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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23065

Title: Implementation of pattern-mixture models in randomized clinical trials
Authors: Bunouf, Pierre
Molenberghs, Geert
Issue Date: 2016
Citation: PHARMACEUTICAL STATISTICS, 15(6), p. 494-506
Abstract: Modern analysis of incomplete longitudinal outcomes involves formulating assumptions about the missingness mechanisms and then using a statistical method that produces valid inferences under this assumption. In this manuscript, we define missingness strategies for analyzing randomized clinical trials (RCTs) based on plausible clinical scenarios. Penalties for dropout are also introduced in an attempt to balance benefits against risks. Some missingness mechanisms are assumed to be non-future dependent, which is a subclass of missing not at random. Non-future dependent stipulates that missingness depends on the past and the present information but not on the future. Missingness strategies are implemented in the pattern-mixture modeling framework using multiple imputation (MI), and it is shown how to estimate the marginal treatment effect. Next, we outline how MI can be used to investigate the impact of dropout strategies in subgroups of interest. Finally, we provide the reader with some points to consider when implementing pattern-mixture modeling-MI analyses in confirmatory RCTs. The data set that motivated our investigation comes from a placebo-controlled RCT design to assess the effect on pain of a new compound.
Notes: [Bunouf, P.] Labs Pierre Fabre, Toulouse, France. [Molenberghs, G.] Univ Hasselt, I BioStat, Hasselt, Belgium. [Molenberghs, G.] Katholieke Univ Leuven, I BioStat, Hasselt, Belgium.
URI: http://hdl.handle.net/1942/23065
DOI: 10.1002/pst.1780
ISI #: 000388565400005
ISSN: 1539-1604
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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