Cox regression models for quality adjusted survival analysis

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Abstract
We develop a method for incorporating covariates as regressors in a quality adjusted survival analysis (Q‐TWiST) using Cox's proportional hazards model. The standard Q‐TWiST method assumes that patients progress through a series of health states which differ in quality of life. The Kaplan—Meier product limit method is used to estimate the mean duration of each state by estimating the survival curves for the health state transition times. These estimates provide the basis for quality adjusted survival analysis. In this paper, the survival curves are modelled using Cox's proportional hazards regression. Quality adjusted survival is estimated given sets of covariate values, allowing one to profile patients. The results are useful for investigating how prognostic factors affect treatment benefits in terms of quality of life. We give a brief review of the standard Q‐TWiST method and illustrate the extended methodology with an example from the International Breast Cancer Study Group Trial V comparing short duration versus long duration chemotherapy in the treatment of node‐positive breast cancer.