Post-stratification based direct adjustment approach to a missing data problem in clinical trials

In clinical trials we always expect some missing data. If data are missing completely at random, then missing data can be ignored for the purpose of statistical inference. In most situation, however, ignoring missing data will introduce bias. Adjustment is possible for missing data if the missing mechanism is known, which is rare in real problems. Our approach is to estimate directly the mean outcome of each treatment group in the presence of missing data. To this end, we post-stratify all the subjects by the expected value of outcome (or by a variable predictive of the outcome) so that subjects within a stratum may be considered homogeneous with respect to the expected outcome, and assume that subjects within a stratum are missing at random. We apply this post-stratification approach to a recently concluded clinical trial where a high proportion of data are missing and the missingness depends on the same factors affecting the outcome variable. A simulation study shows that the post-stratification approach reduces the bias substantially compared to the naive approach where only non-missing subjects are analyzed. Published by Elsevier Science B.V.