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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/13637

Title: Analysing intensive longitudinal data after summarization at landmarks: an application to daily pain evaluation in a clinical trial
Authors: Bunouf, P.
Grouin, Jean-Marie
Molenberghs, Geert
Koch, G.
Issue Date: 2012
Abstract: The paper addresses some of the key issues to be considered in analysing intensive longitudinal data after summarization at scheduled landmarks (i.e. prespecified times). In this context, the derivation of outcomes requires rigorous rules and the selection of covariates should be based on a thorough data exploration. To guide the choice of statistical approaches for inferences, we study the missingness mechanism by using a specific dropout model. Then, we compare and contrast statistical approaches based on direct modelling and on multiple imputation applied either to the raw data or to the derived outcomes. The results are interpreted in the light of the model constraints and the missingness mechanism assumption. We show that some statistical approaches based on multiple imputation applied to the raw data are particularly well adapted to our context as they avoid any loss of available information for missing data imputation. We also show that the influence of subjects with incomplete profiles can be described by using individual estimations given by appropriate statistical models. The motivating data set was collected in a double-blind placebo-controlled clinical trial to assess the effect on pain of a new compound in subjects suffering from fibromyalgia.
Notes: [Bunouf, P.] Lab Pierre Fabre, Dept Stat, F-31319 Labege, France. [Grouin, J. -M.] Univ Rouen, F-76821 Mont St Aignan, France. [Molenberghs, G.] Univ Hasselt, Diepenbeek, Belgium. [Molenberghs, G.] Katholieke Univ Leuven, Louvain, Belgium. [Koch, G.] Univ N Carolina, Chapel Hill, NC USA.
URI: http://hdl.handle.net/1942/13637
DOI: 10.1111/j.1467-985X.2011.01014.x
ISI #: 000301535600010
ISSN: 0964-1998
Category: A1
Type: Journal Contribution
Validation: ecoom, 2013
Appears in Collections: Research publications

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