Mixtures of Varying Coefficient Models for Longitudinal Data with Discrete or Continuous Non-ignorable Dropout
The analysis of longitudinal repeated measures data is frequently complicated by missing data due to informative dropout. We describe a mixture model for joint distribution for longitudinal repeated measures, where the dropout distribution may be continuous and the dependence between response and dropout is semiparametric. Specifically, we assume that responses follow a varying coefficient random effects model conditional on dropout time, where the regression coefficients depend on dropout time through unspecified nonparametric functions that are estimated using step functions when dropout time is discrete (e.g., for panel data) and using smoothing splines when dropout time is continuous. Inference under the proposed semiparametric model is hence more robust than the parametric conditional linear model. The unconditional distribution of the repeated measures is a mixture over the dropout distribution. We show that estimation in the semiparametric varying coefficient mixture model can pro- ceed by fitting a parametric mixed-effects model and can be carried out on standard software platforms such as SAS. The model is used to analyze data from a recent AIDS clinical trial and its performance is evaluated using simulations.
Clinical Trials | Design of Experiments and Sample Surveys | Longitudinal Data Analysis and Time Series | Statistical Models
Hogan, Joseph W.; Lin, Xihong; and Herman, Benjamin A., "Mixtures of Varying Coefficient Models for Longitudinal Data with Discrete or Continuous Non-ignorable Dropout" (May 2003). The University of Michigan Department of Biostatistics Working Paper Series. Working Paper 14.
Clinical Trials Commons, Design of Experiments and Sample Surveys Commons, Longitudinal Data Analysis and Time Series Commons, Statistical Models Commons