Linear regression analysis of survival data with missing censoring indicators

Linear regression analysis has been studied extensively in a random censorship setting, but typically all of the censoring indicators are assumed to be observed. In this paper, we develop synthetic data methods for estimating regression parameters in a linear model when some censoring indicators are...

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Bibliographic Details
Published in:Lifetime data analysis 2011-04, Vol.17 (2), p.256-279
Main Authors: Wang, Qihua, Dinse, Gregg E.
Format: Article
Language:English
Subjects:
Age Factors
Brain cancer
Brain Neoplasms - pathology
Calibration
Censorship
Clinical trials
Computer Simulation
Data analysis
Data Interpretation, Statistical
Disease Progression
Economics
Failure
Female
Finance
Glioblastoma - pathology
Glioma
Health Sciences
Humans
Insurance
Linear Models
Male
Management
Mathematics and Statistics
Medicine
Mobility Limitation
Operations Research/Decision Theory
Quality Control
Regression analysis
Reliability
Safety and Risk
Sex Factors
Statistics
Statistics for Business
Statistics for Life Sciences
Studies
Survival Analysis
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Summary:Linear regression analysis has been studied extensively in a random censorship setting, but typically all of the censoring indicators are assumed to be observed. In this paper, we develop synthetic data methods for estimating regression parameters in a linear model when some censoring indicators are missing. We define estimators based on regression calibration, imputation, and inverse probability weighting techniques, and we prove all three estimators are asymptotically normal. The finite-sample performance of each estimator is evaluated via simulation. We illustrate our methods by assessing the effects of sex and age on the time to non-ambulatory progression for patients in a brain cancer clinical trial.
ISSN:1380-7870
1572-9249
DOI:10.1007/s10985-010-9175-8