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Lifetime Data Anal. 2011 Apr;17(2):256-79. doi: 10.1007/s10985-010-9175-8. Epub 2010 Jun 18.

Linear regression analysis of survival data with missing censoring indicators.

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  • 1Department of Mathematics and Statistics, Yunnan University, Kunming 650091, China.

Abstract

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.

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