Smoothing Spline ANOVA Frailty Model for Recurrent Event Data

Gap time hazard estimation is of particular interest in recurrent event data. This article proposes a fully nonparametric approach for estimating the gap time hazard. Smoothing spline analysis of variance (ANOVA) decompositions are used to model the log gap time hazard as a joint function of gap tim...

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Vydáno v:Biometrics Ročník 67; číslo 4; s. 1330 - 1339
Hlavní autoři: Du, Pang, Jiang, Yihua, Wang, Yuedong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Malden, USA Blackwell Publishing Inc 01.12.2011
Wiley-Blackwell
Blackwell Publishing Ltd
Témata:
algorithms
Analysis of Variance
BIOMETRIC METHODOLOGY
Biometrics
biometry
Censorship
Computer Simulation
Data analysis
Data Interpretation, Statistical
Data smoothing
Estimation methods
Event driven simulation
Frailty
Gap time hazard
Health hazards
Humans
Markov chain
Mathematical models
Model selection
Modeling
Models, Statistical
Neoplasm Recurrence, Local - epidemiology
Nonparametric hazard model
Nonparametric models
Numerical Analysis, Computer-Assisted
Parametric models
Prevalence
Proportional Hazards Models
Recurrent event data
Smoothing spline ANOVA
Stochastic approximation
Tensors
Tumors
urinary bladder neoplasms
Urinary Bladder Neoplasms - epidemiology
ISSN:0006-341X, 1541-0420, 1541-0420
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Shrnutí:Gap time hazard estimation is of particular interest in recurrent event data. This article proposes a fully nonparametric approach for estimating the gap time hazard. Smoothing spline analysis of variance (ANOVA) decompositions are used to model the log gap time hazard as a joint function of gap time and covariates, and general frailty is introduced to account for between-subject heterogeneity and within-subject correlation. We estimate the nonparametric gap time hazard function and parameters in the frailty distribution using a combination of the Newton-Raphson procedure, the stochastic approximation algorithm (SAA), and the Markov chain Monte Carlo (MCMC) method. The convergence of the algorithm is guaranteed by decreasing the step size of parameter update and/or increasing the MCMC sample size along iterations. Model selection procedure is also developed to identify negligible components in a functional ANOVA decomposition of the log gap time hazard. We evaluate the proposed methods with simulation studies and illustrate its use through the analysis of bladder tumor data.
Bibliografie:istex:6C8E5D72C9401045B2A33D5B1D067FBE6D53043A
ark:/67375/WNG-QM7GHJM9-W
ArticleID:BIOM1584
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/j.1541-0420.2011.01584.x