THOUGHT LEADERSHIP

This is the last in a series of three posts on missing data imputation.  In the first post, we reviewed the underlying assumptions and limitations of single imputation methods, in particular, last observation carried forward (LOCF).  In the second post, we considered more sophisticated data imputation methods for longitudinal data, Mixed Models for Repeated Measures (MMRM) and Multiple Imputation (MI).  An important assumption for both MMRM and MI is that the data are ‘missing at random’ (MAR), if not ‘missing completely at random’ (MCAR).  But what if this assumption is not true for all the missing data?

In a previous post on missing data imputation, we reviewed the underlying assumptions and limitations of last observation carried forward (LOCF). This method was widely used in the past because of its straightforward application, ease of understanding, and often incorrect assumption that it is a conservative approach. Over the years, more sophisticated data imputation methods for longitudinal data have been developed that have advantages over the single imputation methods, such as LOCF, baseline observation carried forward (BOCF) or worst observation carried forward (WOCF).  We will briefly review two of these methods, mixed models for repeated measures (MMRM) and multiple imputation (MI).

Methods for addressing missing data in longitudinal studies have been written about extensively for many years, but the focus has increased more in the past decade with the publication of the EMA Guideline on Missing Data in Confirmatory Clinical Trials and the National Research Council’s treatise on The Prevention and Treatment of Missing Data in Clinical Trials both in 2010 and the release of ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials in 2017.  Case studies and new methodologies addressing the handling of missing data are published on a regular basis.  Yet for all the attention this topic receives, and perhaps because of the abundance of information on this topic, clinical researchers often do not know what data imputation approach is best for their particular clinical trial.