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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/473

Title: Analyzing Incomplete Longitudinal Clinical Trial Data
Authors: Molenberghs, Geert
Thijs, Herbert
Jansen, Ivy
Beunckens, Caroline
Kenward, Michael
Mallinckrodt, Craig
Carroll, Raymond
Keywords: Clinical trials
Longitudinal data
Missing data
Issue Date: 2003
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.
URI: http://hdl.handle.net/1942/473
Link to publication: https://www.academia.edu/8393352/Analyzing_incomplete_longitudinal_clinical_trial_data
DOI: 10.1093/biostatistics/kxh001
ISI #: 000222723600008
ISSN: 1465-4644
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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