Antedependence Models for Nonstationary Categorical Longitudinal Data with
Ignorable Missingness: Likelihood-Based Inference
 
   Lead Investigator:    Yunlong Xie
   Institution      :    National Institutes of Health
   E-Mail           :    yunlong.xie@nih.gov
   Proposal ID      :    251


   Publications:


1  Xie Y, Zimmerman DL
   Antedependence models for nonstationary categorical longitudinal data with ignorable missingness: likelihood-based inference
   Journal Article  Stat Med,32:3274-3289,2013,PMC3885186

   PUBLISHED





   Proposal Description:
   Time index-ordered random variables are said to be antedependent (AD) of order (p1,p2,...,pk$) if the k-th variable, conditioned on the pk immediately preceding variables, is independent of all further preceding variables. Inferential methods associated with AD models are well developed for continuous (primarily normal) longitudinal data, but not for categorical longitudinal data. In this article, we develop likelihood-based inferential procedures for unstructured AD models for categorical longitudinal data. Specifically, we derive maximum likelihood estimators (mles) of model parameters penalized likelihood criteria and likelihood ratio tests for determining the order of antedependence and likelihood ratio tests for homogeneity across groups, time-invariance of transition probabilities, and strict stationarity. Closed-form expressions for mles and test statistics, which allow for the possibility of empty cells and monotone missing data, are given for all cases save strict stationarity. For data with an arbitrary missingness pattern, we derive an efficient restricted EM algorithm for obtaining mles. The performance of the tests is evaluated by simulation. The methods are applied to data from a longitudinal study.