Handling drop‐out in longitudinal clinical trials: a comparison of the LOCF and MMRM approaches

Abstract

This study compares two methods for handling missing data in longitudinal trials: one using the last‐observation‐carried‐forward (LOCF) method and one based on a multivariate or mixed model for repeated measurements (MMRM). Using data sets simulated to match six actual trials, I imposed several drop‐out mechanisms, and compared the methods in terms of bias in the treatment difference and power of the treatment comparison. With equal drop‐out in Active and Placebo arms, LOCF generally underestimated the treatment effect; but with unequal drop‐out, bias could be much larger and in either direction. In contrast, bias with the MMRM method was much smaller; and whereas MMRM rarely caused a difference in power of greater than 20%, LOCF caused a difference in power of greater than 20% in nearly half the simulations. Use of the LOCF method is therefore likely to misrepresent the results of a trial seriously, and so is not a good choice for primary analysis. In contrast, the MMRM method is unlikely to result in serious misinterpretation, unless the drop‐out mechanism is missing not at random (MNAR) and there is substantially unequal drop‐out. Moreover, MMRM is clearly more reliable and better grounded statistically. Neither method is capable of dealing on its own with trials involving MNAR drop‐out mechanisms, for which sensitivity analysis is needed using more complex methods. Copyright © 2007 John Wiley & Sons, Ltd.