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Related Experiment Videos

Estimating relative risks for common outcome using PROC NLP.

Binbing Yu1, Zhuoqiao Wang

  • 1Laboratory of Epidemiology, Demography and Biometry, National Institute on Aging, Bethesda, MD 20892, USA. yubi@mail.nih.gov

Computer Methods and Programs in Biomedicine
|February 23, 2008
PubMed
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Estimating relative risk in studies with non-rare outcomes is crucial. A new SAS Nonlinear Programming method overcomes log-binomial model convergence issues, providing reliable relative risk estimates for binary outcomes.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Research

Background:

  • Estimating relative risk or prevalence ratios is vital for binary outcomes in cohort and cross-sectional studies, particularly when response rates are not rare.
  • Log-binomial models provide maximum likelihood estimates (MLE) but frequently encounter convergence problems due to parameter space restrictions.
  • Existing alternative methods like Poisson and Cox regressions can yield invalid predicted probabilities.

Purpose of the Study:

  • To introduce a novel computation method using SAS Nonlinear Programming (NLP) to accurately determine MLEs for relative risk.
  • To compare the proposed NLP method with the established COPY method for fitting log-binomial models.
  • To address implementation challenges associated with fitting log-binomial models.

Main Methods:

Related Experiment Videos

  • Development and application of a SAS Nonlinear Programming (NLP) procedure for estimating relative risk.
  • Comparative analysis of the proposed NLP method against the COPY method, a modification for log-binomial model fitting.
  • Utilizing data from the Diabetes Control and Complications Trial (DCCT) for microalbuminuria prevalence analysis.

Main Results:

  • The proposed NLP method offers a viable solution to the convergence issues often encountered with standard log-binomial models.
  • The study demonstrates the practical application of the NLP method using real-world clinical data.
  • Implementation issues and considerations for using the NLP method are discussed.

Conclusions:

  • The SAS NLP procedure provides a robust alternative for estimating relative risk, effectively circumventing convergence problems inherent in log-binomial models.
  • This method ensures valid probability estimates, enhancing the reliability of epidemiological and clinical research findings.
  • The study provides a practical SAS macro for calculating relative risk, aiding researchers in similar analyses.