Problems in dealing with missing data and informative censoring in clinical trials
Current Controlled Trials in Cardiovascular Medicine
CR2u0er0rv2en,ietCwontrolled Trials in Cardiovascular Medicine 3 Problems in dealing with missing data and informative censoring in clinical trials
Weichung Joseph Shih 0 1
0 Cancer Institute of New Jersey, University of Medicine and Dentistry of New Jersey Robert Wood Johnson Medical School , New Brunswick, New Jersey , USA
1 Division of Biometrics, University of Medicine and Dentistry of New Jersey School of Public Health , New Brunswick, New Jersey , USA
A common problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to bias. There are no universally applicable methods for handling missing data. We recommend the following: (1) Report reasons for dropouts and proportions for each treatment group; (2) Conduct sensitivity analyses to encompass different scenarios of assumptions and discuss consistency or discrepancy among them; (3) Pay attention to minimize the chance of dropouts at the design stage and during trial monitoring; (4) Collect postdropout data on the primary endpoints, if at all possible; and (5) Consider the dropout event itself an important endpoint in studies with many.
informative missing data; intent to treat; longitudinal study; missing completely at random
Introduction
With the exception of counting deaths from all causes, a
common problem in clinical trials is the missing data
caused by patients who do not complete the study in full
schedule and drop out of the study without further
measurements. Possible reasons for patients dropping out of
the study (the so-called 'withdrawals') include death,
adverse reactions, unpleasant study procedures, lack of
improvement, early recovery, and other factors related or
unrelated to trial procedure and treatments. Missing data
in a study because of dropouts may cause the usual
statistical analysis for complete or available data to be subject
to a potential bias. This review attempts to raise the
awareness of the problem and to provide some general guidance
to clinical trial practitioners.
Examples
Withdrawals from clinical trials are ubiquitous. The
Nuremberg Code, adopted in 1947, established principles of
ethical conduct in such trials. These principles demand
that the subject be given the choice stop participating at
any time during the clinical study. Under these principles,
the investigator is obliged to stop the experiment if injury
seems likely. I highlight just a few findings from recent
articles in the area of cardiovascular medicine for
illustration.
Example 1:
A multicenter, randomized, double-blind, three parallel
groups trial to compare placebo, candesartan ciltexetil
and enalapril in patients with mild to moderate essential
hypertension [
1
]. The study randomized 205 to
treatment, however, only 178 patients were evaluable by
protocol at the end of an 8-week treatment period. 'The
remaining patients were excluded from the analysis of
blood pressure (BP) data because of major protocol
violations, poor compliance with medical visits, or withdrawal
because of adverse events.'
Example 2:
A multicenter, randomized, open-label, parallel-design
study to compare the treatment effect of niacin and
atorvastatin (for 12 weeks) on lipoprotein subfractions in
patients with atherogenic dyslipidemia [
2
]. 'Of the total 108
patients randomized to treatment, 12 withdrew from the
study. Of those who withdrew, nine were due to adverse
events, two were lost to follow-up, and one did not return
for the final visit.'
Example 3:
A multicenter, randomized, double-blind,
placebo-controlled trial to assess treatment effect of pimobendan on
exercise capacity in patients with chronic heart failure [
3
].
'The primary pre-specified analysis of exercise time was
limited to those patients who had at least the first follow
up (four-week) exercise test carried out and had shown
good compliance up to the day of the test. If subsequent
tests were not performed, whatever the reason, or
performed although compliance between tests had been
poor, the last exercise time value obtained while
compliance was good was carried forward.' Two hundred and
forty of the 317 randomized patients had exercise test done
with good compliance at four, 12, and 24 weeks. Listed
reasons (and number of patients) for missing exercise
time data at 24 weeks were: 'exercise test not done due to
death' (n = 30), 'exercise testing contraindicated' (n = 9),
and 'exercise test not done for other reasons' (n = 10).
Example 4:
A randomized, double-blind study to compare
nifedipine-GITS and verapamil-SR on hemodynamics, left
ventricular mass, and coronary vasodilatory in patients with
advanced hypertension [
4
]. Fifty-four patients were
randomized after the placebo run-in phase. 'Twenty-four failed
to complete the (six-month) trial, and thus were (...truncated)