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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14444

Title: A structured approach to choosing estimands and estimators in longitudinal clinical trials
Authors: Mallinckrodt, C. H.
Lin, Q.
Lipkovich, I.
Molenberghs, G.
Issue Date: 2012
Publisher: WILEY-BLACKWELL
Citation: PHARMACEUTICAL STATISTICS, 11 (6), p. 456-461
Abstract: An important evolution in the missing data arena has been the recognition of need for clarity in objectives. The objectives of primary focus in clinical trials can often be categorized as assessing efficacy or effectiveness. The present investigation illustrated a structured framework for choosing estimands and estimators when testing investigational drugs to treat the symptoms of chronic illnesses. Key issues were discussed and illustrated using a reanalysis of the confirmatory trials from a new drug application in depression. The primary analysis used a likelihood-based approach to assess efficacy: mean change to the planned endpoint of the trial assuming patients stayed on drug. Secondarily, effectiveness was assessed using a multiple imputation approach. The imputation modelderived solely from the placebo groupwas used to impute missing values for both the drug and placebo groups. Therefore, this so-called placebo multiple imputation (a.k.a. controlled imputation) approach assumed patients had reduced benefit from the drug after discontinuing it. Results from the example data provided clear evidence of efficacy for the experimental drug and characterized its effectiveness. Data after discontinuation of study medication were not required for these analyses. Given the idiosyncratic nature of drug development, no estimand or approach is universally appropriate. However, the general practice of pairing efficacy and effectiveness estimands may often be useful in understanding the overall risks and benefits of a drug. Controlled imputation approaches, such as placebo multiple imputation, can be a flexible and transparent framework for formulating primary analyses of effectiveness estimands and sensitivity analyses for efficacy estimands. Copyright (c) 2012 John Wiley & Sons, Ltd.
Notes: [Mallinckrodt, C. H.; Lin, Q.; Lipkovich, I.] Eli Lilly & Co, Lilly Corp Ctr, Indianapolis, IN 46285 USA. [Molenberghs, G.] Katholieke Univ Leuven, Louvain, Belgium. [Molenberghs, G.] Hasselt Univ, I BioStat, Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/14444
DOI: 10.1002/pst.1536
ISI #: 000310789600004
ISSN: 1054-3406
Category: A1
Type: Journal Contribution
Validation: ecoom, 2013
Appears in Collections: Centre for Statistics
Research Institute Center for Statistics

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