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OBJECTIVE: To describe the characteristics of Covid-19 randomized clinical trials (RCTs) and examine the association between trial characteristics and the likelihood of finding a significant effect. STUDY DESIGN: We conducted a systematic review to identify RCTs (up to October 21, 2020) evaluating drugs or blood products to treat or prevent Covid-19. We extracted trial characteristics (number of centers, funding sources, and sample size) and assessed risk of bias (RoB) using the Cochrane RoB 2.0 tool. We performed logistic regressions to evaluate the association between RoB due to randomization, single vs. multicentre, funding source, and sample size, and finding a statistically significant effect. RESULTS: We included 91 RCTs (n = 46,802); 40 (44%) were single-center, 23 (25.3%) enrolled

Background: Since the beginning of the COVID-19 pandemic, no direct antiviral treatment is effective as post-exposure prophylaxis (PEP). Lopinavir/ritonavir (LPV/r) was repurposed as a potential PEP agent against COVID-19. Methods: We conducted a pragmatic open-label, parallel, cluster-randomized superiority trial in four sites in Switzerland and Brazil. Clusters were randomized to receive LPV/r PEP (400/100 mg) twice daily for 5 days or no PEP (surveillance). The primary outcome is the occurrence of COVID-19 within 21 days post-enrollment. Findings: Of 318 participants, 157 (49.4%) were women, median age was 39 (interquartile range, 28-50) years. A total of 209 (179 clusters) participants were randomized to LPV/r PEP and 109 (95 clusters) to surveillance. Baseline characteristics were similar, with the exception of baseline SARS-CoV-2 PCR positivity, which was 3-fold more frequent in the LPV/r arm (34/209 [16.3%] vs 6/109 [5.5%], respectively). During 21-day follow-up, 48/318 (15.1%) participants developed COVID-19: 35/209 (16.7%) in the LPV/r group and 13/109 (11.9%) in the surveillance group (unadjusted hazard ratio 1.44; 95% CI, 0.76 to 2.73). In the primary endpoint analysis adjusted for propensity score to receive LPV/r, the hazard ratio for developing COVID-19 in the LPV/r group vs surveillance was 0.53 (95% CI, 0.23 to 1.23, respectively; P =.14). Interpretation: LPV/r role as PEP for COVID-19 remains unanswered. In this trial, LPV/r over 5 days did not significantly reduce incidence of COVID-19 in exposed individuals. We observed a change in directionality of the effect in favor of LPV/r after adjusting for baseline SARS-CoV-2 PCR results, indicating a potential role of antivirals in COVID-19 prevention. Clinical Trial Registration Details: ClinicalTrials.gov (Identifier: NCT04364022); Swiss National Clinical Trial Portal: SNCTP 000003732. Funding Information: Fondation privee des HUG and Swiss National Fund (project number: 33IC30_166819). Declaration of Interests: None reported. Ethics Approval Statement: The protocol and amendments were approved by Swissmedic and local ethics committees in Switzerland and Brazil. Participants provided written informed consent before study entry.

New approaches to evidence synthesis, which use human effort and machine automation in mutually reinforcing ways, can enhance the feasibility and sustainability of living systematic reviews. Human effort is a scarce and valuable resource, required when automation is impossible or undesirable, and includes contributions from online communities (“crowds”) as well as more conventional contributions from review authors and information specialists. Automation can assist with some systematic review tasks, including searching, eligibility assessment, identification and retrieval of full-text reports, extraction of data, and risk of bias assessment. Workflows can be developed in which human effort and machine automation can each enable the other to operate in more effective and efficient ways, offering substantial enhancement to the productivity of systematic reviews. This paper describes and discusses the potential—and limitations—of new ways of undertaking specific tasks in living systematic reviews, identifying areas where these human/machine “technologies” are already in use, and where further research and development is needed. While the context is living systematic reviews, many of these enabling technologies apply equally to standard approaches to systematic reviewing.

Knowing how to search for evidence that can inform clinical decisions is a fundamental skill for the practice of evidence-based dentistry. There are many available types of evidence-based resources, characterized by their degrees of coverage of preappraised or summarized evidence at varying levels of processing, from primary studies to systematic reviews and clinical guidelines. The practice of evidence-based dentistry requires familiarity with these resources. In this article, the authors describe the process of searching for evidence: defining the question, identifying the question's nature and main components, and selecting the study design that best addresses the question.

OBJECTIVES: To develop a scale and survey the measurement of patient adherence and patient recruitment and to explore how these methods impact the results in randomized controlled trials of interventions to improve patient adherence to medications. STUDY DESIGN: Analytic survey of a purposively selected sample of patient adherence intervention trials from a systematic review assessing the quality of adherence measurement and patient recruitment methods. RESULTS: We identified 44 different measures of adherence with qualities ranging from valid and objective to unreliable and subjective. The median overall quality of measures of adherence was 5 (interquartile range [IQR] 3; range 0 9 9 is high quality). The quality of the measures was associated with variation in the estimate of adherence (Spearman r = 0.66; 95 confidence interval: 0.39 0.83). The median overall quality of patient recruitment methods was 2 (IQR 1; maximum score 6 higher is better). There was no significant correlation between the power of the trial to detect an effect and the quality of the patient recruitment methods. CONCLUSION: Measurement and recruitment methods in adherence trials varied considerably and most methods were of low quality. Adherence research could be advanced by using higher quality measures of adherence and better selection and baseline assessment of study participants.

BACKGROUND Adaptation of guidelines for use at the national or local level can facilitate their implementation. We developed and evaluated an adaptation process in adherence with standards for trustworthy guidelines and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, aiming for efficiency and transparency. This article is the first in a series describing our adaptation of Antithrombotic Therapy and Prevention of Thrombosis, 9th ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines for a Norwegian setting. METHODS Informed by the ADAPTE framework, we developed a five-step adaptation process customized to guidelines developed using GRADE: (1) planning, (2) initial assessment of the recommendations, (3) modification, (4) publication, and (5) evaluation. We developed a taxonomy for describing how and why recommendations from the parent guideline were modified and applied a mixed-methods case study design for evaluation of the process. RESULTS We published the adapted guideline in November 2013 in a novel multilayered format. The taxonomy for adaptation facilitated transparency of the modification process for both the guideline developers and the end users. We excluded 30 and modified 131 of the 333 original recommendations according to the taxonomy and developed eight new recommendations. Unforeseen obstacles related to acquiring a licensing agreement and procuring a publisher resulted in a 9-month delay. We propose modifications of the adaptation process to overcome these obstacles in the future. CONCLUSIONS This case study demonstrates the feasibility of a novel guideline adaptation process. Replication is needed to further validate the usefulness of the process in increasing the organizational and methodologic efficiency of guideline adaptation.

Standards and guidance for developing trustworthy clinical practice guidelines are now available, and a number of leading guidelines adhere to the key standards. Even current trustworthy guidelines, however, generally suffer from a cumbersome development process, suboptimal presentation formats, inefficient dissemination to clinicians at the point of care, high risk of becoming quickly outdated, and suboptimal facilitation of shared decision-making with patients. To address these limitations, we have--in our innovative research program and nonprofit organization, MAGIC (Making GRADE the Irresistible Choice)--constructed a conceptual framework and tools to facilitate the creation, dissemination, and dynamic updating of trustworthy guidelines. We have developed an online application that constitutes an authoring and publication platform that allows guideline content to be written and structured in a database, published directly on our web platform or exported in a computer-interpretable language (eg, XML) enabling dissemination through a wide range of outputs that include electronic medical record systems, web portals, and applications for smartphones/tablets. Modifications in guidelines, such as recommendation updates, will lead to automatic alterations in these outputs with minimal additional labor for guideline authors and publishers, greatly facilitating dynamic updating of guidelines. Semiautomated creation of a new generation of decision aids linked to guideline recommendations should facilitate face-to-face shared decision-making in the clinical encounter. We invite guideline organizations to partner with us (www.magicproject.org) to apply and further improve the tools for their purposes. This work will result in clinical practice guidelines that we cannot only trust, but also easily share and use.

Objective Statistically significant studies may be cited more than negative studies on the same topic. We aimed to assess here whether such citation bias is present across the medical literature. Study Design and Setting We conducted a cohort study of the association between statistical significance and citations. We selected all therapeutic intervention studies included in meta-analyses published between January and March 2010 in the Cochrane database, and retrieved citation counts of all individual studies using ISI Web of Knowledge. The association between the statistical significance of each study and the number of citations it received between 2008 and 2010 was assessed in mixed Poisson models. Results We identified 89 research questions addressed in 458 eligible articles. Significant studies were cited twice as often as nonsignificant studies (multiplicative effect of significance: 2.14, 95% confidence interval: 1.38–3.33). This association was partly because of the higher impact factor of journals where significant studies are published (adjusted multiplicative effect of significance: 1.14, 95% confidence interval: 0.87–1.51). Conclusion A citation bias favoring significant results occurs in medical research. As a consequence, treatments may seem more effective to the readers of medical literature than they really are.

Objective: Logistic regression is commonly used in health research, and it is important to be sure that the parameter estimates can be trusted. A common problem occurs when the outcome has few events; in such a case, parameter estimates may be biased or unreliable. This study examined the relation between correctness of estimation and several data characteristics: number of events per variable (EPV), number of predictors, percentage of predictors that are highly correlated, percentage of predictors that were non-null, size of regression coefficients, and size of correlations. Study Design: Simulation studies. Results: In many situations, logistic regression modeling may pose substantial problems even if the number of EPV exceeds 10. Moreover, the number of EPV is not the only element that impacts on the correctness of parameter estimation. High regression coefficients and high correlations between the predictors may cause large problems in the estimation process. Finally, power is generally very low, even at 20 EPV. Conclusion: There is no single rule based on EPV that would guarantee an accurate estimation of logistic regression parameters. Instead, the number of predictors, probable size of the regression coefficients based on previous literature, and correlations among the predictors must be taken into account as guidelines to determine the necessary sample size. 2011 Elsevier Inc. All rights reserved.

Purpose Optimal management of heart faikure (HF) requires accurate assessment of prognosis. Many prognostic models are available. The aim was to identify studies evaluating the use of risk prediction models for mortality in ambulatory HF patients, describe their performance and clinical applicability. Methods and Materials We searched for studies in MEDLINE, EMBASE and CINAHL in May 2012 and scanned references of included studies. Two reviewers selected citations including HF patients and reporting on model performance in derivation and/or validation cohorts. We abstracted data related to population, outcomes, study quality and model discrimination and calibration. Results Of the 9952 reviewed, we included 32 studies testing 18 models. Only 4 models were validated in independent cohorts: the Heart Failure Survival Score (HFSS), the Seattle Heart Failure Model (SHFM), the PACE risk score and a model by Frankenstein et al. The HFSS was validated in 8 cohorts (2240 patients) showing poor to modest discrimination (c-statistic 0.56-0.79), being lower in more recent cohorts. The SHFM was validated in 14 cohorts (16057 patients) describing poor to acceptable discrimination (0.63-0.81), remaining relatively stable over time. Both models reported adequate calibration, though overestimating survival in specific populations. The other two models were validated in one cohort each, reporting poor discrimination (0.66-0.69). Among the remaining 14 models, six were validated by bootstrapping (c-statistic 0.74-0.85); the rest were not validated. Conclusions Externally validated HF models showed inconsistent performance. The HFSS and SHFM demonstrated modest discrimination and questionable calibration. A new model derived from contemporary patient cohorts may be required for improved prognostic performance.