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

作者：Lan Ling; Bandyopadhyay Dipankar^*; Datta Somnath

来源：Statistica Neerlandica, 2017, 71(1): 31-57.

DOI：10.1111/stan.12099

• censoring
• Markov
• multivariate time-to-event data
• state occupation probability
• periodontal disease

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.

• 出版日期2017-1