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Home >> Publications >> Marginal and Conditional Distribution Estimation from Double-Sampled Semi-Competing Risks Data.

Publication

Author(s):

Yu M, Yiannoutsos CT

Pub Title:

Marginal and Conditional Distribution Estimation from Double-Sampled Semi-Competing Risks Data.

Pub Date:

Mar 1 2016

Pub Region(s):

East Africa

Page Number:
87-103

Journal:

Title: 
Scand Stat Theory Appl

PubMed: 26924877
Pub PDF: PDF icon nihms583951.pdf

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 program 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.

KEYWORDS:

copula model; double sampling; informative dropout; semi-competing risks

 

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Citation:

Yu M, Yiannoutsos CT. Marginal and Conditional Distribution Estimation from Double-Sampled Semi-Competing Risks Data. Scand Stat Theory Appl. 2015 Mar 1;42(1):87-103. PubMed PMID: 26924877; PubMed Central PMCID: PMC4764884.