Latent cure rate model under repair system and threshold effect

In this paper, we formulate a simple latent cure rate model with repair mechanism for a cell exposed to radiation. This latent approach is a flexible alternative to the models proposed by Klebanov et al. [A stochastic model of radiation carcinogenesis: latent time distributions and their properties. Math Biosci. 1993;18:51–75], Kim et al. [A new threshold regression model for survival data with a cure fraction. Lifetime Data Anal. 2011;17:101–122], and is along the lines of the destructive cure rate model formulated recently by Rodrigues et al. [Destructive weighted Poisson cure rate model. Lifetime Data Anal. 2011b;17:333–346]. A new version of the modified Gompertz model and the promotion cure rate model that takes into account the first passage time of reaching the critical point are discussed, and the estimation of tumor size at detection is then addressed from the Bayesian viewpoint. In addition, a simulation study and an application to real data set illustrate the usefulness of the proposed cure rate model.

[1]  J. C. Ahuja ON CERTAIN PROPERTIES OF THE GENERALIZED GOMPERTZ DISTRIBUTION , 2016 .

[2]  Thomas B. L. Kirkwood,et al.  Deciphering death: a commentary on Gompertz (1825) ‘On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies’ , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[3]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[4]  Shuangge Ma,et al.  Survival Analysis in Medicine and Genetics , 2013 .

[5]  Narayanaswamy Balakrishnan,et al.  Correlated destructive generalized power series cure rate models and associated inference with an application to a cutaneous melanoma data , 2012, Comput. Stat. Data Anal..

[6]  Gauss M. Cordeiro,et al.  A unified view on lifetime distributions arising from selection mechanisms , 2011, Comput. Stat. Data Anal..

[7]  Narayanaswamy Balakrishnan,et al.  Destructive weighted Poisson cure rate models , 2011, Lifetime data analysis.

[8]  Ming-Hui Chen,et al.  A new threshold regression model for survival data with a cure fraction , 2011, Lifetime data analysis.

[9]  Jialiang Li,et al.  Interval‐censored data with repeated measurements and a cured subgroup , 2010 .

[10]  By W. R. GILKSt,et al.  Adaptive Rejection Sampling for Gibbs Sampling , 2010 .

[11]  Narayanaswamy Balakrishnan,et al.  COM–Poisson cure rate survival models and an application to a cutaneous melanoma data , 2009 .

[12]  Josemar Rodrigues,et al.  A Bayesian long-term survival model parametrized in the cured fraction. , 2009, Biometrical journal. Biometrische Zeitschrift.

[13]  Josemar Rodrigues,et al.  On the unification of long-term survival models , 2009 .

[14]  Bradley P Carlin,et al.  Flexible Cure Rate Modeling Under Latent Activation Schemes , 2007, Journal of the American Statistical Association.

[15]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[16]  M. May Bayesian Survival Analysis. , 2002 .

[17]  O. Aalen,et al.  Understanding the shape of the hazard rate: A proce ss point of view , 2002 .

[18]  Chin-Shang Li,et al.  Identifiability of cure models , 2001 .

[19]  J G Ibrahim,et al.  Maximum Likelihood Methods for Cure Rate Models with Missing Covariates , 2001, Biometrics.

[20]  O. Aalen,et al.  Understanding the shape of the hazard rate: a process point of view (With comments and a rejoinder by the authors) , 2001 .

[21]  V. Sondak,et al.  High- and low-dose interferon alfa-2b in high-risk melanoma: first analysis of intergroup trial E1690/S9111/C9190. , 2000, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[22]  Joseph G. Ibrahim,et al.  A New Bayesian Model For Survival Data With a Surviving Fraction , 1999 .

[23]  J. Shuster,et al.  Modelling cure rates using the Gompertz model with covariate information. , 1998, Statistics in medicine.

[24]  A. Yakovlev,et al.  Stochastic Models of Tumor Latency and Their Biostatistical Applications , 1996 .

[25]  A. Yakovlev,et al.  A diversity of responses displayed by a stochastic model of radiation carcinogenesis allowing for cell death. , 1996, Mathematical biosciences.

[26]  Y u Yakovlev A,et al.  Parametric versus non-parametric methods for estimating cure rates based on censored survival data. , 1994, Statistics in medicine.

[27]  S T Rachev,et al.  A stochastic model of radiation carcinogenesis: latent time distributions and their properties. , 1993, Mathematical biosciences.

[28]  Hong Chang,et al.  Model Determination Using Predictive Distributions with Implementation via Sampling-Based Methods , 1992 .

[29]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[30]  S. Geisser,et al.  A Predictive Approach to Model Selection , 1979 .

[31]  A. Whittemore,et al.  QUANTITATIVE THEORIES OF CARCINOGENESIS , 1978 .

[32]  M. Zelen,et al.  Application of Exponential Models to Problems in Cancer Research , 1966 .

[33]  W. Feller An Introduction to Probability Theory and Its Applications , 1959 .

[34]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .