to outcomes


Study Types & Data Sources

Study Type

Potential Sources of Information

Preclinical Database

AEGIS AERS WHO Registry Claims

Data

Clinical Trials Database

Literature Cochrane

Systematic Review with Meta Analysis

X X

Randomized Controlled Trial

X X X

Experimental Designs

X X X

Cohort X X XCase Control X X X

Case Report X X X X X X


GRADE

– The Grading of Recommendations Assessment, Development and Evaluation (GRADE )

– Provides a system for rating quality of evidence and strength of recommendations that is explicit, comprehensive, transparent, and pragmatic and is increasingly being adopted by organizations worldwide

• High quality— Further research is very unlikely to change the estimate of effect

• Moderate quality— Further research is likely to have an important impact on the estimate of effect and may change the estimate

• Low quality— Further research is very likely to have an important impact on the estimate of effect and is likely to change the estimate

• Very low quality— Any estimate of effect is very uncertain


Nonsystematic Nonsystematic Clinical Experience Clinical Experience

Case-ControlCase-Control

Case SeriesCase Series

Observational StudiesObservational Studies

RCTRCT

Prospective

Retrospective Less Bias

Meta-analysisMeta-analysis

Uncontrolled

Comparison with bias

Less Bias

Hierarchy of Evidence


Multiple EBM Stakeholders

Levels of Evidence

Chest

Users’ GuidesJAMA

ACP Journal ClubClinical Evidence Cochrane

Collaborative

CONSORTStatement

RCTs

QUORUM Statement

Systematic ReviewMeta-Analysis

Clinical Practice

Guidelines

EMEA

HTAs

FDAAHRQ

NIH

NICE


Where we want to be

Evidence Based

Approach

Evidence Summaries across

All Phases of Development and

Study Designs

Identify Evidence

Gaps and ProposeWays to Fill

Gaps

Case-Control analysis of ambulance, emergency room, or inpatient hospital events for epilepsy and antiepileptic drug formulation changes

Woodie M Zachry, III PhD

Quynhchau D Doan PhD

Jerry D Clewell, PharmD

Brien J Smith MD


Background Epilepsy TreatmentDisease & Treatment

– Incidence: 200,000 cases annually in US, Prevalence: 1% from birth to age 20, then 3% by age 75.6

– Treatment choice dependent upon Partial vs. Generalized presentation, history & secondary causes.

– “A-rated” compounds are considered to be therapeutically bioequivalent to the reference listed drug (United States Food & Drug Administration Center for Drug Evaluation & Research)

• Generic substitution, observational experience

– 65% of US physicians surveyed reported caring for a patient who had a breakthrough seizure after a brand to generic switch.1

– 49.2% of foreign physicians surveyed reported problems when switching from brand alternatives to generics.2

– 67.8% of surveyed neurologists reported breakthrough seizures after a switch.3

– 12.9% of Lamotrigine switches had to be switched back due to medical necessity (v.s 1.5-2.9 for Non-AED).4

– 10.8% of patients switching supplier for CBZ, PHT, & VAL had perceived problems validated by GP.5

1. Berg MJ, Gross RA. Physicians and patients perceive that generic drug substitution of anti-epileptic drugs can cause breakthrough seizures - results from a U.S. survey. 60th Annual Meeting of the American Epilepsy Society; Dec 1-5, 2006; San Diego, California.

2. Kramer G. et al. Experience with generic drugs in epilepsy patients: an electronic survey of members of the German, Austrian and Swiss branches of the ILAE. Epilepsia 2007;48, 609-11.

3. Wilner AN. Therapeutic equivalency of generic antiepileptic drugs: results of a survey. Epilepsy Behavior 2004;5(6):995-8.4. Andermann F, et al. Compulsory generic switching of antiepileptic drugs: high switchback rates ro branded compounds compared with other drug classes. Epilepsia

2007;48(3):464-9.5. Crawford P. et al. Generic Prescribing for epilepsy. Is it safe? Siezure 1996;5:1-5.6. Centers for Disease Control and Prevention 2007. http://www.cdc.gov/epilepsy/ Accessed October 10, 2007.

http://www.cdc.gov/epilepsy/


Confidence in Treatment-Effect Relationship

Case Reports Case-Control Epidemiological Cohort Epidemiological RCCT

Hypothesis generation

Hypothesis test (without temporal relationship)

Hypothesis test (with temporal relationship assessment)

Hypothesis test (Cause – Effect relationship inferred)

Spontaneous reports to authorities with variable completeness and data quality

Subjects selected based on current disease status (yes / no).

Retrospectively evaluate exposure to agent(s) & confounders

Exposed Vs. non-exposed subjects assembled before development of disease.

Baseline confounding variables assessed before disease development.

Treatment and Control groups studied in randomized, blinded trial

Detection bias

Selection bias

Effects of risk factors are most difficult to evaluate

Confounding patient factors often not considered

Cannot establish causality

Usually not possible to calculate rate of development of disease given the presence or absence of exposure.1,2


Treatment-emergent, temporal relationship to exposure, and incidence of disease can be measured.

Most closely resembles RCT design.1,2


Causality can be inferred

Limited ability to detect rare events.

Generalizability limited by inability to detect events in the greater population, and sub-populations.

1Mednick D, Day D. JMCP 1997;3(1):66-75. 2Hennekens, C. Epidemiology in Medicine. 3Harris S. J Cont. Ed. In Health Prof 2000;20:133-45.

Low High


Methods

• Objective: To determine if patients who received epilepsy care in an inpatient setting, emergency room, or ambulance have greater odds of having had a change between A rated AED medication alternatives in the past 6 months when compared to epileptic patients with no evidence of receiving epileptic care in similar settings.


Methods

• Retrospective claims database analysis utilizing the Ingenix LabRx database

• Case-control study

– Unmatched & Matched 1:3 for age within 5 years and epilepsy diagnosis type

– Index date for case patients: 1st seizure event requiring inpatient admission, emergency room visit, or ambulance during 3Q2006 – 4Q2006

– Index date for control patients: 1st office visit during 3Q2006 – 4Q2006

• Index primary ICD-9 diagnosis of 345.xx excluding 345.6

12 and 64 years of age

• No inpatient admission, emergency room visit, or ambulance in 6 months prior to index date

• Possess at least 145 day supply of AED medication for 6 months prior to index event

• Continuous eligibility for 6 months prior to index.


Diagnosis Categories

• Siezure type

– Generalized• Convulsive 345.0

• Non-convulsive 345.1

• Petite mal status 345.2

• Grand mal status 345.3

– Partial • Complex partial 345.4

• Simple partial 345.5

• Epilepsia partialis continua 345.7

– Other• Other forms 345.8

• Epilepsy unspecified 345.9

• Modifier

– XXX.X0 – without mention of intractable epilepsy

– XXX.X1 – with mention of intractable epilepsy


All Patients (Non-Matched)

Variable Case Patients (n=417)

Control Patients (n=5562)

P value (a-priori=0.05)

% Male 44.8% 45.1% NSAge (SD)

Insurance

Commercial

Medicaid

US Region

West

Midwest

South

Northeast

37.4yrs (14.8)

95.4%

4.6%

12.7%

33.1%

42.0%

12.2%

37.2yrs (14.6)

98.1%

1.9%

14.5%

33.8%

40.0%

11.6%

NS

<0.001

NS


Matched Case-Control Patients

Variable Case Patients (n=416)


P value (a-priori=0.05)

% Male 45.0% 44.2% NSAge (SD)

Insurance

Commercial

Medicaid

US Region

West

Midwest

South

Northeast

37.4yrs (14.8)

95.4%

4.6%

12.7 %

33.2 %

41.8 %

12.3 %

37.5yrs (14.7)

98.2%

1.8%

14.3 %

33.6 %

39.2 %

13.0 %

NS

0.004

NS



Seizure Type Case Patients (n=417)


Generalized nonintractable

Generalized intractable

Partial nonintractable

Partial intractable

Other, nonintractable

Other, intractable

30.5%

9.1%

19.2%

26.4%

3.1%

11.8%

35.5%

6.7%

36.0%

16.6%

1.1%

4.1%

2 <0.001



Seizure Type Case Patients (n=416)


Generalized nonintractable

Generalized intractable

Partial nonintractable

Partial intractable

Other, nonintractable

Other, intractable

30.5%

9.1%

19.2%

26.4%

2.9%

11.8%

30.5%

9.1%

19.2%

26.4%

2.9%

11.8%

2 = NS



• Odds of a change between A rated alternatives

Odds ratio = 1.915 (95% CI, 1.387 - 2.644)

Patient switched medications

Patients did NOT switch medications

Case 47

370

Control 346 5216


How to calculate an unmatched odds ratio

Unmatched analysisCohort Status EquationsCase Control

Exposed a b a+b OR = ad/bcNot Exposed c d c+d SE = SQRT(1/a+1/b+1/c+1/d)

a+c b+d n CI = EXP(logeOR + 1.96SE)

Example CalculationCase Control OR estimate 1.91

Switch 47 346 393 SE 0.16No Switch 370 5216 5586 1.96*SE 0.32

417 5562 lnOR 0.65Lower Limit 0.33 1.39Upper Limit 0.97 2.64

Example

Risk factor




Odds ratio = 1.811 (95% CI, 1.247 – 2.629)



Case 47 369

Control 81 1167

21

Matched primary analysis

Case with exposure total sets0 1 2 3

yes 40 7 0 0 47no 298 68 3 0 369

total 416

i = # of exposures i mi ti iti i(4-i)ti (4-i)mi i(ti-mi)1 40 108 108 324 120 682 7 10 20 40 14 63 0 0 0 0 0 0

total 47 118 128 364 134 74

H J

i ti i(4-i)ti (iOR+4-i)2 H/J1 108 324 23.1439 13.999372 10 40 31.60263 1.2657173 0 0 41.37619 0

15.26509

OR estimate 1.810811 Cochran-Mantel-Haenszel StatisticSE 0.190201 0.190201 MH calc 60 58 33641.96*SE 0.372795 MH stat 9.241758lnOR 0.593775 P value 0.0024Lower Limit 0.22098 1.247298

Number of controls with exposure

mi = number of ti where

the case is exposedti = the total number of

sets with i exposures


Matched Case-Control Patients Excluding Medicaid Patients


Odds ratio = 1.855 (95% CI, 1.262 – 2.726)



Case 45 352

Control 79 1146


Matched Case-Control Patients Excluding Patients Who Changed Dosage Schedule


Odds ratio = 2.011 (95% CI, 1.189 – 3.4)



Case 22 205

Control 49 918


Discussion

• This study tested a hypothesis and found a relationship between emergent and inpatient care visits and previous AED formulation switching. This is concordant with problems identified in the survey and case study literature.

– surveyed physicians believe there may be potential safety problems associated with switching between AED formulations for the same medication

– There is some evidence of a significant percentage of patients who must switch back to a branded formulation after trying a generic formulation.


Discussion

• This study assumes that patients experiencing break-through seizures will seek care in emergency and inpatient settings more often than ambulatory settings.

• Study subjects seeking care for break through events in an ambulatory setting may have attenuated the true magnitude of the significant relationship found in this study.

• Attempts were made to strengthen the assumption that subjects were taking AEDs. However, claims data only records the date a prescription was filled, not when or if the patient took the medication.

• Subtle differences in formulations may take time to accumulate and effect outcomes. However, the majority of formulation changes occurred within 2 months of the index event.


Discussion

• Several factors may play a role in break through seizures that were not controlled for in this analysis (e.g., sleep deprivation, alcohol intake, hormonal influences). These effects may be additive to or even supersede formulation changes in precipitating break-through seizures.

• Zonisamide became available as a generic during the study time period. The high percentage of zonisamide formulation changes may have played a role in the significant relationship discovered.

• Case-control studies cannot establish a temporal association between AED formulation switches and outcomes.


Conclusions

• This analysis has found an association between patients who utilized an ER, ambulance or inpatient hospital for epilepsy and the prior occurrence of AED formulation switching involving “A” rated generics.

– After matching by age and epilepsy diagnosis, Cases had 81% greater odds of prior “A” rated switches compared to matched controls.

– The case population had significantly more Medicaid patients.

– Post hoc analyses excluding patients who had a dosage change and Medicaid patients did not change the significance of the original analysis.

– Further investigations are warranted to better understand a possible cause-effect relationship.


Nonsystematic Nonsystematic Clinical Experience Clinical Experience

Case-ControlCase-Control

Case SeriesCase Series

Observational StudiesObservational Studies

RCTRCT

Prospective

Retrospective Less Bias

Meta-analysisMeta-analysis

Uncontrolled

Comparison with bias

Less Bias

Hierarchy of Evidence

clinical epidemiology & analytics – filling the evidence gap woodie m. zachry, iii, phd global...

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