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 periodontal disease status of that subject, leading to an ‘informative cluster size’ scenario. To provide insulation against incorrect model assumptions, we propose a non-parametric 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 unweighted counterparts via a simulation study and illustrate the methodology using a dataset on periodontal disease.
Bibliographical noteFunding Information:
The authors would like to thank an anonymous reviewer whose constructive comments led to a significantly improved version of the manuscript. Bandyopadhyay's research was supported by grants R03DE023372 and R01DE024984 from the National Institute of Dental and Craniofacial Research (NIDCR) of the National Institutes of Health (NIH). Datta's research was supported by NIH grants R03DE020839 and R03DE022538, NSA grant H98230-11-1-0168, and National Science Foundation grant DMS 0706965. Computational resources provided by the University of Minnesota Supercomputing Institute are also acknowledged.
- multivariate time-to-event data
- periodontal disease
- state occupation probability