Linear regression analysis of survival data with missing censoring indicators |
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Authors: | Qihua Wang Gregg E Dinse |
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Institution: | 1.Department of Mathematics and Statistics,Yunnan University,Kunming,China;2.Academy of Mathematics and Systems Science,Chinese Academy of Science,Beijing,China;3.Biostatistics Branch,National Institute of Environmental Health Sciences, Research Triangle Park,North Carolina,USA |
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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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Keywords: | |
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