A Class of Semiparametric Mixture Cure Survival Models With Dependent Censoring

Megan Othus Megan Othus is Graduate Student, Department of Biostatistics, Harvard University and Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115 . Yi Li is Associate Professor, Department of Biostatistics, Harvard University, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115 . Ram Tiwari is Associate Director for Statistical Science and Policy, Office of Biostatistics, Center for Drug Evaluation & Research, Food and Drug Administration, Silver Springs, MD 20993 . The work of Tiwari was conducted while he was at the National Cancer Institute. The views expressed in this article are his own and do not necessarily represent those of the National Cancer Institue or the Food and Drug Administration. This work was supported in part by National Institutes of Health grants CA09337-25 and ES07142-24. The authors thank Eric (Rocky) Feuer and Angela Mariotto for their extensive and insightful comments on the manuscript.

Yi Li Megan Othus is Graduate Student, Department of Biostatistics, Harvard University and Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115 . Yi Li is Associate Professor, Department of Biostatistics, Harvard University, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115 . Ram Tiwari is Associate Director for Statistical Science and Policy, Office of Biostatistics, Center for Drug Evaluation & Research, Food and Drug Administration, Silver Springs, MD 20993 . The work of Tiwari was conducted while he was at the National Cancer Institute. The views expressed in this article are his own and do not necessarily represent those of the National Cancer Institue or the Food and Drug Administration. This work was supported in part by National Institutes of Health grants CA09337-25 and ES07142-24. The authors thank Eric (Rocky) Feuer and Angela Mariotto for their extensive and insightful comments on the manuscript.

Ram C. Tiwari Megan Othus is Graduate Student, Department of Biostatistics, Harvard University and Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115 . Yi Li is Associate Professor, Department of Biostatistics, Harvard University, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115 . Ram Tiwari is Associate Director for Statistical Science and Policy, Office of Biostatistics, Center for Drug Evaluation & Research, Food and Drug Administration, Silver Springs, MD 20993 . The work of Tiwari was conducted while he was at the National Cancer Institute. The views expressed in this article are his own and do not necessarily represent those of the National Cancer Institue or the Food and Drug Administration. This work was supported in part by National Institutes of Health grants CA09337-25 and ES07142-24. The authors thank Eric (Rocky) Feuer and Angela Mariotto for their extensive and insightful comments on the manuscript.

Abstract

Modern cancer treatments have substantially improved cure rates and have generated a great interest in and need for proper statistical tools to analyze survival data with nonnegligible cure fractions. Data with cure fractions often are complicated by dependent censoring, and the analysis of this type of data typically involves untestable parametric assumptions on the dependence of the censoring mechanism and the true survival times. Motivated by the analysis of prostate cancer survival trends, we propose a class of semiparametric transformation cure models that allows for dependent censoring without making parametric assumptions on the dependence relationship. The proposed class of models encompasses a number of common models for the latency survival function, including the proportional hazards model and the proportional odds model, and also allows for time-dependent covariates. An inverse censoring probability reweighting scheme is used to derive unbiased estimating equations. Small-sample properties with simulations are derived, and the method is demonstrated with a data application.

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A Class of Semiparametric Mixture Cure Survival Models With Dependent Censoring

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