Effects of covariates on alternating recurrent events in accelerated failure time models
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Moumita Chatterjee
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Sugata Sen Roy
Abstract
In this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency.
Acknowledgment
The authors are indebted to the anonymous reviewers and the associate editor for their valuable comments which helped to improve the quality of the article substantially.
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Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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© 2020 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Targeted design for adaptive clinical trials via semiparametric model
- Causal mediation analysis in presence of multiple mediators uncausally related
- Summarizing causal differences in survival curves in the presence of unmeasured confounding
- Bayesian information criterion approximations to Bayes factors for univariate and multivariate logistic regression models
- Marginal quantile regression for longitudinal data analysis in the presence of time-dependent covariates
- Parametric models for combined failure time data from an incident cohort study and a prevalent cohort study with follow-up
- Effects of covariates on alternating recurrent events in accelerated failure time models
- Gradient boosting for linear mixed models
- A kernel- and optimal transport- based test of independence between covariates and right-censored lifetimes
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Targeted design for adaptive clinical trials via semiparametric model
- Causal mediation analysis in presence of multiple mediators uncausally related
- Summarizing causal differences in survival curves in the presence of unmeasured confounding
- Bayesian information criterion approximations to Bayes factors for univariate and multivariate logistic regression models
- Marginal quantile regression for longitudinal data analysis in the presence of time-dependent covariates
- Parametric models for combined failure time data from an incident cohort study and a prevalent cohort study with follow-up
- Effects of covariates on alternating recurrent events in accelerated failure time models
- Gradient boosting for linear mixed models
- A kernel- and optimal transport- based test of independence between covariates and right-censored lifetimes
