Deriving Real-World Insights From Real-World Data: Biostatistics to the Rescue

Design, implementation, and inferential issues associated with clinical trials that rely on data in electronic medical records: a narrative review

Real world evidence is now accepted by authorities charged with assessing the benefits and harms of new therapies. Clinical trials based on real world evidence are much less expensive than randomized clinical trials that do not rely on "real world evidence" such as contained in electronic health records (EHR). Consequently, we can expect an increase in the number of reports of these types of trials, which we identify here as 'EHR-sourced trials.' 'In this selected literature review, we discuss the various designs and the ethical issues they raise. EHR-sourced trials have the potential to improve/increase common data elements and other aspects of the EHR and related systems. Caution is advised, however, in drawing causal inferences about the relationships among EHR variables. Nevertheless, we anticipate that EHR-CTs will play a central role in answering research and regulatory questions.

Harnessing Real-World Evidence to Advance Cancer Research

Randomized controlled trials (RCTs) form a cornerstone of oncology research by generating evidence about the efficacy of therapies in selected patient populations. However, their implementation is often resource- and cost-intensive, and their generalisability to patients treated in routine practice may be limited. Real-world evidence leverages data collected about patients receiving clinical care in routine practice outside of clinical trial settings and provides opportunities to identify and address gaps in clinical trial evidence. This review outlines the strengths and limitations of real-world and RCT evidence and proposes a framework for the complementary use of the two bodies of evidence to advance cancer research. There are challenges to the implementation of real-world research in oncology, including heterogeneity of data sources, timely access to high-quality data, and concerns about the quality of methods leveraging real-world data, particularly causal inference. Improved understanding of the strengths and limitations of real-world data and ongoing efforts to optimise the conduct of real-world evidence research will improve its reliability, understanding and acceptance, and enable the full potential of real-world evidence to be realised in oncology practice.

Clinical Trials in the 21st Century - Promising Avenues for Better Studies

Innovation in Clinical Trials in the 21st CenturyMedical evidence is rooted in randomized controlled trials but there is a pressing need for innovative designs. Pencina and Thompson introduce a new series that reviews the most promising innovations in trial design and interpretation.

Integrative Analysis of Randomized Clinical Trial and Observational Study Data to Inform Post-marketing Safety Decision-Making

Safety evaluation is a continual and iterative process throughout the drug development life cycle and requires long time horizons and large amounts of data to fully understand the safety profile of a medical product. Although randomized clinical trials (RCT) provide high-quality data for an initial assessment of safety signals, the safety signals may not all have been known at the time of approval because safety data collected from RCT only involve a relatively small number of subjects during a relatively short follow-up period. The increased accumulation of post-marketing real-world data (RWD) presents an opportunity to utilize them for safety decision-making; these include identifying new safety signals, further characterization of safety concerns that are raised in pre-marketing RCT, and further generalization of RCT findings to the broader patient populations not previously studied in RCT. In this paper, we use cardiovascular safety outcome trial for antidiabetic therapies as an illustrative example and discuss how integrative analysis of RCT and observational study data can answer regulatory concerns about cardiovascular risk in a post-marketing setting. A novel statistical analysis strategy is proposed to combine both sources of safety data in a data fusion approach. The proposed approach includes three stages: (1) feasibility analysis that uses an RCT to validate an observational study, applying estimand framework and emulating RCT with RWD; (2) integrative analysis that combines evidence from the RCT and observational study data cooperatively; and (3) sensitivity analysis that examines the consistency of the previous analyses. Two potential utilities of the proposed integrative analysis for the cardiovascular safety outcome trial are discussed.

Estimation of causal effect in integrating randomized clinical trial and observational data - An example application to cardiovascular outcome trial

Safety evaluation of drug development is a comprehensive process across the product lifecycle. While a randomized clinical trial (RCT) can provide high-quality data to assess the efficacy and safety of a new intervention, the pre-marketing trials are limited in statistical power to detect causal elevation of rare but potentially serious adverse events. On the other hand, real-world data (RWD) sources play a critical role in further understanding the safety profile of the new intervention. Bringing together the breadth and strength of RWD and RCT data, we can maximize the utility of RWD and answer broader questions. In this manuscript, we propose a three-step statistical framework to corroborate findings from both RCT and RWD for evaluating important safety concerns identified in the pre-marketing setting. By the proposed approach, we first match the observational study to RCT, then the causal estimation is validated via the matched observational study with the target RCT by targeted maximum likelihood estimation (TMLE) method, and lastly the evidence from RCT and RWD can be combined in an integrative analysis. A potential application to cardiovascular outcome trials for type 2 diabetes mellitus is illustrated. Finally, simulation results suggest that the heterogeneity of patient population from RCT and RWD can lead to varying degrees of treatment effect estimation and the proposed approach may be able to mitigate such difference in the integrative analysis.

The ACTTION Guide to Clinical Trials of Pain Treatments, part II: mitigating bias, maximizing value

Summaries of the articles included in part II of the ACTTION Guide to Clinical Trials of Pain Treatments are followed by brief overviews of methodologic considerations involving precision pain medicine, pragmatic clinical trials, real world evidence, and patient engagement in clinical trials.

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

Real-world evidence (RWE) is the clinical evidence about benefits or risks of medical products derived from analyzing real world data (RWD), which are data collected through routine clinical practice. This article discusses the advantages and disadvantages of RWE studies, how these studies differ from randomized controlled trials (RCTs), how to overcome barriers to current skepticism about RWE, how FDA is using RWE, how to improve the quality of RWE, and finally the future of RWE trials.

Research-grade data in the real world: challenges and opportunities in data quality from a pragmatic trial in community-based practices

Randomized controlled trials face cost, logistic, and generalizability limitations, including difficulty engaging racial/ethnic minorities. Real-world data (RWD) from pragmatic trials, including electronic health record (EHR) data, may produce intervention evaluation findings generalizable to diverse populations. This case study of Project IMPACT describes unique barriers and facilitators of optimizing RWD to improve health outcomes and advance health equity in small immigrant-serving community-based practices. Project IMPACT tested the effect of an EHR-based health information technology intervention on hypertension control among small urban practices serving South Asian patients. Challenges in acquiring accurate RWD included EHR field availability and registry capabilities, cross-sector communication, and financial, personnel, and space resources. Although using RWD from community-based practices can inform health equity initiatives, it requires multidisciplinary collaborations, clinic support, procedures for data input (including social determinants), and standardized field logic/rules across EHR platforms.