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

Ling Lan, Dipankar Bandyopadhyay, Somnath Datta

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)31-57
Number of pages27
JournalStatistica Neerlandica
Issue number1
StatePublished - Jan 1 2017

Bibliographical note

Publisher Copyright:
© 2016 The Authors. Statistica Neerlandica © 2016 VVS.


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


Dive into the research topics of 'Non-parametric regression in clustered multistate current status data with informative cluster size'. Together they form a unique fingerprint.

Cite this