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Barberio J, Naimi AI, Patzer RE, Kim C, Hernandez RK, Brookhart MA, Gilbertson D, Bradbury BD, Lash TL. Influence of incomplete death information on cumulative risk estimates in US claims data. Am J Epidemiol 2024; 193:1281-1290. [PMID: 38583932 DOI: 10.1093/aje/kwae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 03/05/2024] [Accepted: 04/02/2024] [Indexed: 04/09/2024] Open
Abstract
Administrative claims databases often do not capture date or fact of death, so studies using these data may inappropriately treat death as a censoring event-equivalent to other withdrawal reasons-rather than a competing event. We examined 1-, 3-, and 5-year inverse-probability-of-treatment weighted cumulative risks of a composite cardiovascular outcome among 34 527 initiators of telmisartan (exposure) and ramipril (referent), who were aged ≥55 years, in Optum (United States) claims data from 2003 to 2020. Differences in cumulative risks of the cardiovascular endpoint due to censoring of death (cause-specific), as compared with treating death as a competing event (subdistribution), increased with greater follow-up time and older age, where event and mortality risks were higher. Among ramipril users, 5-year cause-specific and subdistribution cumulative risk estimates per 100, respectively, were 16.4 (95% CI, 15.3-17.5) and 16.2 (95% CI, 15.1-17.3) among ages 55-64 (difference = 0.2) and were 43.2 (95% CI, 41.3-45.2) and 39.7 (95% CI, 37.9-41.4) among ages ≥75 (difference = 3.6). Plasmode simulation results demonstrated the differences in cause-specific versus subdistribution cumulative risks to increase with increasing mortality rate. We suggest researchers consider the cohort's baseline mortality risk when deciding whether real-world data with incomplete death information can be used without concern. This article is part of a Special Collection on Pharmacoepidemiology.
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Noninterventional studies in the COVID-19 era: methodological considerations for study design and analysis. J Clin Epidemiol 2023; 153:91-101. [PMID: 36400263 PMCID: PMC9671552 DOI: 10.1016/j.jclinepi.2022.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 10/27/2022] [Accepted: 11/09/2022] [Indexed: 11/19/2022]
Abstract
The global COVID-19 pandemic has generated enormous morbidity and mortality, as well as large health system disruptions including changes in use of prescription medications, outpatient encounters, emergency department admissions, and hospitalizations. These pandemic-related disruptions are reflected in real-world data derived from electronic medical records, administrative claims, disease or medication registries, and mobile devices. We discuss how pandemic-related disruptions in healthcare utilization may impact the conduct of noninterventional studies designed to characterize the utilization and estimate the effects of medical interventions on health-related outcomes. Using hypothetical studies, we highlight consequences that the pandemic may have on study design elements including participant selection and ascertainment of exposures, outcomes, and covariates. We discuss the implications of these pandemic-related disruptions on possible threats to external validity (participant selection) and internal validity (for example, confounding, selection bias, missing data bias). These concerns may be amplified in populations disproportionately impacted by COVID-19, such as racial/ethnic minorities, rural residents, or people experiencing poverty. We propose a general framework for researchers to carefully consider during the design and analysis of noninterventional studies that use real-world data from the COVID-19 era.
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Buchanan A, Sun T, Wu J, Aroke H, Bratberg J, Rich J, Kogut S, Hogan J. Toward evaluation of disseminated effects of medications for opioid use disorder within provider-based clusters using routinely-collected health data. Stat Med 2022; 41:3449-3465. [PMID: 35673849 PMCID: PMC9288976 DOI: 10.1002/sim.9427] [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: 02/12/2021] [Revised: 04/13/2022] [Accepted: 04/22/2022] [Indexed: 08/17/2023]
Abstract
Routinely-collected health data can be employed to emulate a target trial when randomized trial data are not available. Patients within provider-based clusters likely exert and share influence on each other's treatment preferences and subsequent health outcomes and this is known as dissemination or spillover. Extending a framework to replicate an idealized two-stage randomized trial using routinely-collected health data, an evaluation of disseminated effects within provider-based clusters is possible. In this article, we propose a novel application of causal inference methods for dissemination to retrospective cohort studies in administrative claims data and evaluate the impact of the normality of the random effects distribution for the cluster-level propensity score on estimation of the causal parameters. An extensive simulation study was conducted to study the robustness of the methods under different distributions of the random effects. We applied these methods to evaluate baseline prescription for medications for opioid use disorder among a cohort of patients diagnosed with opioid use disorder and adjust for baseline confounders using information obtained from an administrative claims database. We discuss future research directions in this setting to better address unmeasured confounding in the presence of disseminated effects.
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Affiliation(s)
- Ashley Buchanan
- Department of Pharmacy Practice, University of Rhode Island, Rhode Island, USA
| | - Tianyu Sun
- Department of Pharmacy Practice, University of Rhode Island, Rhode Island, USA
| | - Jing Wu
- Department of Computer Science and Statistics, University of Rhode Island, Rhode Island, USA
| | - Hilary Aroke
- Department of Pharmacy Practice, University of Rhode Island, Rhode Island, USA
| | - Jeffrey Bratberg
- Department of Pharmacy Practice, University of Rhode Island, Rhode Island, USA
| | - Josiah Rich
- The Warren Alpert Medical School, Brown University, Rhode Island, USA
| | - Stephen Kogut
- Department of Pharmacy Practice, University of Rhode Island, Rhode Island, USA
| | - Joseph Hogan
- Department of Biostatistics, Brown University, Rhode Island, USA
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Gantenberg JR, van Aalst R, Zimmerman N, Limone B, Chaves SS, La Via WV, Nelson CB, Rizzo C, Savitz DA, Zullo AR. Medically Attended Illness due to Respiratory Syncytial Virus Infection Among Infants Born in the United States Between 2016 and 2020. J Infect Dis 2022; 226:S164-S174. [PMID: 35968869 PMCID: PMC9377038 DOI: 10.1093/infdis/jiac185] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 05/04/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Respiratory syncytial virus (RSV) is a leading cause of infant hospitalization in the United States. Preterm infants and those with select comorbidities are at highest risk of RSV-related complications. However, morbidity due to RSV infection is not confined to high-risk infants. We estimated the burden of medically attended (MA) RSV-associated lower respiratory tract infection (LRTI) among infants in the United States. METHODS We analyzed commercial (MarketScan Commercial [MSC], Optum Clinformatics [OC]), and Medicaid (MarketScan Medicaid [MSM]) insurance claims data for infants born between April 2016 and February 2020. Using both specific and sensitive definitions of MA RSV LRTI, we estimated the burden of MA RSV LRTI during infants' first RSV season, stratified by gestational age, comorbidity status, and highest level of medical care associated with the MA RSV LRTI diagnosis. RESULTS According to the specific definition 75.0% (MSC), 78.6% (MSM), and 79.6% (OC) of MA RSV LRTI events during infants' first RSV season occurred among term infants without known comorbidities. CONCLUSIONS Term infants without known comorbidities account for up to 80% of the MA RSV LRTI burden in the United States during infants' first RSV season. Future prevention efforts should consider all infants.
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Affiliation(s)
- Jason R Gantenberg
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, Rhode Island, USA
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Robertus van Aalst
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, Rhode Island, USA
- Department of Modeling, Epidemiology, and Data Science, Vaccines Medical Affairs, Sanofi, Lyon, France
- Department of Health Sciences, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | | | - Sandra S Chaves
- Department of Modeling, Epidemiology, and Data Science, Vaccines Medical Affairs, Sanofi, Lyon, France
| | | | | | | | - David A Savitz
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Andrew R Zullo
- Department of Health Services, Policy and Practice, Brown University School of Public Health, Providence, Rhode Island, USA
- Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, USA
- Providence VA Medical Center, Providence, Rhode Island, USA
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Sinyavskaya L, Schnitzer M, Renoux C, Guertin JR, Talbot D, Durand M. Evidence of the Different Associations of Prognostic Factors With Censoring Across Treatment Groups and Impact on Censoring Weight Model Specification: The Example of Anticoagulation in Atrial Fibrillation. Am J Epidemiol 2021; 190:2671-2679. [PMID: 34165152 DOI: 10.1093/aje/kwab186] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 06/17/2021] [Accepted: 06/22/2021] [Indexed: 01/14/2023] Open
Abstract
Inverse probability of censoring weights (IPCWs) may reduce selection bias due to informative censoring in longitudinal studies. However, in studies with an active comparator, the associations between predictors and censoring may differ across treatment groups. We used the clinical example of anticoagulation treatment with warfarin or a direct oral anticoagulant (DOAC) in atrial fibrillation to illustrate this. The cohort of individuals initiating an oral anticoagulant during 2010-2016 was identified from the Régie de l'assurance maladie du Québec (RAMQ) databases. The parameter of interest was the hazard ratio (HR) of the composite of stroke, major bleeding, myocardial infarction, or death associated with continuous use of warfarin versus DOACs. Two strategies for the specification of the model for estimation of censoring weights were explored: exposure-unstratified and exposure-stratified. The HR associated with continuous treatment with warfarin versus DOACs adjusted with exposure-stratified IPCWs was 1.26 (95% confidence interval: 1.20, 1.33). Using exposure-unstratified IPCWs, the HR differed by 15% in favor of DOACs (1.41, 95% confidence interval: 1.34, 1.48). Not accounting for the different associations between the predictors and informative censoring across exposure groups may lead to misspecification of censoring weights and biased estimate on comparative effectiveness and safety.
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Abstract
BACKGROUND Important questions exist regarding the comparative effectiveness of alternative childhood vaccine schedules; however, optimal approaches to studying this complex issue are unclear. METHODS We applied methods for studying dynamic treatment regimens to estimate the comparative effectiveness of different rotavirus vaccine (RV) schedules for preventing acute gastroenteritis-related emergency department (ED) visits or hospitalization. We studied the effectiveness of six separate protocols: one- and two-dose monovalent rotavirus vaccine (RV1); one-, two-, and three-dose pentavalent rotavirus vaccine (RV5); and no RV vaccine. We used data on all infants to estimate the counterfactual cumulative risk for each protocol. Infants were censored when vaccine receipt deviated from the protocol. Inverse probability of censoring-weighted estimation addressed potentially informative censoring by protocol deviations. A nonparametric group-based bootstrap procedure provided statistical inference. RESULTS The method yielded similar 2-year effectiveness estimates for the full-series protocols; weighted risk difference estimates comparing unvaccinated children to those adherent to either full-series (two-dose RV1, three-dose RV5) corresponded to four fewer hospitalizations and 12 fewer ED visits over the 2-year period per 1,000 children. We observed dose-response relationships, such that additional doses further reduced risk of acute gastroenteritis. Under a theoretical intervention to fully vaccinate all children, the 2-year risk differences comparing full to observed adherence were 0.04% (95% CI = 0.03%, 0.05%) for hospitalizations and 0.17% (95% CI = 0.14%, 0.19%) for ED visits. CONCLUSIONS The proposed approach can generate important evidence about the consequences of delaying or skipping vaccine doses, and the impact of interventions to improve vaccine schedule adherence.
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Affiliation(s)
- Anne M. Butler
- Department of Medicine, Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA
- Department of Surgery, Division of Public Health Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | | | - John M. Sahrmann
- Department of Medicine, Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, USA
| | - M. Alan Brookhart
- NoviSci, Durham, NC, USA
- Department of Population Health Sciences, Duke University, Durham, NC, USA
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Koffman L, Levis AW, Arterburn D, Coleman KJ, Herrinton LJ, Cooper J, Ewing J, Fischer H, Fraser JR, Johnson E, Taylor B, Theis MK, Liu L, Courcoulas A, Li R, Fisher DP, Amsden L, Haneuse S. Investigating Bias from Missing Data in an Electronic Health Records-Based Study of Weight Loss After Bariatric Surgery. Obes Surg 2021; 31:2125-2135. [PMID: 33462670 DOI: 10.1007/s11695-021-05226-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/05/2021] [Accepted: 01/06/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE Missing data is common in electronic health records (EHR)-based obesity research. To avoid bias, it is critical to understand mechanisms that underpin missingness. We conducted a survey among bariatric surgery patients in three integrated health systems to (i) investigate predictors of disenrollment and (ii) examine differences in weight between disenrollees and enrollees at 5 years. MATERIALS AND METHODS We identified 2883 patients who had bariatric surgery between 11/2013 and 08/2014. Patients who disenrolled before their 5-year anniversary were invited to participate in a survey to ascertain reasons for disenrollment and current weight. Logistic regression was used to investigate predictors of disenrollment. Five-year percent weight change distributions were estimated using inverse-probability weighting to adjust for (un)availability of EHR weight data at 5 years among enrollees and survey (non-)response among disenrollees. RESULTS Among 536 disenrolled patients, 104 (19%) completed the survey. Among 2347 patients who maintained enrollment, 384 (16%) had no weight measurement in the EHR near 5 years. Insurance, age, Hispanic ethnicity, and site predicted disenrollment. Disenrollees had slightly greater weight loss than enrollees. CONCLUSION We found little evidence of weight loss differences by enrollment status. Collecting information through surveys can be an effective tool to investigate and adjust for missingness in EHR-based studies.
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Affiliation(s)
- Lily Koffman
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, 655 Huntington Ave Building 2, Boston, MA, 02115, USA.
| | - Alexander W Levis
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, 655 Huntington Ave Building 2, Boston, MA, 02115, USA
| | - David Arterburn
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Karen J Coleman
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA
| | | | - Julie Cooper
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - John Ewing
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Heidi Fischer
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA
| | - James R Fraser
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Eric Johnson
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Brianna Taylor
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA
| | - Mary Kay Theis
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Liyan Liu
- Kaiser Permanente Northern California, Oakland, CA, USA
| | | | - Robert Li
- Kaiser Permanente Northern California, Oakland, CA, USA
| | | | - Laura Amsden
- Kaiser Permanente Northern California, Oakland, CA, USA
| | - Sebastien Haneuse
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, 655 Huntington Ave Building 2, Boston, MA, 02115, USA
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Schultze A, Graham S, Nordstrom BL, Mehmud F, Ramagopalan SV. Commonly used definitions in real-world studies may underestimate the prevalence of renal disease among nonvalvular atrial fibrillation patients. J Comp Eff Res 2019; 8:961-968. [PMID: 31317772 DOI: 10.2217/cer-2019-0070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: To describe comorbidities among treated nonvalvular atrial fibrillation (NVAF) patients and assess the impact of using different time ('look back' windows) on the prevalence estimates. Patients & methods: We included all adult nonvalvular atrial fibrillation patients newly initiating treatment in the Clinical Practice Research Datalink. Comorbidities included in the Charlson Comorbidity Index were defined using an all available, 3- and 1-year look back window before the start of treatment. Results: The prevalence of comorbidities was high and increased when using longer look back windows; the largest difference was observed for renal disease (+15.6%). Conclusion: Our findings emphasize the importance of using all available data when characterizing chronic conditions and highlights the high comorbidity burden in this population.
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Affiliation(s)
| | | | | | - Faisal Mehmud
- Centre for Observational Research & Data Sciences, Bristol-Myers Squibb, Uxbridge, UB8 1DH, UK
| | - Sreeram V Ramagopalan
- Centre for Observational Research & Data Sciences, Bristol-Myers Squibb, Uxbridge, UB8 1DH, UK
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