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Systematic reviews and meta-analyses (MA) are accepted modalities for evidence synthesis in evidence-based medicine. However, as MA uses aggregate data that includes averaging patient characteristics and pooled effect estimates, it has limitations when considering personalised medicine. In contrast, individual participant data meta-analysis (IPD-MA) includes and segregates individual patient data to study new outcomes, identify outcome predictors, and analyse multiple covariate effects on treatments. IPD-MA requires data from multiple investigators, review board approvals, clear communication with collaborators, and statistical recalculation of cumulative data. IPD-MA can be performed as a single-stage process where data from all included studies is pooled and reanalysed or as a two-stage process where additionally the data from individual studies is re-analysed before being pooled. This review aims to orient clinicians about IPD-MA, including the process of performing it, comparing it with other types of meta-analyses and considering the potential barriers in conducting it. [ABSTRACT FROM AUTHOR]

Aim To identify data sharing practices of authors of randomized-controlled trials (RCTs) in indexed orthodontic journals and explore associations between published reports and several publication characteristics. Materials and methods RCTs from indexed orthodontic journals in major databases, namely PubMed® (Medline), Scopus®, EMBASE®, and Web of Science™, were included from January 2019 to December 2023. Data extraction was conducted for outcome and predictor variables such as data and statistical code sharing practices reported, protocol registration, funding sources, and other publication characteristics, including the year of publication, journal ranking, the origin of authorship, number of authors, design of the RCT, and outcome-related variables (e.g. efficacy/safety). Statistical analyses included descriptive statistics, cross-tabulations, and univariable and multivariable logistic regression. Results A total of 318 RCTs were included. Statement for intention of the authors to provide their data upon request was recorded in 51 of 318 RCTs (16.0%), while 6 of 318 (1.9%) openly provided their data in repositories. No RCT provided any code or script for statistical analysis. A significant association was found between data sharing practices and the year of publication, with increasing odds for data sharing by 1.56 times across the years (odds ratio [OR]: 1.56; 95% confidence interval [CI]: 1.22, 2.01; P  < .001). RCTs reporting on safety outcomes presented 62% lower odds for including positive data sharing statements compared to efficacy outcomes (OR: 0.38; 95% CI: 0.17, 0.88). There was evidence that funded RCTs were more likely to report on data sharing compared to non-funded (P  = .02). Conclusions Albeit progress has been made towards credibility and transparency in the presentation of findings from RCTs in orthodontics, less than 20% of published orthodontic trials include a positive data sharing statement while less than 2% openly provide their data with publication. [ABSTRACT FROM AUTHOR]

Systematically missing data in distributed data networks presents practical and methodological challenges. Failure to handle it appropriately can bias statistical inference. Multiple imputations can be used to address systematic missingness. However, when data from different study sites cannot be pooled into a unified file, conventional imputation approaches become unavailable due to the absence of a basis for imputation. To address such challenges, we introduce an imputation method based on conditional quantiles – conditional quantile imputation (CQI) – which involves four steps: (i) estimating 99 quantiles for the systematically missing variable in studies with observed data; (ii) deriving a weighted average of regression coefficients across studies and transmitting it to sites with systematically missing data; (iii) imputing the systematically missing values based on observed data and the set of regression coefficients from step ii; and (iv) combining estimates of the substantive outcome model across imputations using Rubin's rules. We evaluate CQI in different simulation scenarios and illustrate it with an applied data example. We conclude that CQI can be a suitable approach for the imputation of systematically missing data when data from multiple studies cannot be pooled. [ABSTRACT FROM AUTHOR]

Increasing integrity concerns in medical research have prompted the development of tools to detect untrustworthy studies. Existing tools primarily assess published aggregate data (AD), though scrutiny of individual participant data (IPD) is often required to detect trustworthiness issues. Thus, we developed the IPD Integrity Tool for detecting integrity issues in randomised trials with IPD available. This manuscript describes the development of this tool. We conducted a literature review to collate and map existing integrity items. These were discussed with an expert advisory group; agreed items were included in a standardised tool and automated where possible. We piloted this tool in two IPD meta‐analyses (including 116 trials) and conducted preliminary validation checks on 13 datasets with and without known integrity issues. We identified 120 integrity items: 54 could be conducted using AD, 48 required IPD, and 18 were possible with AD, but more comprehensive with IPD. An initial reduced tool was developed through consensus involving 13 advisors, featuring 11 AD items across four domains, and 12 IPD items across eight domains. The tool was iteratively refined throughout piloting and validation. All studies with known integrity issues were accurately identified during validation. The final tool includes seven AD domains with 13 items and eight IPD domains with 18 items. The quality of evidence informing healthcare relies on trustworthy data. We describe the development of a tool to enable researchers, editors, and others to detect integrity issues using IPD. Detailed instructions for its application are published as a complementary manuscript in this issue. [ABSTRACT FROM AUTHOR]

Increasing concerns about the trustworthiness of research have prompted calls to scrutinise studies' Individual Participant Data (IPD), but guidance on how to do this was lacking. To address this, we developed the IPD Integrity Tool to screen randomised controlled trials (RCTs) for integrity issues. Development of the tool involved a literature review, consultation with an expert advisory group, piloting on two IPD meta‐analyses (including 73 trials with IPD), preliminary validation on 13 datasets with and without known integrity issues, and evaluation to inform iterative refinements. The IPD Integrity Tool comprises 31 items (13 study‐level, 18 IPD‐specific). IPD‐specific items are automated where possible, and are grouped into eight domains, including unusual data patterns, baseline characteristics, correlations, date violations, patterns of allocation, internal and external inconsistencies, and plausibility of data. Users rate each item as having either no issues, some/minor issue(s), or many/major issue(s) according to decision rules, and justification for each rating is recorded. Overall, the tool guides decision‐making by determining whether a trial has no concerns, some concerns requiring further information, or major concerns warranting exclusion from evidence synthesis or publication. In our preliminary validation checks, the tool accurately identified all five studies with known integrity issues. The IPD Integrity Tool enables users to assess the integrity of RCTs via examination of IPD. The tool may be applied by evidence synthesists, editors and others to determine whether an RCT should be considered sufficiently trustworthy to contribute to the evidence base that informs policy and practice. [ABSTRACT FROM AUTHOR]

Background: Reference intervals, which define an interval in which a specific proportion of measurements from a healthy population are expected to fall, are commonly used in medical practice. Synthesizing information from multiple studies through meta-analysis can provide a more precise and representative reference interval than one derived from a single study. However, the current approaches for estimating the reference interval from a meta-analysis mainly rely on aggregate data and require parametric distributional assumptions that cannot always be checked. Methods: With the availability of individual participant data (IPD), non-parametric methods can be used to estimate reference intervals without any distributional assumptions. Furthermore, patient-level covariates can be introduced to estimate personalized reference intervals that may be more applicable to specific patients. This paper introduces quantile regression as a method to estimate the reference interval from an IPD meta-analysis under the fixed effects model. Results: We compared several non-parametric bootstrap methods through simulation studies to account for within-study correlation. Under fixed effects model, we recommend keeping the studies fixed and only randomly sampling subjects with replacement within each study. Conclusion: We proposed to use the quantile regression in the IPD meta-analysis to estimate the reference interval. Based on the simulation results, we identify an optimal bootstrap strategy for estimating the uncertainty of the estimated reference interval. An example of liver stiffness measurements, a clinically important diagnostic test without explicitly established reference range in children, is provided to demonstrate the use of quantile regression in estimating both overall and subject-specific reference intervals. [ABSTRACT FROM AUTHOR]

Background: Induction of labour (IOL) is common practice and different methods carry different effectiveness and safety profiles. Objectives: To compare the effectiveness, and maternal and perinatal safety outcomes of IOL with vaginal misoprostol versus vaginal dinoprostone using individual participant data from randomised clinical trials. Search strategy: The following databases were searched from inception to March 2023: CINAHL Plus, ClinicalTrials.gov, Cochrane Pregnancy and Childbirth Group Trial Register, Ovid Embase, Ovid Emcare, Ovid MEDLINE, Scopus and the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP). Selection criteria: Randomised controlled trials (RCTs), with viable singleton gestation, no language restrictions, and all published and unpublished data. Data collection and analysis: An individual participant data meta‐analysis was carried out. Main results: Ten of 52 eligible trials provided individual participant data, of which two were excluded after checking data integrity. The remaining eight trials compared low‐dose vaginal misoprostol versus dinoprostone, including 4180 women undergoing IOL, which represents 32.8% of all participants in the published RCTs. Of these, 2077 were assigned to low‐dose vaginal misoprostol and 2103 were assigned to vaginal dinoprostone. Compared with vaginal dinoprostone, low‐dose vaginal misoprostol had a comparable rate of vaginal birth. Composite adverse perinatal outcomes did not differ between the groups. Compared with vaginal dinoprostone, composite adverse maternal outcomes were significantly lower with low‐dose vaginal misoprostol (aOR 0.80, 95% CI 0.65–0.98, P = 0.03, I2 = 0%). Conclusions: Low‐dose vaginal misoprostol and vaginal dinoprostone for IOL are comparable in terms of effectiveness and perinatal safety. However, low‐dose vaginal misoprostol is likely to lead to a lower rate of composite adverse maternal outcomes than vaginal dinoprostone. This article includes Author Insights, a video abstract available at: https://vimeo.com/915799731. [ABSTRACT FROM AUTHOR]

Background Major depressive disorder (MDD) is highly prevalent and burdensome for individuals and society. While there are psychological interventions able to prevent and treat MDD, uptake remains low. To overcome structural and attitudinal barriers, an indirect approach of using online insomnia interventions seems promising because insomnia is less stigmatized, predicts MDD onset, is often comorbid and can outlast MDD treatment. This individual-participant-data meta-analysis evaluated the potential of the online insomnia intervention GET.ON Recovery as an indirect treatment to reduce depressive symptom severity (DSS) and potential MDD onset across a range of participant characteristics. Methods Efficacy on depressive symptom outcomes was evaluated using multilevel regression models controlling for baseline severity. To identify potential effect moderators, clinical, sociodemographic, and work-related variables were investigated using univariable moderation and random-forest methodology before developing a multivariable decision tree. Results IPD were obtained from four of seven eligible studies (N = 561); concentrating on workers with high work-stress. DSS was significantly lower in the intervention group both at post-assessment (d = −0.71 [95% CI−0.92 to −0.51]) and at follow-up (d = −0.84 [95% CI −1.11 to −0.57]). In the subsample (n = 121) without potential MDD at baseline, there were no significant group differences in onset of potential MDD. Moderation analyses revealed that effects on DSS differed significantly across baseline severity groups with effect sizes between d = −0.48 and −0.87 (post) and d = − 0.66 to −0.99 (follow-up), while no other sociodemographic, clinical, or work-related characteristics were significant moderators. Conclusions An online insomnia intervention is a promising approach to effectively reduce DSS in a preventive and treatment setting. [ABSTRACT FROM AUTHOR]

The cornerstone of obesity treatment is behavioural weight management, resulting in significant improvements in cardio-metabolic and psychosocial health. However, there is ongoing concern that dietary interventions used for weight management may precipitate the development of eating disorders. Systematic reviews demonstrate that, while for most participants medically supervised obesity treatment improves risk scores related to eating disorders, a subset of people who undergo obesity treatment may have poor outcomes for eating disorders. This review summarises the background and rationale for the formation of the Eating Disorders In weight-related Therapy (EDIT) Collaboration. The EDIT Collaboration will explore the complex risk factor interactions that precede changes to eating disorder risk following weight management. In this review, we also outline the programme of work and design of studies for the EDIT Collaboration, including expected knowledge gains. The EDIT studies explore risk factors and the interactions between them using individual-level data from international weight management trials. Combining all available data on eating disorder risk from weight management trials will allow sufficient sample size to interrogate our hypothesis: that individuals undertaking weight management interventions will vary in their eating disorder risk profile, on the basis of personal characteristics and intervention strategies available to them. The collaboration includes the integration of health consumers in project development and translation. An important knowledge gain from this project is a comprehensive understanding of the impact of weight management interventions on eating disorder risk. [ABSTRACT FROM AUTHOR]

Background: Few studies have systematically examined the safety and effectiveness of antidepressant versus mood stabilizer monotherapy of bipolar II depression. To date, there are no aggregated or mega‐analyses of prospective trials of individual participant‐level data (IPD) to inform future treatment guidelines on the relative safety and effectiveness of antidepressant or lithium monotherapy. Methods: Data from a series of four independent, similarly designed trials of antidepressant or lithium monotherapy (where longitudinal IPD were available) (n = 393) were aggregated into an IPD dataset (i.e., mega‐analysis). Hierarchical log‐linear growth models were used to analyze primary outcome of change over time in Hamilton Rating Scale for Depression (HRSD) scores; while secondary outcomes examined Clinical Global Impressions severity (CGI/S) and change (CGI/C) scores, and change over time in Young Mania Rating (YMR) scores. Results: Relative to lithium monotherapy, antidepressant monotherapy demonstrated significantly greater symptom reduction on HRSD scores across time (b = −2.33, t = −6.68, p < 0.0001), significantly greater symptom reduction on the CGI/S across time (b = −0.414, t = −6.32, p < 0.001), and a significant improvement in CGI/C across time (b = −0.47, t = −7.43, p < 0.0001). No differences were observed in change over time for YMR scores between antidepressant and lithium monotherapy (b = 0.06, t = 0.49, p = 0.62). Conclusion: Findings from this IPD mega‐analysis of bipolar II depression trials suggest a divergence from current evidence‐based guidelines recommending combined mood stabilizer plus antidepressant therapy. The current mega‐analysis suggests that antidepressant monotherapy may provide superior short‐term effectiveness without clinically meaningful increase in treatment‐emergent hypomanic symptoms compared to lithium monotherapy. [ABSTRACT FROM AUTHOR]

Observational data provide invaluable real-world information in medicine, but certain methodological considerations are required to derive causal estimates. In this systematic review, we evaluated the methodology and reporting quality of individual-level patient data meta-analyses (IPD-MAs) conducted with non-randomized exposures, published in 2009, 2014, and 2019 that sought to estimate a causal relationship in medicine. We screened over 16,000 titles and abstracts, reviewed 45 full-text articles out of the 167 deemed potentially eligible, and included 29 into the analysis. Unfortunately, we found that causal methodologies were rarely implemented, and reporting was generally poor across studies. Specifically, only three of the 29 articles used quasi-experimental methods, and no study used G-methods to adjust for time-varying confounding. To address these issues, we propose stronger collaborations between physicians and methodologists to ensure that causal methodologies are properly implemented in IPD-MAs. In addition, we put forward a suggested checklist of reporting guidelines for IPD-MAs that utilize causal methods. This checklist could improve reporting thereby potentially enhancing the quality and trustworthiness of IPD-MAs, which can be considered one of the most valuable sources of evidence for health policy. [ABSTRACT FROM AUTHOR]

Background: The provision of data sharing statements (DSS) for clinical trials has been made mandatory by different stakeholders. DSS are a device to clarify whether there is intention to share individual participant data (IPD). What is missing is a detailed assessment of whether DSS are providing clear and understandable information about the conditions for data sharing of IPD for secondary use. Methods: A random sample of 200 COVID-19 clinical trials with explicit DSS was drawn from the ECRIN clinical research metadata repository. The DSS were assessed and classified, by two experienced experts and one assessor with less experience in data sharing (DS), into different categories (unclear, no sharing, no plans, yes but vague, yes on request, yes with specified storage location, yes but with complex conditions). Results: Between the two experts the agreement was moderate to substantial (kappa=0.62, 95% CI [0.55, 0.70]). Agreement considerably decreased when these experts were compared with a third person who was less experienced and trained in data sharing ("assessor") (kappa=0.33, 95% CI [0.25, 0.41]; 0.35, 95% CI [0.27, 0.43]). Between the two experts and under supervision of an independent moderator, a consensus was achieved for those cases, where both experts had disagreed, and the result was used as "gold standard" for further analysis. At least some degree of willingness of DS (data sharing) was expressed in 63.5% (127/200) cases. Of these cases, around one quarter (31/127) were vague statements of support for data sharing but without useful detail. In around half of the cases (60/127) it was stated that IPD could be obtained by request. Only in in slightly more than 10% of the cases (15/127) it was stated that the IPD would be transferred to a specific data repository. In the remaining cases (21/127), a more complex regime was described or referenced, which could not be allocated to one of the three previous groups. As a result of the consensus meetings, the classification system was updated. Conclusion: The study showed that the current DSS that imply possible data sharing are often not easy to interpret, even by relatively experienced staff. Machine based interpretation, which would be necessary for any practical application, is currently not possible. Machine learning and / or natural language processing techniques might improve machine actionability, but would represent a very substantial investment of research effort. The cheaper and easier option would be for data providers, data requestors, funders and platforms to adopt a clearer, more structured and more standardised approach to specifying, providing and collecting DSS. Trial registration: The protocol for the study was pre-registered on ZENODO (https://zenodo.org/record/7064624#.Y4DIAHbMJD8). [ABSTRACT FROM AUTHOR]

BACKGROUND IVF and IUI with ovarian stimulation (IUI-OS) are widely used in managing unexplained infertility. IUI-OS is generally considered first-line therapy, followed by IVF only if IUI-OS is unsuccessful after several attempts. However, there is a growing interest in using IVF for immediate treatment because it is believed to lead to higher live birth rates and shorter time to pregnancy. OBJECTIVE AND RATIONALE Randomized controlled trials (RCTs) comparing IVF versus IUI-OS had varied study designs and findings. Some RCTs used complex algorithms to combine IVF and IUI-OS, while others had unequal follow-up time between arms or compared treatments on a per-cycle basis, which introduced biases. Comparing cumulative live birth rates of IVF and IUI-OS within a consistent time frame is necessary for a fair head-to-head comparison. Previous meta-analyses of RCTs did not consider the time it takes to achieve pregnancy, which is not possible using aggregate data. Individual participant data meta-analysis (IPD-MA) allows standardization of follow-up time in different trials and time-to-event analysis methods. We performed this IPD-MA to investigate if IVF increases cumulative live birth rate considering the time leading to pregnancy and reduces multiple pregnancy rate compared to IUI-OS in couples with unexplained infertility. SEARCH METHODS We searched MEDLINE, EMBASE, CENTRAL, PsycINFO, CINAHL, and the Cochrane Gynaecology and Fertility Group Specialised Register to identify RCTs that completed data collection before June 2021. A search update was carried out in January 2023. RCTs that compared IVF/ICSI to IUI-OS in couples with unexplained infertility were eligible. We invited author groups of eligible studies to join the IPD-MA and share the deidentified IPD of their RCTs. IPD were checked and standardized before synthesis. The quality of evidence was assessed using the Risk of Bias 2 tool. OUTCOMES Of eight potentially eligible RCTs, two were considered awaiting classification. In the other six trials, four shared IPD of 934 women, of which 550 were allocated to IVF and 383 to IUI-OS. Because the interventions were unable to blind, two RCTs had a high risk of bias, one had some concerns, and one had a low risk of bias. Considering the time to pregnancy leading to live birth, the cumulative live birth rate was not significantly higher in IVF compared to that in IUI-OS (4 RCTs, 908 women, 50.3% versus 43.2%, hazard ratio 1.19, 95% CI 0.81–1.74, I 2 = 42.4%). For the safety primary outcome, the rate of multiple pregnancy was not significantly lower in IVF than IUI-OS (3 RCTs, 890 women, 3.8% versus 5.2% of all couples randomized, odds ratio 0.78, 95% CI 0.41–1.50, I 2 = 0.0%). WIDER IMPLICATIONS There is no robust evidence that in couples with unexplained infertility IVF achieves pregnancy leading to live birth faster than IUI-OS. IVF and IUI-OS are both viable options in terms of effectiveness and safety for managing unexplained infertility. The associated costs of interventions and the preference of couples need to be weighed in clinical decision-making. [ABSTRACT FROM AUTHOR]

Background: Selective reporting of results from only well-performing cut-offs leads to biased estimates of accuracy in primary studies of questionnaire-based screening tools and in meta-analyses that synthesize results. Individual participant data meta-analysis (IPDMA) of sensitivity and specificity at each cut-off via bivariate random-effects models (BREMs) can overcome this problem. However, IPDMA is laborious and depends on the ability to successfully obtain primary datasets, and BREMs ignore the correlation between cut-offs within primary studies. Methods: We compared the performance of three recent multiple cut-off models developed by Steinhauser et al., Jones et al., and Hoyer and Kuss, that account for missing cut-offs when meta-analyzing diagnostic accuracy studies with multiple cut-offs, to BREMs fitted at each cut-off. We used data from 22 studies of the accuracy of the Edinburgh Postnatal Depression Scale (EPDS; 4475 participants, 758 major depression cases). We fitted each of the three multiple cut-off models and BREMs to a dataset with results from only published cut-offs from each study (published data) and an IPD dataset with results for all cut-offs (full IPD data). We estimated pooled sensitivity and specificity with 95% confidence intervals (CIs) for each cut-off and the area under the curve. Results: Compared to the BREMs fitted to the full IPD data, the Steinhauser et al., Jones et al., and Hoyer and Kuss models fitted to the published data produced similar receiver operating characteristic curves; though, the Hoyer and Kuss model had lower area under the curve, mainly due to estimating slightly lower sensitivity at lower cut-offs. When fitting the three multiple cut-off models to the full IPD data, a similar pattern of results was observed. Importantly, all models had similar 95% CIs for sensitivity and specificity, and the CI width increased with cut-off levels for sensitivity and decreased with an increasing cut-off for specificity, even the BREMs which treat each cut-off separately. Conclusions: Multiple cut-off models appear to be the favorable methods when only published data are available. While collecting IPD is expensive and time consuming, IPD can facilitate subgroup analyses that cannot be conducted with published data only. [ABSTRACT FROM AUTHOR]

Individual participant data (IPD) meta‐analyses of randomised trials are considered a reliable way to assess participant‐level treatment effect modifiers but may not make the best use of the available data. Traditionally, effect modifiers are explored one covariate at a time, which gives rise to the possibility that evidence of treatment‐covariate interaction may be due to confounding from a different, related covariate. We aimed to evaluate current practice when estimating treatment‐covariate interactions in IPD meta‐analysis, specifically focusing on involvement of additional covariates in the models. We reviewed 100 IPD meta‐analyses of randomised trials, published between 2015 and 2020, that assessed at least one treatment‐covariate interaction. We identified four approaches to handling additional covariates: (1) Single interaction model (unadjusted): No additional covariates included (57/100 IPD meta‐analyses); (2) Single interaction model (adjusted): Adjustment for the main effect of at least one additional covariate (35/100); (3) Multiple interactions model: Adjustment for at least one two‐way interaction between treatment and an additional covariate (3/100); and (4) Three‐way interaction model: Three‐way interaction formed between treatment, the additional covariate and the potential effect modifier (5/100). IPD is not being utilised to its fullest extent. In an exemplar dataset, we demonstrate how these approaches lead to different conclusions. Researchers should adjust for additional covariates when estimating interactions in IPD meta‐analysis providing they adjust their main effects, which is already widely recommended. Further, they should consider whether more complex approaches could provide better information on who might benefit most from treatments, improving patient choice and treatment policy and practice. [ABSTRACT FROM AUTHOR]

Introduction: Numerous arguments have been advanced for broadly sharing de‐identified, participant‐level clinical trial data. However, data sharing in pragmatic clinical trials (PCTs) presents ethical challenges. While prior scholarship has described aspects of PCTs that raise distinct considerations for data sharing, there have been no reports of the experiences of those at the leading edge of data‐sharing efforts for PCTs, including how these particular challenges have been navigated. To address this gap, we conducted interviews with key stakeholders, with a focus on the ethical issues presented by sharing data from PCTs. Methods: We recruited respondents using purposive sampling to reflect the range of stakeholder groups affected by efforts to expand PCT data sharing. Through semi‐structured interviews, we explored respondents' experiences and perceptions about sharing de‐identified, individual‐level data from PCTs. An integrated approach was used to identify and describe key themes. Results: We conducted 40 interviews between April and September 2022. Five overarching themes emerged through analysis: (1) challenges in sharing data collected under a waiver or alteration of consent; (2) conflicting views regarding PCT patient‐subject preferences for data sharing; (3) identification of respect‐promoting practices beyond consent; (4) concerns about elevated risks or burdens from sharing PCT data; and (5) diverse views about the likely benefits resulting from sharing PCT data. Conclusion: Our data indicate unresolved tensions in how to fulfill the expectation to broadly share de‐identified, individual‐level data from PCTs, and suggest that those promulgating and implementing data‐sharing policies must be sensitive to PCT‐specific considerations. Future work could inform efforts to tailor data‐sharing policy and practice to reflect the challenges presented by PCTs, including sharing experiences from trials that have successfully navigated these tensions. [ABSTRACT FROM AUTHOR]

Background: Depression is a highly common and recurrent condition. Predicting who is at most risk of relapse or recurrence can inform clinical practice. Applying machine-learning methods to Individual Participant Data (IPD) can be promising to improve the accuracy of risk predictions. Methods: Individual data of four Randomized Controlled Trials (RCTs) evaluating antidepressant treatment compared to psychological interventions with tapering ( N = 714 ) were used to identify predictors of relapse and/or recurrence. Ten baseline predictors were assessed. Decision trees with and without gradient boosting were applied. To study the robustness of decision-tree classifications, we also performed a complementary logistic regression analysis. Results: The combination of age, age of onset of depression, and depression severity significantly enhances the prediction of relapse risk when compared to classifiers solely based on depression severity. The studied decision trees can (i) identify relapse patients at intake with an accuracy, specificity, and sensitivity of about 55% (without gradient boosting) and 58% (with gradient boosting), and (ii) slightly outperform classifiers that are based on logistic regression. Conclusions: Decision tree classifiers based on multiple–rather than single–risk indicators may be useful for developing treatment stratification strategies. These classification models have the potential to contribute to the development of methods aimed at effectively prioritizing treatment for those individuals who require it the most. Our results also underline the existing gaps in understanding how to accurately predict depressive relapse. [ABSTRACT FROM AUTHOR]

Prior research suggests that brief interventions (BIs) for alcohol and other drug use may vary in effectiveness across patient sociodemographic factors. The objective of this individual participant data (IPD) meta-analysis was to explore for whom BIs delivered in general healthcare settings are more or less effective. We examined variability in BI effects by patient age, sex, employment, education, relationship status, and baseline severity of substance use using a two-stage IPD meta-analysis approach. All trials included in a parent aggregate data meta-analysis (k = 116) were invited to contribute IPD, and 29 trials provided patient-level data (12,074 participants). Among females, BIs led to significant reductions in binge alcohol consumption ( g ¯ = 0.09, 95% CI [0.03, 0.14]), frequency of alcohol consumption ( g ¯ = 0.10, 95% CI [0.03, 0.17]), and alcohol-related consequences ( g ¯ = 0.16, 95% CI [0.08, 0.25]), as well as greater substance use treatment utilization ( g ¯ = 0.25, 95% CI [0.21, 0.30]). BIs yielded larger reductions in frequency of alcohol consumption at 3-month follow-up for individuals with less than a high school level education ( g ¯ = 0.16, 95% CI [0.09, 0.22]). Given evidence demonstrating modest BI effects on alcohol use and mixed or null findings for BI effects on other drug use, BI research should continue to investigate potential drivers of effect magnitude and variation. Protocol registration details: The protocol for this review was pre-registered in PROSPERO #CRD42018086832 and the analysis plan was pre-registered in OSF: osf.io/m48g6. [ABSTRACT FROM AUTHOR]

Evidence synthesis involves drawing conclusions from trial samples that may differ from the target population of interest, and there is often heterogeneity among trials in sample characteristics, treatment implementation, study design, and assessment of covariates. Stitching together this patchwork of evidence requires subject-matter knowledge, a clearly defined target population, and guidance on how to weigh evidence from different trials. Transportability analysis has provided formal identifiability conditions required to make unbiased causal inference in the target population. In this manuscript, we review these conditions along with an additional assumption required to address systematic missing data. The identifiability conditions highlight the importance of accounting for differences in treatment effect modifiers between the populations underlying the trials and the target population. We perform simulations to evaluate the bias of conventional random effect models and multiply imputed estimates using the pooled trials sample and describe causal estimators that explicitly address trial-to-target differences in key covariates in the context of systematic missing data. Results indicate that the causal transportability estimators are unbiased when treatment effect modifiers are accounted for in the analyses. Results also highlight the importance of carefully evaluating identifiability conditions for each trial to reduce bias due to differences in participant characteristics between trials and the target population. Bias can be limited by adjusting for covariates that are strongly correlated with missing treatment effect modifiers, including data from trials that do not differ from the target on treatment modifiers, and removing trials that do differ from the target and did not assess a modifier. [ABSTRACT FROM AUTHOR]

To evaluate and optimize brief alcohol interventions (BAIs), it is critical to have a credible overall effect size estimate as a benchmark. Estimating such an effect size has been challenging because alcohol outcomes often represent responses from a mixture of individuals: those at high risk for alcohol misuse, occasional nondrinkers, and abstainers. Moreover, some BAIs exclusively focus on heavy drinkers, whereas others take a universal prevention approach. Depending on sample characteristics, the outcome distribution might have many zeros or very few zeros and overdispersion; consequently, the most appropriate statistical model may differ across studies. We synthesized individual participant data (IPD) from 19 studies in Project INTEGRATE (Mun et al., 2015b) that randomly allocated participants to intervention and control groups (N = 7,704 participants, 38.4% men, 74.7% White, 58.5% first-year students). We sequentially estimated marginalized zero-inflated Poisson (Long et al., 2014) or negative binomial regression models to obtain covariate-adjusted, study-specific intervention effect estimates in the first step, which were subsequently combined in a random-effects meta-analysis model in the second step. BAIs produced a statistically significant 8% advantage in the mean number of drinks at both 1–3 months (RR = 0.92, 95% CI = [0.85, 0.98]) and 6 months (RR = 0.92, 95% CI = [0.85, 0.99]) compared to controls. At 9–12 months, there was no statistically significant difference in the mean number of drinks between BAIs and controls. In conclusion, BAIs are effective at reducing the mean number of drinks through at least 6 months post intervention. IPD can play a critical role in deriving findings that could not be obtained in original individual studies or standard aggregate data meta-analyses. [ABSTRACT FROM AUTHOR]

Introduction: Evidence comparing double‐balloon vs single‐balloon catheter for induction of labor is divided. We aim to compare the efficacy and safety of double‐vs single‐balloon catheters using individual participant data. Material and methods: A search of Ovid MEDLINE, Embase, Ovid Emcare, CINAHL Plus, Scopus, and clinicaltrials.gov was conducted for randomized controlled trials published from March 2019 until April 13, 2021. Earlier trials were identified from the Cochrane Review on Mechanical Methods for Induction of Labour. Randomized controlled trials that compared double‐balloon with single‐balloon catheters for induction of labor in singleton gestations were eligible. Participant‐level data were sought from trial investigators and an individual participant data meta‐analysis was performed. The primary outcomes were rates of vaginal birth achieved, a composite measure of adverse maternal outcomes and a composite measure of adverse perinatal outcomes. We used a two‐stage random‐effects model. Data were analyzed from the intention‐to‐treat perspective. Results: Of the eight eligible randomized controlled trials, three shared individual‐level data with a total of 689 participants, 344 women in the double‐balloon catheter group and 345 women in the single‐balloon catheter group. The difference in the rate of vaginal birth between double‐balloon catheter and single‐balloon catheter was not statistically significant (relative risk [RR] 0.93, 95% confidence interval [CI] 0.86–1.00, p = 0.050; I2 0%; moderate‐certainty evidence). Both perinatal outcomes (RR 0.81, 95% CI 0.54–1.21, p = 0.691; I2 0%; moderate‐certainty evidence) and maternal composite outcomes (RR 0.65, 95% CI 0.15–2.87, p = 0.571; I2 55.46%; low‐certainty evidence) were not significantly different between the two groups. Conclusions: Single‐balloon catheter is at least comparable to double‐balloon catheter in terms of vaginal birth rate and maternal and perinatal safety outcomes. [ABSTRACT FROM AUTHOR]

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Objective: Identify independent and novel risk factors for late‐preterm (28–36 weeks) and term (≥37 weeks) stillbirth and explore development of a risk‐prediction model. Design: Secondary analysis of an Individual Participant Data (IPD) meta‐analysis investigating modifiable stillbirth risk factors. Setting: An IPD database from five case–control studies in New Zealand, Australia, the UK and an international online study. Population: Women with late‐stillbirth (cases, n = 851), and ongoing singleton pregnancies from 28 weeks' gestation (controls, n = 2257). Methods: Established and novel risk factors for late‐preterm and term stillbirth underwent univariable and multivariable logistic regression modelling with multiple sensitivity analyses. Variables included maternal age, body mass index (BMI), parity, mental health, cigarette smoking, second‐hand smoking, antenatal‐care utilisation, and detailed fetal movement and sleep variables. Main outcome measures: Independent risk factors with adjusted odds ratios (aOR) for late‐preterm and term stillbirth. Results: After model building, 575 late‐stillbirth cases and 1541 controls from three contributing case–control studies were included. Risk factor estimates from separate multivariable models of late‐preterm and term stillbirth were compared. As these were similar, the final model combined all late‐stillbirths. The single multivariable model confirmed established demographic risk factors, but additionally showed that fetal movement changes had both increased (decreased frequency) and reduced (hiccoughs, increasing strength, frequency or vigorous fetal movements) aOR of stillbirth. Poor antenatal‐care utilisation increased risk while more‐than‐adequate care was protective. The area‐under‐the‐curve was 0.84 (95% CI 0.82–0.86). Conclusions: Similarities in risk factors for late‐preterm and term stillbirth suggest the same approach for risk‐assessment can be applied. Detailed fetal movement assessment and inclusion of antenatal‐care utilisation could be valuable in late‐stillbirth risk assessment. This article includes Author Insights, a video abstract available at: https://players.brightcove.net/3806881048001/default%5fdefault/index.html?videoId=6320088103112. Linked article: This article is commented on by J. Jardine, pp. 1071 in this issue. To view this mini commentary visit https://doi.org/10.1111/1471‐0528.17469. [ABSTRACT FROM AUTHOR]

Background: Brief motivational interventions (BMIs) are one of the most effective individually focused alcohol intervention strategies for college students. Despite the central theoretical role of motivation for change in BMIs, it is unclear whether BMIs increase motivation to change drinking behavior. We conducted a two‐step meta‐analysis of individual participant data (IPD) to examine whether BMIs increase motivation for change. N = 5903;59% women, 72% White) from Project INTEGRATE. The BMIs included individually delivered motivational interviewing with personalized feedback (MI + PF), stand‐alone personalized feedback (PF), and group‐based motivational interviewing (GMI). Methods: We included 15 trials of BMI (N = 5903;59% women, 72% White) from Project INTEGRATE. The BMIs included individually‐delivered motivational interviewing with personalized feedback (MI + PF), stand‐alone personalized feedback (PF), and group‐based motivational interviewing (GMI). Different measures and responses used in the original trials were harmonized. Effect size estimates were derived from a model that adjusted for baseline motivation and demographic variables for each trial (step 1) and subsequently combined in a random‐effects meta‐analysis (step 2). Results: The overall intervention effect of BMIs on motivation for change was not statistically significant (standard mean difference [SMD]: 0.026, 95% CI: [−0.001, 0.053], p = 0.06, k = 19 comparisons). Of the three subtypes of BMIs, GMI, which tended to provide motivation‐targeted content, had a statistically significant intervention effect on motivation, compared with controls (SMD: 0.055, 95% CI: [0.007, 0.103], p = 0.025, k = 5). By contrast, there was no evidence that MI + PF (SMD = 0.04, 95% CI: [−0.02, 0.10], k = 6, p = 0.20) nor PF increased motivation (SMD = 0.005, 95% CI: [−0.028, 0.039], k = 8, p = 0.75), compared with controls. Post hoc meta‐regression analysis suggested that motivation sharply decreased each month within the first 3 months postintervention (b = −0.050, z = −2.80, p = 0.005 for k = 14). Conclusions: Although BMIs provide motivational content and normative feedback and are assumed to motivate behavior change, the results do not wholly support the hypothesis that BMIs improve motivation for change. Changing motivation is difficult to assess during and following interventions, but it is still a theoretically important clinical endpoint. Further, the evidence cautiously suggests that changing motivation may be achievable, especially if motivation‐targeted content components are provided. [ABSTRACT FROM AUTHOR]

A common problem in the analysis of multiple data sources, including individual participant data meta‐analysis (IPD‐MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate unbiased estimation of adjusted and unadjusted exposure‐outcome associations and between‐study heterogeneity in IPD‐MA, where the extent and nature of exposure misclassification may vary across studies. We present Bayesian methods that allow misclassification of binary exposure variables to depend on study‐ and participant‐level characteristics. In an example of the differential diagnosis of dengue using two variables, where the gold standard measurement for the exposure variable was unavailable for some studies which only measured a surrogate prone to misclassification, our methods yielded more accurate estimates than analyses naive with regard to misclassification or based on gold standard measurements alone. In a simulation study, the evaluated misclassification model yielded valid estimates of the exposure‐outcome association, and was more accurate than analyses restricted to gold standard measurements. Our proposed framework can appropriately account for the presence of binary exposure misclassification in IPD‐MA. It requires that some studies supply IPD for the surrogate and gold standard exposure, and allows misclassification to follow a random effects distribution across studies conditional on observed covariates (and outcome). The proposed methods are most beneficial when few large studies that measured the gold standard are available, and when misclassification is frequent. [ABSTRACT FROM AUTHOR]

Background: Bacterial vaginosis (BV) increases preterm delivery (PTD) risk, but treatment trials showed mixed results in preventing PTD. Objectives: Determine, using individual participant data (IPD), whether BV treatment during pregnancy reduced PTD or prolonged time‐to‐delivery. Data Sources: Cochrane Systematic Review (2013), MEDLINE, EMBASE, journal searches, and searches (January 2013–September 2022) ("bacterial vaginosis AND pregnancy") of (i) clinicaltrials.gov; (ii) Cochrane Central Register of Controlled Trials; (iii) World Health Organization International Clinical Trials Registry Platform Portal; and (iv) Web of Science ("bacterial vaginosis"). Study Selection and Data Extraction: Studies randomising asymptomatic pregnant individuals with BV to antibiotics or control, measuring delivery gestation. Extraction was from original data files. Bias risk was assessed using the Cochrane tool. Analysis used "one‐step" logistic and Cox random effect models, adjusting gestation at randomisation and PTD history; heterogeneity by I2. Subgroup analysis tested interactions with treatment. In sensitivity analyses, studies not providing IPD were incorporated by "multiple random‐donor hot‐deck" imputation, using IPD studies as donors. Results: There were 121 references (96 studies) with 23 eligible trials (11,979 participants); 13 studies (6915 participants) provided IPD; 12 (6115) were incorporated. Results from 9 (4887 participants) not providing IPD were imputed. Odds ratios for PTD for metronidazole and clindamycin versus placebo were 1.00 (95% CI 0.84, 1.17), I2 = 62%, and 0.59 (95% CI 0.42, 0.82), I2 = 0 before; and 0.95 (95% CI 0.81, 1.11), I2 = 59%, and 0.90 (95% CI: 0.72, 1.12), I2 = 0, after imputation. Time‐to‐delivery did not differ from null with either treatment. Including imputed IPD, there was no evidence that either drug was more effective when administered earlier, or among those with a PTD history. Conclusions: Clindamycin, but not metronidazole, was beneficial in studies providing IPD, but after imputing data from missing IPD studies, treatment of BV during pregnancy did not reduce PTD, nor prolong pregnancy, in any subgroup or when started earlier in gestation. [ABSTRACT FROM AUTHOR]

Descriptive analyses of socially important or theoretically interesting phenomena and trends are a vital component of research in the behavioral, social, economic, and health sciences. Such analyses yield reliable results when using representative individual participant data (IPD) from studies with complex survey designs, including educational large‐scale assessments (ELSAs) or social, health, and economic survey and panel studies. The meta‐analytic integration of these results offers unique and novel research opportunities to provide strong empirical evidence of the consistency and generalizability of important phenomena and trends. Using ELSAs as an example, this tutorial offers methodological guidance on how to use the two‐stage approach to IPD meta‐analysis to account for the statistical challenges of complex survey designs (e.g., sampling weights, clustered and missing IPD), first, to conduct descriptive analyses (Stage 1), and second, to integrate results with three‐level meta‐analytic and meta‐regression models to take into account dependencies among effect sizes (Stage 2). The two‐stage approach is illustrated with IPD on reading achievement from the Programme for International Student Assessment (PISA). We demonstrate how to analyze and integrate standardized mean differences (e.g., gender differences), correlations (e.g., with students' socioeconomic status [SES]), and interactions between individual characteristics at the participant level (e.g., the interaction between gender and SES) across several PISA cycles. All the datafiles and R scripts we used are available online. Because complex social, health, or economic survey and panel studies share many methodological features with ELSAs, the guidance offered in this tutorial is also helpful for synthesizing research evidence from these studies. [ABSTRACT FROM AUTHOR]

Numerous arguments have been advanced for broadly sharing de-identified, participant-level clinical trials data, and trial sponsors and journals are increasingly requiring it. However, data sharing in pragmatic clinical trials presents ethical challenges related to the use of waivers or alterations of informed consent for some pragmatic clinical trials and corresponding limitations of informed consent to guide sharing decisions; the potential for data sharing in pragmatic clinical trials to present risks not only for individual patient-subjects, but also for health systems and the clinicians within them; sharing of data from electronic health records instead of data newly collected for research purposes; and researchers' limited capacity to control sensitive data within an electronic health record and potential implications of such limits for meeting obligations inherent to Certificates of Confidentiality. These challenges raise questions about the extent to which traditional research ethics governance structures are capable of guiding decisions about pragmatic clinical trial data sharing. This article identifies and examines these ethical challenges for pragmatic clinical trial data sharing. We suggest several areas for future empirical scholarship, including the need to identify patient and public attitudes regarding pragmatic clinical trial data sharing as well as to assess the demand for pragmatic clinical trial data and the correspondingly likely benefit of such sharing. Further conceptual work is also needed to explore how requirements to respect patient-subjects about whom data are shared in the context of pragmatic clinical trials should be understood, particularly in the absence of informed consent for initial research activities, and the appropriate balance between promoting the generation of socially valuable knowledge and respecting autonomy. [ABSTRACT FROM AUTHOR]

Background: Stroke rehabilitation interventions are routinely personalized to address individuals' needs, goals, and challenges based on evidence from aggregated randomized controlled trials (RCT) data and meta-syntheses. Individual participant data (IPD) meta-analyses may better inform the development of precision rehabilitation approaches, quantifying treatment responses while adjusting for confounders and reducing ecological bias. Aim: We explored associations between speech and language therapy (SLT) interventions frequency (days/week), intensity (h/week), and dosage (total SLT-hours) and language outcomes for different age, sex, aphasia severity, and chronicity subgroups by undertaking prespecified subgroup network meta-analyses of the RELEASE database. Methods: MEDLINE, EMBASE, and trial registrations were systematically searched (inception-Sept2015) for RCTs, including ⩾ 10 IPD on stroke-related aphasia. We extracted demographic, stroke, aphasia, SLT, and risk of bias data. Overall-language ability, auditory comprehension, and functional communication outcomes were standardized. A one-stage, random effects, network meta-analysis approach filtered IPD into a single optimal model, examining SLT regimen and language recovery from baseline to first post-intervention follow-up, adjusting for covariates identified a-priori. Data were dichotomized by age (⩽/> 65 years), aphasia severity (mild–moderate/ moderate–severe based on language outcomes' median value), chronicity (⩽/> 3 months), and sex subgroups. We reported estimates of means and 95% confidence intervals. Where relative variance was high (> 50%), results were reported for completeness. Results: 959 IPD (25 RCTs) were analyzed. For working-age participants, greatest language gains from baseline occurred alongside moderate to high-intensity SLT (functional communication 3-to-4 h/week; overall-language and comprehension > 9 h/week); older participants' greatest gains occurred alongside low-intensity SLT (⩽ 2 h/week) except for auditory comprehension (> 9 h/week). For both age-groups, SLT-frequency and dosage associated with best language gains were similar. Participants ⩽ 3 months post-onset demonstrated greatest overall-language gains for SLT at low intensity/moderate dosage (⩽ 2 SLT-h/week; 20-to-50 h); for those > 3 months, post-stroke greatest gains were associated with moderate-intensity/high-dosage SLT (3–4 SLT-h/week; ⩾ 50 hours). For moderate–severe participants, 4 SLT-days/week conferred the greatest language gains across outcomes, with auditory comprehension gains only observed for ⩾ 4 SLT-days/week; mild–moderate participants' greatest functional communication gains were associated with similar frequency (⩾ 4 SLT-days/week) and greatest overall-language gains with higher frequency SLT (⩾ 6 days/weekly). Males' greatest gains were associated with SLT of moderate (functional communication; 3-to-4 h/weekly) or high intensity (overall-language and auditory comprehension; (> 9 h/weekly) compared to females for whom the greatest gains were associated with lower-intensity SLT (< 2 SLT-h/weekly). Consistencies across subgroups were also evident; greatest overall-language gains were associated with 20-to-50 SLT-h in total; auditory comprehension gains were generally observed when SLT > 9 h over ⩾ 4 days/week. Conclusions: We observed a treatment response in most subgroups' overall-language, auditory comprehension, and functional communication language gains. For some, the maximum treatment response varied in association with different SLT-frequency, intensity, and dosage. Where differences were observed, working-aged, chronic, mild–moderate, and male subgroups experienced their greatest language gains alongside high-frequency/intensity SLT. In contrast, older, moderate–severely impaired, and female subgroups within 3 months of aphasia onset made their greatest gains for lower-intensity SLT. The acceptability, clinical, and cost effectiveness of precision aphasia rehabilitation approaches based on age, sex, aphasia severity, and chronicity should be evaluated in future clinical RCTs. [ABSTRACT FROM AUTHOR]

One-stage meta-analysis of individual participant data (IPD) poses several statistical and computational challenges. For time-to-event outcomes, the approach requires the estimation of complicated nonlinear mixed-effects models that are flexible enough to realistically capture the most important characteristics of the IPD. We present a model class that incorporates general normally distributed random effects into linear transformation models. We discuss extensions to model between-study heterogeneity in baseline risks and covariate effects and also relax the assumption of proportional hazards. Within the proposed framework, data with arbitrary random censoring patterns can be handled. The accompanying $\textsf{R}$ package tramME utilizes the Laplace approximation and automatic differentiation to perform efficient maximum likelihood estimation and inference in mixed-effects transformation models. We compare several variants of our model to predict the survival of patients with chronic obstructive pulmonary disease using a large data set of prognostic studies. Finally, a simulation study is presented that verifies the correctness of the implementation and highlights its efficiency compared to an alternative approach. [ABSTRACT FROM AUTHOR]

Summary: Background: Although early childhood obesity prevention has become an important issue internationally, little evidence exists regarding longer term effects (i.e., sustainability) of early interventions. Objective: To determine whether intervention benefits at 2 years of age were sustained at 3.5 and 5 years. Methods: Follow‐up of the Early Prevention of Obesity in Children (EPOCH) individual participant data prospective meta‐analysis of four randomized controlled trials including 2196 mother–child dyads at baseline. Interventions were home‐ or community‐based, commenced within 6 months of birth, ended by 2 years of age, and comprised multiple sessions. Controls received standard care. BMI z‐score (primary outcome), other anthropometric measures and weight‐related behaviours were initially measured at 1.5–2 years and followed up at 3.5 and 5 years. Results: Positive intervention effects on BMI z‐scores at 1.5–2 years of age were not apparent by 3.5 years (−0.04 adjusted mean difference; 95% CI:−0.14, 0.06; p = 0.424), and 5 years (0.03; 95% CI: −0.08, 0.14; p = 0.60). While prolonged intervention benefits were detected for a few, but not the majority of, weight‐related behaviours at 3.5 years, these effects diminished over time. Conclusion: This meta‐analysis found that initial positive effects of childhood obesity interventions faded out after interventions ended, pointing toward the importance of a suite of interventions implemented at multiple stages across childhood. [ABSTRACT FROM AUTHOR]

Background: Intrauterine insemination with ovarian stimulation (IUI-OS) is a first-line treatment for unexplained infertility. Gonadotrophins, letrozole and clomiphene citrate (CC) are commonly used agents during IUI-OS and have been compared in multiple aggregate data meta-analyses, with substantial heterogeneity and no analysis on time-to-event outcomes. Individual participant data meta-analysis (IPD-MA) is considered the gold standard for evidence synthesis as it can offset inadequate reporting of individual studies by obtaining the IPD, and allows analyses on treatment-covariate interactions to identify couples who benefit most from a particular treatment.Objective and Rationale: We performed this IPD-MA to compare the effectiveness and safety of ovarian stimulation with gonadotrophins, letrozole and CC and to explore treatment-covariate interactions for important baseline characteristics in couples undergoing IUI.Search Methods: We searched electronic databases including MEDLINE, EMBASE, CENTRAL, CINAHL, and PsycINFO from their inception to 28 June 2021. We included randomized controlled trials (RCTs) comparing IUI-OS with gonadotrophins, letrozole and CC among couples with unexplained infertility. We contacted the authors of eligible RCTs to share the IPD and established the IUI IPD-MA Collaboration. The primary effectiveness outcome was live birth and the primary safety outcome was multiple pregnancy. Secondary outcomes were other reproductive outcomes, including time to conception leading to live birth. We performed a one-stage random effects IPD-MA.Outcomes: Seven of 22 (31.8%) eligible RCTs provided IPD of 2495 couples (62.4% of the 3997 couples participating in 22 RCTs), of which 2411 had unexplained infertility and were included in this IPD-MA. Six RCTs (n = 1511) compared gonadotrophins with CC, and one (n = 900) compared gonadotrophins, letrozole and CC. Moderate-certainty evidence showed that gonadotrophins increased the live birth rate compared to CC (6 RCTs, 2058 women, RR 1.30, 95% CI 1.12-1.51, I2 = 26%). Low-certainty evidence showed that gonadotrophins may also increase the multiple pregnancy rate compared to CC (6 RCTs, 2058 women, RR 2.17, 95% CI 1.33-3.54, I2 = 69%). Heterogeneity on multiple pregnancy could be explained by differences in gonadotrophin starting dose and choice of cancellation criteria. Post-hoc sensitivity analysis on RCTs with a low starting dose of gonadotrophins (≤75 IU) confirmed increased live birth rates compared to CC (5 RCTs, 1457 women, RR 1.26, 95% CI 1.05-1.51), but analysis on only RCTs with stricter cancellation criteria showed inconclusive evidence on live birth (4 RCTs, 1238 women, RR 1.15, 95% CI 0.94-1.41). For multiple pregnancy, both sensitivity analyses showed inconclusive findings between gonadotrophins and CC (RR 0.94, 95% CI 0.45-1.96; RR 0.81, 95% CI 0.32-2.03, respectively). Moderate certainty evidence showed that gonadotrophins reduced the time to conception leading to a live birth when compared to CC (6 RCTs, 2058 women, HR 1.37, 95% CI 1.15-1.63, I2 = 22%). No strong evidence on the treatment-covariate (female age, BMI or primary versus secondary infertility) interactions was found.Wider Implications: In couples with unexplained infertility undergoing IUI-OS, gonadotrophins increased the chance of a live birth and reduced the time to conception compared to CC, at the cost of a higher multiple pregnancy rate, when not differentiating strategies on cancellation criteria or the starting dose. The treatment effects did not seem to differ in women of different age, BMI or primary versus secondary infertility. In a modern practice where a lower starting dose and stricter cancellation criteria are in place, effectiveness and safety of different agents seem both acceptable, and therefore intervention availability, cost and patients' preferences should factor in the clinical decision-making. As the evidence for comparisons to letrozole is based on one RCT providing IPD, further RCTs comparing letrozole and other interventions for unexplained infertility are needed. [ABSTRACT FROM AUTHOR]

Background: Increasingly in network meta-analysis (NMA), there is a need to incorporate non-randomised evidence to estimate relative treatment effects, and in particular in cases with limited randomised evidence, sometimes resulting in disconnected networks of treatments. When combining different sources of data, complex NMA methods are required to address issues associated with participant selection bias, incorporating single-arm trials (SATs), and synthesising a mixture of individual participant data (IPD) and aggregate data (AD). We develop NMA methods which synthesise data from SATs and randomised controlled trials (RCTs), using a mixture of IPD and AD, for a dichotomous outcome.Methods: We propose methods under both contrast-based (CB) and arm-based (AB) parametrisations, and extend the methods to allow for both within- and across-trial adjustments for covariate effects. To illustrate the methods, we use an applied example investigating the effectiveness of biologic disease-modifying anti-rheumatic drugs for rheumatoid arthritis (RA). We applied the methods to a dataset obtained from a literature review consisting of 14 RCTs and an artificial dataset consisting of IPD from two SATs and AD from 12 RCTs, where the artificial dataset was created by removing the control arms from the only two trials assessing tocilizumab in the original dataset.Results: Without adjustment for covariates, the CB method with independent baseline response parameters (CBunadjInd) underestimated the effectiveness of tocilizumab when applied to the artificial dataset compared to the original dataset, albeit with significant overlap in posterior distributions for treatment effect parameters. The CB method with exchangeable baseline response parameters produced effectiveness estimates in agreement with CBunadjInd, when the predicted baseline response estimates were similar to the observed baseline response. After adjustment for RA duration, there was a reduction in across-trial heterogeneity in baseline response but little change in treatment effect estimates.Conclusions: Our findings suggest incorporating SATs in NMA may be useful in some situations where a treatment is disconnected from a network of comparator treatments, due to a lack of comparative evidence, to estimate relative treatment effects. The reliability of effect estimates based on data from SATs may depend on adjustment for covariate effects, although further research is required to understand this in more detail. [ABSTRACT FROM AUTHOR]

Purpose: Many systematic reviews have reported on the effectiveness of spinal manipulative therapy (SMT) for low back pain (LBP) in adults. Much less is known about the older population regarding the effects of SMT. Objective: To assess the effects of SMT on pain and function in older adults with chronic LBP in an individual participant data (IPD) meta-analysis. Setting: Electronic databases from 2000 until June 2020, and reference lists of eligible trials and related reviews. Design and subjects: Randomized controlled trials (RCTs) which examined the effects of SMT in adults with chronic LBP compared to interventions recommended in international LBP guidelines. Methods: Authors of trials eligible for our IPD meta-analysis were contacted to share data. Two review authors conducted a risk of bias assessment. Primary results were examined in a one-stage mixed model, and a two-stage analysis was conducted in order to confirm findings. Main outcomes and measures: Pain and functional status examined at 4, 13, 26, and 52 weeks. Results: 10 studies were retrieved, including 786 individuals, of which 261 were between 65 and 91 years of age. There is moderate-quality evidence that SMT results in similar outcomes at 4 weeks (pain: mean difference [MD] − 2.56, 95% confidence interval [CI] − 5.78 to 0.66; functional status: standardized mean difference [SMD] − 0.18, 95% CI − 0.41 to 0.05). Second-stage and sensitivity analysis confirmed these findings. Conclusion: SMT provides similar outcomes to recommended interventions for pain and functional status in the older adult with chronic LBP. SMT should be considered a treatment for this patient population. [ABSTRACT FROM AUTHOR]

Background: Use of fall prevention strategies requires detection of high-risk patients. Our goal was to develop prediction models for falls and recurrent falls in community-dwelling older adults and to improve upon previous models by using a large, pooled sample and by considering a wide range of candidate predictors, including medications.Methods: Harmonized data from 2 Dutch (LASA, B-PROOF) and 1 German cohort (ActiFE Ulm) of adults aged ≥65 years were used to fit 2 logistic regression models: one for predicting any fall and another for predicting recurrent falls over 1 year. Model generalizability was assessed using internal-external cross-validation.Results: Data of 5 722 participants were included in the analyses, of whom 1 868 (34.7%) endured at least 1 fall and 702 (13.8%) endured a recurrent fall. Positive predictors for any fall were: educational status, depression, verbal fluency, functional limitations, falls history, and use of antiepileptics and drugs for urinary frequency and incontinence; negative predictors were: body mass index (BMI), grip strength, systolic blood pressure, and smoking. Positive predictors for recurrent falls were: educational status, visual impairment, functional limitations, urinary incontinence, falls history, and use of anti-Parkinson drugs, antihistamines, and drugs for urinary frequency and incontinence; BMI was a negative predictor. The average C-statistic value was 0.65 for the model for any fall and 0.70 for the model for recurrent falls.Conclusion: Compared with previous models, the model for recurrent falls performed favorably while the model for any fall performed similarly. Validation and optimization of the models in other populations are warranted. [ABSTRACT FROM AUTHOR]

Background: The role of vitamin D on muscle health is debated. Methods: An individual participant metanalysis of 4 randomized placebo-controlled trials, investigating short-term (3-9months) effects of vitamin D3 in moderate (2800 IU) to high (7000 IU) daily oral doses on muscle health and quality of life (QoL). Inclusion criteria were either obesity (n = 52), newly diagnosed primary hyperparathyroidism (n = 41), Graves’ disease (n = 86), or secondary hyperparathyroidism (n = 81). Results: Overall (n = 260) as well as in a subgroup analysis including only vitamin D insufficient [25(OH)D < 50 nmol/L] individuals (n = 176), vitamin D supplementation did not affect measures of muscle health (isometric muscle strength, Timed Up and Go test, chair rising test, body composition, and balance) or QoL. However, a beneficial effect was present on QoL (physical component score) in vitamin D deficient [25(OH) D < 25 nmol/L] individuals (n = 34). Overall, relative changes in 25(OH)D inversely affected maximum muscle strength in a dose-response manner. Stratified into body mass index 30 kg/m², vitamin D supplementation had divergent effects on isometric muscle strength, with beneficial effects in obese individuals (n = 93) at knee flexion 90° (P = 0.04), and adverse effects in nonobese individuals (n = 167) at handgrip (P = 0.02), knee extension 60° (P = 0.03) and knee flexion 60° (P < 0.01). Conclusion: Overall, short-term treatment with moderate to high daily doses of vitamin D did not affect muscle health or QoL. A potential beneficial effect was present on muscle strength in severely obese individuals and on QoL in vitamin D deficient individuals. Subgroup analyses, however, suggested negative effects of large relative increases in p-25(OH)D. [ABSTRACT FROM AUTHOR]

Often both aggregate data (AD) studies and individual participant data (IPD) studies are available for specific treatments. Combining these two sources of data could improve the overall meta‐analytic estimates of treatment effects. Moreover, often for some studies with AD, the associated IPD maybe available, albeit at some extra effort or cost to the analyst. We propose a method for combining treatment effects across trials when the response is from the exponential family of distribution and hence a generalized linear model structure can be used. We consider the case when treatment effects are fixed and common across studies. Using the proposed combination method, we study the relative efficiency of analyzing all IPD studies vs combining various percentages of AD and IPD studies. For many different models, design constraints under which the AD estimators are the IPD estimators, and hence fully efficient, are known. For such models, we advocate a selection procedure that chooses AD studies over IPD studies in a manner that force least departure from design constraints and hence ensures an efficient combined AD and IPD estimator. [ABSTRACT FROM AUTHOR]

Collation of aphasia research data across settings, countries and study designs using big data principles will support analyses across different language modalities, levels of impairment, and therapy interventions in this heterogeneous population. Big data approaches in aphasia research may support vital analyses, which are unachievable within individual trial datasets. However, we lack insight into the requirements for a systematically created database, the feasibility and challenges and potential utility of the type of data collated. To report the development, preparation and establishment of an internationally agreed aphasia after stroke research database of individual participant data (IPD) to facilitate planned aphasia research analyses. Data were collated by systematically identifying existing, eligible studies in any language (≥10 IPD, data on time since stroke, and language performance) and included sourcing from relevant aphasia research networks. We invited electronic contributions and also extracted IPD from the public domain. Data were assessed for completeness, validity of value-ranges within variables, and described according to pre-defined categories of demographic data, therapy descriptions, and language domain measurements. We cleaned, clarified, imputed and standardised relevant data in collaboration with the original study investigators. We presented participant, language, stroke, and therapy data characteristics of the final database using summary statistics. From 5256 screened records, 698 datasets were potentially eligible for inclusion; 174 datasets (5928 IPD) from 28 countries were included, 47/174 RCT datasets (1778 IPD) and 91/174 (2834 IPD) included a speech and language therapy (SLT) intervention. Participants' median age was 63 years (interquartile range [53, 72]), 3407 (61.4%) were male and median recruitment time was 321 days (IQR 30, 1156) after stroke. IPD were available for aphasia severity or ability overall (n = 2699; 80 datasets), naming (n = 2886; 75 datasets), auditory comprehension (n = 2750; 71 datasets), functional communication (n = 1591; 29 datasets), reading (n = 770; 12 datasets) and writing (n = 724; 13 datasets). Information on SLT interventions were described by theoretical approach, therapy target, mode of delivery, setting and provider. Therapy regimen was described according to intensity (1882 IPD; 60 datasets), frequency (2057 IPD; 66 datasets), duration (1960 IPD; 64 datasets) and dosage (1978 IPD; 62 datasets). Our international IPD archive demonstrates the application of big data principles in the context of aphasia research; our rigorous methodology for data acquisition and cleaning can serve as a template for the establishment of similar databases in other research areas. [ABSTRACT FROM AUTHOR]

Evidence-based decisions on clinical and cost-effectiveness of interventions are ideally informed by meta-analyses of intervention trial data. However, when undertaken, such meta-analyses in ageing research have typically been conducted using standard methods whereby summary (aggregate) data are extracted from published trial reports. Although meta-analysis of aggregate data can provide useful insights into the average effect of interventions within a selected trial population, it has limitations regarding robust conclusions on which subgroups of people stand to gain the greatest benefit from an intervention or are at risk of experiencing harm. Future evidence synthesis using individual participant data from ageing research trials for meta-analysis could transform understanding of the effectiveness of interventions for older people, supporting evidence-based and sustainable commissioning. A major advantage of individual participant data meta-analysis (IPDMA) is that it enables examination of characteristics that predict treatment effects, such as frailty, disability, cognitive impairment, ethnicity, gender and other wider determinants of health. Key challenges of IPDMA relate to the complexity and resources needed for obtaining, managing and preparing datasets, requiring a meticulous approach involving experienced researchers, frequently with expertise in designing and analysing clinical trials. In anticipation of future IPDMA work in ageing research, we are establishing an international Ageing Research Trialists collective, to bring together trialists with a common focus on transforming care for older people as a shared ambition across nations. [ABSTRACT FROM AUTHOR]

Objective To quantify the risk and predictors of relapse among individuals with schizophrenia randomly withdrawn from antipsychotic maintenance treatment. Methods We re-analyzed time-to-event and baseline predictors from placebo arms in five placebo-controlled randomized trials of antipsychotics (n = 688 individuals; 173 stabilized on oral antipsychotic [OAP] and 515 on long-acting injectables [LAI]) for relapse-prevention available in the Yale Open Data Access repository. Using a survival and Cox-proportional hazards regression analyses, we estimated survival rates of "relapse-free" individuals by the end of follow-up (median = 118 days, IQR = 52.0–208.0), the rate of study-confirmed relapse, and adjusted hazard ratios (aHR, 95% confidence intervals [CI]) associated with baseline predictors. We also estimated these parameters for individuals followed for >5 half-lives of the stabilizing antipsychotic, and studied predictors of "rebound psychosis" in OAP-stabilized participants, defined as occurring within 30 days of antipsychotic withdrawal. Results 29.9% (95%CI = 23.2–38.5) remained relapse-free by the end of follow-up, 11.1% (95%CI = 5.65–21.9) among those OAP-stabilized, 36.4% (95%CI = 28.4–46.7) among those LAI-stabilized. The study-confirmed relapse rate was 45.2%, 62.4% among those OAP-stabilized and 39.4% among those LAI-stabilized. Predictors of relapse included smoking (aHR = 1.54, 95%CI = 1.19–2.00), female sex (aHR = 1.37, 95%CI = 1.08–1.79), and having been stabilized on OAPs vs LAIs (aHR = 3.56, 95%CI = 2.68–4.72). Greater risk of relapse on OAP persisted even after sufficient time had elapsed to clear antipsychotic plasma level among LAI-stabilized (aHR = 5.0, 95%CI = 3.5–7.1). "Rebound psychosis" did not show predictors. Conclusions and relevance Our results corroborate the high relapse risk following antipsychotic withdrawal after symptom stabilization with limited patient-related predictors of safe treatment discontinuation. Stabilization with LAIs reduces the short-/medium-term relapse risk. [ABSTRACT FROM AUTHOR]

Background: In non-randomized studies (NRSs) where a continuous outcome variable (e.g., depressive symptoms) is assessed at baseline and follow-up, it is common to observe imbalance of the baseline values between the treatment/exposure group and control group. This may bias the study and consequently a meta-analysis (MA) estimate. These estimates may differ across statistical methods used to deal with this issue. Analysis of individual participant data (IPD) allows standardization of methods across studies. We aimed to identify methods used in published IPD-MAs of NRSs for continuous outcomes, and to compare different methods to account for baseline values of outcome variables in IPD-MA of NRSs using two empirical examples from the Thyroid Studies Collaboration (TSC). Methods: For the first aim we systematically searched in MEDLINE, EMBASE, and Cochrane from inception to February 2021 to identify published IPD-MAs of NRSs that adjusted for baseline outcome measures in the analysis of continuous outcomes. For the second aim, we applied analysis of covariance (ANCOVA), change score, propensity score and the naïve approach (ignores the baseline outcome data) in IPD-MA from NRSs on the association between subclinical hyperthyroidism and depressive symptoms and renal function. We estimated the study and meta-analytic mean difference (MD) and relative standard error (SE). We used both fixed- and random-effects MA. Results: Ten of 18 (56%) of the included studies used the change score method, seven (39%) studies used ANCOVA and one the propensity score (5%). The study estimates were similar across the methods in studies in which groups were balanced at baseline with regard to outcome variables but differed in studies with baseline imbalance. In our empirical examples, ANCOVA and change score showed study results on the same direction, not the propensity score. In our applications, ANCOVA provided more precise estimates, both at study and meta-analytical level, in comparison to other methods. Heterogeneity was higher when change score was used as outcome, moderate for ANCOVA and null with the propensity score. Conclusion: ANCOVA provided the most precise estimates at both study and meta-analytic level and thus seems preferable in the meta-analysis of IPD from non-randomized studies. For the studies that were well-balanced between groups, change score, and ANCOVA performed similarly. [ABSTRACT FROM AUTHOR]

Objective: Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance.Methods: MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit.Results: Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes.Conclusions: The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Background: Previous narrative reviews have concluded that dietary nitrate (NO3−) improves maximal neuromuscular power in humans. This conclusion, however, was based on a limited number of studies, and no attempt has been made to quantify the exact magnitude of this beneficial effect. Such information would help ensure adequate statistical power in future studies and could help place the effects of dietary NO3− on various aspects of exercise performance (i.e., endurance vs. strength vs. power) in better context. We therefore undertook a systematic review and individual participant data meta-analysis to quantify the effects of NO3− supplementation on human muscle power. Methods: The literature was searched using a strategy developed by a health sciences librarian. Data sources included Medline Ovid, Embase, SPORTDiscus, Scopus, Clinicaltrials.gov, and Google Scholar. Studies were included if they used a randomized, double-blind, placebo-controlled, crossover experimental design to measure the effects of dietary NO3− on maximal power during exercise in the non-fatigued state and the within-subject correlation could be determined from data in the published manuscript or obtained from the authors. Results: Nineteen studies of a total of 268 participants (218 men, 50 women) met the criteria for inclusion. The overall effect size (ES; Hedge's g) calculated using a fixed effects model was 0.42 (95% confidence interval (CI) 0.29, 0.56; p = 6.310 × 10− 11). There was limited heterogeneity between studies (i.e., I2 = 22.79%, H2 = 1.30, p = 0.3460). The ES estimated using a random effects model was therefore similar (i.e., 0.45, 95% CI 0.30, 0.61; p = 1.064 × 10− 9). Sub-group analyses revealed no significant differences due to subject age, sex, or test modality (i.e., small vs. large muscle mass exercise). However, the ES in studies using an acute dose (i.e., 0.54, 95% CI 0.37, 0.71; p = 6.774 × 10− 12) was greater (p = 0.0211) than in studies using a multiple dose regimen (i.e., 0.22, 95% CI 0.01, 0.43; p = 0.003630). Conclusions: Acute or chronic dietary NO3− intake significantly increases maximal muscle power in humans. The magnitude of this effect–on average, ~ 5%–is likely to be of considerable practical and clinical importance. [ABSTRACT FROM AUTHOR]

Meta‐analysis is the synthesis of findings from research projects, which enables an estimate of the average or pooled effect across various studies. This study presents findings from the intention to treat analysis for a series of educational evaluations in England using a two‐stage meta‐analysis with standardised outcome data and individual participant data meta‐analyses. The research estimates the overall impact of educational trials on pupils eligible for Free School Meals (FSM) and the attainment gap in literacy and mathematics performance between FSM and non‐FSM pupils based on analysis of 88 trials and data from over half a million pupils. For the meta‐analyses, frequentist and Bayesian multilevel models were used to estimate the individual and pooled effect size across categories of explanatory variables such as age groups (key stages in England) and aspects of the type of interventions (one‐to‐one, small group, whole class). Results indicated that the overall impact of interventions on the literacy outcomes of FSM pupils was positive, with a pooled effect size of 0.06 (0.03, 0.08). However, for mathematics, no overall effect on FSM pupils was observed. Analysis of the attainment gap indicated that literacy outcomes for FSM pupils were improved by interventions marginally more than for non‐FSM pupils (pooled attainment gap 0.01 (−0.01, 0.04)). The risk of bias assessment showed that estimates were consistent across different methodological approaches. Overall, evidence from this study can be used to identify, test and scale educational interventions in schools to improve educational outcomes for disadvantaged pupils. [ABSTRACT FROM AUTHOR]

Summary: Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero‐inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study‐level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta‐analysis. The current study proposes a novel statistical approach, the Zero‐inflation Bias Correction (ZIBC) method, that can account for the bias introduced when using the Poisson regression model, despite a high rate of inflated zeros in the outcome distribution of a randomized clinical trial. This correction method only requires summary information from individual studies to correct intervention effect estimates as if they were appropriately estimated using the zero‐inflated Poisson regression model, thus it is attractive for meta‐analysis when individual participant‐level data are not available in some studies. Simulation studies and real data analyses showed that the ZIBC method performed well in correcting zero‐inflation bias in most situations. [ABSTRACT FROM AUTHOR]

Background: Previous narrative reviews have concluded that dietary nitrate (NO3−) improves maximal neuromuscular power in humans. This conclusion, however, was based on a limited number of studies, and no attempt has been made to quantify the exact magnitude of this beneficial effect. Such information would help ensure adequate statistical power in future studies and could help place the effects of dietary NO3− on various aspects of exercise performance (i.e., endurance vs. strength vs. power) in better context. We therefore undertook a systematic review and individual participant data meta-analysis to quantify the effects of NO3− supplementation on human muscle power. Methods: The literature was searched using a strategy developed by a health sciences librarian. Data sources included Medline Ovid, Embase, SPORTDiscus, Scopus, Clinicaltrials.gov, and Google Scholar. Studies were included if they used a randomized, double-blind, placebo-controlled, crossover experimental design to measure the effects of dietary NO3− on maximal power during exercise in the non-fatigued state and the within-subject correlation could be determined from data in the published manuscript or obtained from the authors. Results: Nineteen studies of a total of 268 participants (218 men, 50 women) met the criteria for inclusion. The overall effect size (ES; Hedge's g) calculated using a fixed effects model was 0.42 (95% confidence interval (CI) 0.29, 0.56; p = 6.310 × 10− 11). There was limited heterogeneity between studies (i.e., I2 = 22.79%, H2 = 1.30, p = 0.3460). The ES estimated using a random effects model was therefore similar (i.e., 0.45, 95% CI 0.30, 0.61; p = 1.064 × 10− 9). Sub-group analyses revealed no significant differences due to subject age, sex, or test modality (i.e., small vs. large muscle mass exercise). However, the ES in studies using an acute dose (i.e., 0.54, 95% CI 0.37, 0.71; p = 6.774 × 10− 12) was greater (p = 0.0211) than in studies using a multiple dose regimen (i.e., 0.22, 95% CI 0.01, 0.43; p = 0.003630). Conclusions: Acute or chronic dietary NO3− intake significantly increases maximal muscle power in humans. The magnitude of this effect–on average, ~ 5%–is likely to be of considerable practical and clinical importance. [ABSTRACT FROM AUTHOR]

In the past years, network meta‐analysis (NMA) has been widely used among clinicians, guideline makers, and health technology assessment agencies and has played an important role in clinical decision‐making and guideline development. To inform further development of NMAs, we conducted a bibliometric analysis to assess the current status of published NMA methodological studies, summarized the methodological progress of seven types of NMAs, and discussed the current challenges of NMAs. [ABSTRACT FROM AUTHOR]

Data unavailability impedes research transparency and is a major problem for individual participant data (IPD) meta‐analyses as it reduces statistical power, increases risk of bias, and may even preclude completion. The primary objectives of this study were to determine IPD sharing plans reported in recently registered clinical trial registration records, how data sharing commitment relates to clinical trial characteristics, and principal investigators' attitudes, motivations and barriers to data sharing. The secondary objective was to derive recommendations to overcome identified barriers to data sharing. This was a retrospective cohort study of all interventional trials registered on the Australian New Zealand Clinical Trials Registry (ANZCTR) from 1 December 2018 to 30 November 2019, and an online cross‐sectional survey of their principal investigators. In the cohort study of all clinical trials registered on the ANZCTR in the study period (n = 1517), commitment to share data was low (22%, 329/1517). In the cross‐sectional survey (n = 281, 23% response rate), principal investigators showed strong support for the concept of data sharing (77%, 216/281) but a substantially lower intention to actually share data from their clinical trials (40%, 111/281). Major barriers to data sharing included lacking informed consent to share data, protecting participant confidentiality and preventing misinterpretation of data or misleading secondary analyses. There is a gap between high in‐principle support for data sharing, and low in‐practice intention from investigators to share data from their own clinical trials. Multiple pathways exist to bridge this gap by addressing the identified barriers to data sharing. [ABSTRACT FROM AUTHOR]