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Joint Regression Analysis of Survival and Quality‐Adjusted Survival
Author(s) -
Fine Jason P.,
Gelber Richard D.
Publication year - 2001
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2001.00376.x
Subject(s) - censoring (clinical trials) , statistics , resampling , joint probability distribution , inference , estimator , context (archaeology) , bivariate analysis , parametric statistics , regression analysis , econometrics , regression , computer science , univariate , mathematics , multivariate statistics , artificial intelligence , paleontology , biology
Summary. In this paper, a semiparametric bivariate linear regression model for survival and quality‐adjusted survival is investigated. Even with a parametric specification for the joint distribution, maximum likelihood is not applicable because of induced informative censoring. We propose inference procedures based on estimating functions. The estimators are consistent and asymptotically normal. Hypothesis tests and confidence intervals may be constructed with easy‐to‐implement resampling techniques. Simultaneous regression modeling of survival and quality‐adjusted survival has not been studied formally. Our methodology gives parameter estimates that are highly interpretable in the context of a cost‐effectiveness analysis. The usefulness of the proposal is illustrated with a breast cancer dataset.