Cargando…

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

BACKGROUND: 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...

Descripción completa

Detalles Bibliográficos
Autores principales: Arano, Ichiro, Sugimoto, Tomoyuki, Hamasaki, Toshimitsu, Ohno, Yuko
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2880122/
https://www.ncbi.nlm.nih.gov/pubmed/20412598
http://dx.doi.org/10.1186/1471-2288-10-33
_version_ 1782181992383643648
author Arano, Ichiro
Sugimoto, Tomoyuki
Hamasaki, Toshimitsu
Ohno, Yuko
author_facet Arano, Ichiro
Sugimoto, Tomoyuki
Hamasaki, Toshimitsu
Ohno, Yuko
author_sort Arano, Ichiro
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: The method is illustrated using data from a sertraline randomized withdrawal study in patients with major depressive disorder. CONCLUSIONS: 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.
format Text
id pubmed-2880122
institution National Center for Biotechnology Information
language English
publishDate 2010
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-28801222010-06-03 Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder Arano, Ichiro Sugimoto, Tomoyuki Hamasaki, Toshimitsu Ohno, Yuko BMC Med Res Methodol Technical Advance BACKGROUND: 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. METHODS: 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. RESULTS: The method is illustrated using data from a sertraline randomized withdrawal study in patients with major depressive disorder. CONCLUSIONS: 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. BioMed Central 2010-04-23 /pmc/articles/PMC2880122/ /pubmed/20412598 http://dx.doi.org/10.1186/1471-2288-10-33 Text en Copyright ©2010 Arano et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Advance
Arano, Ichiro
Sugimoto, Tomoyuki
Hamasaki, Toshimitsu
Ohno, Yuko
Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder
title Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder
title_full Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder
title_fullStr Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder
title_full_unstemmed Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder
title_short Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder
title_sort practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2880122/
https://www.ncbi.nlm.nih.gov/pubmed/20412598
http://dx.doi.org/10.1186/1471-2288-10-33
work_keys_str_mv AT aranoichiro practicalapplicationofcuremixturemodelforlongtermcensoredsurvivordatafromawithdrawalclinicaltrialofpatientswithmajordepressivedisorder
AT sugimototomoyuki practicalapplicationofcuremixturemodelforlongtermcensoredsurvivordatafromawithdrawalclinicaltrialofpatientswithmajordepressivedisorder
AT hamasakitoshimitsu practicalapplicationofcuremixturemodelforlongtermcensoredsurvivordatafromawithdrawalclinicaltrialofpatientswithmajordepressivedisorder
AT ohnoyuko practicalapplicationofcuremixturemodelforlongtermcensoredsurvivordatafromawithdrawalclinicaltrialofpatientswithmajordepressivedisorder