An accelerated failure time mixture cure model with masked event.
We extend the Dahlberg and Wang (Biometrics 2007, 63, 1237-1244) proportional hazards (PH) cure model for the analysis of time-to-event data that is subject to a cure rate with masked event to a setting where the PH assumption does not hold. Assuming an accelerated failure time (AFT) model with unspecified error distribution for the time to the event of interest, we propose rank-based estimating equations for the model parameters and use a generalization of the EM algorithm for parameter estimation. Applying our proposed AFT model to the same motivating breast cancer dataset as Dahlberg and Wang (Biometrics 2007, 63, 1237-1244), our results are more intuitive for the treatment arm in which the PH assumption may be violated. We also conduct a simulation study to evaluate the performance of the proposed method.
Zhang
Jenny J
JJ
Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, MA 02115, USA. jjzhang6@gmail.com
Wang
Molin
M
D015984:Causality; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination / Q000379:methods*; D005260:Female; D006801:Humans; D015233:Models, Statistical; 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; D017211:Treatment Failure; D016896:Treatment Outcome