title: Statistical analysis of repeated measurements with informative censoring times.
abstract: Incomplete repeated measurement data often arise in medical studies. A problem that has recently drawn much attention in the literature in this situation is that the incompleteness or missingness is informative or related to the underlying variable of interest. In this paper we propose a non-parametric global test for treatment comparison in the presence of informative incompleteness. A semi-parametric regression model is also presented for assessing conditional treatment effects given the drop-out patterns, adopting the idea similar to that behind the pattern-mixture modelling approach and discussed in Shih and Quan. The proposed methods can be easily implemented and are conceptually simple and similar too, but can be applied to more general cases than those given in Yao et al. They are evaluated by numerical studies and applied to data from a clinical trial of adult schizophrenics.
authors.affiliation: Department of Statistics, University of Missouri-Columbia, 222 Math Sciences Building, Columbia, MO 65211, USA. tsun@stat.missouri.edu
authors.forename: J
authors.initials: J
authors.lastname: Sun
authors.forename: P X
authors.initials: PX
authors.lastname: Song
mesh_terms: D014150:Antipsychotic Agents / Q000627:therapeutic use; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D004311:Double-Blind Method; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic; D012044:Regression Analysis; D012559:Schizophrenia / Q000188:drug therapy; D018709:Statistics, Nonparametric
chemical_list: D014150:Antipsychotic Agents