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A computer program for the statistical analysis of repeated event data using a mixed effects regression model

R Natarajan1, B W Turnbull, E H Slate

  • 1School of Operations Research and Industrial Engineering, Cornell University, Ithaca, NY 14850-3801.

Computer Methods and Programs in Biomedicine
|April 1, 1994
PubMed
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This study introduces a computer program for analyzing repeated events data using mixed effects regression models. It helps researchers understand how factors influence event rates in longitudinal studies.

Area of Science:

  • Biostatistics
  • Statistical Computing
  • Longitudinal Data Analysis

Background:

  • Repeated events data are common in longitudinal studies.
  • Analyzing such data requires specialized statistical methods.
  • Existing methods may not efficiently handle complex covariate influences.

Purpose of the Study:

  • To present a computer program for fitting mixed effects regression models to repeated events data.
  • To provide a tool for stepwise model construction and covariate influence assessment.
  • To demonstrate the program's utility with real-world examples.

Main Methods:

  • Implementation of mixed effects regression models for event rate analysis.
  • Stepwise model building approach for identifying significant covariates.

Related Experiment Videos

  • Application of the program to analyze mammary tumor incidence and skin tumor recurrence data.

    • Main Results:

    • The program successfully fits mixed effects models to repeated events data.
    • Stepwise construction allows for the examination of covariate effects on event rates.
    • Demonstrated applicability on both simple and complex, large-scale datasets.

    Conclusions:

    • The developed computer program is a valuable tool for analyzing repeated events data in longitudinal studies.
    • It facilitates the understanding of covariate impacts on event rates.
    • The program is robust, handling diverse and complex datasets effectively.