Design and Analysis of Clinical Study 12. Randomized Clinical Trials

Dr. Tuan V. Nguyen

Garvan Institute of Medical Research

Sydney, Australia

Basic Design of Clinical Trials

Sample

Subjects

Randomise

Treatment

Placebo

Cured Same

Cured SameBlinding

Blocking

Variations in Basic Design - 1

Run-in Design

Admission On placebo Compliers randomised

Cross-over Design

Recruitment Randomised

Treatment 1

Treatment 2

A

S

S

E

S

S

A

S

S

E

S

S

Variations in Basic Design - 2

Time - Series Design

Recruitment Treatment

Assess

No Treatment

Assess

Treatment

Assess

Factorial Design

Recruitment Randomise

Treatment 1

Treatment 2

Low Dose

High Dose

Low Dose

High Dose

Issues of Methodology - I

• Entry criteria– Strict for explanatory trials

– Less strict for pragmatic trials

• Diagnosis– How accurate?

• Intervention– Compliance

– Drop-out

– Competing intervention

Issues of Methodology - II

• Subject allocation– Different rates of drop-out between groups cause

under or over estimate of outcome

• Treatment allocation– Randomisation and blinding help to remove bias.

Blocking needed if outcomes vary because of age, sex or other attributes.

Challenges in Designing Clinical Trials

• Control of bias– In allocation of subjects to treatment

– In assessment of outcome

• Sample size– How small a difference is clinically important?

– What tests of significance will be used?

– What outcome is expected in the control group?

• Drop-outs and withdrawals– How to handle them during analysis?

Generalizability of Results

Population of Patients

Sample

Treated

Control

Outcome

Outcome

Difference in Outcome by Chance?

GeneralisabilityOther

Populations

Rigour of study

Randomization works!

• Volunteerism• Eligibility• Placebo effect• Hawthorne effect• Regression towards the mean

Volunteerism, Eligibility, Placebo Effect

• Volunteerism– People who agree to participate in clinical trials are an “elite”

group of patients with extremely good prognosis.

• Eligibility: – Patients have to meet stringent eligibility criteria before

randomization, or they would be excluded

• Placebo can do just about anything (prolong life, cure cancer).

• Placebo can also cause side effects.• Placebo effect is very useful in medicine but in

epidemiology it causes problems, so we try to equalize it between the 2 groups.

Regression Towards the Mean

• Weather game• Individuals with initially abnormal results tend on average

to have more normal (closer to the mean) results later.• Lab tests, BP etc.• Recheck before randomization. Run-in period.• Sophomore slump, medical school, Airforce landing

feedback

Objectives of Subgroup Analysis

• Support the main finding• Check the consistency of main finding• Address specific concerns re efficacy or safety in specific

subgroup• Generate hypotheses for future studies

Inappropriate Uses of Subgroup Analysis

• Rescue a negative trial• Rescue a harmful trial• Data dredging: find interesting results without a

prespecified plan or hypothesis

To Avoid Inappropriate Uses of Subgroup Analysis

• Prespecify analysis plan• Prespecify hypotheses to be tested based on prior

evidence• Plan adequate power in the subgroups• Avoid the previous pitfalls

Problems with Subgroup Analysis

1. Low power

2. Multiplicity

3. Test for interaction

4. Comparability of the treatment groups maybe compromized

5. Over interpretation

ITT

• Intention to treat analysis• Once randomized always analyzed• Why ?

1. Change in therapy may be related to outcome or eligibility

2. To get the full benefit of randomization

3. Effectiveness versus efficacy

Five-Year Mortality in Coronary Drug Project

ADHERENCE CLOFIBRATE PLACEBO

Total 18.2 19.4

> 80% 15.0 15.1

< 80% 24.6 28.2 (p<5X10-16)

Screening Mammography

Population Incidence of breast cancer /1000 woman years

Not offered screening 2.03

Screened 2.20

Refused screening 1.80

Descriptions of “Trials”

• 34% relative decrease in the incidence of MI. The decrease is statistically significant. The 95% CI ranges from 55% relative decrease to a 9% relative decrease.

• 1.4% decrease in …. (2.5% versus 3.9%). The decrease is statistically significant. The 95% confidence interval ranges from a 2.5% decrease to a ..

• 77 persons must be treated for an average of just over 5 years to prevent 1 MI.

Ethical Issues

• When is it unethical to randomize ? • When Do you stop a trial?• Data Safety Monitoring Board• Early Termination rules• O’Brien Fleming

1. Early vs. late

2. Benefit vs. harm (blinding?)

3. Multiplicity

4. Rules. Scenarios.