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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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A simple approach for fitting linear relative rate models in SAS.

David B Richardson¹

¹Department of Epidemiology, CB 7435, School of Public Health, University of North Carolina, Chapel Hill, NC 27599, USA. david.richardson@unc.edu

American Journal of Epidemiology

|October 28, 2008

Summary

This study introduces a straightforward method for fitting the linear relative rate model to epidemiologic data using SAS software. This approach aids in analyzing environmental and occupational exposures and disease risk.

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Area of Science:

Epidemiology
Biostatistics
Environmental Health

Background:

The linear relative rate model is crucial for analyzing environmental and occupational exposures.
This model assumes excess disease risk changes additively with exposure.
Existing software like EPICURE can fit this model, but a SAS-based approach is presented.

Purpose of the Study:

To present a simple method for fitting the linear relative rate model using PROC NLMIXED in SAS.
To demonstrate the application of this SAS-based approach with real-world data.

Main Methods:

Utilized PROC NLMIXED within the SAS statistical software package.
Applied the method to analyze mortality data from a cohort of South Carolina asbestos textile workers (1940-2001).

Main Results:

Successfully fitted the linear relative rate model using PROC NLMIXED.
The approach provides a viable alternative for epidemiologic analyses in SAS.

Conclusions:

The PROC NLMIXED approach offers a flexible and accessible method for fitting linear relative rate models.
This method can be effectively used for analyzing occupational and environmental health data.