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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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A Simple Method for Evaluating Within Sample Prognostic Balance Achieved by Published Comorbidity Summary Measures.

Brian L Egleston1, Robert G Uzzo2, J Robert Beck3

  • 1Biostatistics and Bioinformatics Facility, Fox Chase Cancer Center, Temple University Health System, 333 Cottman Avenue, Philadelphia, PA, 19111.

Health Services Research
|December 20, 2014
PubMed
Summary
This summary is machine-generated.

Researchers can assess if a comorbidity summary measure fits their data by examining survival curves. The Charlson Comorbidity Index weights were mostly adequate, but may underestimate one disease's impact.

Keywords:
Comorbidity scoresSEER-Medicarediagnosticsprognostic balanceprognostic scores

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Area of Science:

  • Health Services Research
  • Epidemiology
  • Biostatistics

Background:

  • Comorbidity summary measures are crucial for adjusting outcomes in research.
  • Published measures, like the Charlson Comorbidity Index, are widely used but require validation for specific populations.
  • Assessing the appropriateness of these measures is essential for accurate research findings.

Purpose of the Study:

  • To demonstrate a method for evaluating the suitability of a published comorbidity summary measure for a specific research sample.
  • To assess the validity of the Charlson Comorbidity Index in a cohort with early-stage kidney cancer.

Main Methods:

  • Utilized Surveillance, Epidemiology, and End Results (SEER) linked to Medicare claims data.
  • Examined Kaplan-Meier survival curves for four diseases stratified by the Charlson Comorbidity Index.
  • Included individuals diagnosed with early-stage kidney cancer between 1995 and 2009, recording pre-diagnosis comorbidities.

Main Results:

  • The Charlson Comorbidity Index weights were found to be adequate for three of the four studied diseases.
  • Evidence suggests the Charlson Comorbidity Index may underestimate the impact of one specific disease within the sample population.
  • The appropriateness of comorbidity measures depends on comparable relationships between comorbidities and outcomes in the researcher's population versus the algorithm's original population.

Conclusions:

  • Examining survival curves within strata defined by a comorbidity summary measure is a valuable tool for validation.
  • Researchers must ensure that the variables included in a comorbidity score accurately reflect their population's health status.
  • Published comorbidity measures require careful evaluation for appropriate application in diverse patient cohorts.