4.1 Article

A novel approach to improve health status measurement in observational claims-based studies of cancer treatment and outcomes

Journal

JOURNAL OF GERIATRIC ONCOLOGY
Volume 4, Issue 2, Pages 157-165

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.jgo.2012.12.005

Keywords

Medicare; Cancer; Health status; Observational study; Treatment selection bias; Functional status

Funding

  1. NCI NIH HHS [R21 CA137283] Funding Source: Medline

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Objectives: To develop and provide initial validation for a multivariate, claims-based prediction model for disability status (DS), a proxy measure of performance status (PS), among older adults. The model was designed to augment information on health status at the point of cancer diagnosis in studies using insurance claims to examine cancer treatment and outcomes. Materials and Methods: We used data from the 2001-2005 Medicare Current Beneficiary Survey (MCBS), with observations randomly split into estimation and validation subsamples. We developed an algorithm linking self-reported functional status measures to a DS scale, a proxy for the Eastern Cooperative Oncology Group (ECOG) PS scale. The DS measure was dichotomized to focus on good [ECOG 0-2] versus poor [ECOG 3-4] PS. We identified potential claims-based predictors, and estimated multivariate logistic regression models, with poor DS as the dependent measure, using a stepwise approach to select the optimal model. Construct validity was tested by determining whether the predicted DS measure generated by the model was a significant predictor of survival within a validation sample from the MCBS. Results and Conclusion: One-tenth of the beneficiaries met the definition for poor DS. The base model yielded high sensitivity (0.79) and specificity (0.92); positive predictive value=48.3% and negative predictive value=97.8%, c-statistic=0.92 and good model calibration. Adjusted poor claims-based DS was associated with an increased hazard of death (HR=3.53, 95% CI 3.18, 3.92). The ability to assess DS should improve covariate control and reduce indication bias in observational studies of cancer treatment and outcomes based on insurance claims. (C) 2013 Elsevier Ltd. All rights reserved.

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