000340129 001__ 340129
000340129 005__ 20240228054314.0
000340129 022__ $$a1536-8634
000340129 0247_ $$2doi$$a10.22004/ag.econ.340129
000340129 037__ $$a199-2024-511
000340129 041__ $$aeng
000340129 245__ $$aReference-based sensitivity analysis via multiple imputation for longitudinal trials with protocol deviation
000340129 260__ $$c2016-04
000340129 269__ $$a2016-04
000340129 336__ $$aJournal Article
000340129 520__ $$aRandomized controlled trials provide essential evidence for the evaluation of new and existing medical treatments. Unfortunately, the statistical analysis is often complicated by the occurrence of protocol deviations, which mean we cannot always measure the intended outcomes for individuals who deviate, resulting in a missing-data problem. In such settings, however one approaches the analysis, an untestable assumption about the distribution of the unobserved data must be made. To understand how far the results depend on these assumptions, the primary analysis should be supplemented by a range of sensitivity analyses, which explore how the conclusions vary over a range of different credible assumptions for the missing data. In this article, we describe a new command, mimix, that can be used to perform reference-based sensitivity analyses for randomized controlled trials with longitudinal quantitative outcome data, using the approach proposed by Carpenter, Roger, and Kenward (2013, Journal of Biopharmaceutical Statistics 23: 1352–1371). Under this approach, we make qualitative assumptions about how individuals’ missing outcomes relate to those observed in relevant groups in the trial, based on plausible clinical scenarios. Statistical analysis then proceeds using the method of multiple imputation.
000340129 546__ $$aEnglish
000340129 6531_ $$ast0440
000340129 6531_ $$amimix
000340129 6531_ $$aclinical trial
000340129 6531_ $$aprotocol deviation
000340129 6531_ $$amissing data
000340129 6531_ $$amultiple imputation
000340129 6531_ $$asensitivity analysis
000340129 700__ $$aCro, Suzie
000340129 700__ $$aMorris, Tim P.
000340129 700__ $$aKenward, Michael G.
000340129 700__ $$aCarpenter, James R.
000340129 773__ $$j16$$k2$$q443$$o463$$tStata Journal
000340129 8560_ $$fbecke073@umn.edu
000340129 8564_ $$917e77499-40fe-40d0-bdbc-719a4819b46a$$s282236$$uhttps://ageconsearch.umn.edu/record/340129/files/Cro.pdf
000340129 909CO $$ooai:ageconsearch.umn.edu:340129$$pGLOBAL_SET
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000340129 980__ $$a199