Bias through selective inclusion and attrition: Representativeness when comparing provider performance with routine outcome monitoring data
Bias through selective inclusion and attrition: Representativeness when comparing provider performance with routine outcome monitoring data
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DOI：
10.1002/cpp.2364
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发表时间：
2019-07-01
影响因子：
3.6
通讯作者：
Twisk, Jos
中科院分区：
文献类型：
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作者：
de Beurs, Edwin;Warmerdam, Lisanne;Twisk, Jos
背景基于常规结果监测的观察性研究容易出现数据缺失，并且由于基线时的选择性纳入或后测时的选择性脱落，结果可能存在偏倚。由于具有完整数据的患者可能不能代表提供者的所有患者，缺失数据可能会使结果产生偏差，特别是当缺失不是随机的而是系统性的时。方法本研究建立了临床和人口统计学患者变量相关的结果信息的代表性。它应用策略来估计样本选择偏差（按纳入倾向加权）和选择性损耗偏差（基于多级回归分析的多重插补），并估计其对供应商绩效指数的影响程度。估计偏差和响应率之间的关联也进行了研究。结果提供者为基础的分析表明，在目前的实践中，选择性纳入的影响是最小的，但磨损有更大的影响，偏向两个方向的结果：夸大和低估的性能。对于22%的供应商，损耗偏差估计超过0.05 ES。偏倚与总体应答率相关（r = 0.50）。当选择性包容和自然减员使提供者的答复低于50%时，选择偏见更有可能超过临界水平，关于这些提供者的比较业绩的结论可能会产生误导。结论估计供应商的表现有偏见的选择，特别是在后测缺失的数据。结果的程度和方向的偏见和最低要求的答复率，以达到公正的业绩指标进行了讨论。
Background Observational research based on routine outcome monitoring is prone to missing data, and outcomes can be biased due to selective inclusion at baseline or selective attrition at posttest. As patients with complete data may not be representative of all patients of a provider, missing data may bias results, especially when missingness is not random but systematic. Methods The present study establishes clinical and demographic patient variables relevant for representativeness of the outcome information. It applies strategies to estimate sample selection bias (weighting by inclusion propensity) and selective attrition bias (multiple imputation based on multilevel regression analysis) and estimates the extent of their impact on an index of provider performance. The association between estimated bias and response rate is also investigated. Results Provider-based analyses showed that in current practice, the effect of selective inclusion was minimal, but attrition had a more substantial effect, biasing results in both directions: overstating and understating performance. For 22% of the providers, attrition bias was estimated to be in excess of 0.05 ES. Bias was associated with overall response rate (r = .50). When selective inclusion and attrition bring providers' response below 50%, it is more likely that selection bias increased beyond a critical level, and conclusions on the comparative performance of such providers may be misleading. Conclusions Estimates of provider performance were biased by selection, especially by missing data at posttest. Results on the extent and direction of bias and minimal requirements for response rates to arrive at unbiased performance indicators are discussed.