Reference-based pattern-mixture models for analysis of longitudinal binary data.

Statistical Methods in Medical Research
Kaifeng Lu

Abstract

Pattern-mixture model (PMM)-based controlled imputations have become a popular tool to assess the sensitivity of primary analysis inference to different post-dropout assumptions or to estimate treatment effectiveness. The methodology is well established for continuous responses but less well established for binary responses. In this study, we formulate the copy-reference and jump-to-reference PMMs for longitudinal binary data using a multivariate probit model with latent variables. We discuss the maximum likelihood, Bayesian, and multiple imputation methods for estimating the treatment effect under the specified PMM. Simulation studies are conducted to evaluate the performance of these methods. These methods are also illustrated using data from a bipolar mania study.

References

Nov 3, 2010·Technometrics : a Journal of Statistics for the Physical, Chemical, and Engineering Sciences·Huiping Xu, Bruce A Craig
Mar 18, 2011·New Solutions : a Journal of Environmental and Occupational Health Policy : NS·C Eduardo Siqueira
Oct 5, 2012·The New England Journal of Medicine·Roderick J LittleHal Stern
Dec 18, 2013·Pharmaceutical Statistics·Kaifeng Lu
Mar 14, 2017·Journal of Biopharmaceutical Statistics·Fei GaoJoseph G Ibrahim
Oct 11, 2017·Journal of Affective Disorders·Willie EarleyLakshmi N Yatham
Jan 18, 2019·Statistics in Medicine·Tim P MorrisMichael J Crowther
Mar 5, 2019·Journal of the Royal Statistical Society. Series A, (Statistics in Society)·Suzie CroMichael G Kenward
Mar 1, 2016·The Stata Journal·John Mullahy

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Methods Mentioned

BETA
FCS

Software Mentioned

Stata
statistical analysis system
SAS
R mvord package
SAS IML
R mvord

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