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药物流行病学中工具变量分析的功效计算器。

Power calculator for instrumental variable analysis in pharmacoepidemiology.

机构信息

School of Social and Community Medicine.

MRC University of Bristol Integrative Epidemiology Unit.

出版信息

Int J Epidemiol. 2017 Oct 1;46(5):1627-1632. doi: 10.1093/ije/dyx090.

DOI:10.1093/ije/dyx090
PMID:28575313
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5837396/
Abstract

BACKGROUND

Instrumental variable analysis, for example with physicians' prescribing preferences as an instrument for medications issued in primary care, is an increasingly popular method in the field of pharmacoepidemiology. Existing power calculators for studies using instrumental variable analysis, such as Mendelian randomization power calculators, do not allow for the structure of research questions in this field. This is because the analysis in pharmacoepidemiology will typically have stronger instruments and detect larger causal effects than in other fields. Consequently, there is a need for dedicated power calculators for pharmacoepidemiological research.

METHODS AND RESULTS

The formula for calculating the power of a study using instrumental variable analysis in the context of pharmacoepidemiology is derived before being validated by a simulation study. The formula is applicable for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome. An online calculator, as well as packages in both R and Stata, are provided for the implementation of the formula by others.

CONCLUSIONS

The statistical power of instrumental variable analysis in pharmacoepidemiological studies to detect a clinically meaningful treatment effect is an important consideration. Research questions in this field have distinct structures that must be accounted for when calculating power. The formula presented differs from existing instrumental variable power formulae due to its parametrization, which is designed specifically for ease of use by pharmacoepidemiologists.

摘要

背景

在药物流行病学领域，工具变量分析（例如，以医生的处方偏好作为初级保健中开具药物的工具）是一种越来越受欢迎的方法。现有的用于工具变量分析研究的功效计算器，如孟德尔随机化功效计算器，无法满足该领域研究问题的结构。这是因为药物流行病学中的分析通常具有更强的工具变量，并能检测到比其他领域更大的因果效应。因此，需要专门为药物流行病学研究设计功效计算器。

方法和结果

在药物流行病学背景下，推导出了使用工具变量分析进行研究的功效计算公式，并通过模拟研究进行了验证。该公式适用于使用单个二进制工具分析二进制暴露对连续结局的因果效应的研究。提供了在线计算器以及 R 和 Stata 中的包，供其他人实现该公式。

结论

药物流行病学研究中使用工具变量分析检测临床有意义的治疗效果的统计功效是一个重要的考虑因素。该领域的研究问题具有独特的结构，在计算功效时必须考虑到这些结构。由于其参数化，本文提出的公式与现有的工具变量功效公式不同，这是专门为便于药物流行病学家使用而设计的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/50da/5837396/63bd1538a8e9/dyx090f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/50da/5837396/63bd1538a8e9/dyx090f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/50da/5837396/63bd1538a8e9/dyx090f1.jpg

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Int J Epidemiol. 2014 Jun;43(3):922-9. doi: 10.1093/ije/dyu005. Epub 2014 Mar 6.
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Physicians' prescribing preferences were a potential instrument for patients' actual prescriptions of antidepressants.
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Health Econ. 2023 Sep;32(9):2113-2126. doi: 10.1002/hec.4719. Epub 2023 Jun 11.
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Smoking Cessation and Changes in Anxiety and Depression in Adults With and Without Psychiatric Disorders.戒烟与伴或不伴精神障碍的成年人焦虑和抑郁变化。
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