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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16081

Title: Missing Data: Turning Guidance Into Action
Authors: Mallinckrodt, Craig
Roger, James
Chuang-Stein, Christy
Molenberghs, Geert
Lane, Peter W.
Kelly, Michael O.
Ratitch, Bohdana
Xu, Lei
Gilbert, Steve
Mehrotra, Devan V.
Wolfinger, Russ
Thijs, Herbert
Issue Date: 2013
Citation: STATISTICS IN BIOPHARMACEUTICAL RESEARCH, 5 (4), p. 369-382
Abstract: Recent research has fostered new guidance on preventing and treating missing data. This article is the consensus opinion of the Drug Information Association's Scientific Working Group on Missing Data. Common elements from recent guidance are distilled and means for putting the guidance into action are proposed. The primary goal is to maximize the proportion of patients that adhere to the protocol specified interventions. In so doing, trial design and trial conduct should be considered. Completion rate should be focused upon as much as enrollment rate, with particular focus on minimizing loss to follow-up. Whether or not follow-up data after discontinuation of the originally randomized medication and/or initiation of rescue medication contribute to the primary estimand depends on the context. In outcomes trials (intervention thought to influence disease process) follow-up data are often included in the primary estimand, whereas in symptomatic trials (intervention alters symptom severity but does not change underlying disease) follow-up data are often not included. Regardless of scenario, the confounding influence of rescue medications can render follow-up data of little use in understanding the causal effects of the randomized interventions. A sensible primary analysis can often be formulated in the missing at random (MAR) framework. Sensitivity analyses assessing robustness to departures from MAR are crucial. Plausible sensitivity analyses can be prespecified using controlled imputation approaches to either implement a plausibly conservative analysis or to stress test the primary result, and used in combination with other model-based MNAR approaches such as selection, shared parameter, and pattern-mixture models. The example dataset and analyses used in this article are freely available for public use at www.missingdata.org.uk.
Notes: Mallinckrodt, C (reprint author), Eli Lilly & Co, Lilly Res Labs, Indianapolis, IN 46285 USA. mallinckrodt_craig@lilly.com; james.h.roger@gsk.com; christy.j.chuang-stein@pfizer.com; Geert.Molenberghs@luc.ac.be; Peter.W.Lane@GSK.com; Michael.O'Kelly@quintiles.com; Bohdana.Ratitch@Quintiles.com; Lei.Xu@biogenidec.com; steve.gilbert@rhoworld.com; devan.mehrotra@merck.com; Russ.Wolfinger@jmp.com; herbert.thijs@uhasselt.be
URI: http://hdl.handle.net/1942/16081
DOI: 10.1080/19466315.2013.848822
ISI #: 000327394600009
ISSN: 1946-6315
Category: A1
Type: Journal Contribution
Validation: ecoom, 2014
Appears in Collections: Research publications

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