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Objectives: Two designs are frequently used in cluster randomized trials in nursing homes: closed cohort and open cohort. The former design includes residents at the beginning of the trial and then follows them. In the latter design, participants are enrolled at the beginning of the trial or although it is ongoing; at dates of assessment, all residents present in the nursing home are assessed. The open-cohort design is much less used than the closed-cohort design, but it offers several advantages such as less exposure to individual attrition. Objective was to assess whether an open-cohort design could have been feasible in trials with a closed-cohort design., Study Design and Setting: Twenty-two closed-cohort trials in nursing homes., Results: An open-cohort design was considered a relevant alternative for 20 trials. For 16 trials, a resident newly admitted could not opt out of the intervention, and for all trials, the resident could benefit from an intervention effect if it existed. For two trials, newly admitted residents could not benefit from the intervention effect, if it existed., Conclusion: The open-cohort design is well-adapted for most of the interventions assessed in nursing homes by means of a cluster randomized trial and should be considered more often., Competing Interests: Declaration of Competing Interest The authors report no conflicts of interest related to this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objectives: Randomized trials labelled as "pragmatic" are attractive to funders, patients, and clinicians as the label implies that the results are directly applicable to clinical care. We examined how authors justify use of the label (e.g., by referring to one or more PRECIS [PRagmatic Explanatory Continuum Indicator Summary]-2 domains)., Study Design and Setting: We reviewed primary trial reports published 2014-2019, registered in ClinicalTrials.gov and using the pragmatic label anywhere in the report., Results: Among 415 trials, the label was justified by reference to at least one design element in 282 (68.0%); of these, 240 (85.1%) referenced trial characteristics that can be mapped to one or more of the PRECIS-2 domains, most commonly eligibility (91, 32.3%), setting (90, 31.9%), flexibility delivery (89, 31.6%), and organization (75, 26.6%); 42 (14.9%) referenced characteristics that are not PRECIS-2 domains, most commonly type of intervention/comparator (48, 17%), recruitment without consent (22, 7.8%), routinely collected data (22, 7.8%), and cluster randomization (20, 7.1%). Most reports referenced only one or two design elements. Overall, 9/415 (2.2%) provided PRECIS wheels., Conclusion: Current use of pragmatic labels is uninformative. Authors should clarify the decision the trial is intended to support and include a PRECIS-2 table to make the design transparent., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: A noncompleter is defined as a participant who leaves a trial before the end of the planned follow-up. Research in nursing homes is highly exposed to this problem because of high death rates., Objectives: The aim of this trial is to assess the statistical management of noncompleters in cluster randomized trials carried out in nursing homes., Study Design and Setting: A methodological review of published cluster randomized trials., Results: We selected 37 articles. For 22 (59%) trials, the design was closed-cohort (i.e., participants included all at the same time when randomizing clusters). In those 22 closed-cohort trials, the median follow-up was 6.5 months (interquartile range 4-12). The median noncompleter rate was 19.5% and the median noncompletions due to death was 73.2%. In only one trial were the baseline characteristics of completers and noncompleters compared. Strategies to deal with noncompleters were an inflation of the planned sample size (11 trials), the use of repeated measurements of the outcome (12 trials), and the use of imputation methods when analyzing data (7 trials)., Conclusion: In cluster randomized trials of nursing homes, noncompleters are managed as for any missing data, but they are essentially due to death. Methodological and statistical developments and guidance are needed., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objectives: To describe prevalence of multiple primary outcomes, changes in primary outcomes and target sample sizes between protocols and final reports, and how issues of multiplicity are addressed in pragmatic trials., Study Design and Setting: Individually randomized trials labeled as pragmatic, published 2014-2019 in MEDLINE and registered with ClinicalTrials.gov., Results: We identified 262 final reports and located protocols for 159 (61%); primary outcomes were clearly reported in 145 (91%) protocols and 256 (98%) final reports. Thirty (19%) protocols and 38 (15%) final reports had multiple primary outcomes. Primary outcomes were present and identical in 128 (81%) matched protocol-final reports. Among 140 pairs with target sample sizes reported, 28 (20.0%) reduced their target sample size (mean 543 fewer participants per trial) and 16 (11.4%) increased it (mean 192 more participants per trial). Thirteen (29.5%) provided an explanation. Only 2 of 30 (7%) protocols and 4 of 38 (11%) final reports with co-primary outcomes explained how results would be interpreted in light of multiplicity; 21 of 30 (70%) protocols and 20 of 38 (53%) final reports accounted for co-primary outcomes in power calculations., Conclusion: Co-primary outcomes are common in pragmatic trials; improved transparency around design and analysis decisions involving co-primary outcomes is required., (Copyright © 2021. Published by Elsevier Inc.)

Objective: It is now more than 50 years since the concepts of explanatory and pragmatic attitudes toward trials were first discussed by Schwartz and Lellouch in their influential 1967 paper. Since then, there has been increasing focus on design aspects that may be consistent with more pragmatic attitudes within clinical trials, and a number of tools developed to assist investigators prospectively think about their trial design. Researchers have subsequently expressed interest in using these tools retrospectively to characterize trials as pragmatic or explanatory., Results: We suggest that recent attempts to retrospectively dichotomize trials solely on the basis of quantitative scoring of trial design features are flawed. Instead, we argue that there is a need to consider both the intent and design when assessing the degree of pragmatism within a trial., Conclusion: The practical implication of our suggestion for trial reporting is that investigators should explicitly state the intent of the trial through a clear articulation of the decision that they hope will be informed by the trial results. This should be coupled with a completed PRagmatic-Explanatory Continuum Indicator Summary 2 assessment (or similar) with an explanation of study design choices to appropriately assess whether the study design is consistent with the study intent. We believe this will assist reviewers and knowledge users in making assessments of trials., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objective: The objective of the study was to estimate the proportion of secondary publications of randomized controlled trials (RCTs) that provide new results relative to the primary publication., Study Design and Setting: We searched for RCTs published in 2014 in the five medical journals with the highest impact factors. Secondary publications for each primary publication were then identified by their registration number. The main outcome measure was the proportion of secondary publications providing results already reported in the primary publication and/or nonprespecified analyses and/or a meta-analysis pooling results of studies not identified by systematic review., Results: A total of 144 primary publications were identified; 94 (65%) had at least one secondary publication within 30 months after a primary publication. Of the secondary publications, 20% reported only results present in the primary publication, and 35% reported results not prespecified or pooled analyses not based on a systematic review. Factors associated with having at least one secondary publication were a large number of randomized trial participants (odds ratio [95% confidence interval]: 3.2 [1.1-9.3] for trials with >1,000 vs. ≤500 participants), investigating a biologic product (4.8 [1.4‒16.3] vs. a nonbiologic product) and cardiologic field vs. other fields (7.6 [1.46-39.8])., Conclusion: Most drug RCTs with results published in high-impact-factor journals had secondary publications. More than half of these secondary publications provided results already reported in the primary publication or results of nonprespecified analyses., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Objectives: The Core Outcome Measures in Effectiveness Trial initiative aims at developing core outcome set (COS) for research. Consensus on a COS for renoprotection research is lacking. We performed a systematic review (SR) of SRs of renoprotection to identify outcomes used in renoprotection research and to assess how frequently these outcomes could be meta-analyzed in the SRs., Study Design and Setting: We searched for SRs with meta-analyses of renoprotection treatments in the Database of Abstracts of Reviews of Effects and in MEDLINE and collected outcomes that were meta-analyzed. For each outcome and SR, we assessed the proportion of trials/patients that could be meta-analyzed., Results: We retrieved 66 SRs. A total of 609 outcomes were extracted for 20 distinct renoprotection outcomes. The median (interquartile range) proportion of SRs in which these outcomes had been meta-analyzed was 8% (2%, 27%) (range 2-50%). The proportion of trials that had been aggregated was >75% for only 36% of the outcomes., Conclusion: The outcomes meta-analyzed in renoprotection trials are heterogeneous, with a low proportion of trials or patients pooled for each outcome and each SR. A COS is needed in renoprotection research to limit this waste of research. The outcomes identified in the present work could be a basis for this COS., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Objectives: We aimed to assess whether the clinical equipoise principle is satisfied in unbalanced randomized controlled trials (RCTs) (i.e., with an unequal probability of subjects being allocated to one group than another)., Study Design and Setting: Observational and comparative study between unbalanced and balanced RCTs. We searched the "core clinical journals" of MEDLINE to identify reports of two-parallel group superiority unbalanced RCTs published between January 2009 and December 2010. For each unbalanced RCT, we identified a maximum of four reports (to maximize power) of matched balanced RCTs dealing with the same population. Our primary outcome was the proportion of positive RCTs [i.e., with statistically significant results for the primary outcome (P < 0.05), showing greater efficacy with the new treatment than the control treatment]., Results: We selected 46 reports of unbalanced RCTs and 164 of balanced RCTs; 65.2% unbalanced RCTs and 43.9% of balanced RCTs were positive (odds ratio, 2.38; 95% confidence interval: 1.23, 4.63). As compared with balanced RCTs, unbalanced RCTs were more often industry funded and their control treatments were more often inactive. Adjusting for these latter variables did not modify the results., Conclusion: This result questions the respect of clinical equipoise in unbalanced RCTs., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Objectives: To assess the reporting of the unequal randomization ratio in reports of trials with this design and to identify the justification for the design., Study Design and Setting: Systematic review of reports of trials with unequal randomization. We selected all original reports of two parallel-group randomized controlled trials with unequal randomization, which were published in 2009 and 2010 in core clinical journals in MEDLINE on the basis of the abstract (and full text, if necessary). Additional information was collected by an author survey., Results: We retrieved 106 reports (prevalence, 4.7%). The randomization ratio was not stated in 8.5% of reports and 51.9% of abstracts. Sample size calculation was reported in 70 reports, with unequal randomization not taken into account in 25.7% (n = 18). Justification for unequal randomization was not reported in 77.4% (n = 82) of reports. Combining information from reports and author surveys, we had justification for 41 trials. The main justification was safety issues for 20 trials. In 11 of those latter 20 reports, adverse events were not fully reported., Conclusion: A better reporting of the randomization ratio, sample size calculation, and justification to unequal randomization could help readers appraise the quality and risk of bias of such trials., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Objective: Outcome Measures in Rheumatology promotes standardized outcome measures. No such organization exists for nephrology. We compared the reporting and homogeneity of outcome measures in registered protocols of randomized trials in rheumatology and nephrology., Study Design and Setting: Data were extracted from protocols for rheumatoid arthritis or nephroprotection registered in ClinicalTrials.gov. We rated five outcome items (domain, specific measurement, specific metric, method of aggregating data, and time frame) to obtain a 5-point score. We split outcomes into clusters that could be pooled for meta-analysis, and assessed the proportion of trials and patients by cluster., Results: We selected 75 protocols for rheumatology and 66 for nephrology. A high adjusted score for outcomes was associated with rheumatology protocols (odds ratio, 4.2; 95% confidence interval, 2.39, 7.39). We retained 13 clusters of outcomes for rheumatology, and one of one outcome (American College Rheumatology Criteria) could pool 87.1% of trials and 92.8% of patients. We retained eight clusters for nephrology, and one of four outcomes (assessing proteinuria) could pool 83.1% of trials and 44.7% of patients., Conclusions: The reporting and homogeneity of outcomes is better in registered protocols of rheumatology than nephrology. The presence of international guidelines on outcome measurement may explain the differences., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Objective: The minimal clinically important difference (MCID) and the patient acceptable symptom state (PASS) are cutoffs dichotomizing continuous values into variables such as improved or not improved (MCID), or in an acceptable state or not in an acceptable state (PASS), allowing to report the success rate (proportion of patients improved or in an acceptable state) in trial arms. The objective was to investigate the influence of the choice of MCID and/or PASS values on the difference in success rate between arms., Study Design and Setting: Analytic study was carried out. In two hypothetic trials (prespecified mean and standard deviation (SD) for control arm score, effect sizes [ES] of 0.25 and 0.5), we calculated the success rates in control and experimental arms for different MCID and PASS values, and derived the difference in success rates between arms., Results: For a 0-100 score and MCID values from -40 to -10, the difference in success rate between arms ranges from 7.9% to 9.9% (ES=0.25) and from 15.9% to 19.7% (ES=0.50). For PASS values from 20 to 50, the difference in success rate between arms ranges from 7.1% to 9.9% (ES=0.25) and from 15.6% to 19.7% (ES=0.50)., Conclusion: The MCID or PASS value has a low impact on the difference in the success rate between the arms in a trial.

Background and Objective: To develop a checklist of items measuring the quality of reports of randomized clinical trials (RCTs) assessing nonpharmacological treatments (NPTs)., Study Design and Setting: The Delphi consensus method was used to select and reduce the number of items in the checklist. A total of 154 individuals were invited to participate: epidemiologists and statisticians involved in the field of methodology of RCTs (n = 55), members of the Cochrane Collaboration (n = 41), and clinicians involved in planning NPT clinical trials (n = 58). Participants ranked on a 10-point Likert scale whether an item should be included in the checklist., Results: Fifty-five experts (36%) participated in the survey. They were experienced in systematic reviews (68% were involved in the Cochrane Collaboration) and in planning RCTs (76%). Three rounds of the Delphi method were conducted to achieve consensus. The final checklist contains 10 items and 5 subitems, with items related to the standardization of the intervention, care provider influence, and additional measures to minimize the potential bias from lack of blinding of participants, care providers, and outcome assessors., Conclusions: This tool can be used to critically appraise the medical literature, design NPT studies, and assess the quality of trial reports included in systematic reviews.

Objective: To compare the feasibility of blinding and the perceived risk of unblinding in trials evaluating pharmacologic (PT) and nonpharmacologic treatments (NPT) of hip or knee osteoarthritis., Study Design and Setting: Two independent reviewers assessed the feasibility of blinding patients, care providers, and outcome assessors, the perceived risk of unblinding, and whether blinding was reported in 110 reports of randomized controlled trials (RCTs) evaluating PT and NPT in patients with hip or knee osteoarthritis., Results: Blinding was considered to be possible less often in NPT trials than in PT trials for patients (42 vs. 96%; P <.001), care providers (12 vs. 96%; P <.001), and outcome assessors (34 vs. 98%; P <.001). When blinding was judged feasible, the perceived risk of unblinding was more often considered moderate or important in NPT than PT trials for patients (35 vs. 14%, P=.02) and outcome assessors (44 vs. 10%, P=.0004). When blinding was judged feasible, it was reported less often in NPT reports than in PT reports for patients (46 vs. 98%, P <.001), care providers (43 vs. 83%, P=.03), and outcome assessors (72 vs. 98%, P=.0006)., Conclusion: Blinding appears to be more difficult to achieve and unblinding may occur more often in NPT than PT trials.

Objective: Results of studies estimating osteoarthritis progression may be affected by missing values. In clinical trials assessing disease-modifying osteoarthritis drugs, sample sizes should be calculated using close estimates of outcome variables., Study Design and Setting: Supposing a two-parallel group design in hip osteoarthritis clinical trials, we estimated sample sizes using the joint space width (JSW), number of patients with JSW progression >0.5 mm (JSN), time to total hip arthroplasty (THA), and time to JSN or THA using several approaches to deal with missing data., Results: Three-year clinical trials testing a treatment effect of 50%, with a power of 80%, could require sample sizes of 121 patients for JSW, 57 for JS progression using multiple imputation for handling missing values; 200 for THA; and 47 for JSN or THA. These numbers vary greatly depending on the approach chosen for handling missing data., Conclusions: These results can help investigators plan clinical trials to select the primary outcome and a priori specify the way missing data will be handled.

Interpreting changes in continuous structural outcome measures is a common problem in clinical research and in daily practice. We propose a method for estimating whether difference observed between two successive measures in an individual constitutes a statistically relevant change or a change induced by variability. This statistically relevant change is based on an analysis of reproducibility. The continuous structural outcome measure investigated as an example was joint space width (JSW) measurement on standard X-rays, which is known to be the primary end-point for assessing structural osteoarthritis progression. The results of the present study demonstrate that cutoffs are closely dependent on all sources of variabilities in JSW measurement such as joint positioning, radiographic procedure, and the measurement process itself. Therefore, we suggest to determine cutoffs for each study using a representative sample of the population studied and using the procedures and methods of measurement of the specific study. This approach may easily be extended to other continuous structural outcome measures.