Obtaining and managing data sets for individual participant data meta-analysis: scoping review and practical guide

Understanding East-West differences in subsolid nodules: prevalence and overdiagnosis implications in lung cancer screening

BACKGROUND

Owing to the widespread opportunistic LDCT screening leading to increased overdiagnosis in Asian countries, such as South Korea, mainland China, and Taiwan, this study seeks to analyze the divergence in SSN prevalence between Eastern and Western nations, focusing on the influence of SSN on the growing overdiagnosis trend, notably among females.

METHODS

This retrospective study collected data from 4166 participants who underwent baseline LDCT in a hospital-based cohort between January 2014 and August 2021. Clinical parameters, including age, sex, lung imaging reporting and data system (Lung-RADS) categories, smoking history, pack-year dose, and SSN characteristics, were extracted from electronic medical records. Additionally, a narrative review and pooled analysis integrated relevant published studies on the prevalence of subsolid nodules and sex disparities.

RESULTS

The study encompassed 4166 participants, with females accounting for 49.3% and males for 50.7%, with a mean age of 53.38 ± 10.89. The prevalence of SSNs was significantly higher in females (20.1%) than in males (12.6%). Pooled analysis across seven studies revealed a significantly higher prevalence of SSN in Eastern countries (12.6%) compared to the prevalence in Western countries (3.6%) (test for subgroup differences: p < 0.01; I2 = 100%). Additionally, a notable sex difference was observed in the prevalence of SSNs (risk ratio = 0.489, 95% CI: 0.301-0.796, p < 0.01; reference group: male group).

CONCLUSIONS

Apart from differences in clinical management and health literacy regarding SSNs between Eastern and Western countries, the high prevalence of SSNs in Asian nations, particularly among females, significantly contributes to the issue of overdiagnosis in opportunistic lung cancer screening in Asian countries. Tailored sex-specific strategies and risk prediction models are essential for effective screening optimization.

How to: share and reuse data-challenges and solutions from predicting the impact of monoclonal antibodies & vaccines on antimicrobial resistance project

BACKGROUND

Data sharing accelerates scientific progress and improves evidence quality. Even though journals and funding institutions require investigators to share data, only a small part of studies made their data publicly available upon publication. The procedures necessary to share retrospective data for reuse in secondary data analysis projects can be cumbersome.

OBJECTIVES

Predicting the Impact of Monoclonal Antibodies & Vaccines on Antimicrobial Resistance is a European research project that aims to develop mathematical models and an epidemiological repository to assess the impact of vaccines and monoclonal antibodies on antimicrobial resistance (AMR). To accomplish the project aim, Work Package 3 was responsible for gathering historical anonymized individual patient datasets.

SOURCES

Through a systematic search we have identified 108 eligible studies for data sharing; of which eight have completed all legal requirements and shared their datasets, with data from four infectious syndromes and seven resistant pathogens. The AMR data gathered in Predicting the Impact of Monoclonal Antibodies & Vaccines on Antimicrobial Resistance project are publicly available in European Clinical Research Alliance on Infectious Disease epidemiology network platform (https://epi-net.eu/primavera/about/anonymized-individual-patient-data/).

CONTENT

Challenges and possible solutions in data sharing activities were mapped and discussed: lack of researchers' interest in sharing data, cumbersome ethical and legal requirements, laborious data management procedures, specific requirements for public data access, insufficient training and funding.

IMPLICATIONS

We expect that experience gained in this project can be useful to improve data sharing; and that the datasets gathered can be used in future AMR projects.

Identifying Modifiers of CAR T-Cell Therapeutic Efficacy and Safety: A Systematic Review and Individual Patient Data Meta-Analysis

CAR T-cell therapy is effective in relapsed/refractory hematologic malignancies, but its use has been tempered by heterogeneity in response and safety outcomes. We performed individual patient data meta-analysis (IPDMA) of CAR T-cell therapy in patients with hematologic malignancies to explore whether patient-level factors modify therapeutic efficacy/safety. We searched MEDLINE, Embase, and Cochrane CENTRAL for relevant trials. IPD was collected and pooled from each included trial, and prevalence of outcomes among strata of potential modifiers was explored. Our primary outcome was complete response, and the secondary outcomes were cytokine release syndrome (CRS), and immune effector cell associated neurotoxicity syndrome (ICANS). We identified 89 trials comprising 2,331 patients for the IPDMA. Complete response proportion ranged from 25% to 75% depending on cancer type. Decreased complete response was seen in those that received bridging therapy compared to those that did not (34% vs 58%, RR:0.55, 95% CI:0.30-0.98), as well as with autologous cell sources compared to allogeneic sources (53% vs 67%, RR:0.61, 95% CI:0.43-0.87). Compared to CAR T-cell therapies targeting CD19 alone, therapies that combine CD19 targeting with additional targets such as CD20, CD22, CD30, CD33, LeY, NKG2D, or BCMA were associated with higher complete response rates (72% vs 58%, RR:1.69, 95% CI:1.15-2.50). Autologous cell sources demonstrated increased risk of ICANS relative to allogeneic sources (24% vs 3%, RR:10.48, 95% CI:1.87-58.57). Safety and efficacy of CAR T-cell therapy within specific cancer types was also affected by modifiers including bridging therapy, CAR T-cell source, CAR T-cell target, sex, age, number of cell infusions, co-stimulatory domain, and dose.

Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study

BACKGROUND

Insufficient patient accrual is a major challenge in clinical trials and can result in underpowered studies, as well as exposing study participants to toxicity and additional costs, with limited scientific benefit. Real-world data can provide external controls, but insufficient accrual affects all arms of a study, not just controls. Studies that used generative models to simulate more patients were limited in the accrual scenarios considered, replicability criteria, number of generative models, and number of clinical trials evaluated.

OBJECTIVE

This study aimed to perform a comprehensive evaluation on the extent generative models can be used to simulate additional patients to compensate for insufficient accrual in clinical trials.

METHODS

We performed a retrospective analysis using 10 datasets from 9 fully accrued, completed, and published cancer trials. For each trial, we removed the latest recruited patients (from 10% to 50%), trained a generative model on the remaining patients, and simulated additional patients to replace the removed ones using the generative model to augment the available data. We then replicated the published analysis on this augmented dataset to determine if the findings remained the same. Four different generative models were evaluated: sequential synthesis with decision trees, Bayesian network, generative adversarial network, and a variational autoencoder. These generative models were compared to sampling with replacement (ie, bootstrap) as a simple alternative. Replication of the published analyses used 4 metrics: decision agreement, estimate agreement, standardized difference, and CI overlap.

RESULTS

Sequential synthesis performed well on the 4 replication metrics for the removal of up to 40% of the last recruited patients (decision agreement: 88% to 100% across datasets, estimate agreement: 100%, cannot reject standardized difference null hypothesis: 100%, and CI overlap: 0.8-0.92). Sampling with replacement was the next most effective approach, with decision agreement varying from 78% to 89% across all datasets. There was no evidence of a monotonic relationship in the estimated effect size with recruitment order across these studies. This suggests that patients recruited earlier in a trial were not systematically different than those recruited later, at least partially explaining why generative models trained on early data can effectively simulate patients recruited later in a trial. The fidelity of the generated data relative to the training data on the Hellinger distance was high in all cases.

CONCLUSIONS

For an oncology study with insufficient accrual with as few as 60% of target recruitment, sequential synthesis can enable the simulation of the full dataset had the study continued accruing patients and can be an alternative to drawing conclusions from an underpowered study. These results provide evidence demonstrating the potential for generative models to rescue poorly accruing clinical trials, but additional studies are needed to confirm these findings and to generalize them for other diseases.

Salivary cortisol measures across the clinical stages of psychosis: An individual participant data (IPD) meta-analysis

BACKGROUND

Studies of salivary cortisol levels in psychosis have yielded inconsistent findings, which may be attributable to heterogeneity in cortisol measurement, illness stage, and approaches to dealing with sampling factors and potential confounders. To address these issues, we performed an individual participant data (IPD) meta-analysis comparing individuals at different stages of psychosis to controls using five different salivary cortisol measures and explored potential effect modifiers.

METHODS

Salivary cortisol data from five London-based cohorts were used to derive the cortisol awakening response, total daytime cortisol output, basal cortisol, and diurnal slope measures (wake-to-evening and peak-to-evening). Linear regression models were first performed to obtain standardised beta coefficients (β), representing the difference in each cortisol metric between each clinical stage group (cases) and healthy individuals (controls) after accounting for relevant sampling factors; we then used random-effects meta-analyses and meta-regression models to investigate the effect of psychosis stage and sample characteristics on effect sizes.

RESULTS

Data were available for 352 individuals distributed across psychosis clinical stages (1a - distress disorder: N = 35; 1b - clinical high-risk for psychosis: N = 90; 2a - first-episode psychosis: N = 197; 2b - single episode remitted: N = 5; 3 - relapsing/remitting illness: N = 18; 4 - severe and persistent illness: N = 7) and 292 controls. A significant overall main effect of clinical stage on peak-to-evening diurnal slope was observed (χ2=12.83, p = 0.025), with both the clinical high-risk (β=0.21, 95 % CI: 0.06, 0.36) and first-episode psychosis (β=0.20, 95 % CI: 0.10, 0.31) groups characterised by flatter slopes than controls. The clinical stage groups and controls did not differ on any other cortisol measure. Several sample characteristics were significantly associated with diurnal slope effect sizes, but after accounting for clinical stage, only the association between mean age in cases and wake-to-evening diurnal slope retained significance.

CONCLUSION

Clinical high-risk and first-episode psychosis participants differed from healthy controls in the peak-to-evening diurnal cortisol slope. This measure has not been examined in these populations before, and its potential predictive and prognostic utility for psychotic disorders merits further investigation.

Editorial Perspective: Extending IPDMA methodology to drive treatment personalisation in child mental health

To improve outcomes for youth who do not respond optimally to existing treatments, we need to identify robust predictors, moderators, and mediators that are ideal targets for personalisation in mental health care. We propose a solution to leverage the Individual Patient Data Meta-analysis (IPDMA) approach to allow broader access to individual-level data while maintaining methodological rigour. Such a resource has the potential to answer questions that are unable to be addressed by single studies, reduce researcher burden, and enable the application of newer statistical techniques, all to provide data-driven strategies for clinical decision-making. Using childhood anxiety as the worked example, the editorial perspective outlines the rationale for leveraging IPDMA methodology to build a data repository, the Platform for Anxiety Disorder Data in Youth. We also include recommendations to address the methods and challenges inherent in this endeavour.

Survival Analysis Without Sharing of Individual Patient Data by Using a Gaussian Copula

Cox regression and Kaplan-Meier estimations are often needed in clinical research and this requires access to individual patient data (IPD). However, IPD cannot always be shared because of privacy or proprietary restrictions, which complicates the making of such estimations. We propose a method that generates pseudodata replacing the IPD by only sharing non-disclosive aggregates such as IPD marginal moments and a correlation matrix. Such aggregates are collected by a central computer and input as parameters to a Gaussian copula (GC) that generates the pseudodata. Survival inferences are computed on the pseudodata as if it were the IPD. Using practical examples we demonstrate the utility of the method, via the amount of IPD inferential content recoverable by the GC. We compare GC to a summary-based meta-analysis and an IPD bootstrap distributed across several centers. Other pseudodata approaches are also considered. In the empirical results, GC approximates the utility of the IPD bootstrap although it might yield more conservative inferences and it might have limitations in subgroup analyses. Overall, GC avoids many legal problems related to IPD privacy or property while enabling approximation of common IPD survival analyses otherwise difficult to conduct. Sharing more IPD aggregates than is currently practiced could facilitate "second purpose"-research and relax concerns regarding IPD access.

DiScO: novel rapid systems mapping to inform digital transformation of health systems

Background

Global health systems are confronting challenges that intersect climate change with evolving communicable and non-communicable public health risks. Addressing these challenges requires systems integration via citizen big data that exist outside health systems. However, systems integration across jurisdictions is a complex challenge that requires stakeholder input. This study's purpose was to conduct rapid systems mapping with international health system stakeholders to inform the development and implementation of a global digital citizen science observatory (DiScO), which aims to catalyze digital transformation of health systems across jurisdictions.

Methods

A rapid qualitative systems mapping study was conducted during the International Society for Behavioral Nutrition and Physical Activity Annual Global Summit in Uppsala, Sweden, in June 2023. The choice of the venue and approach was informed by three key criteria: (1) Established evidence linking physical activity and nutrition with non-communicable diseases; (2) Concrete existing methods of obtaining citizen big data by physical activity and nutrition researchers; (3) Precedence of physical activity and nutrition researchers conducting citizen science as well behavioral/clinical big data collection. The design of this study was an innovative pre-post systems map development, which consisted of (1) real-time rapid systems mapping (pre/initial map) by engaging with international stakeholders and (2) adjustment of the real-time systems map (post/final map) after analyzing stakeholder discussion data.

Results

Rapid systems mapping resulted in a complex network that included key themes to successfully develop and implement DiScO: priorities, opportunities, risks, challenges, partnerships, and resources. Additionally, a new theme emerged organically through stakeholder group discussions - mitigation strategies. The adapted rapid systems map (i.e., after data analyses) depicts 23 key nodes of intervention across the seven key themes.

Conclusion

Rapid systems mapping at international symposia is a novel methodological approach to capture stakeholder input, particularly to understand complexity across international jurisdictions - an approach that can be replicated across disciplines and sectors to inform digital transformation of health systems. The development and implementation of DiScO, a platform for decentralization and democratization of technology, will take into consideration all the key nodes of intervention identified in the rapid systems map to promote digital health for equity across global jurisdictions.

Increased Risk for Clinically Significant Sleep Disturbances in Mild Traumatic Brain Injury: An Approach to Leveraging the Federal Interagency Traumatic Brain Injury Research Database

STUDY OBJECTIVES

The Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System contains individual-patient-level traumatic brain injury (TBI) data, which when combined, allows for the examination of rates and outcomes for key subpopulations at risk for developing sleep disturbance.

METHODS

This proof-of-concept study creates a model system for harmonizing data (i.e., combining and standardizing data) across FITBIR studies for participants with and without a history of TBI to estimate rates of sleep disturbance and identify risk factors.

RESULTS

Three studies were eligible for harmonization (N = 1753). Sleep disturbance was common among those with a history of mild TBI (63%). Individuals with mild TBI were two to four times more likely to have sleep disturbance compared to those with no history of TBI.

CONCLUSIONS

This study established methods, harmonization code, and meta-databases that are publicly available on the FITBIR website. We demonstrated how the harmonization of FITBIR studies can answer TBI research questions, showing that associations between TBI and sleep disturbance may be influenced by demographic factors.

Effectiveness of pharmacological treatments for severe agitation in real-world emergency settings: protocol of individual-participant-data network meta-analysis

BACKGROUND

Severe psychomotor agitation and aggression often require immediate pharmacological intervention, but clear evidence-based recommendations for choosing among the multiple options are lacking. To address this gap, we plan a systematic review and individual-participant-data network meta-analysis to investigate their comparative effectiveness in real-world emergency settings with increased precision.

METHODS

We will include randomized controlled trials investigating intramuscular or intravenous pharmacological interventions, as monotherapy or in combination, in adults with severe psychomotor agitation irrespective of the underlying diagnosis and requiring rapid tranquilization in general or psychiatric emergency settings. We will exclude studies before 2002, those focusing on specific reasons for agitation and placebo-controlled trials to avoid concerns related to the transitivity assumption and potential selection biases. We will search for eligible studies in BIOSIS, CENTRAL, CINAHL Plus, Embase, LILACS, MEDLINE via Ovid, PubMed, ProQuest, PsycINFO, ClinicalTrials.gov, and WHO-ICTRP. Individual-participant data will be requested from the study authors and harmonized into a uniform format, and aggregated data will also be extracted from the studies. At least two independent reviewers will conduct the study selection, data extraction, risk-of-bias assessment using RoB 2, and applicability evaluation using the RITES tool. The primary outcome will be the number of patients achieving adequate sedation within 30 min after treatment, with secondary outcomes including the need for additional interventions and adverse events, using odds ratios as the effect size. If enough individual-participant data will be collected, we will synthesize them in a network meta-regression model within a Bayesian framework, incorporating study- and participant-level characteristics to explore potential sources of heterogeneity. In cases where individual-participant data are unavailable, potential data availability bias will be explored, and models allowing for the inclusion of studies reporting only aggregated data will be considered. We will assess the confidence in the evidence using the Confidence in Network Meta-Analysis (CINeMA) approach.

DISCUSSION

This individual-participant-data network meta-analysis aims to provide a fine-tuned synthesis of the evidence on the comparative effectiveness of pharmacological interventions for severe psychomotor agitation in real-world emergency settings. The findings from this study can greatly be provided clearer evidence-based guidance on the most effective treatments.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42023402365.

Vaginal misoprostol versus vaginal dinoprostone for cervical ripening and induction of labour: An individual participant data meta-analysis of randomised controlled trials

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.

Teachers' judgment accuracy: A replication check by psychometric meta-analysis

Teachers' judgment accuracy is a core competency in their daily business. Due to its importance, several meta-analyses have estimated how accurately teachers judge students' academic achievements by measuring teachers' judgment accuracy (i.e., the correlation between teachers' judgments of students' academic abilities and students' scores on achievement tests). In our study, we considered previous meta-analyses and updated these databases and the analytic combination of data using a psychometric meta-analysis to explain variations in results across studies. Our results demonstrate the importance of considering aggregation and publication bias as well as correcting for the most important artifacts (e.g., sampling and measurement error), but also that most studies fail to report the data needed for conducting a meta-analysis according to current best practices. We find that previous reviews have underestimated teachers' judgment accuracy and overestimated the variance in estimates of teachers' judgment accuracy across studies because at least 10% of this variance may be associated with common artifacts. We conclude that ignoring artifacts, as in classical meta-analysis, may lead one to erroneously conclude that moderator variables, instead of artifacts, explain any variation. We describe how online data repositories could improve the scientific process and the potential for using psychometric meta-analysis to synthesize results and assess replicability.

Efficacy of iron supplementation on physical capacity in non-anaemic iron-deficient individuals: protocol for an individual patient data meta-analysis

BACKGROUND

A deficiency in iron stores is associated with various adverse health complications, which, if left untreated, can progress to states of anaemia, whereby there is significant detriment to an individual's work capacity and quality of life due to compromised erythropoiesis. The most common methods employed to treat an iron deficiency include oral iron supplementation and, in persistent and/or unresponsive cases, intravenous iron therapy. The efficacy of these treatments, particularly in states of iron deficiency without anaemia, is equivocal. Indeed, both randomised control trials and aggregate data meta-analyses have produced conflicting evidence. Therefore, this study aims to assess the efficacy of both oral and intravenous iron supplementation on physical capacity, quality of life, and fatigue scores in iron-deficient non-anaemic individuals using individual patient data (IPD) meta-analysis techniques.

METHODS

All potential studies, irrespective of design, will be sourced through systematic searches on the following databases: Cochrane Central Register of Controlled Trials, MEDLINE Ovid, Embase Ovid, Web of Science: Science Citation Index Expanded, Web of Science: Conference Proceedings Citation Index-Science, ClinicalTrials.gov, and World Health Organization (WHO) International Clinical Trials Registry Platform. Individual patient data from all available trials will be included and subsequently analysed in a two-stage approach. Predetermined subgroup and sensitivity analyses will be employed to further explain results.

DISCUSSION

The significance of this IPD meta-analysis is one of consolidating a clear consensus to better inform iron-deficient individuals of the physiological response associated with iron supplementation. The IPD approach, to the best of our knowledge, is novel for this research topic. As such, the findings will significantly contribute to the current body of evidence.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020191739.

An individual participant meta-analysis of mirabegron in multiple sclerosis and spinal cord injury

INTRODUCTION

Our objective was to conduct an individual patient data meta-analysis (IPDMA) of the two published randomized placebo-controlled trials of mirabegron in people with neurogenic lower urinary tract dysfunction (NLUTD) due to spinal cord injury (SCI) or multiple sclerosis (MS).

METHODS

We identified two randomized, placebo-controlled trials. We extracted individual patient data from the trials and evaluated two primary outcomes: change in maximum cystometric capacity and change in the patient perception of bladder condition (PPBC). We also evaluated several secondary outcomes related to urodynamic function and quality of life. We conducted three exploratory analyses to test hypotheses based on our clinical experiences with mirabegron in NLUTD. Analysis of covariance with adjustment for baseline values was used for the statistical analysis.

RESULTS

Our IPDMA included 98 patients from the two trials. The results showed that mirabegron was associated with a significant improvement in maximum cystometric capacity (+41 mL, p = 0.04) and in the PPBC (-0.8, p < 0.01) compared to placebo. Secondary outcomes including peak neurogenic detrusor overactivity pressure (-20 cm H2O, p < 0.01), incontinence-QOL score (+12, p < 0.01), and 24 h pad weights (-79 g, p = 0.04) also improved significantly compared to placebo. Exploratory analyses found similar improvements in people with MS and SCI; some outcomes improved to a greater degree among people with incomplete SCI, or SCIs that were below T7.

CONCLUSIONS

Our IPDMA provides evidence supporting the use of mirabegron in patients with NLUTD due to SCI or MS. Further work evaluating differential responses in people with different SCI lesion characteristics may be warranted.

IVF versus IUI with ovarian stimulation for unexplained infertility: a collaborative individual participant data meta-analysis

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, I2 = 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, I2 = 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.

What works for whom and why? Treatment effects and their moderators among forcibly displaced people receiving psychological and psychosocial interventions: study protocol for an individual patient data meta-analysis

INTRODUCTION

Forcibly displaced people (FDP) have a high risk of developing mental disorders such as post-traumatic stress (PTS) disorder. Providing adequate mental healthcare for FDP is crucial but despite overall efficacy of many existing interventions, a large proportion of FDP does not benefit from treatment, highlighting the necessity of further investigating factors contributing to individual differences in treatment outcome. Yet, the few studies that have explored moderators of treatment effects are often insufficiently powered. Therefore, the present Individual Patient Data meta-analysis (IPD-MA) will investigate treatment effects and their moderators-variables related to beneficiaries, providers, intervention and study characteristics in relation to PTS outcomes.

METHODS AND ANALYSIS

A systematic literature search will be conducted from database inception in the databases PsycINFO, Cochrane, Embase, PTSDpubs and Web of Science. Only studies published in English, German, French, Spanish, Portuguese, and Dutch will be considered. Retrieved records will be screened for eligibility. Randomised controlled trials on adult FDP receiving psychological and psychosocial interventions aimed at alleviating symptoms such as PTS compared with a control condition without intervention will be included in this IPD-MA. Subsequently, authors of eligible studies will be contacted to request individual patient data (IPD). All datasets obtained will be synthesised into one large dataset which will be analysed using a one-stage approach by conducting mixed-effects linear regression models (ie, primary analysis). Additionally, aggregate data meta-analyes will be run using a two-stage approach by conducting multivariate regression models including all IPD (transformed) and available meta-data from study reports (ie, secondary analysis). PTS will serve as primary outcome measure, while mental health outcomes other than PTS, attendance, attrition, treatment non-response and adverse outcomes will be examined as secondary outcomes.

ETHICS AND DISSEMINATION

This IPD-MA does not require ethical approval. The results will be published in international peer-reviewed journals.

PROSPERO REGISTRATION NUMBER

CRD42022299510.

Efficacy of beta-blockers on blood pressure control and morbidity and mortality endpoints in hypertensives of African ancestry: an individual patient data meta-analysis

Introduction

Compared with first-line antihypertensives, beta-blockers (BB) have been reported to lower the central aortic blood pressure suboptimally and are associated with increased stroke risk. This observation has not been investigated in hypertensives of African ancestry. We hypothesised that an individual patient data meta-analysis (IPD-MA) on the efficacy of second- or third-generation beta-blockers (STGBBs) in hypertensives of African descent may provide new insights.

Methods

A single-stage IPD-MA analysed the efficacy of STGBB in lowering the mean arterial blood pressure and reducing the composite outcomes: cardiovascular death, stroke, and myocardial infarction.

Results

A total of 11,860 participants from four randomised control trials were included in the analysis. Second- or third-generation beta-blockers reduced the mean arterial pressure by 1.75 mmHg [95% confidence interval (CI):1.16-2.33; P < 0.001] in all participants included in the analysis, and by 1.93 mmHg (95% CI: 0.86-3.00; P < 0.001) in hypertensive Africans. In patients with established cardiovascular disease, where the benefits of BB therapy are well established, STGBBs were associated with an adjusted odds ratio of 1.33 (95% CI: 1.06-1.65; P = 0.015) of the composite outcome, most likely due to confounding. Similarly, the risk of total myocardial infarction was 1.76 times higher (95% CI: 1.15-2.68; P = 0.008) in hypertensives of African ancestry on STGBBs.

Conclusion

The STGBBs reduced the mean arterial pressure comparably to other antihypertensives, and they were not associated with an increased risk of stroke.

Heterogeneity and Utility of Pharmaceutical Company Sharing of Individual-Participant Data Packages

Importance

The pharmaceutical industry has made substantial investments in developing processes for sharing individual-participant data (IPD) from clinical trials. However, the utility and completeness of shared IPD and supporting documents must be evaluated to ensure the potential for scientific advancements from the data sharing ecosystem can be realized.

Objective

To assess the utility and completeness of IPD and supporting documents provided from industry-sponsored clinical trials.

Design, Setting, and Participants

From February 9, 2022, to February 9, 2023, 91 of 203 clinical trials supporting US Food and Drug Administration registrations of anticancer medicines for the treatment of solid tumors from the past decade were confirmed as eligible for IPD request. This quality improvement study performed a retrospective audit of the utility and completeness of the IPD and supporting documents provided from the 91 clinical trials for a planned meta-analysis.

Exposures

Request for IPD from 91 clinical oncology trials indicated as eligible for the request.

Main Outcomes and Measures

The utility and completeness of the IPD and supporting documents provided.

Results

The IPD packages were obtained from 70 of 91 requested clinical trials (77%). The median time to data provision was 123 (range, 117-352) days. Redactions were observed in 18 of the acquired IPD packages (26%) for outcome data, 11 (16%) for assessment variables, and 19 (27%) for adjustment data. Additionally, 20 IPD packages (29%) lacked a clinical study report, 4 (6%) had incomplete or missing data dictionaries, and 20 (29%) were missing anonymization or redaction description files. Access to IPD from 21 eligible trials (23%) was not granted.

Conclusions and Relevance

In this quality improvement study, there was substantial variability within the provided IPD packages regarding the completeness of key data variables and supporting documents. To improve the data sharing ecosystem, key areas for enhancement include (1) ensuring that clinical trials are eligible for IPD sharing, (2) making eligible IPD transparently accessible, and (3) ensuring that IPD packages meet a standard of utility and completeness.

Addressing Systematic Missing Data in the Context of Causally Interpretable Meta-analysis

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.

Evaluating the Utility and Privacy of Synthetic Breast Cancer Clinical Trial Data Sets

PURPOSE

There is strong interest from patients, researchers, the pharmaceutical industry, medical journal editors, funders of research, and regulators in sharing clinical trial data for secondary analysis. However, data access remains a challenge because of concerns about patient privacy. It has been argued that synthetic data generation (SDG) is an effective way to address these privacy concerns. There is a dearth of evidence supporting this on oncology clinical trial data sets, and on the utility of privacy-preserving synthetic data. The objective of the proposed study is to validate the utility and privacy risks of synthetic clinical trial data sets across multiple SDG techniques.

METHODS

We synthesized data sets from eight breast cancer clinical trial data sets using three types of generative models: sequential synthesis, conditional generative adversarial network, and variational autoencoder. Synthetic data utility was evaluated by replicating the published analyses on the synthetic data and assessing concordance of effect estimates and CIs between real and synthetic data. Privacy was evaluated by measuring attribution disclosure risk and membership disclosure risk.

RESULTS

Utility was highest using the sequential synthesis method where all results were replicable and the CI overlap most similar or higher for seven of eight data sets. Both types of privacy risks were low across all three types of generative models.

DISCUSSION

Synthetic data using sequential synthesis methods can act as a proxy for real clinical trial data sets, and simultaneously have low privacy risks. This type of generative model can be one way to enable broader sharing of clinical trial data.

Do brief motivational interventions increase motivation for change in drinking among college students? A two-step meta-analysis of individual participant data

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.

Stenting versus medical therapy alone for symptomatic intracranial arterial stenosis: protocol for a systematic review and individual patient data meta-analysis

INTRODUCTION

Intracranial atherosclerotic stenosis (ICAS) is a common cause of stroke worldwide. However, whether the treatment options for symptomatic ICAS is stent placement or medical therapy alone is still controversial. At present, three multicentre randomised controlled trials (RCTs) have been published, but their research designs are also slightly different and the conclusions are not completely consistent. Therefore, we plan to conduct a systematic review and individual patient data (IPD) meta-analysis of randomised clinical trials to ascertain safety and efficacy of stenting versus medical therapy alone for symptomatic patients with intracranial arterial stenosis.

METHODS AND ANALYSES

We will identify RCTs comparing stenting vs medical therapy alone in patients with symptomatic ICAS stenosis (70%-99%) through a systematic search, mainly including PubMed, MEDLINE, EMBASE, the Cochrane Library and ClinicalTrials.gov. Individual-level patient data for a prespecified list of variables will be sought from authors of all eligible studies. The primary outcome was a composite of stroke or death within 30 days, or stroke in territory of qualifying artery beyond 30 days after randomisation. IPD meta-analysis will be conducted with a one-stage approach.

ETHICS AND DISSEMINATION

Ethical approval and individual patient consent will not be required in most cases since this IPD meta-analysis will use pseudoanonymised data from RCTs. Results will be disseminated through peer-reviewed journals and international conferences.

PROSPERO REGISTRATION NUMBER

CRD42022369922.

A method for generating synthetic longitudinal health data

Getting access to administrative health data for research purposes is a difficult and time-consuming process due to increasingly demanding privacy regulations. An alternative method for sharing administrative health data would be to share synthetic datasets where the records do not correspond to real individuals, but the patterns and relationships seen in the data are reproduced. This paper assesses the feasibility of generating synthetic administrative health data using a recurrent deep learning model. Our data comes from 120,000 individuals from Alberta Health's administrative health database. We assess how similar our synthetic data is to the real data using utility assessments that assess the structure and general patterns in the data as well as by recreating a specific analysis in the real data commonly applied to this type of administrative health data. We also assess the privacy risks associated with the use of this synthetic dataset. Generic utility assessments that used Hellinger distance to quantify the difference in distributions between real and synthetic datasets for event types (0.027), attributes (mean 0.0417), Markov transition matrices (order 1 mean absolute difference: 0.0896, sd: 0.159; order 2: mean Hellinger distance 0.2195, sd: 0.2724), the Hellinger distance between the joint distributions was 0.352, and the similarity of random cohorts generated from real and synthetic data had a mean Hellinger distance of 0.3 and mean Euclidean distance of 0.064, indicating small differences between the distributions in the real data and the synthetic data. By applying a realistic analysis to both real and synthetic datasets, Cox regression hazard ratios achieved a mean confidence interval overlap of 68% for adjusted hazard ratios among 5 key outcomes of interest, indicating synthetic data produces similar analytic results to real data. The privacy assessment concluded that the attribution disclosure risk associated with this synthetic dataset was substantially less than the typical 0.09 acceptable risk threshold. Based on these metrics our results show that our synthetic data is suitably similar to the real data and could be shared for research purposes thereby alleviating concerns associated with the sharing of real data in some circumstances.

Identifying effect modifiers of CAR-T cell therapeutic efficacy: a systematic review and individual patient data meta-analysis protocol

BACKGROUND

Chimeric antigen receptor T cell therapy (CAR-T) represents a promising and exciting new therapy for hematologic malignancies, where prognosis for relapsed/refractory patients remains poor. Encouraging results from clinical trials have often been tempered by heterogeneity in response to treatment among patients, as well as safety concerns including cytokine release syndrome. The identification of specific patient or treatment-specific factors underlying this heterogeneity may provide the key to the long-term sustainability of this complex and expensive therapy. An individual patient data meta-analysis (IPMDA) may provide potential explanations for the high degree of heterogeneity. Therefore, our objective is to perform a systematic review and IPDMA of CAR-T cell therapy in patients with hematologic malignancies to explore potential effect modifiers of CAR-T cell therapy.

METHODS AND ANALYSIS

We will search MEDLINE, Embase, and the Cochrane Central Register of Controlled Clinical Trials. Studies will be screened in duplicate at the abstract level, then at the full-text level by two independent reviewers. We will include any prospective clinical trial of CAR-T cell therapy in patients with hematologic malignancies. Our primary outcome is complete response, while secondary outcomes of interest include overall response, progression-free survival, overall survival, and safety. IPD will be collected from each included trial and, in the case of missing data, corresponding authors/study sponsors will be contacted. Standard aggregate meta-analyses will be performed, followed by the IPD meta-analysis using a one-stage approach. A modified Institute of Health Economics tool will be used to evaluate the risk of bias of included studies.

ETHICS AND DISSEMINATION

Identifying characteristics that may act as modifiers of CAR-T cell efficacy is of paramount importance and can help shape future clinical trials in the field. Results from this study will be submitted for publication in a peer-reviewed scientific journal, presented at relevant conferences and shared with relevant stakeholders.

Synthetic data as an enabler for machine learning applications in medicine

Synthetic data generation is the process of using machine learning methods to train a model that captures the patterns in a real dataset. Then new or synthetic data can be generated from that trained model. The synthetic data does not have a one-to-one mapping to the original data or to real patients, and therefore has the potential of privacy preserving properties. There is a growing interest in the application of synthetic data across health and life sciences, but to fully realize the benefits, further education, research, and policy innovation is required. This article summarizes the opportunities and challenges of SDG for health data, and provides directions for how this technology can be leveraged to accelerate data access for secondary purposes.

Many researchers were not compliant with their published data sharing statement: a mixed-methods study

OBJECTIVES

The objective of the study was to analyze researchers' compliance with their data availability statement (DAS) from manuscripts published in open-access journals with the mandatory DAS.

STUDY DESIGN AND SETTING

We analyzed all articles from 333 open-access journals published during January 2019 by BioMed Central. We categorized types of the DAS. We surveyed corresponding authors who wrote in the DAS that they would share the data. Consent to participate in the study was sought for all included manuscripts. After accessing raw data sets, we checked whether data were available in a way that enabled reanalysis.

RESULTS

Of 3556 analyzed articles, 3416 contained the DAS. The most frequent DAS category (42%) indicated that the data sets are available on reasonable request. Among 1792 manuscripts in which the DAS indicated that authors are willing to share their data, 1669 (93%) authors either did not respond or declined to share their data with us. Among 254 (14%) of 1792 authors who responded to our query for data sharing, only 123 (6.8%) provided the requested data.

CONCLUSION

Even when authors indicate in their manuscript that they will share data upon request, the compliance rate is the same as for authors who do not provide the DAS, suggesting that the DAS may not be sufficient to ensure data sharing.

Measuring re-identification risk using a synthetic estimator to enable data sharing

Background

One common way to share health data for secondary analysis while meeting increasingly strict privacy regulations is to de-identify it. To demonstrate that the risk of re-identification is acceptably low, re-identification risk metrics are used. There is a dearth of good risk estimators modeling the attack scenario where an adversary selects a record from the microdata sample and attempts to match it with individuals in the population.

Objectives

Develop an accurate risk estimator for the sample-to-population attack.

Methods

A type of estimator based on creating a synthetic variant of a population dataset was developed to estimate the re-identification risk for an adversary performing a sample-to-population attack. The accuracy of the estimator was evaluated through a simulation on four different datasets in terms of estimation error. Two estimators were considered, a Gaussian copula and a d-vine copula. They were compared against three other estimators proposed in the literature.

Results

Taking the average of the two copula estimates consistently had a median error below 0.05 across all sampling fractions and true risk values. This was significantly more accurate than existing methods. A sensitivity analysis of the estimator accuracy based on variation in input parameter accuracy provides further application guidance. The estimator was then used to assess re-identification risk and de-identify a large Ontario COVID-19 behavioral survey dataset.

Conclusions

The average of two copula estimators consistently provides the most accurate re-identification risk estimate and can serve as a good basis for managing privacy risks when data are de-identified and shared.

Structured reporting to improve transparency of analyses in prognostic marker studies

BACKGROUND

Factors contributing to the lack of understanding of research studies include poor reporting practices, such as selective reporting of statistically significant findings or insufficient methodological details. Systematic reviews have shown that prognostic factor studies continue to be poorly reported, even for important aspects, such as the effective sample size. The REMARK reporting guidelines support researchers in reporting key aspects of tumor marker prognostic studies. The REMARK profile was proposed to augment these guidelines to aid in structured reporting with an emphasis on including all aspects of analyses conducted.

METHODS

A systematic search of prognostic factor studies was conducted, and fifteen studies published in 2015 were selected, three from each of five oncology journals. A paper was eligible for selection if it included survival outcomes and multivariable models were used in the statistical analyses. For each study, we summarized the key information in a REMARK profile consisting of details about the patient population with available variables and follow-up data, and a list of all analyses conducted.

RESULTS

Structured profiles allow an easy assessment if reporting of a study only has weaknesses or if it is poor because many relevant details are missing. Studies had incomplete reporting of exclusion of patients, missing information about the number of events, or lacked details about statistical analyses, e.g., subgroup analyses in small populations without any information about the number of events. Profiles exhibit severe weaknesses in the reporting of more than 50% of the studies. The quality of analyses was not assessed, but some profiles exhibit several deficits at a glance.

CONCLUSIONS

A substantial part of prognostic factor studies is poorly reported and analyzed, with severe consequences for related systematic reviews and meta-analyses. We consider inadequate reporting of single studies as one of the most important reasons that the clinical relevance of most markers is still unclear after years of research and dozens of publications. We conclude that structured reporting is an important step to improve the quality of prognostic marker research and discuss its role in the context of selective reporting, meta-analysis, study registration, predefined statistical analysis plans, and improvement of marker research.

Fresh and cryopreserved ovarian tissue transplantation for preserving reproductive and endocrine function: a systematic review and individual patient data meta-analysis

BACKGROUND

Ovarian tissue cryopreservation involves freezing and storing of surgically retrieved ovarian tissue in liquid or vapour nitrogen below -190°C. The tissue can be thawed and transplanted back with the aim of restoring fertility or ovarian endocrine function. The techniques for human ovarian tissue freezing and transplantation have evolved over the last 20 years, particularly in the context of fertility preservation in pre-pubertal cancer patients. Fresh ovarian tissue transplantation, using an autograft or donor tissue, is a more recent development; it has the potential to preserve fertility and hormonal function in women who have their ovaries removed for benign gynaecological conditions. The techniques of ovarian tissue cryopreservation and transplantation have progressed rapidly since inception; however, the evidence on the success of this intervention is largely based on case reports and case series.

OBJECTIVE AND RATIONALE

The aim of this study was to systematically review the current evidence by incorporating study-level and individual patient-level meta-analyses of women who received ovarian transplants, including frozen-thawed transplant, fresh or donor graft.

SEARCH METHODS

The review protocol was registered with PROSPERO (CRD42018115233). A comprehensive literature search was performed using MEDLINE, EMBASE, CINAHL and Cochrane Central Register of Controlled Trials from database inception to October 2020. Authors were also contacted for individual patient data if relevant outcomes were not reported in the published manuscripts. Meta-analysis was performed using inverse-variance weighting to calculate summary estimates using a fixed-effects model.

OUTCOMES

The review included 87 studies (735 women). Twenty studies reported on ≥5 cases of ovarian transplants and were included in the meta-analysis (568 women). Fertility outcomes included pregnancy, live birth and miscarriage rates, and endocrine outcomes included oestrogen, FSH and LH levels. The pooled rates were 37% (95% CI: 32-43%) for pregnancy, 28% (95% CI: 24-34%) for live birth and 37% (95% CI: 30-46%) for miscarriage following frozen ovarian tissue transplantation. Pooled mean for pre-transplant oestrogen was 101.6 pmol/l (95% CI: 47.9-155.3), which increased post-transplant to 522.4 pmol/l (95% CI: 315.4-729; mean difference: 228.24; 95% CI: 180.5-276). Pooled mean of pre-transplant FSH was 66.4 IU/l (95% CI: 52.8-84), which decreased post-transplant to 14.1 IU/l (95% CI: 10.9-17.3; mean difference 61.8; 95% CI: 57-66.6). The median time to return of FSH to a value <25 IU/l was 19 weeks (interquartile range: 15-26 weeks; range: 0.4-208 weeks). The median duration of graft function was 2.5 years (interquartile range: 1.4-3.4 years; range: 0.7-5 years). The analysis demonstrated that ovarian tissue cryopreservation and transplantation could restore reproductive and hormonal functions in women. Further studies with larger samples of well-characterized populations are required to define the optimal retrieval, cryopreservation and transplantation processes.

WIDER IMPLICATIONS

Ovarian tissue cryopreservation and transplantation may not only be effective in restoring fertility but also the return of reproductive endocrine function. Although this technology was developed as a fertility preservation option, it may have the scope to be considered for endocrine function preservation.

Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue

Meta-analyses that statistically synthesize evidence from multiple research studies can play an important role in advancing evidence-informed prevention science. When done in the context of a well-conducted systematic review, meta-analysis is a powerful tool for synthesizing evidence and exploring research questions that are difficult to address in individual studies, such as the association of individual study limitations on intervention effect estimates, replicability of empirical findings, and variation of effect estimates across populations and settings. Alongside the rapid growth in the number of published reviews and meta-analyses, there has been a parallel growth in the development of meta-analytic techniques to handle the increasingly complex types of questions and types of evidence relevant to prevention science. Despite this rapid evolution of meta-analytic techniques and approaches, there is still a lag between the development of new techniques and their uptake by researchers in the field. This paper serves as a brief introduction to this special issue of Prevention Science, entitled "Modern Meta-Analytic Methods in Prevention Science," which highlights recent developments in meta-analytic methods and demonstrates their application to prevention research. This special issue makes an important contribution to the field by ensuring these methodological advances are widely accessible to prevention science researchers, thereby improving their uptake and utilization, and ultimately improving the utility and rigor of research syntheses for informing evidence-based decision making in prevention.

Transcranial magnetic stimulation in the treatment of adolescent depression: a systematic review and meta-analysis of aggregated and individual-patient data from uncontrolled studies

Transcranial magnetic stimulation (TMS) is a non-invasive treatment for adolescent major depressive disorder (MDD). Existing evidence on the efficacy of TMS in adolescent MDD awaits quantitative synthesis. A systematic literature search was conducted, and data from eligible studies were synthesized using random-effects models. Treatment-covariate interactions were examined in exploratory analyses of individual-patient data (IPD). Systematic search of the literature yielded 1264 hits, of which 10 individual studies (2 randomized trials) were included for quantitative synthesis of mainly uncontrolled studies. Individual patient data (IPD) were available from five trials (all uncontrolled studies). Quantitative synthesis of aggregated data revealed a statistically significant negative overall standardized mean change (pooled SMCC = 2.04, 95% CI [1.46; 2.61], SE = 0.29, p < .001), as well as a significant overall treatment response rate (Transformed Proportion = 41.30%, 95% CI [31.03; 51.57], SE = 0.05; p < 0.001), considering data from baseline to post-treatment. Exploratory IPD analyses suggests TMS might be more effective in younger individuals and individuals with more severe depression, and efficacy might be enhanced with certain treatment modality settings, including higher number of TMS sessions, longer treatment durations, and unilateral and not bilateral stimulation. Existing studies exhibit methodological shortcomings, including small-study effects and lack of control group, blinding, and randomization-compromising the credibility of the present results. To date, two randomized controlled trials on TMS in adolescent depression have been published, and the only large-scale randomized trial suggests TMS is not more effective than sham stimulation. Future large-scale, randomized, and sham-controlled trials are warranted. Future trials should ensure appropriate selection of patients for TMS treatment and guide precision medicine approaches for stimulation protocols.

Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices

Data integration, the processes by which data are aggregated, combined, and made available for use, has been key to the development and growth of many technological solutions. In health care, we are experiencing a revolution in the use of sensors to collect data on patient behaviors and experiences. Yet, the potential of this data to transform health outcomes is being held back. Deficits in standards, lexicons, data rights, permissioning, and security have been well documented, less so the cultural adoption of sensor data integration as a priority for large-scale deployment and impact on patient lives. The use and reuse of trustworthy data to make better and faster decisions across drug development and care delivery will require an understanding of all stakeholder needs and best practices to ensure these needs are met. The Digital Medicine Society is launching a new multistakeholder Sensor Data Integration Tour of Duty to address these challenges and more, providing a clear direction on how sensor data can fulfill its potential to enhance patient lives.

Survival analysis between laparoscopic and open hepatectomy for hepatocellular carcinoma: a meta-analysis based on reconstructed time-to-event data

BACKGROUND/PURPOSE OF THE STUDY

Laparoscopic hepatectomy (LH) has been widely used in the treatment of hepatocellular carcinoma (HCC). It is generally believed that the long-term outcomes of LH are not inferior to open hepatectomy (OH). However, the quality of evidence is low. The purpose of this study was to reconstruct time-to-event data for meta-analysis based on Kaplan-Meier curves from propensity-score matched studies and compare survival rates following LH and OH for hepatocellular carcinoma.

METHODS

All published propensity-score matched studies reported in English that compared LH and OH for hepatocellular carcinoma with Kaplan-Meier curves were screened. Patients' survival information was reconstructed with the aid of a computer vision program. Different models (fixed-effects model for two-stage survival analysis and Cox regression for one-stage survival analysis) were performed for sensitivity analysis. In addition to the primary meta-analysis, two specific subgroup analyses were performed on patients by types of resection, cirrhosis status.

RESULTS

Time-to-event data were extracted from 45 propensity-score matched studies (N = 8905). According to the time-to-event data and the reconstructed Kaplan-Meier curves, the cumulative overall survival rate was 49.0% and 50.9% in the LH and OH cohorts, respectively, a log-rank test did not demonstrate statistical significance (p > 0.05). The cumulative recurrence-free survival (RFS) probability was both close to 0.0%. The median RFS time was 49.1 (95% CI 46.1 ~ 51.7) and 44.3 (95% CI 41 ~ 46.1) months. The difference in disease status was statistically significant by the Log-rank test (p < 0.05). Using the random-effects model of two-stage analysis, the minor hepatectomy subgroup (HR = 1.32, 95% CI [1.09, 1.55], I² = 6.2%, p = 0.383) and the shared fragile model of one-stage analysis (HR = 1.44 95% CI [1.23, 1.69], p < 0.001) suggested that LH could significantly prolong RFS of patients compared with OH. This result was consistent with sensitivity analysis using different models.

CONCLUSION

This study was the first reconstructed time-to-event data based on a high-quality propensity-score matching study to compare the survival outcomes of LH and OH in the treatment of HCC. Results suggested that LH can improve RFS in patients with HCC undergoing minor hepatectomy and may also benefit long-term RFS in overall patients.

Data sharing-trialists' plans at registration, attitudes, barriers and facilitators: A cohort study and cross-sectional survey

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.

How COVID-19 has fundamentally changed clinical research in global health

COVID-19 has had negative repercussions on the entire global population. Despite there being a common goal that should have unified resources and efforts, there have been an overwhelmingly large number of clinical trials that have been registered that are of questionable methodological quality. As the final paper of this Series, we discuss how the medical research community has responded to COVID-19. We recognise the incredible pressure that this pandemic has put on researchers, regulators, and policy makers, all of whom were doing their best to move quickly but safely in a time of tremendous uncertainty. However, the research community's response to the COVID-19 pandemic has prominently highlighted many fundamental issues that exist in clinical trial research under the current system and its incentive structures. The COVID-19 pandemic has not only re-emphasised the importance of well designed randomised clinical trials but also highlighted the need for large-scale clinical trials structured according to a master protocol in a coordinated and collaborative manner. There is also a need for structures and incentives to enable faster data sharing of anonymised datasets, and a need to provide similar opportunities to those in high-income countries for clinical trial research in low-resource regions where clinical trial research receives considerably less research funding.

Can synthetic data be a proxy for real clinical trial data? A validation study

OBJECTIVES

There are increasing requirements to make research data, especially clinical trial data, more broadly available for secondary analyses. However, data availability remains a challenge due to complex privacy requirements. This challenge can potentially be addressed using synthetic data.

SETTING

Replication of a published stage III colon cancer trial secondary analysis using synthetic data generated by a machine learning method.

PARTICIPANTS

There were 1543 patients in the control arm that were included in our analysis.

PRIMARY AND SECONDARY OUTCOME MEASURES

Analyses from a study published on the real dataset were replicated on synthetic data to investigate the relationship between bowel obstruction and event-free survival. Information theoretic metrics were used to compare the univariate distributions between real and synthetic data. Percentage CI overlap was used to assess the similarity in the size of the bivariate relationships, and similarly for the multivariate Cox models derived from the two datasets.

RESULTS

Analysis results were similar between the real and synthetic datasets. The univariate distributions were within 1% of difference on an information theoretic metric. All of the bivariate relationships had CI overlap on the tau statistic above 50%. The main conclusion from the published study, that lack of bowel obstruction has a strong impact on survival, was replicated directionally and the HR CI overlap between the real and synthetic data was 61% for overall survival (real data: HR 1.56, 95% CI 1.11 to 2.2; synthetic data: HR 2.03, 95% CI 1.44 to 2.87) and 86% for disease-free survival (real data: HR 1.51, 95% CI 1.18 to 1.95; synthetic data: HR 1.63, 95% CI 1.26 to 2.1).

CONCLUSIONS

The high concordance between the analytical results and conclusions from synthetic and real data suggests that synthetic data can be used as a reasonable proxy for real clinical trial datasets.

TRIAL REGISTRATION NUMBER

NCT00079274.

Characteristics and quality of systematic reviews and meta-analyses of observational nutritional epidemiology: a cross-sectional study

BACKGROUND

Dietary recommendations and policies should be guided by rigorous systematic reviews. Reviews that are of poor methodological quality may be ineffective or misleading. Most of the evidence in nutrition comes from nonrandomized studies of nutritional exposures (usually referred to as nutritional epidemiology studies), but to date methodological evaluations of the quality of systematic reviews of such studies have been sparse and inconsistent.

OBJECTIVES

We aimed to investigate the quality of recently published systematic reviews and meta-analyses of nutritional epidemiology studies and to propose guidance addressing major limitations.

METHODS

We searched MEDLINE (January 2018-August 2019), EMBASE (January 2018-August 2019), and the Cochrane Database of Systematic Reviews (January 2018-February 2019) for systematic reviews of nutritional epidemiology studies. We included a random sample of 150 reviews.

RESULTS

Most reviews were published by authors from Asia (n = 49; 32.7%) or Europe (n = 43; 28.7%) and investigated foods or beverages (n = 60; 40.0%) and cancer morbidity and mortality (n = 54; 36%). Reviews often had important limitations: less than one-quarter (n = 30; 20.0%) reported preregistration of a protocol and almost one-third (n = 42; 28.0%) did not report a replicable search strategy. Suboptimal practices and errors in the synthesis of results were common: one-quarter of meta-analyses (n = 30; 26.1%) selected the meta-analytic model based on statistical indicators of heterogeneity and almost half of meta-analyses (n = 50; 43.5%) did not consider dose-response associations even when it was appropriate to do so. Only 16 (10.7%) reviews used an established system to evaluate the certainty of evidence.

CONCLUSIONS

Systematic reviews of nutritional epidemiology studies often have serious limitations. Authors can improve future reviews by involving statisticians, methodologists, and researchers with substantive knowledge in the specific area of nutrition being studied and using a rigorous and transparent system to evaluate the certainty of evidence.

Optimizing the synthesis of clinical trial data using sequential trees

OBJECTIVE

With the growing demand for sharing clinical trial data, scalable methods to enable privacy protective access to high-utility data are needed. Data synthesis is one such method. Sequential trees are commonly used to synthesize health data. It is hypothesized that the utility of the generated data is dependent on the variable order. No assessments of the impact of variable order on synthesized clinical trial data have been performed thus far. Through simulation, we aim to evaluate the variability in the utility of synthetic clinical trial data as variable order is randomly shuffled and implement an optimization algorithm to find a good order if variability is too high.

MATERIALS AND METHODS

Six oncology clinical trial datasets were evaluated in a simulation. Three utility metrics were computed comparing real and synthetic data: univariate similarity, similarity in multivariate prediction accuracy, and a distinguishability metric. Particle swarm was implemented to optimize variable order, and was compared with a curriculum learning approach to ordering variables.

RESULTS

As the number of variables in a clinical trial dataset increases, there is a pattern of a marked increase in variability of data utility with order. Particle swarm with a distinguishability hinge loss ensured adequate utility across all 6 datasets. The hinge threshold was selected to avoid overfitting which can create a privacy problem. This was superior to curriculum learning in terms of utility.

CONCLUSIONS

The optimization approach presented in this study gives a reliable way to synthesize high-utility clinical trial datasets.

Trials number, funding support, and intervention type associated with IPDMA data retrieval: a cross-sectional study

OBJECTIVES

This study aimed to investigate the predictors for high data retrieval and the reporting of individual participant data meta-analyses (IPDMAs).

STUDY DESIGN AND SETTING

We searched EMBASE, MEDLINE, and the Cochrane Library for articles pertaining to IPDMA from 2011 to 2019. Only IPDMA assessing treatment effects, including randomized controlled trials (RCTs), were included. Adherence to the PRISMA-IPD guideline was checked.

RESULTS

A total of 210 IPDMA covering 18 diseases were sampled; 80 (38.1%) and 123 (58.6%) of the IPDMA retrieved IPD from all and ≥80% RCTs, respectively. Non-Cochrane reviews, IPDMA on nonpharmacological interventions, analyses of smaller numbers of RCTs, and having funding supports were predictors of complete IPD retrieval. Owners of RCTs had an increased probability of obtaining IPD. Only 4.3% described the eligibility criteria covering all the PICO components, 11.0% reported the methods for assessing risk of bias across studies, 11.4% mentioned the IPD integrity, and 9.0% presented detailed results of syntheses.

CONCLUSION

IPD retrieval and reporting was not satisfactory among the published IPDMA. Encouraging RCT owners to conduct or join in the IPDMA is a potential strategy to maximize the IPD retrieval. IPDMA are suggested to adhere to the PRISMA-IPD guideline during reporting.