A Two‐Step Framework for Validating Causal Effect Estimates

IF 2.4 4区医学 Q3 PHARMACOLOGY & PHARMACY

Pharmacoepidemiology and Drug Safety Pub Date : 2024-09-10 DOI:10.1002/pds.5873

Lingjie Shen, Erik Visser, Felice van Erning, Gijs Geleijnse, Maurits Kaptein

{"title":"A Two‐Step Framework for Validating Causal Effect Estimates","authors":"Lingjie Shen, Erik Visser, Felice van Erning, Gijs Geleijnse, Maurits Kaptein","doi":"10.1002/pds.5873","DOIUrl":null,"url":null,"abstract":"Background: Comparing causal effect estimates obtained using observational data to those obtained from the gold standard (i.e., randomized controlled trials [RCTs]) helps assess the validity of these estimates. However, comparisons are challenging due to differences between observational data and RCT generated data. The unknown treatment assignment mechanism in the observational data and varying sampling mechanisms between the RCT and the observational data can lead to confounding and sampling bias, respectively.Aims: The objective of this study is to propose a two‐step framework to validate causal effect estimates obtained from observational data by adjusting for both mechanisms.Materials and Methods: An estimator of causal effects related to the two mechanisms is constructed. A two‐step framework for comparing causal effect estimates is derived from the estimator. An R package RCTrep is developed to implement the framework in practice.Results: A simulation study is conducted to show that using our framework observational data can produce causal effect estimates similar to those of an RCT. A real‐world application of the framework to validate treatment effects of adjuvant chemotherapy obtained from registry data is demonstrated.Conclusion: This study constructs a framework for comparing causal effect estimates between observational data and RCT data, facilitating the assessment of the validity of causal effect estimates obtained from observational data.","PeriodicalId":19782,"journal":{"name":"Pharmacoepidemiology and Drug Safety","volume":"35 1","pages":"e5873"},"PeriodicalIF":2.4000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pharmacoepidemiology and Drug Safety","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/pds.5873","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}

引用次数: 0

Abstract

Background: Comparing causal effect estimates obtained using observational data to those obtained from the gold standard (i.e., randomized controlled trials [RCTs]) helps assess the validity of these estimates. However, comparisons are challenging due to differences between observational data and RCT generated data. The unknown treatment assignment mechanism in the observational data and varying sampling mechanisms between the RCT and the observational data can lead to confounding and sampling bias, respectively.Aims: The objective of this study is to propose a two‐step framework to validate causal effect estimates obtained from observational data by adjusting for both mechanisms.Materials and Methods: An estimator of causal effects related to the two mechanisms is constructed. A two‐step framework for comparing causal effect estimates is derived from the estimator. An R package RCTrep is developed to implement the framework in practice.Results: A simulation study is conducted to show that using our framework observational data can produce causal effect estimates similar to those of an RCT. A real‐world application of the framework to validate treatment effects of adjuvant chemotherapy obtained from registry data is demonstrated.Conclusion: This study constructs a framework for comparing causal effect estimates between observational data and RCT data, facilitating the assessment of the validity of causal effect estimates obtained from observational data.

查看原文本刊更多论文

验证因果效应估计值的两步框架

背景：将使用观察数据获得的因果效应估计值与黄金标准（即随机对照试验 [RCT]）获得的估计值进行比较，有助于评估这些估计值的有效性。然而，由于观察数据与随机对照试验产生的数据之间存在差异，因此比较具有挑战性。观察数据中未知的治疗分配机制和 RCT 与观察数据之间不同的抽样机制会分别导致混杂和抽样偏差。研究目的：本研究旨在提出一个两步框架，通过调整这两种机制来验证从观察数据中获得的因果效应估计值：构建与两种机制相关的因果效应估计值。根据该估计器推导出比较因果效应估计值的两步框架。为在实践中实施该框架，开发了一个 RCTrep 软件包：进行了一项模拟研究，表明使用我们的框架，观察数据可以产生与 RCT 类似的因果效应估计值。结果：一项模拟研究表明，利用我们的框架，观察数据可以得出与 RCT 类似的因果效应估计值。该框架在现实世界中的应用，验证了从登记数据中获得的辅助化疗的治疗效果：本研究构建了一个框架，用于比较观察数据和 RCT 数据之间的因果效应估计值，有助于评估从观察数据中获得的因果效应估计值的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Pharmacoepidemiology and Drug Safety 医学-药学

CiteScore

4.80

自引率

7.70%

发文量

173

审稿时长

3 months

期刊介绍： The aim of Pharmacoepidemiology and Drug Safety is to provide an international forum for the communication and evaluation of data, methods and opinion in the discipline of pharmacoepidemiology. The Journal publishes peer-reviewed reports of original research, invited reviews and a variety of guest editorials and commentaries embracing scientific, medical, statistical, legal and economic aspects of pharmacoepidemiology and post-marketing surveillance of drug safety. Appropriate material in these categories may also be considered for publication as a Brief Report. Particular areas of interest include: design, analysis, results, and interpretation of studies looking at the benefit or safety of specific pharmaceuticals, biologics, or medical devices, including studies in pharmacovigilance, postmarketing surveillance, pharmacoeconomics, patient safety, molecular pharmacoepidemiology, or any other study within the broad field of pharmacoepidemiology; comparative effectiveness research relating to pharmaceuticals, biologics, and medical devices. Comparative effectiveness research is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition, as these methods are truly used in the real world; methodologic contributions of relevance to pharmacoepidemiology, whether original contributions, reviews of existing methods, or tutorials for how to apply the methods of pharmacoepidemiology; assessments of harm versus benefit in drug therapy; patterns of drug utilization; relationships between pharmacoepidemiology and the formulation and interpretation of regulatory guidelines; evaluations of risk management plans and programmes relating to pharmaceuticals, biologics and medical devices.