PDF(Pubmed)
Abstract：
Systematic reviews of observational studies can be affected by biases that lead to under- or over-estimates of true intervention effects. Several tools have been reported in the literature that attempt to characterize potential bias. Our objective in this study was to determine the extent to which study-specific bias may have influenced intervention impacts on total costs of care (TCOC) in round 1 of the Health Care Innovation Awards.
We reviewed 82 statistical evaluations of innovation impacts on Medicare TCOC. We developed five risk-of-bias measures and assessed their influence on TCOC impacts using meta-regression.
The majority of evaluations used propensity score matching to create their comparison groups. One third of the non-randomized interventions were judged to have some risk of biased effects due largely to the way they recruited their treatment groups, and 35% had some degree of covariate imbalance remaining after propensity score adjustments. However, in the multivariable analysis of TCOC effects, none of the bias threats we examined (comparison group construction method, risk of bias, or degree of covariate imbalance) had a major impact on the magnitude of HCIA1 innovation effects. Evaluations using propensity score weighting produced larger but imprecise savings effects compared to propensity score matching.
Our results suggest that it is unlikely that HCIA1 TCOC effect sizes were systematically affected by the types of bias we considered. Assessing the risk of bias based on specific study design features is likely to be more useful for identifying problematic characteristics than the subjective quality ratings used by existing risk tools.
We reviewed 82 statistical evaluations of innovation impacts on Medicare TCOC. We developed five risk-of-bias measures and assessed their influence on TCOC impacts using meta-regression.
The majority of evaluations used propensity score matching to create their comparison groups. One third of the non-randomized interventions were judged to have some risk of biased effects due largely to the way they recruited their treatment groups, and 35% had some degree of covariate imbalance remaining after propensity score adjustments. However, in the multivariable analysis of TCOC effects, none of the bias threats we examined (comparison group construction method, risk of bias, or degree of covariate imbalance) had a major impact on the magnitude of HCIA1 innovation effects. Evaluations using propensity score weighting produced larger but imprecise savings effects compared to propensity score matching.
Our results suggest that it is unlikely that HCIA1 TCOC effect sizes were systematically affected by the types of bias we considered. Assessing the risk of bias based on specific study design features is likely to be more useful for identifying problematic characteristics than the subjective quality ratings used by existing risk tools.
摘要：
背景：观察性研究的系统评价可能会受到偏差的影响，这些偏差会导致对真实干预效果的低估或高估。文献中已经报道了几种工具试图表征潜在偏差。我们在这项研究中的目的是确定特定研究的偏见可能影响干预对医疗保健创新奖第1轮护理总成本（TCOC）的影响程度。
方法：我们回顾了82项关于创新对医疗保险TCOC影响的统计评估。我们开发了五种偏差风险度量，并使用元回归评估了它们对TCOC影响的影响。
结果：大多数评估使用倾向评分匹配来创建比较组。三分之一的非随机干预措施被认为有一定的偏倚效应风险，这主要是由于他们招募治疗组的方式。35%的患者在倾向评分调整后仍存在一定程度的协变量失衡.然而,在TCOC效应的多变量分析中，我们检查的偏见威胁都没有(比较组构造方法，偏见的风险，或协变量失衡程度)对HCIA1创新效应的大小有重大影响。与倾向得分匹配相比，使用倾向得分加权的评估产生了更大但不精确的储蓄效果。
结论：我们的结果表明，HCIA1TCOC效应大小不太可能受到我们考虑的偏倚类型的系统性影响。与现有风险工具使用的主观质量评级相比，基于特定研究设计特征评估偏差风险对于识别有问题的特征可能更有用。
方法：我们回顾了82项关于创新对医疗保险TCOC影响的统计评估。我们开发了五种偏差风险度量，并使用元回归评估了它们对TCOC影响的影响。
结果：大多数评估使用倾向评分匹配来创建比较组。三分之一的非随机干预措施被认为有一定的偏倚效应风险，这主要是由于他们招募治疗组的方式。35%的患者在倾向评分调整后仍存在一定程度的协变量失衡.然而,在TCOC效应的多变量分析中，我们检查的偏见威胁都没有(比较组构造方法，偏见的风险，或协变量失衡程度)对HCIA1创新效应的大小有重大影响。与倾向得分匹配相比，使用倾向得分加权的评估产生了更大但不精确的储蓄效果。
结论：我们的结果表明，HCIA1TCOC效应大小不太可能受到我们考虑的偏倚类型的系统性影响。与现有风险工具使用的主观质量评级相比，基于特定研究设计特征评估偏差风险对于识别有问题的特征可能更有用。
