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Tsui JC, Willett K, Cohen JB, Yu Y, VanderBeek BL. Erythropoiesis-Stimulating Agents and the Risk of Vision-Threatening Diabetic Retinopathy. Ophthalmic Epidemiol 2024; 31:249-257. [PMID: 37427852 PMCID: PMC10776797 DOI: 10.1080/09286586.2023.2235001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 06/27/2023] [Accepted: 07/03/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE Animal studies have suggested that Erythropoiesis-Stimulating Agents (ESAs) may increase vascular endothelial growth factor (VEGF)-related retinopathies, but this effect is unclear in humans. This study evaluates the risk of vision-threatening diabetic retinopathy (VTDR), defined as either diabetic macular edema (DME) or proliferative diabetic retinopathy (PDR), in patients exposed to an ESA. METHODS Two analyses were performed. First, a retrospective matched-cohort study was designed using a de-identified commercial and Medicare Advantage medical claims database. The ESA cohort of non-proliferative diabetic retinopathy patients who were new users of an ESA from 2000 to 2022 was matched to controls up to a 3:1 ratio. Exclusion criteria included less than 2 years in the plan, history of VTDR or history of other retinopathy. Multivariable Cox proportional hazards regression with inverse proportional treatment weighting (IPTW) was used to assess the hazard of developing VTDR, DME, and PDR. The second analysis was a self-controlled case series (SCCS) evaluating the incidence rate ratios (IRR) of VTDR during 30-day periods before and after initiating an ESA. RESULTS After inclusion of 1502 ESA-exposed patients compared with 2656 controls, IPTW-adjusted hazard ratios found the ESA cohort had an increased hazard of progressing to VTDR (HR = 3.0 95%CI:2.3-3.8;p < .001) and DME (HR = 3.4,95%CI:2.6-4.4,p < .001), but not PDR (HR = 1.0,95%CI:0.5-2.3,p = .95). Similar results were found within the SCCS which demonstrated higher IRRs for VTDR (IRRs = 1.09-1.18;p < .001) and DME (IRRs = 1.16-1.18;p < .001), but not increased IRRs in PDR (IRR = 0.92-0.97,p = .02-0.39). CONCLUSION ESAs are associated with higher risks for VTDR and DME, but not PDR. Those studying ESAs as adjunctive therapy for DR should be cautious of possible unintended effects.
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Affiliation(s)
- Jonathan C. Tsui
- Scheie Eye Institute, Department of Ophthalmology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Keirnan Willett
- Kittner Eye Center, Department of Ophthalmology, University of North Carolina, Chapel Hill, NC, USA
| | - Jordana B. Cohen
- Renal-Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania. Philadelphia, PA, USA
| | - Yinxi Yu
- Center for Preventative Ophthalmology and Biostatistics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Brian L. VanderBeek
- Scheie Eye Institute, Department of Ophthalmology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Center for Pharmacoepidemiology Research and Training, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Leonard Davis Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
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2
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Abstract
Propensity score matching is commonly used in observational studies to control for confounding and estimate the causal effects of a treatment or exposure. Frequently, in observational studies data are clustered, which adds to the complexity of using propensity score techniques. In this article, we give an overview of propensity score matching methods for clustered data, and highlight how propensity score matching can be used to account for not just measured confounders, but also unmeasured cluster level confounders. We also consider using machine learning methods such as generalized boosted models to estimate the propensity score and show that accounting for clustering when using these methods can greatly reduce the performance, particularly when there are a large number of clusters and a small number of subjects per cluster. In order to get around this we highlight scenarios where it may be possible to control for measured covariates using propensity score matching, while using fixed effects regression in the outcome model to control for cluster level covariates. Using simulation studies we compare the performance of different propensity score matching methods for clustered data across a number of different settings. Finally, as an illustrative example we apply propensity score matching methods for clustered data to study the causal effect of aspirin on hearing deterioration using data from the conservation of hearing study.
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Affiliation(s)
- Benjamin Langworthy
- Department of Biostatistics, Harvard T.H. Chan School of Public
Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public
Health, Boston, MA, USA
| | - Yujie Wu
- Department of Biostatistics, Harvard T.H. Chan School of Public
Health, Boston, MA, USA
| | - Molin Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public
Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public
Health, Boston, MA, USA
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3
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Simoneau G, Pellegrini F, Debray TPA, Rouette J, Muñoz J, Platt RW, Petkau J, Bohn J, Shen C, de Moor C, Karim ME. Recommendations for the use of propensity score methods in multiple sclerosis research. Mult Scler 2022; 28:1467-1480. [PMID: 35387508 PMCID: PMC9260471 DOI: 10.1177/13524585221085733] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/03/2022] [Accepted: 02/17/2022] [Indexed: 01/24/2023]
Abstract
BACKGROUND With many disease-modifying therapies currently approved for the management of multiple sclerosis, there is a growing need to evaluate the comparative effectiveness and safety of those therapies from real-world data sources. Propensity score methods have recently gained popularity in multiple sclerosis research to generate real-world evidence. Recent evidence suggests, however, that the conduct and reporting of propensity score analyses are often suboptimal in multiple sclerosis studies. OBJECTIVES To provide practical guidance to clinicians and researchers on the use of propensity score methods within the context of multiple sclerosis research. METHODS We summarize recommendations on the use of propensity score matching and weighting based on the current methodological literature, and provide examples of good practice. RESULTS Step-by-step recommendations are presented, starting with covariate selection and propensity score estimation, followed by guidance on the assessment of covariate balance and implementation of propensity score matching and weighting. Finally, we focus on treatment effect estimation and sensitivity analyses. CONCLUSION This comprehensive set of recommendations highlights key elements that require careful attention when using propensity score methods.
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Affiliation(s)
| | | | | | - Julie Rouette
- Department of Epidemiology, Biostatistics and
Occupational Health, McGill University, Montreal, QC, Canada/Centre for
Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital,
Montreal, QC, Canada
| | - Johanna Muñoz
- University Medical Center Utrecht, Utretch, The
Netherlands
| | - Robert W. Platt
- Department of Pediatrics, McGill University,
Montreal, QC, Canada/Department of Epidemiology, Biostatistics and
Occupational Health, McGill University, Montreal, QC, Canada/Centre for
Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital,
Montreal, QC, Canada
| | - John Petkau
- Department of Statistics, The University of
British Columbia, Vancouver, BC, Canada
| | | | | | | | - Mohammad Ehsanul Karim
- School of Population and Public Health, The
University of British Columbia, Vancouver, BC, Canada/Centre for Health
Evaluation and Outcome Sciences, The University of British Columbia,
Vancouver, BC, Canada
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4
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Iwagami M, Shinozaki T. Introduction to Matching in Case-Control and Cohort Studies. ANNALS OF CLINICAL EPIDEMIOLOGY 2022; 4:33-40. [PMID: 38504854 PMCID: PMC10760465 DOI: 10.37737/ace.22005] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Matching is a technique through which patients with and without an outcome of interest (in case-control studies) or patients with and without an exposure of interest (in cohort studies) are sampled from an underlying cohort to have the same or similar distributions of some characteristics. This technique is used to increase the statistical efficiency and cost efficiency of studies. In case-control studies, besides time in risk set sampling, controls are often matched for each case with respect to important confounding factors, such as age and sex, and covariates with a large number of values or levels, such as area of residence (e.g., post code) and clinics/hospitals. In the statistical analysis of matched case-control studies, fixed-effect models such as the Mantel-Haenszel odds ratio estimator and conditional logistic regression model are needed to stratify matched case-control sets and remove selection bias artificially introduced by sampling controls. In cohort studies, exact matching is used to increase study efficiency and remove or reduce confounding effects of matching factors. Propensity score matching is another matching method whereby patients with and without exposure are matched based on estimated propensity scores to receive exposure. If appropriately used, matching can improve study efficiency without introducing bias and could also present results that are more intuitive for clinicians.
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Affiliation(s)
- Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine
| | - Tomohiro Shinozaki
- Tokyo University of Science, Department of Information and Computer Technology
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Abstract
Methods based on propensity score (PS) have become increasingly popular as a tool for causal inference. A better understanding of the relative advantages and disadvantages of the alternative analytic approaches can contribute to the optimal choice and use of a specific PS method over other methods. In this article, we provide an accessible overview of causal inference from observational data and two major PS-based methods (matching and inverse probability weighting), focusing on the underlying assumptions and decision-making processes. We then discuss common pitfalls and tips for applying the PS methods to empirical research and compare the conventional multivariable outcome regression and the two alternative PS-based methods (ie, matching and inverse probability weighting) and discuss their similarities and differences. Although we note subtle differences in causal identification assumptions, we highlight that the methods are distinct primarily in terms of the statistical modeling assumptions involved and the target population for which exposure effects are being estimated.
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Affiliation(s)
- Koichiro Shiba
- Department of Epidemiology, Harvard T.H. Chan School of Public Health.,Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health
| | - Takuya Kawahara
- Clinical Research Promotion Center, The University of Tokyo Hospital
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Levenson M, He W, Chen J, Fang Y, Faries D, Goldstein BA, Ho M, Lee K, Mishra-Kalyani P, Rockhold F, Wang H, Zink RC. Biostatistical Considerations When Using RWD and RWE in Clinical Studies for Regulatory Purposes: A Landscape Assessment. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1883473] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
| | - Weili He
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | - Jie Chen
- Overland Pharmaceuticals, Dover, DE
| | - Yixin Fang
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | - Douglas Faries
- Global Statistical Sciences, Eli Lilly & Company, Indianapolis, IN
| | - Benjamin A. Goldstein
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC
- Duke Clinical Research Institute, Duke University, Durham, NC
| | | | - Kwan Lee
- Statistics and Decision Sciences, Janssen Research and Development (retired), Spring House, PA
| | | | - Frank Rockhold
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC
- Duke Clinical Research Institute, Duke University, Durham, NC
| | - Hongwei Wang
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
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Shinozaki T, Suzuki E. Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips. J Epidemiol 2020; 30:377-389. [PMID: 32684529 PMCID: PMC7429147 DOI: 10.2188/jea.je20200226] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Epidemiologists are increasingly encountering complex longitudinal data, in which exposures and their confounders vary during follow-up. When a prior exposure affects the confounders of the subsequent exposures, estimating the effects of the time-varying exposures requires special statistical techniques, possibly with structural (ie, counterfactual) models for targeted effects, even if all confounders are accurately measured. Among the methods used to estimate such effects, which can be cast as a marginal structural model in a straightforward way, one popular approach is inverse probability weighting. Despite the seemingly intuitive theory and easy-to-implement software, misunderstandings (or "pitfalls") remain. For example, one may mistakenly equate marginal structural models with inverse probability weighting, failing to distinguish a marginal structural model encoding the causal parameters of interest from a nuisance model for exposure probability, and thereby failing to separate the problems of variable selection and model specification for these distinct models. Assuming the causal parameters of interest are identified given the study design and measurements, we provide a step-by-step illustration of generalized computation of standardization (called the g-formula) and inverse probability weighting, as well as the specification of marginal structural models, particularly for time-varying exposures. We use a novel hypothetical example, which allows us access to typically hidden potential outcomes. This illustration provides steppingstones (or "tips") to understand more concretely the estimation of the effects of complex time-varying exposures.
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Affiliation(s)
- Tomohiro Shinozaki
- Department of Information and Computer Technology, Faculty of Engineering, Tokyo University of Science
| | - Etsuji Suzuki
- Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
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Gaur L, Kutty S. Surveillance of Repaired Aortic Coarctation: The Quest for a Better Index of Left Ventricular Afterload. Circ Cardiovasc Imaging 2020; 13:e010426. [PMID: 32069117 DOI: 10.1161/circimaging.120.010426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Lasya Gaur
- The Blalock-Taussig-Thomas Heart Center, Johns Hopkins Hospital and School of Medicine, Baltimore, MD
| | - Shelby Kutty
- The Blalock-Taussig-Thomas Heart Center, Johns Hopkins Hospital and School of Medicine, Baltimore, MD
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Abstract
Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are used extensively to determine the variables for which it is sufficient to control for confounding to estimate causal effects. We discuss the following ten pitfalls and tips that are easily overlooked when using DAGs: 1) Each node on DAGs corresponds to a random variable and not its realized values; 2) The presence or absence of arrows in DAGs corresponds to the presence or absence of individual causal effect in the population; 3) “Non-manipulable” variables and their arrows should be drawn with care; 4) It is preferable to draw DAGs for the total population, rather than for the exposed or unexposed groups; 5) DAGs are primarily useful to examine the presence of confounding in distribution in the notion of confounding in expectation; 6) Although DAGs provide qualitative differences of causal structures, they cannot describe details of how to adjust for confounding; 7) DAGs can be used to illustrate the consequences of matching and the appropriate handling of matched variables in cohort and case-control studies; 8) When explicitly accounting for temporal order in DAGs, it is necessary to use separate nodes for each timing; 9) In certain cases, DAGs with signed edges can be used in drawing conclusions about the direction of bias; and 10) DAGs can be (and should be) used to describe not only confounding bias but also other forms of bias. We also discuss recent developments of graphical models and their future directions.
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Affiliation(s)
- Etsuji Suzuki
- Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
| | - Tomohiro Shinozaki
- Department of Information and Computer Technology, Faculty of Engineering, Tokyo University of Science
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