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Registry-based randomized controlled trials are defined as pragmatic trials that use registries as a platform for case records, data collection, randomization, and follow-up. Recently, the application of registry-based randomized controlled trials has attracted increasing attention in health research to address comparative effectiveness research questions in real-world settings, mainly due to their low cost, enhanced generalizability of findings, rapid consecutive enrollment, and the potential completeness of follow-up for the reference population, when compared with conventional randomized effectiveness trials. However several challenges of registry-based randomized controlled trials have to be taken into consideration, including registry data quality, ethical issues, and methodological challenges. In this article, we summarize the advantages, challenges, and areas for future research related to registry-based randomized controlled trials.

Many analyses of observational data are attempts to emulate a target trial. The emulation of the target trial may fail when researchers deviate from simple principles that guide the design and analysis of randomized experiments. We review a framework to describe and prevent biases, including immortal time bias, that result from a failure to align start of follow-up, specification of eligibility, and treatment assignment. We review some analytic approaches to avoid these problems in comparative effectiveness or safety research.

The growing amount of data in operational electronic health record systems provides unprecedented opportunity for its reuse for many tasks, including comparative effectiveness research. However, there are many caveats to the use of such data. Electronic health record data from clinical settings may be inaccurate, incomplete, transformed in ways that undermine their meaning, unrecoverable for research, of unknown provenance, of insufficient granularity, and incompatible with research protocols. However, the quantity and real-world nature of these data provide impetus for their use, and we develop a list of caveats to inform would-be users of such data as well as provide an informatics roadmap that aims to insure this opportunity to augment comparative effectiveness research can be best leveraged.

During the National Neurotrauma Symposium 2010, the DG Research of the European Commission and the National Institutes of Health/National Institute of Neurological Disorders and Stroke (NIH/NINDS) organized a workshop on comparative effectiveness research (CER) in traumatic brain injury (TBI). This workshop reviewed existing approaches to improve outcomes of TBI patients. It had two main outcomes: First, it initiated a process of re-orientation of clinical research in TBI. Second, it provided ideas for a potential collaboration between the European Commission and the NIH/NINDS to stimulate research in TBI. Advances in provision of care for TBI patients have resulted from observational studies, guideline development, and meta-analyses of individual patient data. In contrast, randomized controlled trials have not led to any identifiable major advances. Rigorous protocols and tightly selected populations constrain generalizability. The workshop addressed additional research approaches, summarized the greatest unmet needs, and highlighted priorities for future research. The collection of high-quality clinical databases, associated with systems biology and CER, offers substantial opportunities. Systems biology aims to identify multiple factors contributing to a disease and addresses complex interactions. Effectiveness research aims to measure benefits and risks of systems of care and interventions in ordinary settings and broader populations. These approaches have great potential for TBI research. Although not new, they still need to be introduced to and accepted by TBI researchers as instruments for clinical research. As with therapeutic targets in individual patient management, so it is with research tools: one size does not fit all.

Purpose An essential aspect of patient-centered outcomes research (PCOR) and comparative effectiveness research (CER) is the integration of patient perspectives and experiences with clinical data to evaluate interventions. Thus, PCOR and CER require capturing patient-reported outcome (PRO) data appropriately to inform research, healthcare delivery, and policy. This initiative’s goal was to identify minimum standards for the design and selection of a PRO measure for use in PCOR and CER. Methods We performed a literature review to find existing guidelines for the selection of PRO measures. We also conducted an online survey of the International Society for Quality of Life Research (ISOQOL) membership to solicit input on PRO standards. A standard was designated as “recommended” when >50 % respondents endorsed it as “required as a minimum standard.” Results The literature review identified 387 articles. Survey response rate was 120 of 506 ISOQOL members. The respondents had an average of 15 years experience in PRO research, and 89 % felt competent or very competent providing feedback. Final recommendations for PRO measure standards included: documentation of the conceptual and measurement model; evidence for reliability, validity (content validity, construct validity, responsiveness); interpretability of scores; quality translation, and acceptable patient and investigator burden. Conclusion The development of these minimum measurement standards is intended to promote the appropriate use of PRO measures to inform PCOR and CER, which in turn can improve the effectiveness and efficiency of healthcare delivery. A next step is to expand these minimum standards to identify best practices for selecting decision-relevant PRO measures.

This article presents the current state of patient-reported outcome measures and explains new opportunities for leveraging the recent adoption of electronic health records to expand the application of patient-reported outcomes in both clinical care and comparative effectiveness research. Historic developments of patient-reported outcome, electronic health record, and comparative effectiveness research are analyzed in two dimensions: patient centeredness and digitization. We pose the question, “What needs to be standardized around the collection of patient-reported outcomes in electronic health records for comparative effectiveness research?” We identified three converging trends: the progression of patient-reported outcomes toward greater patient centeredness and electronic adaptation; the evolution of electronic health records into personalized and fully digitized solutions; and the shift toward patient-oriented comparative effectiveness research. Related to this convergence, we propose an architecture for patient-reported outcome standardization that could serve as a first step toward a more comprehensive integration of patient-reported outcomes with electronic health record for both practice and research. The science of patient-reported outcome measurement has matured sufficiently to be integrated routinely into electronic health records and other electronic health solutions to collect data on an ongoing basis for clinical care and comparative effectiveness research. Further efforts and ideally coordinated efforts from various stakeholders are needed to refine the details of the proposed framework for standardization.

The Core Outcome Measures in Effectiveness Trials (COMET) database is a publically available, searchable repository of published and ongoing core outcome set (COS) studies. An annual systematic review update is carried out to maintain the currency of database content. The methods used in the fourth update of the systematic review followed the same approach used in the original review and previous updates. Studies were eligible for inclusion if they reported the development of a COS, regardless of any restrictions by age, health condition or setting. Searches were carried out in March 2018 to identify studies that had been published or indexed between January 2017 and the end of December 2017. Forty-eight new studies, describing the development of 56 COS, were included. There has been an increase in the number of studies clearly specifying the scope of the COS in terms of the population (n = 43, 90%) and intervention (n = 48, 100%) characteristics. Public participation has continued to rise with over half (n = 27, 56%) of studies in the current review including input from members of the public. The rate of inclusion of all stakeholder groups has increased, in particular participation from non-clinical research experts has risen from 32% (mean average in previous reviews) to 62% (n = 29). Input from participants located in Australasia (n = 17; 41%), Asia (n = 18; 44%), South America (n = 13; 32%) and Africa (n = 7; 17%) have all increased since the previous reviews. This update included a pronounced increase in the number of new COS identified compared to the previous three updates. There was an improvement in the reporting of the scope, stakeholder participants and methods used. Furthermore, there has been an increase in participation from Australasia, Asia, South America and Africa. These advancements are reflective of the efforts made in recent years to raise awareness about the need for COS development and uptake, as well as developments in COS methodology.

We present a novel Bayesian adaptive comparative effectiveness trial comparing three treatments for status epilepticus that uses adaptive randomization with potential early stopping. The trial will enroll 720 unique patients in emergency departments and uses a Bayesian adaptive design. The trial design is compared to a trial without adaptive randomization and produces an efficient trial in which a higher proportion of patients are likely to be randomized to the most effective treatment arm while generally using fewer total patients and offers higher power than an analogous trial with fixed randomization when identifying a superior treatment. When one treatment is superior to the other two, the trial design provides better patient care, higher power, and a lower expected sample size.

Observational analyses for causal inference often rely on real world data collected for purposes other than research. A frequent goal of these observational analyses is to use the data to emulate a hypothetical randomized experiment, i.e., the target trial, that mimics the design features of a true experiment, including a clear definition of time zero with synchronization of treatment assignment and determination of eligibility. We review a recent observational analysis that explicitly emulated a target trial of screening colonoscopy using insurance claims from U.S. Medicare. We then compare this explicit emulation with alternative, simpler observational analyses that do not synchronize treatment assignment and eligibility determination at time zero and/or do not allow for repeated eligibility. This empirical comparison suggests that lack of an explicit emulation of the target trial leads to biased estimates, and shows that allowing for repeated eligibility increases the statistical efficiency of the estimates.

Background Bayesian adaptive designs can be more efficient than traditional methods for multi-arm randomised controlled trials. The aim of this work was to demonstrate how Bayesian adaptive designs can be constructed for multi-arm phase III clinical trials and assess potential benefits that these designs offer. Methods We constructed several alternative Bayesian adaptive designs for the Collaborative Ankle Support Trial (CAST), which was a randomised controlled trial that compared four treatments for severe ankle sprain. These designs incorporated response adaptive randomisation (RAR), arm dropping, and early stopping for efficacy or futility. We studied the operating characteristics of the Bayesian designs via simulation. We then virtually re-executed the trial by implementing the Bayesian adaptive designs using patient data sampled from the CAST study to demonstrate the practical applicability of the designs. Results We constructed five Bayesian adaptive designs, each of which had high power and recruited fewer patients on average than the original designs target sample size. The virtual executions showed that most of the Bayesian designs would have led to trials that declared superiority of one of the interventions over the control. Bayesian adaptive designs with RAR or arm dropping were more likely to allocate patients to better performing arms at each interim analysis. Similar estimates and conclusions were obtained from the Bayesian adaptive designs as from the original trial. Conclusions Using CAST as an example, this case study shows how Bayesian adaptive designs can be constructed for phase III multi-arm trials using clinically relevant decision criteria. These designs demonstrated that they can potentially generate earlier results and allocate more patients to better performing arms. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials. Trial registration CAST study registration ISRCTN, ISRCTN37807450 . Retrospectively registered on 25 April 2003.