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      The risk of sudden cardiac arrest and ventricular arrhythmia with rosiglitazone versus pioglitazone: real-world evidence on thiazolidinedione safety

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          Abstract

          Background

          The low cost of thiazolidinediones makes them a potentially valuable therapeutic option for the > 300 million economically disadvantaged persons worldwide with type 2 diabetes mellitus. Differential selectivity of thiazolidinediones for peroxisome proliferator-activated receptors in the myocardium may lead to disparate arrhythmogenic effects. We examined real-world effects of thiazolidinediones on outpatient-originating sudden cardiac arrest (SCA) and ventricular arrhythmia (VA).

          Methods

          We conducted population-based high-dimensional propensity score-matched cohort studies in five Medicaid programs (California, Florida, New York, Ohio, Pennsylvania | 1999–2012) and a commercial health insurance plan (Optum Clinformatics | 2000–2016). We defined exposure based on incident rosiglitazone or pioglitazone dispensings; the latter served as an active comparator. We controlled for confounding by matching exposure groups on propensity score, informed by baseline covariates identified via a data adaptive approach. We ascertained SCA/VA outcomes precipitating hospital presentation using a validated, diagnosis-based algorithm. We generated marginal hazard ratios (HRs) via Cox proportional hazards regression that accounted for clustering within matched pairs. We prespecified Medicaid and Optum findings as primary and secondary, respectively; the latter served as a conceptual replication dataset.

          Results

          The adjusted HR for SCA/VA among rosiglitazone (vs. pioglitazone) users was 0.91 (0.75–1.10) in Medicaid and 0.88 (0.61–1.28) in Optum. Among Medicaid but not Optum enrollees, we found treatment effect heterogeneity by sex (adjusted HRs = 0.71 [0.54–0.93] and 1.16 [0.89–1.52] in men and women respectively, interaction term p-value = 0.01).

          Conclusions

          Rosiglitazone and pioglitazone appear to be associated with similar risks of SCA/VA.

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          Most cited references51

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          Principles of confounder selection

          Selecting an appropriate set of confounders for which to control is critical for reliable causal inference. Recent theoretical and methodological developments have helped clarify a number of principles of confounder selection. When complete knowledge of a causal diagram relating all covariates to each other is available, graphical rules can be used to make decisions about covariate control. Unfortunately, such complete knowledge is often unavailable. This paper puts forward a practical approach to confounder selection decisions when the somewhat less stringent assumption is made that knowledge is available for each covariate whether it is a cause of the exposure, and whether it is a cause of the outcome. Based on recent theoretically justified developments in the causal inference literature, the following proposal is made for covariate control decisions: control for each covariate that is a cause of the exposure, or of the outcome, or of both; exclude from this set any variable known to be an instrumental variable; and include as a covariate any proxy for an unmeasured variable that is a common cause of both the exposure and the outcome. Various principles of confounder selection are then further related to statistical covariate selection methods.
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            High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

            Adjusting for large numbers of covariates ascertained from patients' health care claims data may improve control of confounding, as these variables may collectively be proxies for unobserved factors. Here, we develop and test an algorithm that empirically identifies candidate covariates, prioritizes covariates, and integrates them into a propensity-score-based confounder adjustment model. We developed a multistep algorithm to implement high-dimensional proxy adjustment in claims data. Steps include (1) identifying data dimensions, eg, diagnoses, procedures, and medications; (2) empirically identifying candidate covariates; (3) assessing recurrence of codes; (4) prioritizing covariates; (5) selecting covariates for adjustment; (6) estimating the exposure propensity score; and (7) estimating an outcome model. This algorithm was tested in Medicare claims data, including a study on the effect of Cox-2 inhibitors on reduced gastric toxicity compared with nonselective nonsteroidal anti-inflammatory drugs (NSAIDs). In a population of 49,653 new users of Cox-2 inhibitors or nonselective NSAIDs, a crude relative risk (RR) for upper GI toxicity (RR = 1.09 [95% confidence interval = 0.91-1.30]) was initially observed. Adjusting for 15 predefined covariates resulted in a possible gastroprotective effect (0.94 [0.78-1.12]). A gastroprotective effect became stronger when adjusting for an additional 500 algorithm-derived covariates (0.88 [0.73-1.06]). Results of a study on the effect of statin on reduced mortality were similar. Using the algorithm adjustment confirmed a null finding between influenza vaccination and hip fracture (1.02 [0.85-1.21]). In typical pharmacoepidemiologic studies, the proposed high-dimensional propensity score resulted in improved effect estimates compared with adjustment limited to predefined covariates, when benchmarked against results expected from randomized trials.
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              A comparison of 12 algorithms for matching on the propensity score

              Propensity-score matching is increasingly being used to reduce the confounding that can occur in observational studies examining the effects of treatments or interventions on outcomes. We used Monte Carlo simulations to examine the following algorithms for forming matched pairs of treated and untreated subjects: optimal matching, greedy nearest neighbor matching without replacement, and greedy nearest neighbor matching without replacement within specified caliper widths. For each of the latter two algorithms, we examined four different sub-algorithms defined by the order in which treated subjects were selected for matching to an untreated subject: lowest to highest propensity score, highest to lowest propensity score, best match first, and random order. We also examined matching with replacement. We found that (i) nearest neighbor matching induced the same balance in baseline covariates as did optimal matching; (ii) when at least some of the covariates were continuous, caliper matching tended to induce balance on baseline covariates that was at least as good as the other algorithms; (iii) caliper matching tended to result in estimates of treatment effect with less bias compared with optimal and nearest neighbor matching; (iv) optimal and nearest neighbor matching resulted in estimates of treatment effect with negligibly less variability than did caliper matching; (v) caliper matching had amongst the best performance when assessed using mean squared error; (vi) the order in which treated subjects were selected for matching had at most a modest effect on estimation; and (vii) matching with replacement did not have superior performance compared with caliper matching without replacement. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
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                Author and article information

                Contributors
                celeonar@pennmedicine.upenn.edu
                cbrensin@pennmedicine.upenn.edu
                ghadeer.dawwas@pennmedicine.upenn.edu
                rajat.deo@uphs.upenn.edu
                warren@pennmedicine.upenn.edu
                samantha.soprano@pennmedicine.upenn.edu
                neil.dhopeshwarkar@pennmedicine.upenn.edu
                floryj@mskcc.org
                zbloom@gmail.com
                jgagne@bwh.harvard.edu
                christina.aquilante@cuanschutz.edu
                stevek@pennmedicine.upenn.edu
                hennessy@pennmedicine.upenn.edu
                Journal
                Cardiovasc Diabetol
                Cardiovasc Diabetol
                Cardiovascular Diabetology
                BioMed Central (London )
                1475-2840
                25 February 2020
                25 February 2020
                2020
                : 19
                : 25
                Affiliations
                [1 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Center for Pharmacoepidemiology Research and Training, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, , University of Pennsylvania, ; 423 Guardian Drive, Philadelphia, PA 19104 USA
                [2 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Division of Cardiovascular Medicine, Department of Medicine, Perelman School of Medicine, , University of Pennsylvania, ; 3400 Spruce Street, Philadelphia, PA 19104 USA
                [3 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Endocrinology Service, Department of Subspecialty Medicine, , Memorial Sloan Kettering Cancer Center, ; 1275 York Avenue, New York, NY 10065 USA
                [4 ]GRID grid.59734.3c, ISNI 0000 0001 0670 2351, Division of Endocrinology and Metabolism, Department of Medicine, , Icahn School of Medicine at Mount Sinai, ; 35 East 85th Street, New York, NY 10028 USA
                [5 ]GRID grid.38142.3c, ISNI 000000041936754X, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, , Brigham and Women’s Hospital and Harvard Medical School, Harvard University, ; 1620 Tremont Street, Boston, MA 02120 USA
                [6 ]GRID grid.430503.1, ISNI 0000 0001 0703 675X, Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, Anschutz Medical Campus, , University of Colorado, ; 12850 E. Montview Boulevard, Aurora, CO 80045 USA
                [7 ]GRID grid.25879.31, ISNI 0000 0004 1936 8972, Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA 19104 USA
                Author information
                http://orcid.org/0000-0002-5092-9657
                Article
                999
                10.1186/s12933-020-00999-5
                7041286
                32098624
                c0bf441f-bdd4-4175-a578-426d434050a7
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 20 November 2019
                : 9 February 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000041, American Diabetes Association;
                Award ID: 1-18-ICTS-097
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: R01AG060975
                Award ID: R01AG025152
                Award ID: R01AG064589
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: T32GM075766
                Award Recipient :
                Categories
                Original Investigation
                Custom metadata
                © The Author(s) 2020

                Endocrinology & Diabetes
                thiazolidinediones,type 2 diabetes mellitus,sudden cardiac death,cardiac arrhythmias,cohort studies,pharmacoepidemiology,propensity score,medicaid

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