analyzing rcts

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Analyzing RCTs Arin Basu Prepared for HLTH 460-462 27-04-2010

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Page 1: Analyzing RCTs

Analyzing RCTs

Arin BasuPrepared for

HLTH 460-46227-04-2010

Page 2: Analyzing RCTs

Objectives

• Principles of RCT• Describe RCT• How to analyze results• Advantages of RCT• Limitations of RCT• Interpret RCT

Page 3: Analyzing RCTs

What is an RCT?SELECT POPULATION

RANDOM ASSIGNMENT

INTERVENTION GROUPCONTROL GROUP

FOLLOW UP

COMPARE OUTCOMES FOR BOTH GROUPS

Page 4: Analyzing RCTs

Begin with homogenous group

Individual with disease

Page 5: Analyzing RCTs

Use randomization to split

RANDOMIZATION CAN BE DONE USING RANDOM NUMBERS TABLESOFTWARE PROGRAMME

Page 6: Analyzing RCTs

Follow up to observe outcomes

Individuals with desired outcomes

Individuals with the disease

Page 7: Analyzing RCTs

Comparison of results

Intervention/Control Outcome status

PRESENT ABSENT Total

INTERVENTION A B A+B

CONTROL C D C+DTotal A+C B+D A+B+C+D

Experiment Event Rate (EER) = (A / (A+B)) * 100Control Event Rate (CER) = (C / (C+D)) * 100Absolute Risk Reduction = EER - CERRelative Risk = EER/CER

Page 8: Analyzing RCTs

Types of RCT• Parallel Group RCT• Factorial RCT• Crossover RCT• Cluster RCT• Trial types

– Superiority– Non-inferiority– Equivalence

• Pragmatic RCT vs Traditional RCT

Page 9: Analyzing RCTs

Parallel group RCT

Initial allotment

INTERVENTION CONTROL

Final Evaluation

Diseased individuals

Individuals who got better with treatment

Page 10: Analyzing RCTs

Factorial RCTTREATMENT X

INTERVENTION CONTROL

TREATMENT Y

INTERVENTION

CONTROL

Page 11: Analyzing RCTs

Crossover RCT

Intervention Control

Washout Period

Page 12: Analyzing RCTs

Trial Types based on aims

• Superiority trial– Is treatment X superior to placebo or another

treatment Y?• Non-inferiority trial

– Is treatment X not inferior or as good as another treatment Y?

• Equivalence trials– Is treatment X as good as or certainly as harmful

as no more harmful than another treatment Y?

Page 13: Analyzing RCTs

Pragmatic RCT

• RCT tight selection criteria• Internal validity high generalizability low• Two approaches – not do RCT at all, or

observational studies• Pragmatic RCT is a good middle way• Relaxed inclusion criteria• Concealment of allocation• Appropriate for behavioural intervention studies

Page 14: Analyzing RCTs

Cluster RCT

Clusters of individuals and unit of analysis are clusters

Page 15: Analyzing RCTs

Why are RCTs good?

• Randomization: balancing• Control of chance by appropriate sample size

selection• Control of bias by blinding

– A process where the investigator is unaware of the allocation status (single blinding)

– When the participant is unaware of allocation status (double blinding)

• Control of confounding by randomization

Page 16: Analyzing RCTs

Limitation of RCT

• Expensive in terms of time and money• Not generalizable• Low external validity• Otherwise, RCTs are good study designs

Page 17: Analyzing RCTs

Example of an RCT

Page 18: Analyzing RCTs

Cohort Study

• Observational epidemiological study• Individuals are selected on the basis of their

exposure• Exposed and non-exposed individuals are

followed up in time• Followed up to observe emergence of the

outcomes• Rates of outcomes compared

Page 19: Analyzing RCTs

Steps of Cohort studyStudy Population

Exposure staus

Exposed Non-exposed

Rate of disease occurrence in exposed

Rate of disease occurrence in non-exposed

Page 20: Analyzing RCTs

Type of Cohort study

• Prospective cohort study– Exposure occurs first followed by outcome but

outcome status not known at the time of exposure assignment

• Retrospective cohort study– Exposure precedes outcome– However, outcome status is known at the time of

exposure cohort assembly

Page 21: Analyzing RCTs

Analysis of results

Exposure Outcome Total

Present Absent

Exposed A B A+B

Non exposed C D C+D

Total A+C B+D A+B+C+D

Rate of the disease among exposed = A/(A+B)Rate of disease or outcome among non-exposed = C / (C+D)Relative risk = Rate among exposed / Rate among non-exposed

Page 22: Analyzing RCTs

Why are cohort studies good?

• Since sampling is done on exposure therefore easier to control for sample size therefore chance

• Always followed up, therefore presence of exposure before outcome is guaranteed

• Good for studying many outcomes

Page 23: Analyzing RCTs

Limitations of cohort studies

• Risk of biased observation– Bias during cohort formation– Bias during recording of outcomes

• Role of uncontrolled for confounding is a problem

Page 24: Analyzing RCTs

Summary

• RCTs and cohort studies are two important study designs in epidemiology and health research

• RCTs can be extended to studies beyond health care

• Pragmatic RCTs enable complex interventions to be studied

• Cohort studies are best study designs among observational studies

Page 25: Analyzing RCTs

Example of cohort study