Cnossen Maryse C, van Essen Thomas A, Ceyisakar Iris E, Polinder Suzanne, Andriessen Teuntje M, van der Naalt Joukje, Haitsma Iain, Horn Janneke, Franschman Gaby, Vos Pieter E, Peul Wilco C, Menon David K, Maas Andrew Ir, Steyerberg Ewout W, Lingsma Hester F
Center for Medical Decision Making, Department of Public Health, Erasmus Medical Center Rotterdam, Rotterdam, the Netherlands,
Neurosurgical Cooperative Holland, Department of Neurosurgery, Leiden University Medical Center, Leiden, the Netherlands.
Clin Epidemiol. 2018 Jul 18;10:841-852. doi: 10.2147/CLEP.S154500. eCollection 2018.
Observational studies of interventions are at risk for confounding by indication. The objective of the current study was to define the circumstances for the validity of methods to adjust for confounding by indication in observational studies.
We performed post hoc analyses of data prospectively collected from three European and North American traumatic brain injury studies including 1,725 patients. The effects of three interventions (intracranial pressure [ICP] monitoring, intracranial operation and primary referral) were estimated in a proportional odds regression model with the Glasgow Outcome Scale as ordinal outcome variable. Three analytical methods were compared: classical covariate adjustment, propensity score matching and instrumental variable (IV) analysis in which the percentage exposed to an intervention in each hospital was added as an independent variable, together with a random intercept for each hospital. In addition, a simulation study was performed in which the effect of a hypothetical beneficial intervention (OR 1.65) was simulated for scenarios with and without unmeasured confounders.
For all three interventions, covariate adjustment and propensity score matching resulted in negative estimates of the treatment effect (OR ranging from 0.80 to 0.92), whereas the IV approach indicated that both ICP monitoring and intracranial operation might be beneficial (OR per 10% change 1.17, 95% CI 1.01-1.42 and 1.42, 95% CI 0.95-1.97). In our simulation study, we found that covariate adjustment and propensity score matching resulted in an invalid estimate of the treatment effect in case of unmeasured confounders (OR ranging from 0.90 to 1.03). The IV approach provided an estimate in the similar direction as the simulated effect (OR per 10% change 1.04-1.05) but was statistically inefficient.
The effect estimation of interventions in observational studies strongly depends on the analytical method used. When unobserved confounding and practice variation are expected in observational multicenter studies, IV analysis should be considered.
对干预措施的观察性研究存在指征性混杂的风险。本研究的目的是确定在观察性研究中调整指征性混杂方法有效性的情况。
我们对从三项欧洲和北美创伤性脑损伤研究中前瞻性收集的数据进行了事后分析，共纳入1725例患者。在以格拉斯哥预后量表作为有序结局变量的比例优势回归模型中，估计了三种干预措施（颅内压[ICP]监测、颅内手术和初次转诊）的效果。比较了三种分析方法：经典协变量调整、倾向得分匹配和工具变量（IV）分析，其中将每家医院接受干预的百分比作为自变量，并为每家医院添加一个随机截距。此外，进行了一项模拟研究，在有无未测量混杂因素的情况下模拟了一种假设的有益干预措施（比值比为1.65）的效果。
对于所有三种干预措施，协变量调整和倾向得分匹配得出的治疗效果估计值为负（比值比范围为0.80至0.92），而IV方法表明ICP监测和颅内手术可能有益（每10%变化的比值比为1.17，95%置信区间为1.01 - 1.42；以及1.42，95%置信区间为0.95 - 1.97）。在我们的模拟研究中，我们发现，在存在未测量混杂因素的情况下，协变量调整和倾向得分匹配会导致对治疗效果的无效估计（比值比范围为0.90至1.03）。IV方法得出的估计值方向与模拟效果相似（每10%变化的比值比为1.04 - 1.05），但在统计学上效率较低。
观察性研究中干预措施的效果估计很大程度上取决于所使用的分析方法。当在观察性多中心研究中预期存在未观察到的混杂和实践差异时，应考虑IV分析。
