Joint regression analysis of survival and quality-adjusted survival.
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.
Fine
J P
JP
Department of Statistics, University of Wisconsin, Madison 53706, USA. fine@biostat.wisc.edu
Gelber
R D
RD
D000367:Age Factors; D001943:Breast Neoplasms / Q000401:mortality*; D016001:Confidence Intervals; D005260:Female; D006801:Humans; D017698:Postmenopause; D011379:Prognosis; D011788:Quality of Life; D019057:Quality-Adjusted Life Years; D012044:Regression Analysis; D015203:Reproducibility of Results; D016019:Survival Analysis; D015996:Survival Rate