{"title":"在中等样本量和小样本量的比较效益研究中,评估作为工具变量的医生处方偏好的性能:一项模拟研究。","authors":"Lisong Zhang, Jim Lewsey, David A McAllister","doi":"10.57264/cer-2023-0044","DOIUrl":null,"url":null,"abstract":"<p><p><b>Aim:</b> This simulation study is to assess the utility of physician's prescribing preference (PPP) as an instrumental variable for moderate and smaller sample sizes. <b>Materials & methods:</b> 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. <b>Results:</b> 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. <b>Conclusion:</b> 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.</p>","PeriodicalId":15539,"journal":{"name":"Journal of comparative effectiveness research","volume":"13 5","pages":"e230044"},"PeriodicalIF":1.9000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11036905/pdf/","citationCount":"0","resultStr":"{\"title\":\"Assessing the performance of physician's prescribing preference as an instrumental variable in comparative effectiveness research with moderate and small sample sizes: a simulation study.\",\"authors\":\"Lisong Zhang, Jim Lewsey, David A McAllister\",\"doi\":\"10.57264/cer-2023-0044\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b>Aim:</b> This simulation study is to assess the utility of physician's prescribing preference (PPP) as an instrumental variable for moderate and smaller sample sizes. <b>Materials & methods:</b> 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. <b>Results:</b> 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. <b>Conclusion:</b> 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.</p>\",\"PeriodicalId\":15539,\"journal\":{\"name\":\"Journal of comparative effectiveness research\",\"volume\":\"13 5\",\"pages\":\"e230044\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11036905/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of comparative effectiveness research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.57264/cer-2023-0044\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/4/3 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of comparative effectiveness research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.57264/cer-2023-0044","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/4/3 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
摘要
目的:本模拟研究旨在评估医生处方偏好(PPP)作为工具变量对中等和较小样本量的效用。材料与方法:我们设计了一项模拟研究,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)比较了工具变量法(IV)和非工具变量法的性能。此外,我们还检验了不同形式的购买力平价替代品作为 IV 的性能。结果:2SLS 的偏差百分比约为 20%,而 OLS 的偏差百分比接近 60%。样本量与购买力平价 IV 方法的偏差水平无关。结论无论样本大小如何,PPP IV 方法得出的治疗效果估计值的偏差均小于仅对已知混杂因素进行调整的 OLS 方法。特别是在样本量较小的情况下,我们建议根据长处方历史构建 PPP,以提高统计能力。
Assessing the performance of physician's prescribing preference as an instrumental variable in comparative effectiveness research with moderate and small sample sizes: a simulation study.
Aim: This simulation study is to assess the utility of physician's prescribing preference (PPP) as an instrumental variable for moderate and smaller sample sizes. Materials & methods: 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. Results: 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. Conclusion: 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.
期刊介绍:
Journal of Comparative Effectiveness Research provides a rapid-publication platform for debate, and for the presentation of new findings and research methodologies.
Through rigorous evaluation and comprehensive coverage, the Journal of Comparative Effectiveness Research provides stakeholders (including patients, clinicians, healthcare purchasers, and health policy makers) with the key data and opinions to make informed and specific decisions on clinical practice.