A simple correction to completer analyses and improvement on baseline observation carried forward

Replacing missing data using the baseline observation carried forward (BOCF) technique is known to be fraught with problems. Despite recommendations to the contrary, BOCF and the related last observation carried forward (LOCF) continue to be used in some fields of research. We first show the use of BOCF in testing for a change in a single sample is essentially equivalent to what results from a completer analysis. Next, we derive a simple method based only on summary statistics for adjusting inference from a completer analysis in situations where the estimand of the completer analysis is expected to be close to that of the full analysis set. For those with missing follow‐up data, the method assumes a mean change of zero from baseline, with variance equivalent to that estimated from the patients who complete; in other words, it is essentially BOCF analysis with plausible variance inflation. The extension to two samples is considered and a similar adjustment of the completer analysis is presented. The method presented here can be used to reanalyze results from publications that treat missing data inadequately, to adjust for missing data in power calculations and as a quick way of checking the plausibility of an appropriate treatment of missing data. These practical tools are meant to complement, rather than compete with, established methods for dealing with missing data. Numerical simulations are used to elucidate some properties of the proposed method and, finally, we apply the method to data from a recent trial which evaluated weight loss programs.