A joint model of recurrent events and a terminal event with a nonparametric covariate function

We extend the shared frailty model of recurrent events and a dependent terminal event to allow for a nonparametric covariate function. We include a Gaussian random effect (frailty) in the intensity functions of both the recurrent and terminal events to capture correlation between the two processes. We employ the penalized cubic spline method to describe the nonparametric covariate function in the recurrent events model. We use Laplace approximation to evaluate the marginal penalized partial likelihood without a closed form. We also propose the variance estimates for regression coefficients. Numerical analysis results show that the proposed estimates perform well for both the nonparametric and parametric components. We apply this method to analyze the hospitalization rate of patients with heart failure in the presence of death. Copyright © 2011 John Wiley & Sons, Ltd.

[1]  Walter R. Young,et al.  The Statistical Analysis of Failure Time Data , 1981 .

[2]  F. O’Sullivan Nonparametric Estimation of Relative Risk Using Splines and Cross-Validation , 1988 .

[3]  R. Tibshirani,et al.  Exploring the nature of covariate effects in the proportional hazards model. , 1990, Biometrics.

[4]  Robert Gray,et al.  Flexible Methods for Analyzing Survival Data Using Splines, with Applications to Breast Cancer Prognosis , 1992 .

[5]  N. Breslow,et al.  Approximate inference in generalized linear mixed models , 1993 .

[6]  R. Gray,et al.  Spline-based tests in survival analysis. , 1994, Biometrics.

[7]  B. Silverman,et al.  Nonparametric regression and generalized linear models , 1994 .

[8]  Irène Gijbels,et al.  Local likelihood and local partial likelihood in hazard regression , 1997 .

[9]  T. Lancaster,et al.  Panel Data with Survival: Hospitalization of HIV-Positive Patients , 1998 .

[10]  J. Palmgren,et al.  Estimation of Multivariate Frailty Models Using Penalized Partial Likelihood , 2000, Biometrics.

[11]  J. Kalbfleisch,et al.  The Statistical Analysis of Failure Time Data: Kalbfleisch/The Statistical , 2002 .

[12]  Laurence L. George,et al.  The Statistical Analysis of Failure Time Data , 2003, Technometrics.

[13]  R. Wolfe,et al.  Shared Frailty Models for Recurrent Events and a Terminal Event , 2004, Biometrics.

[14]  P. Janssen,et al.  Penalized Partial Likelihood for Frailties and Smoothing Splines in Time to First Insemination Models for Dairy Cows , 2004, Biometrics.

[15]  B. Ripley,et al.  Semiparametric Regression: Preface , 2003 .

[16]  Virginie Rondeau,et al.  Joint frailty models for recurring events and death using maximum penalized likelihood estimation: application on cancer events. , 2006 .

[17]  Lei Liu,et al.  A Joint Frailty Model for Survival and Gap Times Between Recurrent Events , 2007, Biometrics.

[18]  Richard J. Cook,et al.  The Statistical Analysis of Recurrent Events , 2007 .

[19]  Lei Liu,et al.  Analysis of Longitudinal Data in the Presence of Informative Observational Times and a Dependent Terminal Event, with Application to Medical Cost Data , 2008, Biometrics.

[20]  Joseph G Ibrahim,et al.  Current Methods for Recurrent Events Data With Dependent Termination , 2008, Journal of the American Statistical Association.

[21]  Lei Liu,et al.  A likelihood reformulation method in non‐normal random effects models , 2008, Statistics in medicine.

[22]  Lei Liu,et al.  The use of Gaussian quadrature for estimation in frailty proportional hazards models , 2008, Statistics in medicine.

[23]  Donglin Zeng,et al.  Semiparametric Transformation Models with Random Effects for Joint Analysis of Recurrent and Terminal Events , 2009, Biometrics.

[24]  Richard J. Cook,et al.  Robust Estimation of Mean Functions and Treatment Effects for Recurrent Events Under Event-Dependent Censoring and Termination: Application to Skeletal Complications in Cancer Metastatic to Bone , 2009 .