Open Access

Further evaluation of a claims-based algorithm to determine the effectiveness of biologics for rheumatoid arthritis using commercial claims data

  • Jeffrey R Curtis1Email author,
  • Benjamin Chastek2,
  • Laura Becker2,
  • David J Harrison3,
  • David Collier3,
  • Huifeng Yun4 and
  • George J Joseph3
Arthritis Research & Therapy201315:404

DOI: 10.1186/ar4161

Published: 8 March 2013


As more biologics are approved, there is increasing interest in comparative effectiveness research (CER). Health insurance claims databases contain information about outpatient visits, hospital discharges, procedures, and outpatient pharmacy dispensing but seldom contain clinical outcomes. In a previous issue of Arthritis Research & Therapy, we presented an algorithm that assessed the clinical effectiveness of rheumatoid arthritis (RA) biologics which used Veterans Affairs (VA) claims data and which was validated against the DAS28-ESR (Disease Activity Score 28 using erythrocyte sedimentation rate) [1]. The algorithm had a sensitivity of 72% (95% confidence interval (CI) = 67% to 77%) and a specificity of 91% (95% CI = 89% to 93%). In an editorial in the same issue, Kim and Solomon [2] commented the following: 'a claims-based effectiveness algorithm with acceptable performance characteristics across different data settings will be a powerful and desired tool for CER of RA. Such an algorithm will enable large-scale, population-based studies comparing the effectiveness of different DMARD [disease-modifying antirheumatic drug] regimens. Such studies will facilitate head-to-head comparisons, supplementing typical randomized controlled trials and prospective registries that usually include disease activity. Whether the algorithm will have a similar performance in other claims databases therefore needs to be further examined'. We performed an independent analysis to evaluate the algorithm's positive predictive value (PPV) in a commercial claims data source compared with a clinical gold standard.


Data came from a previous comparative effectiveness study linking outpatient medical records from multiple US institutions and physician practices to commercial claims data from OptumInsight (Eden Prairie, MN, USA) [3] that evaluated the effectiveness of etanercept (ETN), adalimumab (ADA), and infliximab (INF) in biologic naïve adult RA patients persistent on their initial biologic for at least 1 year from 2006 to 2008. Two teams of two rheumatologists reviewed each medical record and categorized clinical change around 1 year as 'much better', 'better', 'no change', 'worse', or 'much worse'. For this study, the biologic was considered effective if the patient was rated as 'better' or 'much better'. Sensitivity, specificity, and negative predictive value could not be determined, because patients switching biologic agents were excluded from the original study. The PPV compared the classification from the algorithm to the rheumatologist rating. Different compliance thresholds with the biologic medications used by the algorithm were evaluated as sensitivity analyses.


The majority (76%) of the 429 patients in the study were female, and the mean age was 51 years. The PPVs were 86.6% in the primary analysis and 86.5% in sensitivity analyses, similar to that of the original algorithm using VA data. PPV did not differ significantly by biologic (P >0.2): INF (PPV = 95%), ETN (PPV = 86%), and ADA (PPV = 85%).


This previously published administrative claims-based effectiveness algorithm had a high PPV across commercial claims data and VA data. This algorithm may be useful in evaluating the effectiveness of biologic agents by administrative claims data in future studies.





confidence interval






positive predictive value


rheumatoid arthritis


Veterans Affairs.



JRC receives support from the National Institutes of Health (AR053351) and the Agency for Healthcare Research and Quality (R01HS018517) and reports grants or consulting work or both with Roche (Basel, Switzerland)/Genentech (South San Francisco, CA, USA), UCB (Raleigh, NC, USA), Centocor (Horsham, PA, USA), Corrona (Southboro, MA, USA), Amgen Inc. (Thousand Oaks, CA, USA), Pfizer Inc (New York, NY, USA), BMS (New York, NY, USA), Crescendo (South San Francisco, CA, USA), and Abbot (Abbott Park, IL, USA). This research was funded by Immunex Corporation (Seattle, WA, USA), a wholly owned subsidiary of Amgen Inc., and by Wyeth (Madison, NJ, USA), which was acquired by Pfizer Inc in October 2009. HY has received research support from Amgen Inc. for unrelated work. BC and LB are employees of OptumInsight. DJH, DC, and GJJ are employees of Amgen Inc.

Authors’ Affiliations

Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham
Amgen Inc., One Amgen Center Drive
Department of Epidemiology, School of Public Health, University of Alabama at Birmingham


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© BioMed Central Ltd 2013