Incentives and payments in pragmatic clinical trials: Scientific, ethical, and policy considerations

Pragmatic clinical trials are increasingly used to generate knowledge about real-world clinical interventions. However, they involve some distinctive ethical and regulatory challenges. In this article, we examine a set of issues related to incentives and other payments to patients in pragmatic clinical trials. Although many of the ethical concerns related to incentives and payments in explanatory trials pertain to pragmatic clinical trials, the pragmatic features may introduce additional challenges. These include those related to the risk of incentives and payments undermining the scientific validity and social value of pragmatic clinical trials, the sources of data used in pragmatic clinical trials, and when the pragmatic clinical trials are conducted under waivers of consent. Based on our examination of these matters, we offer some preliminary recommendations regarding incentives and payments in pragmatic clinical trials, recognizing that additional data and experiences are needed to refine them.

Thoughtful selection and use of scientific terms in clinical research: the case of ‘pragmatic’ trials

Clinical research is a discipline prone to the use of technical terms that may be particularly at risk for misunderstanding given the complex interpretation that is required. In this century, what is happening with the word ‘pragmatic’ when describing a randomized controlled trial (RCT) with medicines deserves a public reflection. Explanatory trials are conducted in ideal conditions to assess the comparative efficacy of interventions and are useful to explain whether interventions work. Pragmatic trials are those conducted in a way that resembles usual clinical practice conditions to assess the comparative effectiveness of interventions in a manner directly applicable for decision-makers. This, however, did not prevent 36% of authors of placebo-controlled, or prelicensing trials to identify their medicines RCTs as pragmatic in the title of their articles. The current situation is such that scientific literature has accepted that ‘pragmatic’ can convey the original meaning—that obtained in trials mimicking usual clinical practice—and a distorted one—that is focused on streamlining any trial procedure. Those involved in clinical trials should emphasize the importance of precision in the use of terms when describing RCTs through standardized solutions when possible. Unless clinical trial stakeholders agree when it would be correct to label an RCT as pragmatic, in a short period of time the term will be in danger of becoming meaningless. It is suggested that the Enhancing the Quality and Transparency of Health Research (EQUATOR) network, the Consolidated Standards of Reporting Trials (CONSORT) group and the International Committee of Medical Journal Editors (ICMJE) could address this topic and provide a consensus way forward.

Effect of pragmatic versus explanatory interventions on medication adherence in people with cardiometabolic conditions: a systematic review and meta-analysis

ObjectiveTo synthesise findings from randomised controlled trials (RCTs) of interventions aimed at increasing medication adherence in individuals with type 2 diabetes (T2DM) and/or cardiovascular disease (CVD). And, in a novel approach, to compare the intervention effect of studies which were categorised as being more pragmatic or more explanatory using the Pragmatic-Explanatory Continuum Indicator Summary-2 (PRECIS-2) tool, to identify whether study design affects outcomes. As explanatory trials are typically held under controlled conditions, findings from such trials may not be relatable to real-world clinical practice. In comparison, pragmatic trials are designed to replicate real-world conditions and therefore findings are more likely to represent those found if the intervention were to be implemented in routine care.DesignSystematic review and meta-analysis.Data sourcesOvid Medline, Ovid Embase, Web of Science and CINAHL from 1 January 2013 to 31 December 2018.Eligibility criteria for selecting studiesRCTs lasting ≥3 months (90 days), involving ≥200 patients in the analysis, with either established CVD and/or T2DM and which measured medication adherence. From 4403 citations, 103 proceeded to full text review. Studies published in any language other than English and conference abstracts were excluded.Main outcome measureChange in medication adherence.ResultsOf 4403 records identified, 34 studies were considered eligible, of which 28, including 30 861 participants, contained comparable outcome data for inclusion in the meta-analysis. Overall interventions were associated with an increase in medication adherence (OR 1.57 (95% CI: 1.33 to 1.84), p<0.001; standardised mean difference 0.24 (95% CI: −0.10 to 0.59) p=0.101). The effectiveness of interventions did not differ significantly between studies considered pragmatic versus explanatory (p=0.598), but did differ by intervention type, with studies that included a multifaceted rather than a single-faceted intervention having a more significant effect (p=0.010). The analysis used random effect models and used the revised Cochrane Risk of Bias Tool to assess study quality.ConclusionsIn this meta-analysis, interventions were associated with a significant increase in medication adherence. Overall multifaceted interventions which included an element of education alongside regular patient contact or follow-up showed the most promise. Effectiveness of interventions between pragmatic and explanatory trials was comparable, suggesting that findings can be transferred from idealised to real-word conditions.PROSPERO registration numberCRD42017059460.

Protocol for developing quality assurance measures to use in surgical trials: an example from the ROMIO study

IntroductionRandomised controlled trials (RCTs) in surgery are frequently criticised because surgeon expertise and standards of surgery are not considered or accounted for during study design. This is particularly true in pragmatic trials (which typically involve multiple centres and surgeons and are based in ‘real world’ settings), compared with explanatory trials (which are smaller and more tightly controlled).ObjectiveThis protocol describes a process to develop and test quality assurance (QA) measures for use within a predominantly pragmatic surgical RCT comparing minimally invasive and open techniques for oesophageal cancer (the NIHR ROMIO study). It builds on methods initiated in the ROMIO pilot RCT.Methods and analysisWe have identified three distinct types of QA measure: (i) entry criteria for surgeons, through assessment of operative videos, (ii) standardisation of operative techniques (by establishing minimum key procedural phases) and (iii) monitoring of surgeons during the trial, using intraoperative photography to document key procedural phases and standardising the pathological assessment of specimens. The QA measures will be adapted from the pilot study and tested iteratively, and the video and photo assessment tools will be tested for reliability and validity.Ethics and disseminationEthics approval was obtained (NRES Committee South West—Frenchay, 25 April 2016, ref: 16/SW/0098). Results of the QA development study will be submitted for publication in a peer-reviewed journal.Trial registration numberISRCTN59036820,ISRCTN10386621.

The 2018 ter Brugge Lecture: Problems with the Introduction of Innovations in Neurovascular Care

ABSTRACT:Most endovascular innovations have been introduced into clinical care by showing good outcomes in small enthusiastic case series of selected patients. Randomized clinical trials (RCTs) have rarely been performed, except for acute ischemic stroke, but even then most trial designs were too explanatory to inform clinical decisions. In this article, we review 2 × 2 tables and forest plots that summarize RCT results to examine methodological issues in the design and interpretation of clinical studies. Research results can apply in practice when RCTs are all-inclusive, pragmatic trials. Common problems include the following: (i) using restrictive eligibility criteria in explanatory trials, instead of including the diversity of patients in need of care, which hampers future generalizability of results; (ii) ignoring an entire line of the 2 × 2 table and excluding patients who do not meet the proposed criteria of a diagnostic test in its evaluation (perfusion studies) which renders clinical inferences misleading; (iii) ignoring an entire column of the 2 × 2 table and comparing different patients treated using the same treatment instead of different treatments in the same patients (the “wrong axis” comparisons of prognostic studies and clinical experience) which leads to unjustified treatment decisions and actions; or (iv) combining all aforementioned problems (case series and epidemiological studies). The most efficient and reliable way to improve patient outcomes, after as well as long before research results are available, is to change the way we practice: to use care trials to guide care in the presence of uncertainty.

Confounding

This chapter gives the definition of confounding, a central issue in epidemiology and its dependence on two associations, with exposure and with outcome. It explains confounding in trials, cohort and case-control studies, and Simpson’s paradox. It explains the five methods of controlling confounding: restriction, randomisation, stratification, matching and multivariate methods. For randomised trials, the limits of randomisation, residual confounding, pre-stratification, intention-to-treat, management and explanatory trials, pragmatic trials are explained. It shows the Mantel–Haenszel risk ratio or odds ratio, direct and indirect standardisation, and effect modification. Frequency and individual matching, their value and limitations, over matching, confounding by indication, and calculation of matched odds ratio are shown. It explains multivariate methods, including linear, logistic, Poisson, and Cox’s proportionate hazards models, including the relationship between coefficients and odds ratios, dummy variables, conditional methods, and propensity scores.