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CRcoder: An Interactive Web Application and SAS Macro to Support Personalized Clinical Decisions.

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This study introduces user-friendly software for joint modeling of electronic health data to estimate typical and personalized risks for concurrent binary outcomes, improving clinical decision-making.

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Area of Science:

  • Biostatistics
  • Health Informatics
  • Clinical Epidemiology

Background:

  • Electronic health care data enable advanced statistical analyses of longitudinal observations to enhance clinical decision-making.
  • Joint modeling of concurrent binary outcomes offers a sophisticated approach to risk estimation.

Purpose of the Study:

  • To describe a Web application and SAS/STAT macro for computing joint models.
  • To estimate the typical and personalized risk of two concurrent binary outcomes.

Main Methods:

  • A Web application was designed for uploading longitudinal data, specifying outcomes, and defining joint or separate models.
  • An SAS macro was developed to perform the statistical analyses based on the Web application's specifications.
  • A sample of older adults with comorbid hypertension and COPD was used to examine polypharmacy's association with functional limitations.

Main Results:

  • Joint models, compared to separate models, typically yielded attenuated associations between variables and outcomes with smaller standard errors.
  • The joint models successfully provided estimates for both personalized and typical concurrent risks.
  • The analysis illustrated the association between polypharmacy and limitations in social activities and mobility in older adults.

Conclusions:

  • Joint modeling of electronic health data can improve clinical decision-making by providing individual probabilities of concurrent risk.
  • The developed user-friendly software facilitates advanced statistical analyses for estimating typical and personalized concurrent risks.