Epidemiologic Reviews最新文献

Randomized trials are often designed to collect outcomes at fixed points in time after randomization. In practice, the number and timing of outcome assessments can vary among participants (i.e., irregular assessment). In fact, the timing of assessments may be associated with the outcome of interest (i.e., informative assessment). For example, in a trial evaluating the effectiveness of treatments for major depressive disorder, not only did the timings of outcome assessments vary among participants but symptom scores were associated with assessment frequency. This type of informative observation requires appropriate statistical analysis. Although analytic methods have been developed, they are rarely used. In this article, we review the literature on irregular assessments with a view toward developing recommendations for analyzing trials with irregular and potentially informative assessment times. We show how the choice of analytic approach hinges on assumptions about the relationship between the assessment and outcome processes. We argue that irregular assessment should be treated with the same care as missing data, and we propose that trialists adopt strategies to minimize the extent of irregularity; describe the extent of irregularity in assessment times; make their assumptions about the relationships between assessment times and outcomes explicit; adopt analytic techniques that are appropriate to their assumptions; and assess the sensitivity of trial results to their assumptions.

In clinical trials, harms (i.e., adverse events) are often reported by simply counting the number of people who experienced each event. Reporting only frequencies ignores other dimensions of the data that are important for stakeholders, including severity, seriousness, rate (recurrence), timing, and groups of related harms. Additionally, application of selection criteria to harms prevents most from being reported. Visualization of data could improve communication of multidimensional data. We replicated and compared the characteristics of 6 different approaches for visualizing harms: dot plot, stacked bar chart, volcano plot, heat map, treemap, and tendril plot. We considered binary events using individual participant data from a randomized trial of gabapentin for neuropathic pain. We assessed their value using a heuristic approach and a group of content experts. We produced all figures using R and share the open-source code on GitHub. Most original visualizations propose presenting individual harms (e.g., dizziness, somnolence) alone or alongside higher level (e.g., by body systems) summaries of harms, although they could be applied at either level. Visualizations can present different dimensions of all harms observed in trials. Except for the tendril plot, all other plots do not require individual participant data. The dot plot and volcano plot are favored as visualization approaches to present an overall summary of harms data. Our value assessment found the dot plot and volcano plot were favored by content experts. Using visualizations to report harms could improve communication. Trialists can use our provided code to easily implement these approaches.

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.

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.

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.

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.

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.

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.