title: Linkage of routinely collected oncology clinical data with health insurance claims data--an example with aromatase inhibitors, tamoxifen, and all-cause mortality.
abstract: OBJECTIVE
  Studies of cancer based solely on health insurance claims data typically lack information on cancer clinical characteristics that are strong predictors of treatment and prognosis. Our objective was to evaluate routinely collected cancer clinical data for adjustment of confounding using an example evaluation of mortality associated with aromatase inhibitors and tamoxifen.
  
  
  METHODS
  This cohort study identified women with breast cancer from 2008 through 2010 using health insurance claims data linked to clinical information on stage at diagnosis, current clinical status, histology, and other clinical markers. Estimated mortality rates (MRs) and 95% confidence intervals (CI) were compared between users of aromatase inhibitors or tamoxifen, adjusted for claims-identified covariates and additionally for the clinical variables using propensity scores and proportional hazards regression models.
  
  
  RESULTS
  The overall (n = 7974) estimated MR was 69/1000 person-years (95%CI = 62-76 person-years), 308/1000 person-years (95% CI = 273-345 person-years) for women with metastasis, and 12/1000 person-years (95%CI = 8-16 person-years) for women without active cancer. Propensity score matching of aromatase inhibitor users (n = 777) with tamoxifen users (n = 535) removed many, but not all, covariate imbalances. The hazard ratios (HRs) of all-cause mortality comparing users of aromatase inhibitors with tamoxifen users ranged from 1.0 to 1.6, with the HR most similar to previous clinical trials (0.87) coming from the claims-only analysis.
  
  
  CONCLUSIONS
  We were able to address potential unmeasured confounders by linking clinical information to the claims data; however, there was no apparent improvement in confounding control in the chosen example. Conditioning eligibility on the clinical data restricted the sample size substantially.
authors.affiliation: Departments of Health Services, Policy & Practice and Epidemiology, Program in Public Health, The Warren Alpert Medical School of Brown University, Providence, RI, USA. david_dore@brown.edu
authors.forename: David D
authors.initials: DD
authors.lastname: Dore
authors.forename: Caihua
authors.initials: C
authors.lastname: Liang
authors.forename: Najat
authors.initials: N
authors.lastname: Ziyadeh
authors.forename: Heather
authors.initials: H
authors.lastname: Norman
authors.forename: Martha
authors.initials: M
authors.lastname: Bayliss
authors.forename: John D
authors.initials: JD
authors.lastname: Seeger
mesh_terms: D000970:Antineoplastic Agents / Q000008:administration & dosage / Q000627:therapeutic use*; D047072:Aromatase Inhibitors / Q000008:administration & dosage / Q000627:therapeutic use*; D001943:Breast Neoplasms / Q000175:diagnosis / Q000188:drug therapy / Q000401:mortality; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D008498:Medical Record Linkage; D017891:Pharmacoepidemiology / Q000379:methods* / Q000706:statistics & numerical data; D016016:Proportional Hazards Models; D013629:Tamoxifen / Q000008:administration & dosage / Q000627:therapeutic use*; D014481:United States
chemical_list: D000970:Antineoplastic Agents; D047072:Aromatase Inhibitors; D013629:Tamoxifen