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Semiparametric Regression Estimation in the Presence of Dependent Censoring

Andrea Rotnitzky and James M. Robins
Biometrika
Vol. 82, No. 4 (Dec., 1995), pp. 805-820
Published by: Oxford University Press on behalf of Biometrika Trust
DOI: 10.2307/2337346
Stable URL: http://www.jstor.org/stable/2337346
Page Count: 16
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Semiparametric Regression Estimation in the Presence of Dependent Censoring
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Abstract

We propose a semiparametric estimation procedure for estimating the regression of an outcome Y, measured at the end of a fixed follow-up period, on baseline explanatory variables X, measured prior to start of follow-up, in the presence of dependent censoring given X. The proposed estimators are consistent when the data are `missing at random' but not `missing completely at random' (Rubin, 1976), and do not require full specification of the complete data likelihood. Specifically, we assume that the probability of censoring at time t is independent of the outcome Y conditional on the recorded history up to t of a vector of time-dependent covariates that are correlated with Y. Our estimators can be used to adjust for dependent censoring and nonrandom noncompliance in randomised trials studying the effect of a treatment on the mean of a response variable of interest. Even with independent censoring, our methods allow the investigator to increase efficiency by exploiting the correlation of the outcome with a vector of time-dependent covariates.

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