Measuring disease-free survival and cancer relapse using Medicare claims from CALGB breast cancer trial participants (companion to 9344).
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To determine the accuracy with which Medicare claims data measure disease-free survival in elderly Medicare beneficiaries with cancer, we performed a criterion validation study. We merged gold-standard clinical trial data of 45 elderly patients with node-positive breast cancer who were treated on the Cancer and Leukemia Group B (CALGB) adjuvant breast trial 9344 with Centers for Medicare and Medicaid Services (CMS) data files and compared the results of a CMS-based algorithm with the CALGB disease-free survival information to determine sensitivity and specificity. For 5-year disease-free survival, the sensitivity of the CMS-based algorithm was 100% (95% confidence interval [CI] = 81% to 100%), the specificity was 97% (95% CI = 83% to 100%), and the area under the receiver operator curve was 98[corrected]% (95% CI = 95[corrected]% to 100%). For 2-year disease-free survival, the test characteristics were less favorable: sensitivity was 83% (95% CI = 36% to 100%), specificity was 95% (95% CI = 83% to 100%), and area under the receiver operator curve was 89[corrected]% (95% CI = 72[corrected]% to 100%).
Area Under Curve
Multicenter Studies as Topic
Neoplasm Recurrence, Local
Sensitivity and Specificity
Published Version (Please cite this version)10.1093/jnci/djj363
Publication InfoCancer and Leukemia Group B; Christakis, NA; Earle, CC; Henderson, IC; Herndon, James Emmett II; Lamont, EB; ... Weeks, JC (2006). Measuring disease-free survival and cancer relapse using Medicare claims from CALGB breast cancer trial participants (companion to 9344). J Natl Cancer Inst, 98(18). pp. 1335-1338. 10.1093/jnci/djj363. Retrieved from http://hdl.handle.net/10161/16103.
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Professor of Biostatistics and Bioinformatics
Current research interests have application to the design and analysis of cancer clinical trials. Specifically, interests include the use of time-dependent covariables within survival models, the design of phase II cancer clinical trials which minimize some of the logistical problems associated with their conduct, and the analysis of longitudinal studies with informative censoring (in particular, quality of life studies of patients with advanced cancer).