Cox regression models for quality adjusted survival analysis.
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.
Cole
B F
BF
Department of Biostatistics, Harvard School of Public Health and Division of Biostatistics, Dana-Farber Cancer Institute, Boston, Massachusetts 02115.
Gelber
R D
RD
Goldhirsch
A
A
D000970:Antineoplastic Agents / Q000008:administration & dosage / Q000009:adverse effects; D001943:Breast Neoplasms / Q000188:drug therapy / Q000401:mortality / Q000473:pathology / Q000523:psychology; D005260:Female; D006801:Humans; D008207:Lymphatic Metastasis; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local; D009367:Neoplasm Staging; D011379:Prognosis; D016016:Proportional Hazards Models; D011788:Quality of Life; D016019:Survival Analysis; D016896:Treatment Outcome
D000970:Antineoplastic Agents