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Abstract
This paper describes some of the statistical considerations in the intent-to-treat
design and analysis of clinical trials. The pivotal property of a clinical trial is
the assignment of treatments to patients at random. Randomization alone, however,
is not sufficient to provide an unbiased comparison of therapies. An additional requirement
is that the set of patients contributing to an analysis provides an unbiased assessment
of treatment effects, or that any missing data are ignorable. A sufficient condition
to provide an unbiased comparison is to obtain complete data on all randomized subjects.
This can be achieved by an intent-to-treat design wherein all patients are followed
until death or the end of the trial, or until the outcome event is reached in a time-to-event
trial, irrespective of whether the patient is still receiving or complying with the
assigned treatment. The properties of this strategy are contrasted with those of an
efficacy subset analysis in which patients and observable patient data are excluded
from the analysis on the basis of information obtained postrandomization. I describe
the potential bias that can be introduced by such postrandomization exclusions and
the pursuant effects on type I error probabilities. Especially in a large study, the
inflation in type I error probability can be severe, 0.50 or higher, even when the
null hypothesis is true. Standard statistical methods for the analysis of censored
or incomplete observations all require the assumption of missing at random to some
degree, and none of these methods adjust for the potential bias introduced by post
hoc subset selection. Nor is such adjustment possible unless one posits a model that
relates the missing observations to other observed information for each subject-models
that are inherently untestable. Further, the subset selection bias is confounded with
the subset-specific treatment effect, and the two components are not identifiable
without additional untestable assumptions. Methods for sensitivity analysis to assess
the impact of bias in the efficacy subset analysis are described. It is generally
believed that the efficacy subset analysis has greater power than the intent-to-treat
analysis. However, even when the efficacy subset analysis is assumed to be unbiased,
or have a true type I error probability equal to the desired level alpha, situations
are described where the intent-to-treat analysis in fact has greater power than the
efficacy subset analysis. The intent-to-treat design, wherein all possible patients
continue to be followed, is especially powerful when an effective treatment arrests
progression of disease during its administration. Thus, a patient benefits long after
the patient becomes noncompliant or the treatment is terminated. In such cases, a
landmark analysis using the observations from the last patient evaluation is likely
to prove more powerful than life-table or longitudinal analyses. Examples are described.