clinical trial. clinical trials strengths: – best measure of causal relationship – best design...

CLINICAL TRIAL

Clinical Trials

Strengths:– Best measure of causal relationship– Best design for controlling bias– Can measure multiple outcomes

Weaknesses:– High cost– Ethical issues may be a problem– Compliance

Intuition and Logic in Research

Dominant Mental ActivityIntuition

Feeling

Judgement

Experience

Analysis

Experiment

Control over variance

Hi

Potential for Misinterpretation

Qualitative

Research

Case Report

Case Series

Cross-sectional Study

Case-control Study

Cohort Study

Clinical trials

Lo

LoHi

Randomised Controlled Trial (RCT)

Strength of evidence

Anecdote

Observational

ProspectiveRetrospective

Experimental

Case series

Cohort studyCase-control study

RCT

Systematic Review

Randomised Controlled Trial (RCT)

RCT is a trial in which subjects are randomly

assigned to two groups: -the experimental group-the comparison group or Controls

Source: Cochrane Collaboration Glossary

CASP

Randomised controlled trial

population

Outcome

Outcome

group 1

group 2

new treatment

control treatment

inclusion/exclusion

Study population (participant) treatment / control

InvestigatorsAssessors Clinical intervention (medical,

surgical,hygiene) Outcome

Who is in control?

• Every experiment should have a “control group.”

• People in control group are treated exactly the same way as the other people in the experiment, except they do not get the “active treatment.”

• A “placebo group” is a special kind of control group.

RANDOMIZATION

Definition

advantage Pseudo randomization( quasi –R) disadvantage

بین افراد تصادفی تقسیم راههایگروهها

• coin• toss

• envelope• Random number table• Computer assisted

Blinding:Open

Single-blind Double blind :with placebo or active

control(double dummy)neither the researcher nor the individuals

know who received what

Triple blind

Potential benefits accruing dependent on those individuals successfully blinded

Individualspsychological More likely to comply with trial regimensLess likely to seek additional interventionsLess likely to leave trial

Trial investigators Less likely to transfer their inclinations or attitudes to participantsLess likely to differentially administer co-interventionsLess likely to differentially adjust doseLess likely to differentially withdraw participantsLess likely to differentially encourage or discourage participants to continue trial

Assessors Less likely to have biases affect their outcome assessments, especially with subjective outcomes

Ascertainment

selection BIAS

Inappropriate

handling of

withdrawals

publication

• SELECTION BIAS Inclusion & exclusion

Intervention

New drug on MS and depression

• Randomization• Allocation concealment

– if both patients and investigators could not predict the next assignment of treatment

Double blinding prevents ascertainment bias and protects randomization after allocation and during study

Allocation concealment prevents selection bias and protects randomization during selection

RCT IS NOT suitable for:

* ETIOLOGY AND CLINICAL COURSE smoking and cancer

* RARE & PROLONGED OUTCOME

ethics

• Phase 1 – 20-80– Toxic and pharmacologic effects

• Phase 2 – 100-200– Efficacy – immunity

• Phase 3– RCT– Multicenter

• Phase 4– After release

Quality of RCT

RCTs - a checklist• Good randomisation procedures• patients blind to treatment• clinicians blind to treatment• all participants followed up• all participants analysed in the groups to

which they were randomised (intention to treat)

limitations

• Loss to follow up• Contamination

– Drop out– Drop in

Effect

7525

8713

Yes No

Cure

A

B

Treatment100

100

16238 200

Total

Randomized Clinical Trials

• ARR(absolute risk reduction)• RR• OR• RRR:Efficacy= (risk in treatment-risk in

control)/risk in control• NNT(Number needed to treat)=1/ARR

Definition

• Number Needed to Treat (NNT):– Number of persons who would have to receive

an intervention for 1 to benefit.– 100/ARR (where ARR is %)– 1/ARR (where ARR is proportion)

NNTs from Controlled Trials

CER% EER% ARR% NNT

Population: hypertensive 60-year-oldsTherapy: oral diureticsOutcome: stroke over 5 years

2.9 1.9 1 100

Population: myocardial infarctionTherapy: ß-blockersOutcome: death over 2 years

9.8 7.3 2.5 40

Population: acute myocardial infarctionTherapy: streptokinase (thrombolytic)Outcome: death over 5 weeks

12 9.2 2.8 36

Cross over studies

Cross over studies

• Types:– planned

• Washout period• Sequence of treatment

– Unplanned

37

Factorial designs

• Two or more independent variables are manipulated in a single experiment

• They are referred to as factors• The major purpose of the research is to

explore their effects jointly• Factorial design produce efficient

experiments, each observation supplies information about all of the factors

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A simple example• Investigate an education

program with a variety of variations to find out the best combination– Amount of time receiving

instruction• 1 hour per week vs. 4 hour per

week– Settings

• In-class vs. pull out• 2 X 2 factorial design

– Number of numbers tells how many factors

– Number values tell how many levels

– The result of multiplying tells how many treatment groups that we have in a factorial design

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Main effects

• Main effect of time• Main effect of setting• Main effects on both

Friday, May 14, 2004 ISYS3015 Analytical Methods for IS ProfessionalsSchool of IT, The University of Sydney 44

A simple example• Investigate an education

program with a variety of variations to find out the best combination– Amount of time receiving

instruction• 1 hour per week vs. 4 hour per

week– Settings

• In-class vs. pull out• 2 X 2 factorial design

– Number of numbers tells how many factors

– Number values tell how many levels

– The result of multiplying tells how many treatment groups that we have in a factorial design

Friday, May 14, 2004 ISYS3015 Analytical Methods for IS ProfessionalsSchool of IT, The University of Sydney 45

Null outcome

• None of the treatment has any effect

• Main effect– is an outcome that is a

consistent difference between levels of a factor.

• Interaction effect– An interaction effect exists

when differences on one factor depend on the level you are on another factor.

Friday, May 14, 2004 ISYS3015 Analytical Methods for IS ProfessionalsSchool of IT, The University of Sydney 46

Main effects

• Main effect of time• Main effect of setting• Main effects on both

Friday, May 14, 2004ISYS3015 Analytical Methods for IS

ProfessionalsSchool of IT, The University of Sydney

47

Interaction effect

• An interaction effect exists when differences on one factor depend on the level of another factor

Friday, May 14, 2004 ISYS3015 Analytical Methods for IS ProfessionalsSchool of IT, The University of Sydney 48

Interaction effect

• Interaction as a difference in magnitude of response

• Interaction as a difference in direction of response

Before after study