Analyzing incomplete longitudinal clinical trial data.
Using standard missing data taxonomy, due to Rubin and co-workers, and simple algebraic derivations, it is argued that some simple but commonly used methods to handle incomplete longitudinal clinical trial data, such as complete case analyses and methods based on last observation carried forward, require restrictive assumptions and stand on a weaker theoretical foundation than likelihood-based methods developed under the missing at random (MAR) framework. Given the availability of flexible software for analyzing longitudinal sequences of unequal length, implementation of likelihood-based MAR analyses is not limited by computational considerations. While such analyses are valid under the comparatively weak assumption of MAR, the possibility of data missing not at random (MNAR) is difficult to rule out. It is argued, however, that MNAR analyses are, themselves, surrounded with problems and therefore, rather than ignoring MNAR analyses altogether or blindly shifting to them, their optimal place is within sensitivity analysis. The concepts developed here are illustrated using data from three clinical trials, where it is shown that the analysis method may have an impact on the conclusions of the study.
Molenberghs
Geert
G
Center for Statistics, Limburgs Universitair Centrum, Universitaire Campus, B-3590 Diepenbeek, Belgium. geert.molenberghs@luc.ac.be
Thijs
Herbert
H
Jansen
Ivy
I
Beunckens
Caroline
C
Kenward
Michael G
MG
Mallinckrodt
Craig
C
Carroll
Raymond J
RJ
D000928:Antidepressive Agents / Q000627:therapeutic use; D002986:Clinical Trials as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D003863:Depression / Q000188:drug therapy; D006801:Humans; D008137:Longitudinal Studies; D010352:Patient Dropouts
D000928:Antidepressive Agents