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

Authors
  • McAvay, Gail J1
  • Murphy, Terrence E1, 2
  • Agogo, George O1
  • Allore, Heather1, 2
  • 1 Department of Internal Medicine, Yale University School of Medicine, New Haven, CT
  • 2 Biostatistics Department, Yale University School of Public Health, New Haven, CT
Type
Published Article
Journal
The Permanente Journal
Publisher
The Permanente Journal
Publication Date
Dec 18, 2019
Volume
24
Identifiers
DOI: 10.7812/TPP/19.078
PMID: 31905337
PMCID: PMC6972556
Source
PubMed Central
Keywords
License
Unknown

Abstract

Introduction Electronic health care data offer an opportunity to improve clinical decision making through advanced statistical analyses of longitudinal observations. Objective To describe a Web application and SAS/STAT macro (SAS Institute Inc, Cary, NC) for computing joint models to estimate the typical and personalized risk of 2 concurrent binary outcomes. Methods Features of the Web application design include uploading longitudinal files formatted with constant or time-varying covariates, specification of 2 binary outcomes, specification of a propensity model for treatment, and joint and separate models of the outcomes. In addition we designed an SAS macro for conducting the analysis. Fitting of joint and separate statistical models was implemented using a model specified in the Web application, with subsequent processing by the SAS macro. To illustrate the fitting of models, a sample of older adults with comorbid hypertension and chronic obstructive pulmonary disease from the Medical Expenditure Panel Survey was created to examine the association between polypharmacy (use of ≥ 5 medication classes) and limitations in social activities and mobility. Results Relative to separate models, the joint models typically estimated attenuated associations between explanatory variables and the 2 outcomes with smaller standard errors. These joint models yielded estimates of personalized concurrent risk and typical concurrent risk. Discussion Clinical decision making based on electronic health data can be improved using joint modeling to generate an individual’s probability of concurrent risk. Conclusion This user-friendly software performs the advanced statistical analyses needed to estimate typical and personalized concurrent risks.

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