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Non‐parametric regression in clustered multistate current status data with informative cluster size
Author(s) -
Lan Ling,
Bandyopadhyay Dipankar,
Datta Somnath
Publication year - 2017
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/stan.12099
Subject(s) - smoothing , regression , estimator , parametric statistics , statistics , cluster (spacecraft) , regression analysis , covariate , periodontal disease , current (fluid) , monotonic function , computer science , mathematics , dentistry , medicine , mathematical analysis , electrical engineering , programming language , engineering
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.