Epidemiologic Reviews最新文献

{"title":"A Qualitative Scoping Review of Early-Terminated Clinical Trials Sponsored by the Department of Veterans Affairs Cooperative Studies Program From 2010 to 2020.","authors":"Alexa Goldberg, Ludmila N Bakhireva, Kimberly Page, Adam M Henrie","doi":"10.1093/epirev/mxac009","DOIUrl":"10.1093/epirev/mxac009","url":null,"abstract":"<p><p>Increasing attention has been paid to the risks and benefits of terminating large clinical trials before reaching prespecified targets, because such decisions can greatly affect the implementation of findings. The Department of Veterans Affairs (VA) Cooperative Studies Program (CSP) is a research infrastructure dedicated to conducting high-quality clinical research. A scoping review was performed to characterize barriers preventing the attainment of prespecified recruitment, statistical power, or sample-size targets in VA CSP trials. A trial was eligible for inclusion if the trial was sponsored by the VA CSP, primary findings were published within the last 10 years, and a decision was made to terminate enrollment or follow-up before meeting a priori recruitment or endpoint targets. In 11 of 29 included trials (37.9%), a decision was made to terminate the trial early. The most common reason for early termination was related to under-recruitment (n = 5). Other reasons included early detection of safety signals (n = 2), futility (n = 1), and benefit (n = 1). This review highlights recruitment as a critical facet of trial conduct that may hinder the production of high-quality data and thus warrant additional attention. Solutions to enhance recruitment now implemented by the VA CSP, including dedicated enrollment infrastructure and screening facilitated by informatics approaches, show promise in reducing this cause for early termination.</p>","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"44 1","pages":"110-120"},"PeriodicalIF":5.2,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362930/pdf/mxac009.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10211471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of the Ring Trial Design for Evaluating Ring Interventions for Infectious Diseases.","authors":"Zachary Butzin-Dozier,&nbsp;Tejas S Athni,&nbsp;Jade Benjamin-Chung","doi":"10.1093/epirev/mxac003","DOIUrl":"https://doi.org/10.1093/epirev/mxac003","url":null,"abstract":"<p><p>In trials of infectious disease interventions, rare outcomes and unpredictable spatiotemporal variation can introduce bias, reduce statistical power, and prevent conclusive inferences. Spillover effects can complicate inference if individual randomization is used to gain efficiency. Ring trials are a type of cluster-randomized trial that may increase efficiency and minimize bias, particularly in emergency and elimination settings with strong clustering of infection. They can be used to evaluate ring interventions, which are delivered to individuals in proximity to or contact with index cases. We conducted a systematic review of ring trials, compare them with other trial designs for evaluating ring interventions, and describe strengths and weaknesses of each design. Of 849 articles and 322 protocols screened, we identified 26 ring trials, 15 cluster-randomized trials, 5 trials that randomized households or individuals within rings, and 1 individually randomized trial. The most common interventions were postexposure prophylaxis (n = 23) and focal mass drug administration and screening and treatment (n = 7). Ring trials require robust surveillance systems and contact tracing for directly transmitted diseases. For rare diseases with strong spatiotemporal clustering, they may have higher efficiency and internal validity than cluster-randomized designs, in part because they ensure that no clusters are excluded from analysis due to zero cluster incidence. Though more research is needed to compare them with other types of trials, ring trials hold promise as a design that can increase trial speed and efficiency while reducing bias.</p>","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"44 1","pages":"29-54"},"PeriodicalIF":5.5,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362935/pdf/mxac003.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9856188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing Health Inequities in Digital Clinical Trials: A Review of Challenges and Solutions From the Field of HIV Research.","authors":"Andrea L Wirtz,&nbsp;Carmen H Logie,&nbsp;Lawrence Mbuagbaw","doi":"10.1093/epirev/mxac008","DOIUrl":"https://doi.org/10.1093/epirev/mxac008","url":null,"abstract":"<p><p>Clinical trials are considered the gold standard for establishing efficacy of health interventions, thus determining which interventions are brought to scale in health care and public health programs. Digital clinical trials, broadly defined as trials that have partial to full integration of technology across implementation, interventions, and/or data collection, are valued for increased efficiencies as well as testing of digitally delivered interventions. Although recent reviews have described the advantages and disadvantages of and provided recommendations for improving scientific rigor in the conduct of digital clinical trials, few to none have investigated how digital clinical trials address the digital divide, whether they are equitably accessible, and if trial outcomes are potentially beneficial only to those with optimal and consistent access to technology. Human immunodeficiency virus (HIV), among other health conditions, disproportionately affects socially and economically marginalized populations, raising questions of whether interventions found to be efficacious in digital clinical trials and subsequently brought to scale will sufficiently and consistently reach and provide benefit to these populations. We reviewed examples from HIV research from across geographic settings to describe how digital clinical trials can either reproduce or mitigate health inequities via the design and implementation of the digital clinical trials and, ultimately, the programs that result. We discuss how digital clinical trials can be intentionally designed to prevent inequities, monitor ongoing access and utilization, and assess for differential impacts among subgroups with diverse technology access and use. These findings can be generalized to many other health fields and are practical considerations for donors, investigators, reviewers, and ethics committees engaged in digital clinical trials.</p>","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"44 1","pages":"87-109"},"PeriodicalIF":5.5,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362940/pdf/mxac008.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10632611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Econometric Issues in Prospective Economic Evaluations Alongside Clinical Trials: Combining the Nonparametric Bootstrap With Methods That Address Missing Data.","authors":"Ali Jalali,&nbsp;Rulla M Tamimi,&nbsp;Sterling M McPherson,&nbsp;Sean M Murphy","doi":"10.1093/epirev/mxac006","DOIUrl":"https://doi.org/10.1093/epirev/mxac006","url":null,"abstract":"<p><p>Prospective economic evaluations conducted alongside clinical trials have become an increasingly popular approach in evaluating the cost-effectiveness of a public health initiative or treatment intervention. These types of economic studies provide improved internal validity and accuracy of cost and effectiveness estimates of health interventions and, compared with simulation or decision-analytic models, have the advantage of jointly observing health and economics outcomes of trial participants. However, missing data due to incomplete response or patient attrition, and sampling uncertainty are common concerns in econometric analysis of clinical trials. Missing data are a particular problem for comparative effectiveness trials of substance use disorder interventions. Multiple imputation and inverse probability weighting are 2 widely recommended methods to address missing data bias, and the nonparametric bootstrap is recommended to address uncertainty in predicted mean cost and effectiveness between trial interventions. Although these methods have been studied extensively by themselves, little is known about how to appropriately combine them and about the potential pitfalls and advantages of different approaches. We provide a review of statistical methods used in 29 economic evaluations of substance use disorder intervention identified from 4 published systematic reviews and a targeted search of the literature. We evaluate how each study addressed missing data bias, whether the recommended nonparametric bootstrap was used, how these 2 methods were combined, and conclude with recommendations for future research.</p>","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"44 1","pages":"67-77"},"PeriodicalIF":5.5,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362933/pdf/mxac006.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10600730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inclusionary Trials: A Review of Lessons Not Learned.","authors":"Paris B Adkins-Jackson,&nbsp;Nancy J Burke,&nbsp;Patricia Rodriguez Espinosa,&nbsp;Juliana M Ison,&nbsp;Susan D Goold,&nbsp;Lisa G Rosas,&nbsp;Chyke A Doubeni,&nbsp;The Stop Covid-California Alliance Trial Participation And Vaccine Hesitancy Working Groups,&nbsp;Arleen F Brown","doi":"10.1093/epirev/mxac007","DOIUrl":"https://doi.org/10.1093/epirev/mxac007","url":null,"abstract":"<p><p>The COVID-19 pandemic revealed weaknesses in the public health infrastructure of the United States, including persistent barriers to engaging marginalized communities toward inclusion in clinical research, including trials. Inclusive participation in clinical trials is crucial for promoting vaccine confidence, public trust, and addressing disparate health outcomes. A long-standing body of literature describes the value of community-based participatory research in increasing marginalized community participation in research. Community-based participatory research emphasizes shared leadership with community members in all phases of the research process, including in the planning and implementation, interpretation, and dissemination. Shared leadership between academic and industry with marginalized communities can assist with inclusive participation in vaccine trials and increase public trust in the development of the vaccines and other therapies used during public emergencies. Nevertheless, epidemiologic and clinical research do not yet have a strong culture of community partnership in the scientific process, which takes time to build and therefore may be difficult to develop and rapidly scale to respond to the pandemic. We outline practices that contribute to a lack of inclusive participation and suggest steps that trialists and other researchers can take to increase marginalized communities' participation in research. Practices include planning for community engagement during the planning and recruitment phases, having regular dialogues with communities about their priorities, supporting them throughout a study, and navigating complex structural determinants of health. Additionally, we discuss how research institutions can support inclusive practices by reexamining their policies to increase participation in clinical trials and instilling institutional trustworthiness.</p>","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"44 1","pages":"78-86"},"PeriodicalIF":5.5,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/7c/75/mxac007.PMC9494445.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9334315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CORRECTION TO \"THE REVOLUTION WILL BE HARD TO EVALUATE: HOW CO-OCCURRING POLICY CHANGES AFFECT RESEARCH ON THE HEALTH EFFECTS OF SOCIAL POLICIES\".","authors":"","doi":"10.1093/epirev/mxac004","DOIUrl":"https://doi.org/10.1093/epirev/mxac004","url":null,"abstract":"","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"44 1","pages":"138"},"PeriodicalIF":5.5,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/4e/fd/mxac004.PMC9780117.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10751603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Note from the Editor.","authors":"David D Celentano","doi":"10.1093/epirev/mxac012","DOIUrl":"https://doi.org/10.1093/epirev/mxac012","url":null,"abstract":"","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"44 1","pages":"1"},"PeriodicalIF":5.5,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10435922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Conundrum of Clinical Trials for the Uveitides: Appropriate Outcome Measures for One Treatment Used in Several Diseases.","authors":"Douglas A Jabs,&nbsp;Meghan K Berkenstock,&nbsp;Michael M Altawee,&nbsp;Janet T Holbrook,&nbsp;Elizabeth A Sugar","doi":"10.1093/epirev/mxac001","DOIUrl":"https://doi.org/10.1093/epirev/mxac001","url":null,"abstract":"<p><p>The uveitides consist of >30 diseases characterized by intraocular inflammation. Noninfectious intermediate, posterior, and panuveitides typically are treated with oral corticosteroids and immunosuppression, with a similar treatment approach for most diseases. Because these uveitides collectively are considered a rare disease, single-disease trials are difficult to impractical to recruit for, and most trials have included several different diseases for a given protocol treatment. However, measures of uveitis activity are disease specific, resulting in challenges for trial outcome measures. Several trials of investigational immunosuppressive drugs or biologic drugs have not demonstrated efficacy, but design problems with the outcome measures have limited the ability to interpret the results. Successful trials have included diseases for which a single uveitis activity measure suffices or a composite measure of uveitis activity is used. One potential solution to this problem is the use of a single, clinically relevant outcome, successful corticosteroid sparing, defined as inactive uveitis with a prednisone dose ≤7.5 mg/day coupled with disease-specific guidelines for determining inactive disease. The clinical relevance of this outcome is that active uveitis is associated with increased risks of visual impairment and blindness, and that prednisone doses ≤7.5 mg/day have a minimal risk of corticosteroid side effects. The consequence of this approach is that trial visits require a core set of measures for all participants and a disease-specific set of measures, both clinical and imaging, to assess uveitis activity. This approach is being used in the Adalimumab Versus Conventional Immunosuppression (ADVISE) Trial.</p>","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"44 1","pages":"2-16"},"PeriodicalIF":5.5,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362938/pdf/mxac001.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9907472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Measurement Error Elephant in the Room: Challenges and Solutions to Measurement Error in Epidemiology.","authors":"Gabriel K Innes, Fiona Bhondoekhan, Bryan Lau, Alden L Gross, Derek K Ng, Alison G Abraham","doi":"10.1093/epirev/mxab011","DOIUrl":"10.1093/epirev/mxab011","url":null,"abstract":"<p><p>Measurement error, although ubiquitous, is uncommonly acknowledged and rarely assessed or corrected in epidemiologic studies. This review offers a straightforward guide to common problems caused by measurement error in research studies and a review of several accessible bias-correction methods for epidemiologists and data analysts. Although most correction methods require criterion validation including a gold standard, there are also ways to evaluate the impact of measurement error and potentially correct for it without such data. Technical difficulty ranges from simple algebra to more complex algorithms that require expertise, fine tuning, and computational power. However, at all skill levels, software packages and methods are available and can be used to understand the threat to inferences that arises from imperfect measurements.</p>","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"43 1","pages":"94-105"},"PeriodicalIF":5.5,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005058/pdf/mxab011.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9612688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Review of Simulation Models to Track and Address the Opioid Crisis.","authors":"Magdalena Cerdá, Mohammad S Jalali, Ava D Hamilton, Catherine DiGennaro, Ayaz Hyder, Julian Santaella-Tenorio, Navdep Kaur, Christina Wang, Katherine M Keyes","doi":"10.1093/epirev/mxab013","DOIUrl":"10.1093/epirev/mxab013","url":null,"abstract":"<p><p>The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models are a tool to help us understand and address thiscomplex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings; created a database of model parameters used for model calibration; and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and agent-based models (16%). Intervention cost-effectiveness was evaluated in 40% of the studies, and 39% focused on services for people with opioid use disorder (OUD). In 61% of the eligible articles, authors discussed calibrating their models to empirical data, and in 31%, validation approaches used in the modeling process were discussed. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation or relapse, emergency medical services, and death parameters. From this database, potential model inputs can be identified and models can be compared with prior work. Simulation models should be used to tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility.</p>","PeriodicalId":50510,"journal":{"name":"Epidemiologic Reviews","volume":"43 1","pages":"147-165"},"PeriodicalIF":5.2,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005056/pdf/mxab013.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9740227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}