Selection models and pattern-mixture models for incomplete data with covariates

Biometrics
B MichielsS R Lipsitz

Abstract

Most models for incomplete data are formulated within the selection model framework. This paper studies similarities and differences of modeling incomplete data within both selection and pattern-mixture settings. The focus is on missing at random mechanisms and on categorical data. Point and interval estimation is discussed. A comparison of both approaches is done on side effects in a psychiatric study.

Citations

Sep 16, 2005·Applied Health Economics and Health Policy·Andrea Manca, Stephen Palmer
Sep 2, 2008·Biometrics·Ying Yuan, Roderick J A Little
Sep 27, 2014·Statistics in Medicine·Niko A Kaciroti, Trivellore Raghunathan
Jul 28, 2007·Statistical Methods in Medical Research·Caroline BeunckensGeert Verbeke
Mar 19, 2004·Journal of Biopharmaceutical Statistics·Desmond CurranGeert Verbeke
May 28, 2019·Statistics in Medicine·Loukia M SpineliKonstantinos Pateras
Jul 12, 2019·Statistical Methods in Medical Research·Jaeil Ahn, Hye Seong Ahn
May 29, 1999·Statistical Methods in Medical Research·M G Kenward, G Molenberghs
Dec 24, 2002·Biometrics·Jolene Birmingham, Garrett M Fitzmaurice
Mar 19, 2004·Journal of Biopharmaceutical Statistics·Diane L FaircloughGeorge Papadopoulos

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