Lifetime Data Anal - Linear regression analysis of survival data with missing censoring indicators.

Tópicos

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Resumo

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.

Resumo Limpo

linear regress analysi studi extens random censorship set typic censor indic assum observ paper develop synthet data method estim regress paramet linear model censor indic miss defin estim base regress calibr imput invers probabl weight techniqu prove three estim asymptot normal finitesampl perform estim evalu via simul illustr method assess effect sex age time nonambulatori progress patient brain cancer clinic trial

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