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The trend-renewal process: a useful model for medical recurrence data
Diana Pietzner1, Andreas Wienke
1Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany. diana.pietzner@medizin.uni-halle.de
Statistics in Medicine
|July 19, 2012
Summary
This study introduces a new statistical model for analyzing recurrent events, like hospital readmissions. The trend-renewal process, using Weibull distributions, effectively models multiple events per subject, accounting for patient and unobserved factors.
Area of Science:
- Statistics
- Biostatistics
- Reliability Engineering
Background:
- Traditional time-to-event models handle single events.
- Recurrent events, where subjects experience multiple events, are common in medicine and engineering.
- Existing models may not fully capture the complexities of recurrent event data.
Purpose of the Study:
- To develop and apply an advanced statistical model for recurrent event time analysis.
- To extend the trend-renewal process to incorporate covariates and random effects for heterogeneity.
- To illustrate the model's utility with hospital readmission data for colon cancer patients.
Main Methods:
- Utilized a trend-renewal process model.
- Employed Weibull processes for both trend and renewal components.
- Incorporated Cox-type covariates for observed heterogeneity.
- Added random effects to account for unobserved heterogeneity.
Main Results:
- The proposed trend-renewal process model was successfully fitted to colon cancer patient readmission data.
- The model demonstrated capability in handling recurrent event times with complex dependencies.
- The inclusion of covariates and random effects improved the model's ability to capture data variations.
Conclusions:
- The extended trend-renewal process provides a flexible framework for analyzing recurrent event data.
- This statistical approach is valuable for understanding disease progression and treatment effects in clinical settings.
- The model offers insights into factors influencing hospital readmissions, aiding clinical decision-making.
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