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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Including injured workers without compensated time loss in Cox regression models: analyzing time loss using all
Jeanne M Sears1, Patrick J Heagerty
1Department of Health Services, School of Public Health and Community Medicine, University of Washington, Box 357660, Seattle, WA 98195, USA. jeannes@u.washington.edu
Journal of Occupational Rehabilitation
|July 19, 2008
Summary
A novel method allows combining medical-only and time loss claims in Cox regression for analyzing worker injury duration. This approach prevents bias by including all cases, expanding time loss data analysis tools.
Area of Science:
- Occupational Health
- Biostatistics
- Survival Analysis
Background:
- Cox proportional hazards regression commonly analyzes time loss duration.
- Standard software excludes cases with no follow-up time, leading to potential bias.
- Excluding medical-only claims or those with short durations limits research scope.
Purpose of the Study:
- To present a novel technique for including all injury claims in time loss duration analysis.
- To address ascertainment bias caused by excluding certain injury cases.
- To expand the utility of survival analysis methods for occupational injury research.
Main Methods:
- A simple technique involves adding a small constant to the time loss duration variable for pooled analysis.
- Comparison with a two-part model using a time-varying step function.
- Standard survival analysis methods are adapted for inclusion of all cases.
Main Results:
- The proposed technique allows for pooled analysis of medical-only and time loss claims.
- Appropriate estimates are produced while accounting for censoring.
- The combined model is statistically justified as a special case of a two-part model.
Conclusions:
- All injury claims (medical-only and time loss) can be combined into a single statistical model.
- This approach is useful for research on factors affecting time loss duration.
- The novel modeling strategy enhances the analysis of time loss data.
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