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Wyss R, Plasek JM, Zhou L, Bessette LG, Schneeweiss S, Rassen JA, Tsacogianis T, Lin KJ. Scalable Feature Engineering from Electronic Free Text Notes to Supplement Confounding Adjustment of Claims-Based Pharmacoepidemiologic Studies. Clin Pharmacol Ther 2023; 113:832-838. [PMID: 36528788 PMCID: PMC10913938 DOI: 10.1002/cpt.2826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
Natural language processing (NLP) tools turn free-text notes (FTNs) from electronic health records (EHRs) into data features that can supplement confounding adjustment in pharmacoepidemiologic studies. However, current applications are difficult to scale. We used unsupervised NLP to generate high-dimensional feature spaces from FTNs to improve prediction of drug exposure and outcomes compared with claims-based analyses. We linked Medicare claims with EHR data to generate three cohort studies comparing different classes of medications on the risk of various clinical outcomes. We used "bag-of-words" to generate features for the top 20,000 most prevalent terms from FTNs. We compared machine learning (ML) prediction algorithms using different sets of candidate predictors: Set1 (39 researcher-specified variables), Set2 (Set1 + ML-selected claims codes), and Set3 (Set1 + ML-selected NLP-generated features), vs. Set4 (Set1 + 2 + 3). When modeling treatment choice, we observed a consistent pattern across the examples: ML models utilizing Set4 performed best followed by Set2, Set3, then Set1. When modeling the outcome risk, there was little to no improvement beyond models based on Set1. Supplementing claims data with NLP-generated features from free text notes improved prediction of prescribing choices but had little or no improvement on clinical risk prediction. These findings have implications for strategies to improve confounding using EHR data in pharmacoepidemiologic studies.
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Affiliation(s)
- Richard Wyss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Joseph M. Plasek
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Li Zhou
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Lily G. Bessette
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | | | - Theodore Tsacogianis
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Kueiyu Joshua Lin
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School
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2
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Johnston S, Jha A, Roy S, Pollack E. Surgical Complication Risk Factor Identification Using High-Dimensional Hospital Data: An Illustrative Example in Hemostasis-Related Complications. CLINICOECONOMICS AND OUTCOMES RESEARCH 2022; 14:683-689. [DOI: 10.2147/ceor.s380004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
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3
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Utility of automated data-adaptive propensity score method for confounding by indication in comparative effectiveness study in real world Medicare and registry data. PLoS One 2022; 17:e0272975. [PMID: 35969535 PMCID: PMC9377588 DOI: 10.1371/journal.pone.0272975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 07/31/2022] [Indexed: 11/19/2022] Open
Abstract
Background Confounding by indication is a serious threat to comparative studies using real world data. We assessed the utility of automated data-adaptive analytic approach for confounding adjustment when both claims and clinical registry data are available. Methods We used a comparative study example of carotid artery stenting (CAS) vs. carotid endarterectomy (CEA) in 2005–2008 when CAS was only indicated for patients with high surgical risk. We included Medicare beneficiaries linked to the Society for Vascular Surgery’s Vascular Registry >65 years old undergoing CAS/CEA. We compared hazard ratios (HRs) for death while adjusting for confounding by combining various 1) Propensity score (PS) modeling strategies (investigator-specified [IS-PS] vs. automated data-adaptive [ada-PS]); 2) data sources (claims-only, registry-only and claims-plus-registry); and 3) PS adjustment approaches (matching vs. quintiles-adjustment with/without trimming). An HR of 1.0 was used as a benchmark effect estimate based on CREST trial. Results The cohort included 1,999 CAS and 3,255 CEA patients (mean age 76). CAS patients were more likely symptomatic and at high surgical risk, and experienced higher mortality (crude HR = 1.82 for CAS vs. CEA). HRs from PS-quintile adjustment without trimming were 1.48 and 1.52 for claims-only IS-PS and ada-PS, 1.51 and 1.42 for registry-only IS-PS and ada-PS, and 1.34 and 1.23 for claims-plus-registry IS-PS and ada-PS, respectively. Estimates from other PS adjustment approaches showed similar patterns. Conclusions In a comparative effectiveness study of CAS vs. CEA with strong confounding by indication, ada-PS performed better than IS-PS in general, but both claims and registry data were needed to adequately control for bias.
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4
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Nguyen T, Collins GS, Pellegrini F, Moons KG, Debray TP. On the aggregation of published prognostic scores for causal inference in observational studies. Stat Med 2020; 39:1440-1457. [PMID: 32022311 PMCID: PMC7187258 DOI: 10.1002/sim.8489] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 12/12/2019] [Accepted: 01/14/2020] [Indexed: 12/23/2022]
Abstract
As real world evidence on drug efficacy involves nonrandomized studies, statistical methods adjusting for confounding are needed. In this context, prognostic score (PGS) analysis has recently been proposed as a method for causal inference. It aims to restore balance across the different treatment groups by identifying subjects with a similar prognosis for a given reference exposure ("control"). This requires the development of a multivariable prognostic model in the control arm of the study sample, which is then extrapolated to the different treatment arms. Unfortunately, large cohorts for developing prognostic models are not always available. Prognostic models are therefore subject to a dilemma between overfitting and parsimony; the latter being prone to a violation of the assumption of no unmeasured confounders when important covariates are ignored. Although it is possible to limit overfitting by using penalization strategies, an alternative approach is to adopt evidence synthesis. Aggregating previously published prognostic models may improve the generalizability of PGS, while taking account of a large set of covariates-even when limited individual participant data are available. In this article, we extend a method for prediction model aggregation to PGS analysis in nonrandomized studies. We conduct extensive simulations to assess the validity of model aggregation, compared with other methods of PGS analysis for estimating marginal treatment effects. We show that aggregating existing PGS into a "meta-score" is robust to misspecification, even when elementary scores wrongfully omit confounders or focus on different outcomes. We illustrate our methods in a setting of treatments for asthma.
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Affiliation(s)
- Tri‐Long Nguyen
- Section of Epidemiology, Department of Public HealthUniversity of CopenhagenCopenhagenDenmark
- Julius Center for Health Sciences and Primary CareUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
- Department of PharmacyNîmes University Hospital CentreNîmesFrance
| | - Gary S. Collins
- National Institute for Health Research Oxford Biomedical Research CentreJohn Radcliffe HospitalOxfordUK
| | | | - Karel G.M. Moons
- Julius Center for Health Sciences and Primary CareUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
- Cochrane NetherlandsUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Thomas P.A. Debray
- Julius Center for Health Sciences and Primary CareUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
- Cochrane NetherlandsUniversity Medical Center UtrechtUtrechtThe Netherlands
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5
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Controlling Confounding in a Study of Oral Anticoagulants: Comparing Disease Risk Scores Developed Using Different Follow-Up Approaches. EGEMS 2019; 7:27. [PMID: 31346542 PMCID: PMC6640656 DOI: 10.5334/egems.254] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Purpose: Little is known about how disease risk score (DRS) development should proceed under different pharmacoepidemiologic follow-up strategies. In an analysis of dabigatran vs. warfarin and risk of major bleeding, we compared the results of DRS adjustment when models were developed under “intention-to-treat” (ITT) and “as-treated” (AT) approaches. Methods: We assessed DRS model discrimination, calibration, and ability to induce prognostic balance via the “dry run analysis”. AT treatment effects stratified on each DRS were compared with each other and with a propensity score (PS) stratified reference estimate. Bootstrap resampling of the historical cohort at 10 percent–90 percent sample size was performed to assess the impact of sample size on DRS estimation. Results: Historically-derived DRS models fit under AT showed greater decrements in discrimination and calibration than those fit under ITT when applied to the concurrent study population. Prognostic balance was approximately equal across DRS models (–6 percent to –7 percent “pseudo-bias” on the hazard ratio scale). Hazard ratios were between 0.76 and 0.78 with all methods of DRS adjustment, while the PS stratified hazard ratio was 0.83. In resampling, AT DRS models showed more overfitting and worse prognostic balance, and led to hazard ratios further from the reference estimate than did ITT DRSs, across sample sizes. Conclusions: In a study of anticoagulant safety, DRSs developed under an AT principle showed signs of overfitting and reduced confounding control. More research is needed to determine if development of DRSs under ITT is a viable solution to overfitting in other settings.
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6
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Tian Y, Schuemie MJ, Suchard MA. Evaluating large-scale propensity score performance through real-world and synthetic data experiments. Int J Epidemiol 2019; 47:2005-2014. [PMID: 29939268 DOI: 10.1093/ije/dyy120] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/24/2018] [Indexed: 12/30/2022] Open
Abstract
Background Propensity score adjustment is a popular approach for confounding control in observational studies. Reliable frameworks are needed to determine relative propensity score performance in large-scale studies, and to establish optimal propensity score model selection methods. Methods We detail a propensity score evaluation framework that includes synthetic and real-world data experiments. Our synthetic experimental design extends the 'plasmode' framework and simulates survival data under known effect sizes, and our real-world experiments use a set of negative control outcomes with presumed null effect sizes. In reproductions of two published cohort studies, we compare two propensity score estimation methods that contrast in their model selection approach: L1-regularized regression that conducts a penalized likelihood regression, and the 'high-dimensional propensity score' (hdPS) that employs a univariate covariate screen. We evaluate methods on a range of outcome-dependent and outcome-independent metrics. Results L1-regularization propensity score methods achieve superior model fit, covariate balance and negative control bias reduction compared with the hdPS. Simulation results are mixed and fluctuate with simulation parameters, revealing a limitation of simulation under the proportional hazards framework. Including regularization with the hdPS reduces commonly reported non-convergence issues but has little effect on propensity score performance. Conclusions L1-regularization incorporates all covariates simultaneously into the propensity score model and offers propensity score performance superior to the hdPS marginal screen.
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Affiliation(s)
- Yuxi Tian
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
| | - Martijn J Schuemie
- Epidemiology Department, Janssen Research and Development LLC, Titusville, NJ, USA
| | - Marc A Suchard
- Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA.,Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, USA.,Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA
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7
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Ju C, Wyss R, Franklin JM, Schneeweiss S, Häggström J, van der Laan MJ. Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data. Stat Methods Med Res 2019; 28:1044-1063. [PMID: 29226777 PMCID: PMC6039292 DOI: 10.1177/0962280217744588] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Propensity score-based estimators are increasingly used for causal inference in observational studies. However, model selection for propensity score estimation in high-dimensional data has received little attention. In these settings, propensity score models have traditionally been selected based on the goodness-of-fit for the treatment mechanism itself, without consideration of the causal parameter of interest. Collaborative minimum loss-based estimation is a novel methodology for causal inference that takes into account information on the causal parameter of interest when selecting a propensity score model. This "collaborative learning" considers variable associations with both treatment and outcome when selecting a propensity score model in order to minimize a bias-variance tradeoff in the estimated treatment effect. In this study, we introduce a novel approach for collaborative model selection when using the LASSO estimator for propensity score estimation in high-dimensional covariate settings. To demonstrate the importance of selecting the propensity score model collaboratively, we designed quasi-experiments based on a real electronic healthcare database, where only the potential outcomes were manually generated, and the treatment and baseline covariates remained unchanged. Results showed that the collaborative minimum loss-based estimation algorithm outperformed other competing estimators for both point estimation and confidence interval coverage. In addition, the propensity score model selected by collaborative minimum loss-based estimation could be applied to other propensity score-based estimators, which also resulted in substantive improvement for both point estimation and confidence interval coverage. We illustrate the discussed concepts through an empirical example comparing the effects of non-selective nonsteroidal anti-inflammatory drugs with selective COX-2 inhibitors on gastrointestinal complications in a population of Medicare beneficiaries.
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Affiliation(s)
- Cheng Ju
- Division of Biostatistics, University of California, USA
| | - Richard Wyss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, USA
| | - Jessica M Franklin
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, USA
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, USA
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8
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Sommet A, Pariente A. Methods in pharmacoepidemiology. Therapie 2019; 74:187-197. [PMID: 30819408 DOI: 10.1016/j.therap.2018.11.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 11/26/2018] [Indexed: 01/04/2023]
Abstract
Pharmacoepidemiology is the study of interactions between drugs and human populations, investigating, in real conditions of life, benefits, risks and use of drugs. Pharmacoepidemiology applies to drugs and their pharmacological evaluations, the different methods also used in epidemiology to assess in real conditions of life, benefits, risks and use of drugs. Pharmacoepidemiologic studies are ad-hoc studies or studies on databases. Specific methods exist to measure drug exposure, as well as indicators of compliance and misuse of drugs. Various designs for descriptive and explanatory studies exist, in a context in which a growing proportion of studies are carried out using medico-administrative data. The limits traditionally affecting the study designs are modified in this context, almost any design selected for the conduct of a study from these databases then deriving from a cohort in whom the information has been recorded prospectively and exhaustively.
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Affiliation(s)
- Agnès Sommet
- Service de pharmacologie médicale et clinique, UMR Inserm 1027, CIC Inserm 1436, faculté de médecine, centre hospitalier universitaire, 31000 Toulouse, France.
| | - Antoine Pariente
- Inserm, Bordeaux population health research centre, team pharmacoepidemiology, UMR 1219, university of Bordeaux, 33076 Bordeaux, France; Pôle de santé publique, centre régional de pharmacovigilance de Bordeaux, service de pharmacologie médicale, CHU de Bordeaux, 33076 Bordeaux, France
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9
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Schneeweiss S, Gopalakrishnan C, Bartels DB, Franklin JM, Zint K, Kulldorff M, Huybrechts KF. Sequential Monitoring of the Comparative Effectiveness and Safety of Dabigatran in Routine Care. Circ Cardiovasc Qual Outcomes 2019; 12:e005173. [DOI: 10.1161/circoutcomes.118.005173] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA (S.S., C.G., J.M.F., M.K., K.F.H.)
| | - Chandrasekar Gopalakrishnan
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA (S.S., C.G., J.M.F., M.K., K.F.H.)
| | - Dorothee B. Bartels
- Global Epidemiology, Boehringer Ingelheim International GmbH, Ingelheim, Germany (D.B.B., K.Z.)
- BI X GmbH, Ingelheim, Germany (D.B.B.)
| | - Jessica M. Franklin
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA (S.S., C.G., J.M.F., M.K., K.F.H.)
| | - Kristina Zint
- Global Epidemiology, Boehringer Ingelheim International GmbH, Ingelheim, Germany (D.B.B., K.Z.)
| | - Martin Kulldorff
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA (S.S., C.G., J.M.F., M.K., K.F.H.)
| | - Krista F. Huybrechts
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA (S.S., C.G., J.M.F., M.K., K.F.H.)
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10
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Ju C, Gruber S, Lendle SD, Chambaz A, Franklin JM, Wyss R, Schneeweiss S, van der Laan MJ. Scalable collaborative targeted learning for high-dimensional data. Stat Methods Med Res 2019; 28:532-554. [PMID: 28936917 PMCID: PMC6086775 DOI: 10.1177/0962280217729845] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typically, only one of the latter is optimized for the sake of constructing a well-behaved estimator of the low-dimensional parameter of interest. Optimizing more than one of them for the sake of achieving a better bias-variance trade-off in the estimation of the parameter of interest is the core idea driving the general template of the collaborative targeted minimum loss-based estimation procedure. The original instantiation of the collaborative targeted minimum loss-based estimation template can be presented as a greedy forward stepwise collaborative targeted minimum loss-based estimation algorithm. It does not scale well when the number p of covariates increases drastically. This motivates the introduction of a novel instantiation of the collaborative targeted minimum loss-based estimation template where the covariates are pre-ordered. Its time complexity is O ( p ) as opposed to the original O ( p 2 ) , a remarkable gain. We propose two pre-ordering strategies and suggest a rule of thumb to develop other meaningful strategies. Because it is usually unclear a priori which pre-ordering strategy to choose, we also introduce another instantiation called SL-C-TMLE algorithm that enables the data-driven choice of the better pre-ordering strategy given the problem at hand. Its time complexity is O ( p ) as well. The computational burden and relative performance of these algorithms were compared in simulation studies involving fully synthetic data or partially synthetic data based on a real world large electronic health database; and in analyses of three real, large electronic health databases. In all analyses involving electronic health databases, the greedy collaborative targeted minimum loss-based estimation algorithm is unacceptably slow. Simulation studies seem to indicate that our scalable collaborative targeted minimum loss-based estimation and SL-C-TMLE algorithms work well. All C-TMLEs are publicly available in a Julia software package.
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Affiliation(s)
- Cheng Ju
- University of California, Berkeley, CA, USA
| | - Susan Gruber
- Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA
| | | | - Antoine Chambaz
- University of California, Berkeley, CA, USA
- Modal’X, UPL, Univ Paris Nanterre, Nanterre, France
| | - Jessica M Franklin
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA
| | - Richard Wyss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA, USA
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11
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Nguyen TL, Debray TPA. The use of prognostic scores for causal inference with general treatment regimes. Stat Med 2019; 38:2013-2029. [PMID: 30652333 PMCID: PMC6590249 DOI: 10.1002/sim.8084] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 12/03/2018] [Accepted: 12/09/2018] [Indexed: 01/29/2023]
Abstract
In nonrandomised studies, inferring causal effects requires appropriate methods for addressing confounding bias. Although it is common to adopt propensity score analysis to this purpose, prognostic score analysis has recently been proposed as an alternative strategy. While both approaches were originally introduced to estimate causal effects for binary interventions, the theory of propensity score has since been extended to the case of general treatment regimes. Indeed, many treatments are not assigned in a binary fashion and require a certain extent of dosing. Hence, researchers may often be interested in estimating treatment effects across multiple exposures. To the best of our knowledge, the prognostic score analysis has not been yet generalised to this case. In this article, we describe the theory of prognostic scores for causal inference with general treatment regimes. Our methods can be applied to compare multiple treatments using nonrandomised data, a topic of great relevance in contemporary evaluations of clinical interventions. We propose estimators for the average treatment effects in different populations of interest, the validity of which is assessed through a series of simulations. Finally, we present an illustrative case in which we estimate the effect of the delay to Aspirin administration on a composite outcome of death or dependence at 6 months in stroke patients.
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Affiliation(s)
- Tri-Long Nguyen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen K, Denmark.,Department of Pharmacy, Nîmes University Hospital Centre, Nîmes, France
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.,Cochrane Netherlands, University Medical Center Utrecht, Utrecht, The Netherlands.,Botnar Research Centre, University of Oxford, Oxford, UK.,Institute of Health Informatics, University College London, London, UK
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12
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Douros A, Filion KB, Yin H, Yu OH, Etminan M, Udell JA, Azoulay L. Glucagon-Like Peptide 1 Receptor Agonists and the Risk of Incident Diabetic Retinopathy. Diabetes Care 2018; 41:2330-2338. [PMID: 30150234 DOI: 10.2337/dc17-2280] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 07/29/2018] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Previous studies suggested that glucagon-like peptide 1 receptor agonists (GLP-1 RAs) may initially worsen and possibly increase the risk of diabetic retinopathy. However, data on this possible association remain limited. Thus, this population-based study aimed to determine whether use of GLP-1 RAs is associated with an increased risk of incident diabetic retinopathy. RESEARCH DESIGN AND METHODS Using the U.K. Clinical Practice Research Datalink (CPRD), we conducted a cohort study among 77,115 patients with type 2 diabetes initiating antidiabetic drugs between January 2007 and September 2015. Adjusted hazard ratios (HRs) and 95% CIs of incident diabetic retinopathy were estimated using time-dependent Cox proportional hazards models, comparing use of GLP-1 RAs with current use of two or more oral antidiabetic drugs. In an ancillary analysis, new users of GLP-1 RAs were compared with new users of insulin. RESULTS During 245,825 person-years of follow-up, 10,763 patients were newly diagnosed with diabetic retinopathy. Compared with current use of two or more oral antidiabetic drugs, use of GLP-1 RAs was not associated with an increased risk of incident diabetic retinopathy overall (HR 1.00, 95% CI 0.85-1.17). Compared with insulin, GLP-1 RAs were associated with a decreased risk of diabetic retinopathy (HR 0.67, 95% CI 0.51-0.90). CONCLUSIONS The associations with diabetic retinopathy varied according to the type of comparator. When compared with use of two or more oral antidiabetic drugs, use of GLP-1 RAs was not associated with an increased risk of incident diabetic retinopathy. The apparent lower risk of diabetic retinopathy associated with GLP-1 RAs compared with insulin may be due to residual confounding.
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Affiliation(s)
- Antonios Douros
- Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montreal, Quebec, Canada.,Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.,Institute of Clinical Pharmacology and Toxicology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Kristian B Filion
- Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montreal, Quebec, Canada.,Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.,Division of Clinical Epidemiology, Department of Medicine, McGill University, Montreal, Quebec, Canada
| | - Hui Yin
- Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montreal, Quebec, Canada
| | - Oriana Hoi Yu
- Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montreal, Quebec, Canada.,Division of Endocrinology, Jewish General Hospital, Montreal, Quebec, Canada
| | - Mahyar Etminan
- Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jacob A Udell
- Women's College Research Institute and Cardiovascular Division, Department of Medicine, Women's College Hospital, University of Toronto, Toronto, Ontario, Canada.,Cardiovascular Division, Department of Medicine, Peter Munk Cardiac Centre, Toronto General Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Laurent Azoulay
- Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montreal, Quebec, Canada .,Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada
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13
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Bezin J, Mansiaux Y, Noize P, Salvo F, Bégaud B, Pariente A. Use of Lipid-Lowering Drugs and the Risk of Cataract: A Population-Based Nested Case-Control Study. Clin Pharmacol Ther 2018; 105:458-465. [PMID: 29992538 DOI: 10.1002/cpt.1176] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 06/30/2018] [Indexed: 11/11/2022]
Abstract
Eye lens membrane cells require high cholesterol concentrations that might be counteracted by lipid-lowering drugs. Using a nationwide database, we conducted a nested case-control study to evaluate the risk of cataract development associated with the use of lipid-lowering drugs. Patients aged 45 years and over with first cataract surgery in 2014 (cases) and up to four controls matched on age, gender, diabetes, hypothyroidism, glucocorticoid use, cardiovascular risk, and area of residence were included in the study. Among the 2,811 cases and 11,106 matched controls included, analyses showed a significantly increased risk of cataract surgery for a cumulative exposure to fibrates exceeding 5 years (adjusted odds ratio (aOR) 1.58; 95% confidence interval (CI): 1.17-2.15), unlike cumulative exposure to statins, whatever the dose or duration of treatment (aORs from 1.00-1.08, none being significant). This study highlighted an increased risk of cataract surgery with prolonged use of fibrates but not of statins.
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Affiliation(s)
- Julien Bezin
- Bordeaux Population Health Research Center, Team Pharmacoepidemiology, UMR 1219, Inserm, DRUGS-SAFE National Platform of Pharmacoepidemiology, University of Bordeaux, Bordeaux, France
| | - Yohann Mansiaux
- Bordeaux Population Health Research Center, Team Pharmacoepidemiology, UMR 1219, Inserm, DRUGS-SAFE National Platform of Pharmacoepidemiology, University of Bordeaux, Bordeaux, France
| | - Pernelle Noize
- Bordeaux Population Health Research Center, Team Pharmacoepidemiology, UMR 1219, Inserm, DRUGS-SAFE National Platform of Pharmacoepidemiology, University of Bordeaux, Bordeaux, France.,Service de Pharmacologie Médicale, CHU Bordeaux, Bordeaux, France
| | - Francesco Salvo
- Bordeaux Population Health Research Center, Team Pharmacoepidemiology, UMR 1219, Inserm, DRUGS-SAFE National Platform of Pharmacoepidemiology, University of Bordeaux, Bordeaux, France.,Service de Pharmacologie Médicale, CHU Bordeaux, Bordeaux, France
| | - Bernard Bégaud
- Bordeaux Population Health Research Center, Team Pharmacoepidemiology, UMR 1219, Inserm, DRUGS-SAFE National Platform of Pharmacoepidemiology, University of Bordeaux, Bordeaux, France.,Service de Pharmacologie Médicale, CHU Bordeaux, Bordeaux, France
| | - Antoine Pariente
- Bordeaux Population Health Research Center, Team Pharmacoepidemiology, UMR 1219, Inserm, DRUGS-SAFE National Platform of Pharmacoepidemiology, University of Bordeaux, Bordeaux, France.,Service de Pharmacologie Médicale, CHU Bordeaux, Bordeaux, France
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14
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Schneeweiss S. Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects. Clin Epidemiol 2018; 10:771-788. [PMID: 30013400 PMCID: PMC6039060 DOI: 10.2147/clep.s166545] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Decision makers in health care increasingly rely on nonrandomized database analyses to assess the effectiveness, safety, and value of medical products. Health care data scientists use data-adaptive approaches that automatically optimize confounding control to study causal treatment effects. This article summarizes relevant experiences and extensions. METHODS The literature was reviewed on the uses of high-dimensional propensity score (HDPS) and related approaches for health care database analyses, including methodological articles on their performance and improvement. Articles were grouped into applications, comparative performance studies, and statistical simulation experiments. RESULTS The HDPS algorithm has been referenced frequently with a variety of clinical applications and data sources from around the world. The appeal of HDPS for database research rests in 1) its superior performance in situations of unobserved confounding through proxy adjustment, 2) its predictable efficiency in extracting confounding information from a given data source, 3) its ability to automate estimation of causal treatment effects to the extent achievable in a given data source, and 4) its independence of data source and coding system. Extensions of the HDPS approach have focused on improving variable selection when exposure is sparse, using free text information and time-varying confounding adjustment. CONCLUSION Semiautomated and optimized confounding adjustment in health care database analyses has proven successful across a wide range of settings. Machine-learning extensions further automate its use in estimating causal treatment effects across a range of data scenarios.
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Affiliation(s)
- Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital,
- Harvard Medical School, Boston, MA, USA,
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15
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Connolly JG, Wang SV, Fuller CC, Toh S, Panozzo CA, Cocoros N, Zhou M, Gagne JJ, Maro JC. Development and application of two semi-automated tools for targeted medical product surveillance in a distributed data network. CURR EPIDEMIOL REP 2017; 4:298-306. [PMID: 29204333 PMCID: PMC5710750 DOI: 10.1007/s40471-017-0121-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
PURPOSE OF REVIEW An important component of the Food and Drug Administration's Sentinel Initiative is the active post-market risk identification and analysis (ARIA) system, which utilizes semi-automated, parameterized computer programs to implement propensity-score adjusted and self-controlled risk interval designs to conduct targeted surveillance of medical products in the Sentinel Distributed Database. In this manuscript, we review literature relevant to the development of these programs and describe their application within the Sentinel Initiative. RECENT FINDINGS These quality-checked and publicly available tools have been successfully used to conduct rapid, replicable, and targeted safety analyses of several medical products. In addition to speed and reproducibility, use of semi-automated tools allows investigators to focus on decisions regarding key methodological parameters. We also identified challenges associated with the use of these methods in distributed and prospective datasets like the Sentinel Distributed Database, namely uncertainty regarding the optimal approach to estimating propensity scores in dynamic data among data partners of heterogeneous size. SUMMARY Future research should focus on the methodological challenges raised by these applications as well as developing new modular programs for targeted surveillance of medical products.
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Affiliation(s)
- John G. Connolly
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School Boston, MA
| | - Shirley V. Wang
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School Boston, MA
| | - Candace C. Fuller
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA
| | - Sengwee Toh
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA
| | - Catherine A. Panozzo
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA
| | - Noelle Cocoros
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA
| | - Meijia Zhou
- Center for Clinical Epidemiology and Biostatistics, Pereleman School of Medicine at the University of Pennsylvania, Philadelphia, PA
- Center for Pharmacoepidemiology Research and Training, University of Pennsylvania Pereleman School of Medicine, Philadelphia, PA
| | - Joshua J. Gagne
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School Boston, MA
| | - Judith C. Maro
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA
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16
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Wyss R, Hansen BB, Ellis AR, Gagne JJ, Desai RJ, Glynn RJ, Stürmer T. The "Dry-Run" Analysis: A Method for Evaluating Risk Scores for Confounding Control. Am J Epidemiol 2017; 185:842-852. [PMID: 28338910 DOI: 10.1093/aje/kwx032] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 06/29/2016] [Indexed: 12/18/2022] Open
Abstract
A propensity score (PS) model's ability to control confounding can be assessed by evaluating covariate balance across exposure groups after PS adjustment. The optimal strategy for evaluating a disease risk score (DRS) model's ability to control confounding is less clear. DRS models cannot be evaluated through balance checks within the full population, and they are usually assessed through prediction diagnostics and goodness-of-fit tests. A proposed alternative is the "dry-run" analysis, which divides the unexposed population into "pseudo-exposed" and "pseudo-unexposed" groups so that differences on observed covariates resemble differences between the actual exposed and unexposed populations. With no exposure effect separating the pseudo-exposed and pseudo-unexposed groups, a DRS model is evaluated by its ability to retrieve an unconfounded null estimate after adjustment in this pseudo-population. We used simulations and an empirical example to compare traditional DRS performance metrics with the dry-run validation. In simulations, the dry run often improved assessment of confounding control, compared with the C statistic and goodness-of-fit tests. In the empirical example, PS and DRS matching gave similar results and showed good performance in terms of covariate balance (PS matching) and controlling confounding in the dry-run analysis (DRS matching). The dry-run analysis may prove useful in evaluating confounding control through DRS models.
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17
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Wyss R, Glynn RJ, Gagne JJ. A Review of Disease Risk Scores and Their Application in Pharmacoepidemiology. CURR EPIDEMIOL REP 2016. [DOI: 10.1007/s40471-016-0088-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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