The Expanding Role of Real-World Evidence Trials in Health Care Decision Making

Klonoff, David C.

doi:10.1177/1932296819832653

Cited by 54 publications

(36 citation statements)

References 43 publications

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“…The Coordinating Center seeks to support the sustainable generation and use of timely, reliable, and cost-effective real-world evidence (RWE) throughout the medical device lifecycle. This can be achieved by using real-world data 12 that meets robust methodological standards; 2) is generated in the course of clinical care and everyday life by patients, providers, or payers; and 3) is intended for enhancing regulatory and clinical decision making. 13 On November 20, 2018, FDA commissioner Scott Gottlieb, MD, and Jeff Shuren, MD, director of the Center for Devices and Radiological Health, together announced updates to the FDA Medical Device Safety Action Plan to enhance postmarket safety.…”

Section: Current Fda Position On Postmarket Testing Of Devicesmentioning

confidence: 99%

Postmarket Surveillance of Blood Glucose Monitor Systems Is Needed for Safety of Subjects and Accurate Determination of Effectiveness in Clinical Trials of Diabetes Drugs and Devices

Klonoff

2019

J Diabetes Sci Technol

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Section: Current Fda Position On Postmarket Testing Of Devicesmentioning

confidence: 99%

Postmarket Surveillance of Blood Glucose Monitor Systems Is Needed for Safety of Subjects and Accurate Determination of Effectiveness in Clinical Trials of Diabetes Drugs and Devices

Klonoff

2019

J Diabetes Sci Technol

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“…While randomized controlled trials (RCT) have long been seen as the "gold standard" for evaluating the efficacy of interventions, there are well-known limitations to their generalizability [1]. Accordingly, there have been growing interests in real-world studies (RWS) to generate real-world evidence (RWE) that are more realistic and generalizable [2][3][4][5][6][7][8][9], and RWE is increasingly valued by regulators and payers [10]. In addition, RWE and the RCT can happily co-exist and complement each other [9].…”

Section: Introductionmentioning

confidence: 99%

Generalizing findings from a randomized controlled trial to a real-world study of the iLookOut, an online education program to improve early childhood care and education providers’ knowledge and attitudes about reporting child maltreatment

et al. 2020

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In recent years, real-world studies (RWS) are gaining increasing interests, because they can generate more realistic and generalizable results than randomized controlled clinical trials (RCT). In 2017, we published a RCT in 741 early childhood care and education providers (CCPs). It is the Phase I of our iLookOut for Child Abuse project (iLookOut), an online, interactive learning module about reporting suspected child maltreatment. That study demonstrated that in a RCT setting, the iLookOut is efficient at improving CCPs' knowledge of and attitudes towards child maltreatment reporting. However, the generalizability of that RCT's results in a RWS setting remains unknown. To address this question, we design and conduct this large RWS in 11,065 CCPs, which is the Phase II of the iLookOut. We hypothesize replication of the earlier RCT findings, i.e., the iLookOut can improve CCPs' knowledge of and attitudes toward child maltreatment reporting in a real world setting. In addition, this RWS also explores whether demographic factors affect CCPs' performance. Results of this RWS confirmed the generalizability of the previous RCT's results in a real world setting. It yielded similar effect sizes for knowledge and attitudes as were found in the earlier RCT. Cohen's d for knowledge improvement was 0.95 in that RCT, 0.96 in this RWS; Cohen's d for attitude improvement was 0.98 in that RCT, 0.80 in this RWS. Also, we found several

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“…Over time, the limited utility of this approach has led to a resurgence of interest in the analysis of non-experimental (observational) data and the synthesis of ‘real world evidence’ (e.g. Klonoff 2020; though see also: Losilla et al 2018). Indeed, there is broad consensus that such analyses will remain far more common in many applied disciplines, not least in the era of ‘medical tech’ and ‘Big Data’ (Eustace 2018); and in contexts where intervention complexity, ethics, governance, safety, participation and cost make experimentation impossible or simply undesirable and undesired (Meyer et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Introducing causal inference to the medical curriculum using temporal logic to draw directed acyclic graphs

Ellison

2020

Preprint

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Directed acyclic graphs (DAGs) might yet transform the statistical modelling of observational data for causal inference. This is because they offer a principled approach to analytical design that draws on existing contextual, empirical and theoretical knowledge, but ultimately relies on temporality alone to objectively specify probabilistic causal relationships amongst measured (and unmeasured) covariates, and the associated exposure and outcome variables. While a working knowledge of phenomenology, critical realism and epistemology seem likely to be useful for mastering the application of DAGs, drawing a DAG appears to require limited technical expertise and might therefore be accessible to even inexperienced and novice analysts. The present study evaluated the inclusion of a novel four-task directed learning exercise for medical undergraduates, which culminated in temporality-driven covariate classification, followed by DAG specification itself. The exercise achieved high levels of student engagement, although the proportion of students completing each of the exercise's four key tasks declined from close to 100% in tasks 1 and 2 (exposure and outcome specification; and covariate selection) to 83.5% and 77.6% in the third and fourth tasks, respectively. Fewer than 15% of the students successfully classified all of their covariates (as confounders, mediators or competing exposures) using temporality-driven classification, but this improved to more than 35% following DAG specification - an unexpected result given that all of the DAGs displayed at least one substantive technical error. These findings suggest that drawing a DAG, in and of itself, increases the utility of temporality-driven covariate classification for causal inference analysis; although further research is required to better understand: why even poorly specified DAGs might reduce covariate misclassification; how 'wrong but useful' DAGs might be identified; and how these marginal benefits might be enhanced with or without improvements in DAG specification.

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The Expanding Role of Real-World Evidence Trials in Health Care Decision Making

Cited by 54 publications

References 43 publications

Postmarket Surveillance of Blood Glucose Monitor Systems Is Needed for Safety of Subjects and Accurate Determination of Effectiveness in Clinical Trials of Diabetes Drugs and Devices

Postmarket Surveillance of Blood Glucose Monitor Systems Is Needed for Safety of Subjects and Accurate Determination of Effectiveness in Clinical Trials of Diabetes Drugs and Devices

Generalizing findings from a randomized controlled trial to a real-world study of the iLookOut, an online education program to improve early childhood care and education providers’ knowledge and attitudes about reporting child maltreatment

Introducing causal inference to the medical curriculum using temporal logic to draw directed acyclic graphs

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