Introduction to Clinical Trials
Introduction to Clinical Trials
Issues in Analysis of Randomized Clinical Trials
Sources of Bias
1. Patient selection
2. Treatment assignment
3. Patient Evaluation
4. Data Analysis
Minimize Bias
1. Randomized Controls
2. Double blind (masked)
3. Analyze what is randomized
What Data Should Be Analyzed?
* Basic Intention-to-Treat (ITT) Procedure
o Analyze what is randomized!
* Randomized control trial “gold” standard
o Beware of lookalikes
* Definitions
Exclusions
o Screened but not randomized
o Affects generalizability
o Validity OK
Withdrawals from Analysis
o Randomized, but not included in data analysis
o Possible to introduce bias!
Patient Closeout
* ICH E9 Glossary
o “Intention-to-treat principle - …It has the consequence that subjects allocated to a treatment group should be followed up, assessed, and analyzed as members of that group irrespective of their compliance with the planned course of treatment.”
Patient Withdrawn in Analysis
* Common Practice - 1980s
o Over 3 years, 37/109 trials in New England Journal of Medicine
* Typical Reasons Given
a. Patient ineligible (in retrospect)
b. Noncompliance
c. Competing events
d. Missing data
I. Patient Withdrawn in Analysis
A. Patient INELIGIBLE
o After randomization, discover some patients did not in fact meet entry criteria
o Concern ineligible patients may dilute treatment effect
o Temptation to withdraw ineligibles
o Withdrawl of ineligible patients, post hoc, may introduce bias
Betablocker Heart Attack Trial
* 3837 post MI patients randomized
* 341 patients found by Central Review to be ineligible
* Results
Anturane Reinfarction Trial (1980) NEJM
* Randomized, double blind, placebo controlled
Anturane Reinfarction Trial (1980)
* 1629 patients randomized
o 1631 entered, but two patients randomized twice
o Need to delete 03013, 17008
o Use first randomization!
* Declared post hoc 71 “ineligible” patients
Analyzable Deaths - Within 7 days of being off drug
1980 Anturane Mortality Results
Total Mortality
Anturane Reinfarction Trial (1980)
Total Mortality
Anturane Sudden Death (SD)
for Total Follow-up
Anturane Analysis
Acceptable Policies For Ineligible Subjects
1. Delay randomization, confirm eligibility and allow no withdrawals (e.g. AMIS) (Chronic Studies)
2. Accept ineligibles, allow no withdrawals
(e.g. BHAT, MILIS) (Acute Studies)
3. Allow withdrawals if:
a. Procedures defined in advance
b. Decision made early (before event)
c. Decision independent and blinded
d. Use baseline covariates only (two subgroups)
e. Analysis done with and without
B. WITHDRAWL FOR NON-COMPLIANCE
References: Sackett & Gent (1979) NEJM, p. 1410
Coronary Drug Project (1980) NEJM, p. 1038
* Two Types of Trials
1. Management
- "Intent to Treat" Principle
- Compare all subjects, regardless of compliance
2. Explanatory
- Estimate optimum effect, understand mechanism
- Analyze subjects who fully comply
WITHDRAWALS FOR NON-COMPLIANCE MAY LEAD TO BIAS!
Breast Cancer Adjuvant Therapy Probability of Disease Free Survival for Years Post Mastectomy (Method I)
Breast Cancer Adjuvant Therapy
Probability of Disease Free Survival for
Years Post Mastectomy (Method II)
Redmond et al (1983) Cancer Treatment Report
Example: Coronary Drug Project 5-Year Mortality
Comments
* Higher % of estrogens patients did not comply
* Beneficial to be randomized to estrogen & not take it
* (6.1% vs. 9.9%)
* Best to be randomized to placebo & comply (4.8%)
Aspirin Myocardial Infarction Study (AMIS)
Summary of Compliance
* No consistent pattern
Example Non-compliance Did Worse
CDP Clofibrate, AMIS Both Treatment & Control
CDP Estrogen Control Only
Beta-blocker, Wilcox Two Treatments, Not Control
* Compliance an outcome, not always independent of treatment
* Withdrawal of non-compliers can lead to bias
* Non-compliers dilute treatment
* Try hard not to randomize non-compliers
II. Competing Events
* Subject may be censored from primary event by some other event (e.g. cancer vs. heart disease)
* Must assume independence
* If cause specific mortality used, should also look at total death
* If non-fatal event is primary, should also look at total death and non-fatal event
* Problem for some response measures
Problem of Definitions
Classification Anturane Placebo P-value
Anturane Reinfarction Trial Sudden Death
Category Source Placebo Anturane P-value
III. "Wrong", Inconsistent, Outlying Data
* "Wrong" or "outlying" data may in fact be real
* Decisions must be made blind of group assignment
* All modifications or withdrawals must be documented
IV. Missing Outcome Data
* Design with zero
o missingness may be associated with treatment
+ for analysis, data are not missing at random
+ even if same number missing, missing may be for different reason in each treatment group
* Implement with minimum possible
* Analyze exploring different approaches
o if all, or most, agree, then more persuasive
“Best” and “Worst” Case Analyses
VI. Poor Clinic Performance in a Multicenter Study
* If randomization was stratified by clinic, then withdrawal of a clinic is theoretically valid
* Withdrawal must be done independent of the outcome at that clinic
VIII. Fishing or Dichotomizing Outcomes
* Common practice to define a response (S,F) from a non-dichotomous variable
* By changing our definition, we can alter results
* Thus, definitions stated in advance
* Definitions should be based on external data
Dichotomizing Outcomes
Example
IX. Time Dependent Covariate Adjustment
* Classic covariate adjustment uses baseline prognostic factors only
o Adjust for Imbalance
o Gain Efficiency
* Adjustment by time dependent variates not recommended in clinical trials (despite Cox time dependent regression model)
* Habit from epidemiology studies
Intent To Treat (ITT) Principle
* Analyze all patients randomized, regardless of compliance to assigned intervention
* Analyze all events in the follow-up, regardless of compliance
Introduction to Clinical Trials.ppt
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