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There is a growing trend to include nonrandomized studies of interventions (NRSIs) in meta-analyses of randomized controlled trials (RCTs) for health decision-making. The study aimed to quantify the impact of integrating NRSI on the evidence derived from RCTs within the same systematic review. We searched PubMed for systematic reviews published between December 9, 2017, and December 9, 2022, that included both RCTs and NRSIs under the same outcome. Using the DerSimonian–Laird random-effects model, we reanalyzed the pooled estimates to compare those derived from RCTs with those from combined RCTs and NRSIs. We examined changes in point estimates, subgroup differences, statistical heterogeneity, and the weight of RCTs in pooled estimates. Results were defined as being in qualitative agreement if both estimates demonstrated statistical significance in the same direction or if neither achieved statistical significance. A total of 220 eligible systematic reviews were identified and 217 meta-analyses were reanalyzed. Qualitative disagreement between RCTs only and pooled estimates combining RCTs and NRSIs was observed in 78 meta-analyses (35.9%), of which 69 (88.5%) gained statistical significance after the inclusion of NRSIs. Point estimates in 58 meta-analyses (26.7%) failed to meet predefined agreement criteria, and statistically significant subgroup differences between RCTs and NRSIs were identified in 32 meta-analyses (14.8%). The incorporation of NRSIs raised the heterogeneity from 21.8% to 36.9%, whereas RCTs accounted for a median weight of 33.9% in the pooled estimates. These findings highlight the need for caution in conducting and interpreting meta-analyses combining RCTs and NRSIs, particularly in scenarios where RCTs yield nonsignificant results whereas the inclusion of NRSIs achieves statistical significance. Although randomized controlled trials (RCTs) remain the gold standard for clinical evidence, they are often insufficient to address complex clinical questions. Nonrandomized studies of interventions (NRSIs), leveraging real-world clinical data, are increasingly used to supplement RCT findings. Despite growing interest in integrating NRSIs into meta-analyses with RCTs, the clinical and statistical implications of this approach remain uncertain. To address this gap, we conducted a systematic evaluation of how NRSI inclusion impacts meta-analytic results by analyzing 220 systematic reviews that combined RCTs and NRSIs under the same outcome. Our analysis revealed that incorporating NRSIs altered effect estimates in over one-third of cases, with 88.5% of meta-analyses achieving statistical significance only after NRSI inclusion–a finding with critical implications for decision-making. In addition, NRSI integration elevated statistical heterogeneity, although RCTs accounted for less than one-third of the weight in pooled estimates. These findings collectively underscore the necessity for robust evaluation and cautious interpretation when merging NRSI data with RCTs in meta-analyses. [Display omitted] •Including NRSIs in meta-analyses of RCTs altered the estimates in more than one-third of the studies.•The inclusion of NRSIs increased statistical heterogeneity of the pooled estimates. •The median weight of the RCTs in the pooled estimates was approximately one-third.

To review the literature and obtain preferences and perceptions from experts regarding the role of randomized studies (RSs) and nonrandomized studies (NRSs) in systematic reviews of intervention effects. Scoping review and survey of experts. Using levels of certainty developed by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) working group, experts expressed their preferences about the use of RS and NRS in health syntheses. Of 189 respondents, 123 had the expertise required to answer the questionnaire; 116 provided their extent of agreement with approaches to use NRS with RS. Most respondents would include NRS when RS was unfeasible (83.6%) or unethical (71.5%) and a majority to maximize the body of evidence (66.3%), compare results in NRS and RS (53.5%) and to identify subgroups (51.7%). Sizable minorities would include NRS and RS to address the effect of randomization (29.5%) or because the question being addressed was a public-health intervention (36.5%). In summary of findings tables, most respondents would include both bodies of evidence–in two rows in the same table—when RS provided moderate, low, or very-low certainty evidence; even when RS provided high certainty evidence, a sizable minority (25%) would still present results from both bodies of evidence. Very few (3.6%) would, under realistic circumstances, pool RS and NRS results. Most experts would include both RS and NRS in the same review under a wide variety of circumstances, but almost all would present results of two bodies of evidence separately.

This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews. A machine learning classifier for retrieving randomized controlled trials (RCTs) was developed (the “Cochrane RCT Classifier”), with the algorithm trained using a data set of title–abstract records from Embase, manually labeled by the Cochrane Crowd. The classifier was then calibrated using a further data set of similar records manually labeled by the Clinical Hedges team, aiming for 99% recall. Finally, the recall of the calibrated classifier was evaluated using records of RCTs included in Cochrane Reviews that had abstracts of sufficient length to allow machine classification. The Cochrane RCT Classifier was trained using 280,620 records (20,454 of which reported RCTs). A classification threshold was set using 49,025 calibration records (1,587 of which reported RCTs), and our bootstrap validation found the classifier had recall of 0.99 (95% confidence interval 0.98–0.99) and precision of 0.08 (95% confidence interval 0.06–0.12) in this data set. The final, calibrated RCT classifier correctly retrieved 43,783 (99.5%) of 44,007 RCTs included in Cochrane Reviews but missed 224 (0.5%). Older records were more likely to be missed than those more recently published. The Cochrane RCT Classifier can reduce manual study identification workload for Cochrane Reviews, with a very low and acceptable risk of missing eligible RCTs. This classifier now forms part of the Evidence Pipeline, an integrated workflow deployed within Cochrane to help improve the efficiency of the study identification processes that support systematic review production. •Systematic review processes need to become more efficient.•Machine learning is sufficiently mature for real-world use.•A machine learning classifier was built using data from Cochrane Crowd.•It was calibrated to achieve very high recall.•It is now live and in use in Cochrane review production systems.

Over the past 70 years, randomized, controlled trials (RCTs) have reshaped medical knowledge and practice. Popularized by mid-20th-century clinical researchers and statisticians aiming to reduce bias and enhance the accuracy of clinical experimentation, RCTs have often functioned well in that role. Yet the past seven decades also bear witness to many limitations of this new “gold standard.” The scientific and political history of RCTs offers lessons regarding the complexity of medicine and disease and the economic and political forces that shape the production and circulation of medical knowledge. The Rise of RCTs Physicians and medical researchers have attempted for millennia . . .

Background Multiple primary outcomes may be specified in randomised controlled trials (RCTs). When analysing multiple outcomes it’s important to control the family wise error rate (FWER). A popular approach to do this is to adjust the p -values corresponding to each statistical test used to investigate the intervention effects by using the Bonferroni correction. It’s also important to consider the power of the trial to detect true intervention effects. In the context of multiple outcomes, depending on the clinical objective, the power can be defined as: ‘disjunctive power’ , the probability of detecting at least one true intervention effect across all the outcomes or ‘ marginal power’ the probability of finding a true intervention effect on a nominated outcome. We provide practical recommendations on which method may be used to adjust for multiple comparisons in the sample size calculation and the analysis of RCTs with multiple primary outcomes. We also discuss the implications on the sample size for obtaining 90% disjunctive power and 90% marginal power. Methods We use simulation studies to investigate the disjunctive power, marginal power and FWER obtained after applying Bonferroni, Holm, Hochberg, Dubey/Armitage-Parmar and Stepdown-minP adjustment methods. Different simulation scenarios were constructed by varying the number of outcomes, degree of correlation between the outcomes, intervention effect sizes and proportion of missing data. Results The Bonferroni and Holm methods provide the same disjunctive power. The Hochberg and Hommel methods provide power gains for the analysis, albeit small, in comparison to the Bonferroni method. The Stepdown-minP procedure performs well for complete data. However, it removes participants with missing values prior to the analysis resulting in a loss of power when there are missing data. The sample size requirement to achieve the desired disjunctive power may be smaller than that required to achieve the desired marginal power. The choice between whether to specify a disjunctive or marginal power should depend on the clincial objective.

Substantial COVID-19 research investment has been allocated to randomized clinical trials (RCTs) on hydroxychloroquine/chloroquine, which currently face recruitment challenges or early discontinuation. We aim to estimate the effects of hydroxychloroquine and chloroquine on survival in COVID-19 from all currently available RCT evidence, published and unpublished. We present a rapid meta-analysis of ongoing, completed, or discontinued RCTs on hydroxychloroquine or chloroquine treatment for any COVID-19 patients (protocol: https://osf.io/QESV4/ ). We systematically identified unpublished RCTs (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, Cochrane COVID-registry up to June 11, 2020), and published RCTs (PubMed, medRxiv and bioRxiv up to October 16, 2020). All-cause mortality has been extracted (publications/preprints) or requested from investigators and combined in random-effects meta-analyses, calculating odds ratios (ORs) with 95% confidence intervals (CIs), separately for hydroxychloroquine and chloroquine. Prespecified subgroup analyses include patient setting, diagnostic confirmation, control type, and publication status. Sixty-three trials were potentially eligible. We included 14 unpublished trials (1308 patients) and 14 publications/preprints (9011 patients). Results for hydroxychloroquine are dominated by RECOVERY and WHO SOLIDARITY, two highly pragmatic trials, which employed relatively high doses and included 4716 and 1853 patients, respectively (67% of the total sample size). The combined OR on all-cause mortality for hydroxychloroquine is 1.11 (95% CI: 1.02, 1.20; I² = 0%; 26 trials; 10,012 patients) and for chloroquine 1.77 (95%CI: 0.15, 21.13, I² = 0%; 4 trials; 307 patients). We identified no subgroup effects. We found that treatment with hydroxychloroquine is associated with increased mortality in COVID-19 patients, and there is no benefit of chloroquine. Findings have unclear generalizability to outpatients, children, pregnant women, and people with comorbidities. Hydroxychloroquine and chloroquine have been investigated as a potential treatment for Covid-19 in several clinical trials. Here the authors report a meta-analysis of published and unpublished trials, and show that treatment with hydroxychloroquine for patients with Covid-19 was associated with increased mortality, and there was no benefit from chloroquine.

Randomized controlled trials (RCTs) are conducted under idealized and rigorously controlled conditions that may compromise their external validity. A literature review was conducted of published English language articles that reported the findings of studies assessing external validity by a comparison of the patient sample included in RCTs reporting on pharmaceutical interventions with patients from everyday clinical practice. The review focused on publications in the fields of cardiology, mental health, and oncology. A range of databases were interrogated (MEDLINE; EMBASE; Science Citation Index; Cochrane Methodology Register). Double-abstract review and data extraction were performed as per protocol specifications. Out of 5,456 de-duplicated abstracts, 52 studies met the inclusion criteria (cardiology, n = 20; mental health, n = 17; oncology, n = 15). Studies either performed an analysis of the baseline characteristics (demographic, socioeconomic, and clinical parameters) of RCT-enrolled patients compared with a real-world population, or assessed the proportion of real-world patients who would have been eligible for RCT inclusion following the application of RCT inclusion/exclusion criteria. Many of the included studies concluded that RCT samples are highly selected and have a lower risk profile than real-world populations, with the frequent exclusion of elderly patients and patients with co-morbidities. Calculation of ineligibility rates in individual studies showed that a high proportion of the general disease population was often excluded from trials. The majority of studies (n = 37 [71.2 %]) explicitly concluded that RCT samples were not broadly representative of real-world patients and that this may limit the external validity of the RCT. Authors made a number of recommendations to improve external validity. Findings from this review indicate that there is a need to improve the external validity of RCTs such that physicians treating patients in real-world settings have the appropriate evidence on which to base their clinical decisions. This goal could be achieved by trial design modification to include a more representative patient sample and by supplementing RCT evidence with data generated from observational studies. In general, a thoughtful approach to clinical evidence generation is required in which the trade-offs between internal and external validity are considered in a holistic and balanced manner.

BackgroundSubstantial amounts of public funds are invested in health research worldwide. Publicly funded randomised controlled trials (RCTs) often recruit participants at a slower than anticipated rate. Many trials fail to reach their planned sample size within the envisaged trial timescale and trial funding envelope.ObjectivesTo review the consent, recruitment and retention rates for single and multicentre randomised control trials funded and published by the UK's National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme.Data sources and study selectionHTA reports of individually randomised single or multicentre RCTs published from the start of 2004 to the end of April 2016 were reviewed.Data extractionInformation was extracted, relating to the trial characteristics, sample size, recruitment and retention by two independent reviewers.Main outcome measuresTarget sample size and whether it was achieved; recruitment rates (number of participants recruited per centre per month) and retention rates (randomised participants retained and assessed with valid primary outcome data).ResultsThis review identified 151 individually RCTs from 787 NIHR HTA reports. The final recruitment target sample size was achieved in 56% (85/151) of the RCTs and more than 80% of the final target sample size was achieved for 79% of the RCTs (119/151). The median recruitment rate (participants per centre per month) was found to be 0.92 (IQR 0.43–2.79) and the median retention rate (proportion of participants with valid primary outcome data at follow-up) was estimated at 89% (IQR 79–97%).ConclusionsThere is considerable variation in the consent, recruitment and retention rates in publicly funded RCTs. Investigators should bear this in mind at the planning stage of their study and not be overly optimistic about their recruitment projections.

PurposeTo compare the efficacy and safety of thulium laser VapoResection of the prostate (ThuVaRP) versus standard traditional transurethral resection of the prostate (TURP) or plasmakinetic resection of prostate (PKRP) for benign prostatic obstruction.MethodsSystematic searches were performed in the Medline, EMBASE, the Cochrane Library, Web of Science, and CNKI in December 2017. The outcomes of demographic and clinical characteristics, perioperative variables, complications, and postoperative efficacy including International Prostate Symptom Score (IPSS), quality of life (QoL), maximum flow rate (Qmax), and postvoid residual (PVR) were assessed.Results16 studies were selected in the meta-analysis including nine randomized controlled trials (RCTs) and seven non-RCTs. Among of them, nine studies compared ThuVaRP with PKRP, while seven studies compared ThuVaRP with TURP. It seemed that ThuVaRP needed longer operation time than TURP (WMD = 6.41, 95% CI 1.38–11.44, p = 0.01) and PKRP (WMD = 10.15, 95% CI 5.20–15.10, p < 0.0001). ThuVaRP was associated with less serum hemoglobin decreased, catheterization time, and the length of hospital stay compared with TURP (WMD = − 0.58, 95% CI − 0.77 to 0.38, p < 0.00001; WMD = − 1.89, 95% CI − 2.67 to 1.11, p < 0.00001; WMD = − 2.25, 95% CI − 2.91 to 1.60, p < 0.00001) and PKRP (WMD = − 0.28, 95% CI − 0.46 to 0.10, p = 0.002; WMD = − 1.88, 95% CI − 2.87 to 0.89, p = 0.0002; WMD = − 2.08, 95% CI − 2.63 to 1.54, p<0.00001). According to our assessment, there was no significantly difference in postoperative efficacy.ConclusionsThe pooled data indicated that ThuVaRP had a nearly efficacy to TURP and PKRP based on IPSS, QoL, Qmax, and PVR. Although ThuVaRP was associated with longer operation time, it got distinct superiority on serum hemoglobin decreased, catheterization time, and hospital stay.

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.