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Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder

Ichiro Arano12, Tomoyuki Sugimoto3, Toshimitsu Hamasaki3* and Yuko Ohno2

Author Affiliations

1 Pfizer Global Research and Development, Pfizer Japan Inc., Yoyogi 3-22-7, Shibuya 151-8589, Tokyo Japan

2 Department of Mathematical Health Science, Osaka University Graduate School of Medicine, Yamadaoka 1-7, Suita 567-0871, Osaka, Japan

3 Department of Biomedical Statistics, Osaka University Graduate School of Medicine, Yamadaoka 2-2, Suita 567-0871, Osaka, Japan

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BMC Medical Research Methodology 2010, 10:33  doi:10.1186/1471-2288-10-33

Published: 23 April 2010



Survival analysis methods such as the Kaplan-Meier method, log-rank test, and Cox proportional hazards regression (Cox regression) are commonly used to analyze data from randomized withdrawal studies in patients with major depressive disorder. However, unfortunately, such common methods may be inappropriate when a long-term censored relapse-free time appears in data as the methods assume that if complete follow-up were possible for all individuals, each would eventually experience the event of interest.


In this paper, to analyse data including such a long-term censored relapse-free time, we discuss a semi-parametric cure regression (Cox cure regression), which combines a logistic formulation for the probability of occurrence of an event with a Cox proportional hazards specification for the time of occurrence of the event. In specifying the treatment's effect on disease-free survival, we consider the fraction of long-term survivors and the risks associated with a relapse of the disease. In addition, we develop a tree-based method for the time to event data to identify groups of patients with differing prognoses (cure survival CART). Although analysis methods typically adapt the log-rank statistic for recursive partitioning procedures, the method applied here used a likelihood ratio (LR) test statistic from a fitting of cure survival regression assuming exponential and Weibull distributions for the latency time of relapse.


The method is illustrated using data from a sertraline randomized withdrawal study in patients with major depressive disorder.


We concluded that Cox cure regression reveals facts on who may be cured, and how the treatment and other factors effect on the cured incidence and on the relapse time of uncured patients, and that cure survival CART output provides easily understandable and interpretable information, useful both in identifying groups of patients with differing prognoses and in utilizing Cox cure regression models leading to meaningful interpretations.