Dynamic regression with recurrent events
Corresponding Author
J. E. Soh
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
Correspondence J. E. Soh, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322.
Email: [email protected]
Search for more papers by this authorYijian Huang
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
Search for more papers by this authorCorresponding Author
J. E. Soh
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
Correspondence J. E. Soh, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322.
Email: [email protected]
Search for more papers by this authorYijian Huang
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
Search for more papers by this authorAbstract
Recurrent events often arise in follow-up studies where a subject may experience multiple occurrences of the same event. Most regression models with recurrent events tacitly assume constant effects of covariates over time, which may not be realistic in practice. To address time-varying effects, we develop a dynamic regression model to target the mean frequency of recurrent events. We propose an estimation procedure which fully exploits observed data. Consistency and weak convergence of the proposed estimator are established. Simulation studies demonstrate that the proposed method works well, and two real data analyses are presented for illustration.
Supporting Information
| Filename | Description |
|---|---|
| Dynamic_Regression_with_Recurrent_Events.zip100.7 KB | Supplementary Information |
| biom13105-sup-0002-Supporting Information for_Dynamic_Regression_with_Recurrent_Events_____Biometrics 20190613.pdf122.8 KB | Supplementary Information |
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