Marginal and conditional distribution estimation from double-sampled semi-competing risks data

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Informative dropout is a vexing problem for any biomedical study. Most existing statistical methods attempt to correct estimation bias related to this phenomenon by specifying unverifiable assumptions about the dropout mechanism. We consider a cohort study in Africa that uses an outreach programme to ascertain the vital status for dropout subjects. These data can be used to identify a number of relevant distributions. However, as only a subset of dropout subjects were followed, vital status ascertainment was incomplete. We use semi-competing risk methods as our analysis framework to address this specific case where the terminal event is incompletely ascertained and consider various procedures for estimating the marginal distribution of dropout and the marginal and conditional distributions of survival. We also consider model selection and estimation efficiency in our setting. Performance of the proposed methods is demonstrated via simulations, asymptotic study and analysis of the study data.

Original languageEnglish
Pages (from-to)87-103
Number of pages17
JournalScandinavian Journal of Statistics
Volume42
Issue number1
DOIs
StatePublished - Mar 1 2015

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Competing Risks
Drop out
Marginal Distribution
Conditional Distribution
Informative Dropout
Cohort Study
Model Selection
Statistical method
Subset
Conditional distribution
Competing risks
Simulation

Keywords

  • Copula model, double sampling, informative dropout, semi-competing risks

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Marginal and conditional distribution estimation from double-sampled semi-competing risks data. / Yu, Menggang; Yiannoutsos, Constantin.

In: Scandinavian Journal of Statistics, Vol. 42, No. 1, 01.03.2015, p. 87-103.

Research output: Contribution to journalArticle

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