Non-parametric regression in clustered multistate current status data with informative cluster size

Stat Neerl. 2017 Jan;71(1):31-57. doi: 10.1111/stan.12099. Epub 2016 Oct 25.

Abstract

Datasets examining periodontal disease records current (disease) status information of tooth-sites, whose stochastic behavior can be attributed to a multistate system with state occupation determined at a single inspection time. In addition, the tooth-sites remain clustered within a subject, and the number of available tooth-sites may be representative of the true PD status of that subject, leading to an 'informative cluster size' scenario. To provide insulation against incorrect model assumptions, we propose a nonparametric regression framework to estimate state occupation probabilities at a given time and state exit/entry distributions, utilizing weighted monotonic regression and smoothing techniques. We demonstrate the superior performance of our proposed weighted estimators over the un-weighted counterparts via. a simulation study, and illustrate the methodology using a dataset on periodontal disease.

Keywords: Markov; censoring; multivariate time-to-event data; periodontal disease; state-occupation probability.