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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18606

Title: A multiple-imputation based approach to sensitivity analysis and effectiveness assessment in longitudinal clinical trials
Authors: Teshome Ayele, Birhanu
Lipkovich, Ilya
Molenberghs, Geert
Mallinckrodt, Craig H.
Issue Date: 2014
Citation: Journal of Biopharmaceutical Statistics, 24 (2), p. 211-228
Abstract: It is important to understand the effects of a drug as actually taken (effectiveness) and when taken as directed (efficacy). The primary objective of this investigation was to assess the statistical performance of a method referred to as placebo multiple imputation (pMI) as an estimator of effectiveness and as a worst reasonable case sensitivity analysis in assessing efficacy. The pMI method assumes the statistical behavior of placebo- and drug-treated patients after dropout is the statistical behavior of placebo-treated patients. Thus, in the effectiveness context, pMI assumes no pharmacological benefit of the drug after dropout. In the efficacy context, pMI is a specific form of a missing not at random analysis expected to yield a conservative estimate of efficacy. In a simulation study with 18 scenarios, the pMI approach generally provided unbiased estimates of effectiveness and conservative estimates of efficacy. However, the confidence interval coverage was consistently greater than the nominal coverage rate. In contrast, last and baseline observation carried forward (LOCF and BOCF) were conservative in some scenarios and anti-conservative in others with respect to efficacy and effectiveness. As expected, direct likelihood (DL) and standard multiple imputation (MI) yielded unbiased estimates of efficacy and tended to overestimate effectiveness in those scenarios where a drug effect existed. However, in scenarios with no drug effect, and therefore where the true values for both efficacy and effectiveness were zero, DL and MI yielded unbiased estimates of efficacy and effectiveness.
Notes: Address correspondence to Birhanu Teshome Ayele, Medical Statistics Department, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK E-mail: birhanu.teshome@lshtm.ac.uk
URI: http://hdl.handle.net/1942/18606
DOI: 10.1080/10543406.2013.859148
ISI #: 000333938500001
ISSN: 1054-3406
Category: A1
Type: Journal Contribution
Validation: ecoom, 2015
Appears in Collections: Research publications

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