Departments of Health Services, Policy & Practice and Epidemiology, Program in Public Health, The Warren Alpert Medical School of Brown University, Providence, RI, USA.
Pharmacoepidemiol Drug Saf. 2012 May;21 Suppl 2:29-36. doi: 10.1002/pds.3244.
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.
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.
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.
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.
仅基于健康保险索赔数据进行的癌症研究通常缺乏癌症临床特征信息，这些信息是治疗和预后的重要预测因素。我们的目的是评估常规收集的癌症临床数据，以调整混杂因素，并以评估与芳香化酶抑制剂和他莫昔芬相关的死亡率为例。
本队列研究使用健康保险索赔数据，通过与诊断时的分期、当前临床状况、组织学和其他临床标志物相关的临床信息，确定了 2008 年至 2010 年间患有乳腺癌的女性。比较了使用芳香化酶抑制剂或他莫昔芬的患者的估计死亡率（MR）和 95%置信区间（CI），并对索赔确定的混杂因素进行了调整，此外还使用倾向评分和比例风险回归模型对临床变量进行了调整。
总体（n=7974）估计的 MR 为 69/1000人年（95%CI=62-76 人年），转移患者为 308/1000 人年（95%CI=273-345 人年），无活跃癌症患者为 12/1000 人年（95%CI=8-16 人年）。对 777 名芳香化酶抑制剂使用者（n=777）和 535 名他莫昔芬使用者进行倾向评分匹配，消除了许多，但不是全部，混杂因素的不平衡。与他莫昔芬使用者相比，使用芳香化酶抑制剂的患者的全因死亡率的风险比（HR）范围为 1.0 至 1.6，与之前临床试验最相似的 HR（0.87）来自仅索赔分析。
我们能够通过将临床信息与索赔数据相关联来解决潜在的未测量混杂因素；然而，在所选择的例子中，混杂控制似乎没有明显改善。将临床数据作为入选条件限制了样本量。
