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Van Lancker K, Vo T, Akacha M. Estimands in heath technology assessment: a causal inference perspective. Stat Med 2022; 41:5577-5585. [DOI: 10.1002/sim.9539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 07/14/2022] [Accepted: 07/15/2022] [Indexed: 11/18/2022]
Affiliation(s)
- Kelly Van Lancker
- Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA
| | - Tat‐Thang Vo
- Department of Statistics and Data Science The Wharton School, University of Pennsylvania Philadelphia Pennsylvania USA
| | - Mouna Akacha
- Statistical Methodology and Consulting Novartis Pharma AG Basel Switzerland
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2
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Gao Q, Zhang Y, Liang J, Sun H, Wang T. High-dimensional generalized propensity score with application to omics data. Brief Bioinform 2021; 22:6354024. [PMID: 34410351 DOI: 10.1093/bib/bbab331] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 01/09/2023] Open
Abstract
Propensity score (PS) methods are popular when estimating causal effects in non-randomized studies. Drawing causal conclusion relies on the unconfoundedness assumption. This assumption is untestable and is considered more plausible if a large number of pre-treatment covariates are included in the analysis. However, previous studies have shown that including unnecessary covariates into PS models can lead to bias and efficiency loss. With the ever-increasing amounts of available data, such as the omics data, there is often little prior knowledge of the exact set of important covariates. Therefore, variable selection for causal inference in high-dimensional settings has received considerable attention in recent years. However, recent studies have focused mainly on binary treatments. In this study, we considered continuous treatments and proposed the generalized outcome-adaptive LASSO (GOAL) to select covariates that can provide an unbiased and statistically efficient estimation. Simulation studies showed that when the outcome model was linear, the GOAL selected almost all true confounders and predictors of outcome and excluded other covariates. The accuracy and precision of the estimates were close to ideal. Furthermore, the GOAL is robust to model misspecification. We applied the GOAL to seven DNA methylation datasets from the Gene Expression Omnibus database, which covered four brain regions, to estimate the causal effects of epigenetic aging acceleration on the incidence of Alzheimer's disease.
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Affiliation(s)
- Qian Gao
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yu Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Jie Liang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Hongwei Sun
- Department of Health Statistics, School of Public Health and Management, Binzhou Medical University, Yantai, China
| | - Tong Wang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
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3
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Goetghebeur E, le Cessie S, De Stavola B, Moodie EEM, Waernbaum I. Formulating causal questions and principled statistical answers. Stat Med 2020; 39:4922-4948. [PMID: 32964526 PMCID: PMC7756489 DOI: 10.1002/sim.8741] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Revised: 05/10/2020] [Accepted: 08/05/2020] [Indexed: 12/13/2022]
Abstract
Although review papers on causal inference methods are now available, there is a lack of introductory overviews on what they can render and on the guiding criteria for choosing one particular method. This tutorial gives an overview in situations where an exposure of interest is set at a chosen baseline ("point exposure") and the target outcome arises at a later time point. We first phrase relevant causal questions and make a case for being specific about the possible exposure levels involved and the populations for which the question is relevant. Using the potential outcomes framework, we describe principled definitions of causal effects and of estimation approaches classified according to whether they invoke the no unmeasured confounding assumption (including outcome regression and propensity score-based methods) or an instrumental variable with added assumptions. We mainly focus on continuous outcomes and causal average treatment effects. We discuss interpretation, challenges, and potential pitfalls and illustrate application using a "simulation learner," that mimics the effect of various breastfeeding interventions on a child's later development. This involves a typical simulation component with generated exposure, covariate, and outcome data inspired by a randomized intervention study. The simulation learner further generates various (linked) exposure types with a set of possible values per observation unit, from which observed as well as potential outcome data are generated. It thus provides true values of several causal effects. R code for data generation and analysis is available on www.ofcaus.org, where SAS and Stata code for analysis is also provided.
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Affiliation(s)
- Els Goetghebeur
- Department of Applied Mathematics, Computer Science and StatisticsGhent UniversityGhentBelgium
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
| | - Saskia le Cessie
- Department of Clinical Epidemiology/Biomedical Data SciencesLeiden University Medical CenterLeidenThe Netherlands
| | - Bianca De Stavola
- Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
| | - Erica EM Moodie
- Division of BiostatisticsMcGill UniversityMontrealQuebecCanada
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4
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Webster-Clark M, Stürmer T, Wang T, Man K, Marinac-Dabic D, Rothman KJ, Ellis AR, Gokhale M, Lunt M, Girman C, Glynn RJ. Using propensity scores to estimate effects of treatment initiation decisions: State of the science. Stat Med 2020; 40:1718-1735. [PMID: 33377193 DOI: 10.1002/sim.8866] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 12/11/2020] [Accepted: 12/14/2020] [Indexed: 02/02/2023]
Abstract
Confounding can cause substantial bias in nonexperimental studies that aim to estimate causal effects. Propensity score methods allow researchers to reduce bias from measured confounding by summarizing the distributions of many measured confounders in a single score based on the probability of receiving treatment. This score can then be used to mitigate imbalances in the distributions of these measured confounders between those who received the treatment of interest and those in the comparator population, resulting in less biased treatment effect estimates. This methodology was formalized by Rosenbaum and Rubin in 1983 and, since then, has been used increasingly often across a wide variety of scientific disciplines. In this review article, we provide an overview of propensity scores in the context of real-world evidence generation with a focus on their use in the setting of single treatment decisions, that is, choosing between two therapeutic options. We describe five aspects of propensity score analysis: alignment with the potential outcomes framework, implications for study design, estimation procedures, implementation options, and reporting. We add context to these concepts by highlighting how the types of comparator used, the implementation method, and balance assessment techniques have changed over time. Finally, we discuss evolving applications of propensity scores.
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Affiliation(s)
| | - Til Stürmer
- Department of Epidemiology, UNC Chapel Hill, Chapel Hill, North Carolina, USA
| | - Tiansheng Wang
- Department of Epidemiology, UNC Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kenneth Man
- Research Department of Practice and Policy, UCL School of Pharmacy, London, UK.,Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, University of Hong Kong, Hong Kong
| | - Danica Marinac-Dabic
- Office of Clinical Evidence and Analysis, FDA Center for Devices and Radiological Health, Silver Springs, Maryland, USA
| | - Kenneth J Rothman
- RTI Health Solutions, Raleigh, North Carolina, USA.,Department of Epidemiology, Boston University, Boston, Massachusetts, USA
| | - Alan R Ellis
- Department of Social Work, NC State University, Raleigh, North Carolina, USA
| | - Mugdha Gokhale
- Department of Epidemiology, UNC Chapel Hill, Chapel Hill, North Carolina, USA.,Pharmacoepidemiology, Center for Observational & Real-World Evidence, Merck, West Point, Pennsylvania, USA
| | - Mark Lunt
- The Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, UK
| | - Cynthia Girman
- Department of Epidemiology, UNC Chapel Hill, Chapel Hill, North Carolina, USA.,CERobs Consulting, LLC, Chapel Hill, North Carolina, USA
| | - Robert J Glynn
- Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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6
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Dukes O, Avagyan V, Vansteelandt S. Doubly robust tests of exposure effects under high‐dimensional confounding. Biometrics 2020; 76:1190-1200. [DOI: 10.1111/biom.13231] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 01/10/2020] [Accepted: 01/17/2020] [Indexed: 11/26/2022]
Affiliation(s)
- Oliver Dukes
- Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium
| | - Vahe Avagyan
- Mathematical and Statistical Methods Group Wageningen University and Research Wageningen The Netherlands
| | - Stijn Vansteelandt
- Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium
- Department of Medical Statistics London School of Hygiene and Tropical Medicine London United Kingdom
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