Zhang Lisong, Lewsey Jim, McAllister David A
Department of Population Health Sciences, University of Leicester, Leicester, LE1 7RH, UK.
School of Health and Well-being, University of Glasgow, Glasgow, G12 8TB, UK.
J Comp Eff Res. 2024 May;13(5):e230044. doi: 10.57264/cer-2023-0044. Epub 2024 Apr 3.
This simulation study is to assess the utility of physician's prescribing preference (PPP) as an instrumental variable for moderate and smaller sample sizes. We designed a simulation study to imitate a comparative effectiveness research under different sample sizes. We compare the performance of instrumental variable (IV) and non-IV approaches using two-stage least squares (2SLS) and ordinary least squares (OLS) methods, respectively. Further, we test the performance of different forms of proxies for PPP as an IV. The percent bias of 2SLS is around approximately 20%, while the percent bias of OLS is close to 60%. The sample size is not associated with the level of bias for the PPP IV approach. Irrespective of sample size, the PPP IV approach leads to less biased estimates of treatment effectiveness than OLS adjusting for known confounding only. Particularly for smaller sample sizes, we recommend constructing PPP from long prescribing histories to improve statistical power.
本模拟研究旨在评估医生处方偏好(PPP)作为中等及较小样本量的工具变量的效用。我们设计了一项模拟研究,以模仿不同样本量下的比较效果研究。我们分别使用两阶段最小二乘法(2SLS)和普通最小二乘法(OLS)比较工具变量(IV)和非IV方法的性能。此外,我们测试了PPP作为IV的不同形式代理的性能。2SLS的偏差百分比约为20%,而OLS的偏差百分比接近60%。样本量与PPP IV方法的偏差水平无关。无论样本量如何,PPP IV方法比仅针对已知混杂因素进行调整的OLS能产生偏差更小的治疗效果估计。特别是对于较小的样本量,我们建议从长期处方历史中构建PPP以提高统计功效。