A proportional hazards cure model for the analysis of time to event with frequently unidentifiable causes.
We propose a semiparametric method for the analysis of masked-cause failure data that are also subject to a cure. We present estimators for the failure time distribution, the cure rate, and the covariate effect on each of these, assuming a proportional hazards cure model for the time to event of interest and we use the expectation-maximization algorithm to conduct the likelihood maximization. The method is applied to data from a breast cancer clinical trial.
Dahlberg
Suzanne E
SE
Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA. dahlberg@jimmy.harvard.edu
Wang
Molin
M
D001943:Breast Neoplasms / Q000453:epidemiology* / Q000628:therapy*; D015984:Causality; D005260:Female; D006801:Humans; D017063:Outcome Assessment, Health Care / Q000379:methods*; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D013997:Time Factors; D016896:Treatment Outcome