Gilbertson David T, Bradbury Brian D, Wetmore James B, Weinhandl Eric D, Monda Keri L, Liu Jiannong, Brookhart M Alan, Gustafson Sally K, Roberts Tricia, Collins Allan J, Rothman Kenneth J
Chronic Disease Research Group, Minneapolis Medical Research Foundation, Minneapolis, MN, USA.
Center for Observational Research, Amgen, Inc., Thousand Oaks, CA, USA.
Pharmacoepidemiol Drug Saf. 2016 Mar;25(3):269-77. doi: 10.1002/pds.3922. Epub 2015 Nov 26.
Confounding, a concern in nonexperimental research using administrative claims, is nearly ubiquitous in claims-based pharmacoepidemiology studies. A fixed-length look-back window for assessing comorbidity from claims is common, but it may be advantageous to use all historical claims. We assessed how the strength of association between a baseline-identified condition and subsequent mortality varied by when the condition was measured and investigated methods to control for confounding.
For Medicare beneficiaries undergoing maintenance hemodialysis on 1 January 2008 (n = 222 343), we searched all Medicare claims, 1 January 2001 to 31 December 2007, for four conditions representing chronic and acute diseases, and classified claims by number of months preceding the index date. We used proportional hazard models to estimate the association between time of condition and subsequent mortality. We simulated a confounded comorbidity-exposure relationship and investigated an alternative method of adjustment when the association between the condition and mortality varied by proximity to follow-up start.
The magnitude of the mortality hazard ratio estimates for each condition investigated decreased toward unity as time increased between index date and most recent manifestation of the condition. Simulation showed more biased estimates of exposure-outcome associations if proximity to follow-up start was not considered.
Using all-available claims information during a baseline period, we found that for all conditions investigated, the association between a comorbid condition and subsequent mortality varied considerably depending on when the condition was measured. Improved confounding control may be achieved by considering the timing of claims relative to follow-up start.
在使用行政索赔数据的非实验性研究中，混杂因素是一个需要关注的问题，在基于索赔数据的药物流行病学研究中几乎无处不在。使用固定时长的回顾窗口从索赔数据中评估合并症是常见做法，但使用所有历史索赔数据可能更具优势。我们评估了基线确定的疾病与后续死亡率之间的关联强度如何因疾病测量时间的不同而变化，并研究了控制混杂因素的方法。
对于2008年1月1日正在接受维持性血液透析的医疗保险受益人（n = 222343），我们检索了2001年1月1日至2007年12月31日期间的所有医疗保险索赔数据，以查找代表慢性和急性疾病的四种疾病，并按索引日期前的月数对索赔进行分类。我们使用比例风险模型来估计疾病发生时间与后续死亡率之间的关联。我们模拟了一个存在混杂的合并症 - 暴露关系，并研究了一种替代调整方法，用于当疾病与死亡率之间的关联因距随访开始的时间接近程度而异时。
随着索引日期与疾病最近一次出现之间的时间增加，所研究的每种疾病的死亡率风险比估计值的大小都朝着1降低。模拟结果表明，如果不考虑距随访开始的时间接近程度，暴露 - 结局关联的估计会有更大偏差。
在基线期使用所有可用的索赔信息，我们发现对于所有研究的疾病，合并症与后续死亡率之间的关联因疾病测量时间的不同而有很大差异。通过考虑索赔相对于随访开始的时间，可以更好地控制混杂因素。
