[논문]Analysis of Recurrent Gap Time Data with a Binary Time-Varying Covariate

Kim, Yang-Jin

doi:10.5351/csam.2014.21.5.387

[국내논문] Analysis of Recurrent Gap Time Data with a Binary Time-Varying Covariate 원문보기

Communications for statistical applications and methods = 한국통계학회논문집, v.21 no.5, 2014년, pp.387 - 393

Kim, Yang-Jin (Department of Statistics, Sookmyung Women's University)

Abstract ▼ AI-Helper

Recurrent gap times are analyzed with diverse methods under several assumptions such as a marginal model or a frailty model. Several resampling techniques have been recently suggested to estimate the covariate effect; however, these approaches can be applied with a time-fixed covariate. According to simulation results, these methods result in biased estimates for a time-varying covariate which is often observed in a longitudinal study. In this paper, we extend a resampling method by incorporating new weights and sampling scheme. Simulation studies are performed to compare the suggested method with previous resampling methods. The proposed method is applied to estimate the effect of an educational program on traffic conviction data where a program participation occurs in the middle of the study.

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제안 방법

As Darlington and Dixon (2013) remarked, these two methods cannot be applied to a time-varying covariate. In this paper, we adjust a resampling technique to estimate the effect of a binary time-varying covariate on recurrent gap times.
. In this paper, our interest is to adjust a resampling technique for analyzing a recurrent gap time data. Before presenting the suggested method, the methods proposed by Luo and Huang (2011) and Darlington and Dixon (2013) are briefly summarized.
To evaluate the performance of a suggested method for a binary time-varying covariate, a Monte Carlo simulation is conducted. Sample size n is set to be 100 and 200.
A resampling technique has been applied to consider an informative risk set. The purpose of this study is to extend a resampling method to estimate a time-varying covariate which often occurs in a longitudinal data. A simulation study was performed using a R-package in order to compare previous two methods which do not consider a time-varying covariate.

대상 데이터

Main interest is to evaluate the effect of an educational program which is experienced in the middle of a study. That is, every subject had included in a Non-YTOP group at start of study and then among 192 drivers, 98 drivers became program participants. We applied a suggested method to estimate the effect of two covariates; a time-varying covariate, YTOP participation(I(t >participation time)) and a time independent covariate, gender(Male = 1, Female = 0).

참고문헌 (13)

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Darlington, G. A. and Dixon, S. N. (2013). Event-weighted proportional hazards modeling for recurrent gap time data, Statistics in Medicine, 32, 124-130.

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Huang, Y. and Chen, Y. Q. (2003). Marginal regression of gaps between recurrent events, Lifetime Data Analysis, 9, 293-303.

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Luo, X. and Huang, C. Y. (2011). Analysis of recurrent gap time data using the weighted risk-set method and the modified within-cluster resampling method, Statistics in Medicine, 30, 301-311.

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Zhao, X., Liu, L., Liu, Y. and Xu, W. (2012). Analysis of multivariate recurrent event data with time-dependent covariates and informative censoring, Biometrical Journal, 54, 585-599.

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