Mixture cure model with random effects for clustered interval-censored survival data. 2011

Liming Xiang, and Xiangmei Ma, and Kelvin K W Yau
Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore. lmxiang@ntu.edu.sg

The mixture cure model is an effective tool for analysis of survival data with a cure fraction. This approach integrates the logistic regression model for the proportion of cured subjects and the survival model (either the Cox proportional hazards or accelerated failure time model) for uncured subjects. Methods based on the mixture cure model have been extensively investigated in the literature for data with exact failure/censoring times. In this paper, we propose a mixture cure modeling procedure for analyzing clustered and interval-censored survival time data by incorporating random effects in both the logistic regression and PH regression components. Under the generalized linear mixed model framework, we develop the REML estimation for the parameters, as well as an iterative algorithm for estimation of the survival function for interval-censored data. The estimation procedure is implemented via an EM algorithm. A simulation study is conducted to evaluate the performance of the proposed method in various practical situations. To demonstrate its usefulness, we apply the proposed method to analyze the interval-censored relapse time data from a smoking cessation study whose subjects were recruited from 51 zip code regions in the southeastern corner of Minnesota.

UI MeSH Term Description Entries
D008910 Minnesota State bordered on the north by Canada, on the east by Lake Superior and Wisconsin, on the south by Iowa, and on the west by North Dakota and South Dakota.
D002986 Clinical Trials as Topic Works about pre-planned studies of the safety, efficacy, or optimum dosage schedule (if appropriate) of one or more diagnostic, therapeutic, or prophylactic drugs, devices, or techniques selected according to predetermined criteria of eligibility and observed for predefined evidence of favorable and unfavorable effects. This concept includes clinical trials conducted both in the U.S. and in other countries. Clinical Trial as Topic
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000465 Algorithms A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task. Algorithm
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016019 Survival Analysis A class of statistical procedures for estimating the survival function (function of time, starting with a population 100% well at a given time and providing the percentage of the population still well at later times). The survival analysis is then used for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates on the function. Analysis, Survival,Analyses, Survival,Survival Analyses
D016540 Smoking Cessation Discontinuing the habit of SMOKING. Giving Up Smoking,Quitting Smoking,Stopping Smoking,Cessation, Smoking,Smoking Cessations,Smoking, Giving Up,Smoking, Quitting,Smoking, Stopping,Smokings, Giving Up,Up Smoking, Giving

Related Publications

Liming Xiang, and Xiangmei Ma, and Kelvin K W Yau
April 2024, Lifetime data analysis,
Liming Xiang, and Xiangmei Ma, and Kelvin K W Yau
January 2021, Lifetime data analysis,
Liming Xiang, and Xiangmei Ma, and Kelvin K W Yau
June 2013, Statistics in medicine,
Liming Xiang, and Xiangmei Ma, and Kelvin K W Yau
January 2008, Statistics in medicine,
Liming Xiang, and Xiangmei Ma, and Kelvin K W Yau
March 2016, Statistics in medicine,
Liming Xiang, and Xiangmei Ma, and Kelvin K W Yau
September 2013, Biometrical journal. Biometrische Zeitschrift,
Liming Xiang, and Xiangmei Ma, and Kelvin K W Yau
February 2023, Statistics in medicine,
Liming Xiang, and Xiangmei Ma, and Kelvin K W Yau
January 2018, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America,
Liming Xiang, and Xiangmei Ma, and Kelvin K W Yau
March 2010, Statistics in medicine,
Liming Xiang, and Xiangmei Ma, and Kelvin K W Yau
June 2017, Statistical methods in medical research,
Copied contents to your clipboard!