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Background Most meta-analyses in systematic reviews, including Cochrane ones, do not have sufficient statistical power to detect or refute even large intervention effects. This is why a meta-analysis ought to be regarded as an interim analysis on its way towards a required information size. The results of the meta-analyses should relate the total number of randomised participants to the estimated required meta-analytic information size accounting for statistical diversity. When the number of participants and the corresponding number of trials in a meta-analysis are insufficient, the use of the traditional 95% confidence interval or the 5% statistical significance threshold will lead to too many false positive conclusions (type I errors) and too many false negative conclusions (type II errors). Methods We developed a methodology for interpreting meta-analysis results, using generally accepted, valid evidence on how to adjust thresholds for significance in randomised clinical trials when the required sample size has not been reached. Results The Lan-DeMets trial sequential monitoring boundaries in Trial Sequential Analysis offer adjusted confidence intervals and restricted thresholds for statistical significance when the diversity-adjusted required information size and the corresponding number of required trials for the meta-analysis have not been reached. Trial Sequential Analysis provides a frequentistic approach to control both type I and type II errors. We define the required information size and the corresponding number of required trials in a meta-analysis and the diversity (D 2 ) measure of heterogeneity. We explain the reasons for using Trial Sequential Analysis of meta-analysis when the actual information size fails to reach the required information size. We present examples drawn from traditional meta-analyses using unadjusted naïve 95% confidence intervals and 5% thresholds for statistical significance. Spurious conclusions in systematic reviews with traditional meta-analyses can be reduced using Trial Sequential Analysis. Several empirical studies have demonstrated that the Trial Sequential Analysis provides better control of type I errors and of type II errors than the traditional naïve meta-analysis. Conclusions Trial Sequential Analysis represents analysis of meta-analytic data, with transparent assumptions, and better control of type I and type II errors than the traditional meta-analysis using naïve unadjusted confidence intervals.

Background Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms. Methods We describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention’s effects. Results We provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial. Conclusions Trial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

Recent events have led psychologists to acknowledge that the inherent uncertainty encapsulated in an inductive science is amplified by problematic research practices. In this article, we provide a practical introduction to recently developed statistical tools that can be used to deal with these uncertainties when performing and evaluating research. In Part 1, we discuss the importance of accurate and stable effect size estimates as well as how to design studies to reach a corridor of stability around effect size estimates. In Part 2, we explain how, given uncertain effect size estimates, well-powered studies can be designed with sequential analyses. In Part 3, we (a) explain what p values convey about the likelihood that an effect is true, (b) illustrate how the v statistic can be used to evaluate the accuracy of individual studies, and (c) show how the evidential value of multiple studies can be examined with a p-curve analysis. We end by discussing the consequences of incorporating our recommendations in terms of a reduced quantity, but increased quality, of the research output. We hope that the practical recommendations discussed in this article will provide researchers with the tools to make important steps toward a psychological science that allows researchers to differentiate among all possible truths on the basis of their likelihood.

Trial sequential analysis (TSA) is an increasingly used tool in systematic reviews to monitor synthesized evidence. However, the current practice of TSAs often overlooks the order of same-year studies, which are typically ordered alphabetically based on the last names of the studies’ authors by default in the widely used TSA software application. This practice is inappropriate and contrary to the TSA’s definition. This issue is particularly concerning in systematic reviews on time-sensitive topics, such as COVID-19, where reviews include many studies within a short period. In this article, we use a case study to illustrate the impact of the order of same-year studies on TSA conclusions. It shows dramatically different patterns of evidence accumulation when same-year studies are ordered alphabetically vs in their actual temporal order. This article offers suggestions for authors to pay attention to study ordering in future TSAs. •Trial sequential analysis (TSA) is increasingly becoming a critical component in systematic reviews to dynamically monitor the accumulation of synthesized evidence and determine whether more studies are needed to achieve conclusive evidence.•In current practice, the majority of published TSAs include same-year studies in the order determined by the authors' last names, as this is the default option in the widely used TSA software application, leading to questionable TSA conclusions.•This issue about same-year studies is particularly significant for TSAs dealing with time-sensitive topics, such as COVID-19, as our case study illustrates that different orders of same-year studies produced dramatically different patterns of evidence accumulation.•We suggest that researchers should be cautious about the order of same-year studies in TSAs, ensuring that the order reflects the actual temporal sequence of individual studies by using formal publication times as they appear in citations for long study timelines or the earliest availability of study results for short-term TSAs.

Background: The ideal timing for tracheostomy in critically ill patients is still debated. This systematic review and meta-analysis examined whether early tracheostomy improves clinical outcomes compared to late tracheostomy or prolonged intubation in critically ill patients on mechanical ventilation. Methods: We conducted a comprehensive search of randomized controlled trials (RCTs) assessing the risk of clinical outcomes in intensive care unit (ICU) patients who underwent early (within 7–10 days of intubation) versus late tracheostomy or prolonged intubation. Databases searched included PubMed, Embase, and the Cochrane Library up to June 2023. The primary outcome evaluated was mortality, while secondary outcomes included the incidence of ventilator-associated pneumonia (VAP), ICU length of stay, and duration of mechanical ventilation. No language restriction was applied. Eligible studies were RCTs comparing early to late tracheostomy or prolonged intubation in critically ill patients that reported on mortality. The risk of bias was evaluated using the Cochrane Risk of Bias Tool for RCTs, and evidence certainty was assessed via the GRADE approach. Results: This systematic review and meta-analysis included 19 RCTs, covering 3586 critically ill patients. Early tracheostomy modestly decreased mortality compared to the control (RR −0.1511 [95% CI: −0.2951 to −0.0070], p = 0.0398). It also reduced ICU length of stay (SMD −0.6237 [95% CI: −0.9526 to −0.2948], p = 0.0002) and the duration of mechanical ventilation compared to late tracheostomy (SMD −0.3887 [95% CI: −0.7726 to −0.0048], p = 0.0472). However, early tracheostomy did not significantly reduce the duration of mechanical ventilation compared to prolonged intubation (SMD −0.1192 [95% CI: −0.2986 to 0.0601], p = 0.1927) or affect VAP incidence (RR −0.0986 [95% CI: −0.2272 to 0.0299], p = 0.1327). Trial sequential analysis (TSA) for each outcome indicated that additional trials are needed for conclusive evidence. Conclusions: Early tracheostomy appears to offer some benefits across all considered clinical outcomes when compared to late tracheostomy and prolonged intubation.

Blended learning (BL) is regarded as an effective strategy for combining traditional face-to-face classroom activities with various types of online learning tools (e.g., e-books). An effective feature of e-books is the ability to use digital notes. When e-books are used in BL, the strategic adoption of note-taking provides benefits that influence the learners' progress for self-regulated learning (SRL) and course achievements. However, learners tend to be unsure about how note-taking is performed using online learning materials and lack knowledge of effective strategies for SRL. Furthermore, few studies have investigated blended learners' sequential patterns of e-book note-taking for SRL. Thus, in this paper, an exploratory study was conducted in an undergraduate course that implemented the BL design. The learning task for the blended learners in the present study was to study the learning material using BookRoll, an e-book system, during in-class and out-of-class learning sessions. Lag sequential analysis of the e-book learning behavior data was conducted to identify the blended learners' sequential behaviors of e-book note-taking for the cognitive strategy use of SRL. Moreover, the difference between higher- and lower-achievement blended learners in terms of their sequential behaviors of e-book note-taking for SRL was revealed. This study can help educators provide evidence-based educational feedback to learners regarding the identified sequential patterns of e-book note-taking that can be applied as effective strategies for promoting the cognitive strategy use of SRL and improvement of course achievement in BL.

This meta-analysis and trial sequential analysis (TSA) of randomized controlled trials (RCTs) on the psychological treatment of body dysmorphic disorder (BDD) was conducted to evaluate the intervention effects and robustness of the evidence. This study included 15 RCTs up until 15 June 2024, with 905 participants. Results showed significant improvements in BDD symptoms ( g = −0.97), depression ( g = −0.51), anxiety ( g = −0.72), insight/delusion ( g = −0.57), psychosocial functioning ( g = 0.45), and quality of life ( g = 0.44), with effects sustained from 1 to 6 months follow-up. RCTs with a waitlist/inactive control reported larger effect sizes for post-intervention BDD symptoms compared to those with a placebo/active control group. In addition, studies with low risk of bias demonstrate larger effect sizes for post-intervention psychosocial functioning compared to studies with some concerns. Notably, the presence of exposure and response prevention in the treatment, as well as the mode of delivery (face-to-face or digital), did not have a significant impact on the intervention outcomes. Females exhibited greater effect sizes in post-intervention BDD symptoms and psychosocial functioning than males. With increasing age, the effect size for insight/delusion symptoms diminished. Longer session duration was associated with larger effect sizes for BDD symptoms, depression at post-treatment, and depression at follow-up. TSA indicated robust evidence for depression at post-treatment and BDD symptoms, while the remaining outcome variables did not meet the desired level of evidence. In conclusion, this study underscores the effectiveness of psychological treatments in reducing BDD symptoms and improving related outcomes, highlighting the need for further research to confirm the impact of these therapies on other outcomes.

To investigate the prevalence of meta-analyses containing potentially redundant randomized controlled trials (RCTs) and the factors associated with the presence of redundancy. This is a cross-sectional study, based on a random sample of references (n = 4500) that were published during 2020 and 2021, indexed in PubMed, Embase, or the Cochrane Database of Systematic Reviews, and retrieved through comprehensive searches using terms about systematic reviews and meta-analysis. From each systematic review, one meta-analysis fulfilling all the following criteria, if available, was included in this study: (1) assessing the effect of the intervention on a primary outcome of the systematic review; (2) combining RCTs only. The primary outcome was prevalence of meta-analyses containing potentially redundant RCTs. Potentially redundant RCTs referred to the trials that started 1 year after the overall effect estimate from cumulative meta-analysis had been statistically robust, as determined by trial sequential analysis when appropriate. The number of potentially redundant trials (if any) in each eligible meta-analysis and the number of participants involved in those trials were documented and contrasted across groups. Logistic regression analysis was conducted to explore the factors associated with presence of potential redundancy. Of the 448 eligible meta-analyses, 57 (12.7%, 95% confidence interval (CI) 9.8–16.2%) contained potentially redundant RCTs. When limited to the 333 low-heterogeneity meta-analyses, the prevalence was 17.1% (95% CI 13.5–21.5%). The total number of potentially redundant RCTs was 295 (involving 85,385 participants), accounting for 38.5% of the RCTs (and 30.3% of the participants) included in the 57 meta-analyses. In these meta-analyses, the median number of potentially redundant RCTs and the participants involved were 2 (range: 1–50) and 352 (range: 17–26997), respectively. Potentially redundant RCTs were more likely to be present in the meta-analyses evaluating pharmaceutical intervention (odds ratio [OR] 2.31, 95% CI 1.16–4.49), assessing efficacy outcomes (OR 7.25, 95% CI 0.85–61.87), containing more than 5 RCTs (OR 6.47, 95% CI 3.22–12.99), or with the earliest RCT reporting statistically significant effect estimate (OR 5.30, 95% CI 2.64–10.64). This study found that 12.7% to 17.1% of recently published meta-analyses contained potentially redundant RCTs, highlighting the importance of conducting or examining systematic reviews of existing evidence to justify new RCTs. More importantly, the study identified some scenarios in which redundancy was more likely to occur and thus has implications for trialists, funding agencies, ethics committees, and journal editors. •Of the selected meta-analyses, 12.7%–17.1% contained potentially redundant randomized controlled trials (RCTs).•The 295 potentially redundant RCTs (with 85,385 participants) accounted for 38.5% of the RCTs (and 30.3% of the participants) included in the 57 meta-analyses.•The potentially redundant RCTs were more prevalent in the meta-analyses evaluating pharmaceutical interventions, assessing efficacy outcomes, including more than 5 RCTs, or with the earliest RCT reporting statistically significant effect estimate.