Epidemiology Department of Pfizer Inc, 500 Arcola Road, Collegeville, PA, USA.
Pharmacoepidemiol Drug Saf. 2012 May;21 Suppl 2:114-20. doi: 10.1002/pds.3252.
We studied the application of the generalized structural mean model (GSMM) of instrumental variable (IV) methods in estimating treatment odds ratios (ORs) for binary outcomes in pharmacoepidemiologic studies and evaluated the bias of GSMM compared to other IV methods.
Because of the bias of standard IV methods, including two-stage predictor substitution (2SPS) and two-stage residual inclusion (2SRI) with binary outcomes, we implemented another IV approach based on the GSMM of Vansteelandt and Goetghebeur. We performed simulations under the principal stratification setting and evaluated whether GSMM provides approximately unbiased estimates of the causal OR and compared its bias and mean squared error to that of 2SPS and 2SRI. We then applied different IV methods to a study comparing bezafibrate versus other fibrates on the risk of diabetes.
Our simulations showed that unlike the standard logistic, 2SPS, and 2SRI procedures, our implementation of GSMM provides an approximately unbiased estimate of the causal OR even under unmeasured confounding. However, for the effect of bezafibrate versus other fibrates on the risk of diabetes, the GSMM and two-stage approaches yielded similarly attenuated and statistically non-significant OR estimates. The attenuation of the OR by the two-stage and GSMM IV approaches suggests unmeasured confounding, although violations of the IV assumptions or differences in the parameters estimated could be playing a role.
The GSMM IV approach provides approximately unbiased adjustment for unmeasured confounding on binary outcomes when a valid IV is available.
我们研究了广义结构均值模型（GSMM）在估计药物流行病学研究中二元结局治疗比值比（OR）的工具变量（IV）方法中的应用，并评估了 GSMM 与其他 IV 方法相比的偏差。
由于标准 IV 方法（包括二阶段预测变量替代法 2SPS 和二阶段残差纳入法 2SRI）存在偏倚，我们采用了另一种基于 Vansteelandt 和 Goetghebeur 的 GSMM 的 IV 方法。我们在主要分层设置下进行了模拟，并评估了 GSMM 是否为因果 OR 提供了近似无偏估计，将其偏差和均方误差与 2SPS 和 2SRI 进行了比较。然后，我们将不同的 IV 方法应用于一项比较苯扎贝特与其他贝特类药物对糖尿病风险的研究。
我们的模拟表明，与标准逻辑、2SPS 和 2SRI 程序不同，我们实施的 GSMM 即使在存在未测量混杂的情况下，也提供了因果 OR 的近似无偏估计。然而，对于苯扎贝特与其他贝特类药物对糖尿病风险的影响，GSMM 和两阶段方法得出的 OR 估计值相似，且具有统计学意义。两阶段和 GSMM IV 方法对 OR 的衰减表明存在未测量的混杂，尽管可能存在违反 IV 假设或估计参数不同的情况。
当存在有效的 IV 时，GSMM IV 方法为二元结局提供了对未测量混杂的近似无偏调整。
