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|Title: ||Analyzing incomplete longitudinal clinical trial data|
|Authors: ||Molenberghs, Geert|
Kenward, Michael G.
Carroll, Raymond J.
|Issue Date: ||2004|
|Publisher: ||OXFORD UNIV PRESS|
|Citation: ||BIOSTATISTICS, 5(3). p. 445-464|
|Abstract: ||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.|
|Notes: ||Limburgs Univ Ctr, Ctr Stat, B-3590 Diepenbeek, Belgium. Univ London London Sch Hyg & Trop Med, London WC1E 7HT, England. Eli Lilly & Co, Indianapolis, IN 46285 USA. Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA.Molenberghs, G, Limburgs Univ Ctr, Ctr Stat, Univ Campus, B-3590 Diepenbeek, Belgium.firstname.lastname@example.org|
|ISI #: ||000222723600008|
|Type: ||Journal Contribution|
|Validation: ||ecoom, 2005|
|Appears in Collections: ||Research publications|
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