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Abstract Background Individual-patient data meta-analysis (IPD-MA) is an increasingly popular approach because of its analytical benefits. IPD-MA of observational studies must overcome the problem of confounding, otherwise biased estimates of treatment effect may be obtained. One approach to reducing confounding bias could be the use of propensity score matching (PSM). IPD-MA can be considered as two-stage clustered data (patients within studies) and propensity score matching can be implemented within studies, across studies, and combining both. Methods This article focuses on implementation of four PSM-based approaches for the analysis of data structure that exploit IPD-MA in two ways: (i) estimation of propensity score model using single-level or random-effects logistic regression; and (ii) matching of propensity scores (PS) across studies, within studies or preferential-within studies. We investigated the performance of these approaches through a simulation study, which considers an IPD-MA that examined the success of different treatments for multidrug-resistant tuberculosis (MDR-TB). The simulation parameters were varied according to three treatment prevalences (according to studies, 50% and 30%), three levels of heterogeneity between studies (low, moderate and high) and three levels of pooled odds ratio (1, 1.5, 3). Results All approaches showed greater biases at the higher levels of heterogeneity regardless of the choices of treatment prevalences. However, matching of propensity scores using within-study and preferential-within study reported better performance compared to matching across studies when treatment prevalence varied across-studies. For fixed prevalences, a random-effect propensity score model to estimate propensity scores followed by matching of propensity scores across-studies achieved lower biases compared to other PSM-based approaches. Conclusions Propensity score matching has wide application in health research while only limited literature is available on the implementation of PSM methods in IPD-MA, and until now methodological performance of PSM methods have not been examined. We believe, this work offers an intuition to the applied researcher for the choice of the PSM-based approaches.

Background: People with either Type 1/Type 2 diabetes experiencing anxiety or depression experience worse clinical and social outcomes. Efficacy of available psychological and pharmacological treatments for anxiety and depression is unclear. Aggregate data meta-analyses (AD-MAs) have failed to consider the clinical relevance of any change these treatments elicit. Thus, we sought to complete an individual participant data meta-analysis (IPD-MA) to evaluate this.Methods: Eligible RCTs of psychological treatments (PTs) and pharmacological treatments (PhTs) were systematically identified and assessed using the Cochrane Risk of Bias Tool-2. IPD was requested and Jacobson's methodology was used to determine the clinical relevance of symptom-change. Traditional effect sizes were calculated to permit comparison of trials providing and not providing IPD and to compare with AD-MAs.Results: Sufficient data was obtained to conduct an IPD-MA for PTs (12/25) but not PhTs (1/5). Across PT trials, rates of 'recovery' for depression post-intervention were low. Whilst significantly more treated patients did recover (17% [95% CI 0.10, 0.25]) than controls (9% [95% CI 0.03, 0.17]), the difference was small (6% [95% CI 0.02, 0.10]).Limitations: Only 50% of eligible trials provided IPD; we were also only able to examine outcomes immediately following the end of an intervention.Conclusion: Current psychological interventions offer limited benefit in treating anxiety and depression in people with Type 1 or Type 2 diabetes (83% remain depressed). More efficacious interventions are urgently needed. [ABSTRACT FROM AUTHOR]

Background: Individual-patient data meta-analysis (IPD-MA) is an increasingly popular approach because of its analytical benefits. IPD-MA of observational studies must overcome the problem of confounding, otherwise biased estimates of treatment effect may be obtained. One approach to reducing confounding bias could be the use of propensity score matching (PSM). IPD-MA can be considered as two-stage clustered data (patients within studies) and propensity score matching can be implemented within studies, across studies, and combining both.Methods: This article focuses on implementation of four PSM-based approaches for the analysis of data structure that exploit IPD-MA in two ways: (i) estimation of propensity score model using single-level or random-effects logistic regression; and (ii) matching of propensity scores (PS) across studies, within studies or preferential-within studies. We investigated the performance of these approaches through a simulation study, which considers an IPD-MA that examined the success of different treatments for multidrug-resistant tuberculosis (MDR-TB). The simulation parameters were varied according to three treatment prevalences (according to studies, 50% and 30%), three levels of heterogeneity between studies (low, moderate and high) and three levels of pooled odds ratio (1, 1.5, 3).Results: All approaches showed greater biases at the higher levels of heterogeneity regardless of the choices of treatment prevalences. However, matching of propensity scores using within-study and preferential-within study reported better performance compared to matching across studies when treatment prevalence varied across-studies. For fixed prevalences, a random-effect propensity score model to estimate propensity scores followed by matching of propensity scores across-studies achieved lower biases compared to other PSM-based approaches.Conclusions: Propensity score matching has wide application in health research while only limited literature is available on the implementation of PSM methods in IPD-MA, and until now methodological performance of PSM methods have not been examined. We believe, this work offers an intuition to the applied researcher for the choice of the PSM-based approaches. [ABSTRACT FROM AUTHOR]

Infectious disease (ID) cohorts are key to advancing public health surveillance, public policies, and pandemic responses. Unfortunately, ID cohorts often lack funding to store and share clinical-epidemiological (CE) data and high-dimensional laboratory (HDL) data long term, which is evident when the link between these data elements is not kept up to date. This becomes particularly apparent when smaller cohorts fail to successfully address the initial scientific objectives due to limited case numbers, which also limits the potential to pool these studies to monitor long-term cross-disease interactions within and across populations. CE data from 9 arbovirus (arthropod-borne viruses) cohorts in Latin America were retrospectively harmonized using the Maelstrom Research methodology and standardized to Clinical Data Interchange Standards Consortium (CDISC). We created a harmonized and standardized meta-cohort that contains CE and HDL data from 9 arbovirus studies from Latin America. To facilitate advancements in cross-population inference and reuse of cohort data, the Reconciliation of Cohort Data for Infectious Diseases (ReCoDID) Consortium harmonized and standardized CE and HDL from 9 arbovirus cohorts into 1 meta-cohort. Interested parties will be able to access data dictionaries that include information on variables across the data sets via Bio Studies. After consultation with each cohort, linked harmonized and curated human cohort data (CE and HDL) will be made accessible through the European Genome-phenome Archive platform to data users after their requests are evaluated by the ReCoDID Data Access Committee. This meta-cohort can facilitate various joint research projects (eg, on immunological interactions between sequential flavivirus infections and for the evaluation of potential biomarkers for severe arboviral disease)., (©Gustavo Gómez, Heather Hufstedler, Carlos Montenegro Morales, Yannik Roell, Anyela Lozano-Parra, Adriana Tami, Tereza Magalhaes, Ernesto T A Marques, Angel Balmaseda, Guilherme Calvet, Eva Harris, Patricia Brasil, Victor Herrera, Luis Villar, Lauren Maxwell, Thomas Jaenisch, ReCoDID Arbovirus harmonization study group. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 23.07.2024.)

Background: Subclinical and overt thyroid dysfunction is easily detectable, often modifiable, and, in younger age groups, has been associated with clinically relevant outcomes. Robust associations in very old persons, however, are currently lacking. This study aimed to investigate the associations between (sub-)clinical thyroid dysfunction and disability in daily living, cognitive function, depressive symptoms, physical function, and mortality in people aged 80 years and older. Methods: Four prospective cohorts participating in the Towards Understanding Longitudinal International older People Studies (TULIPS) consortium were included. We performed a two-step individual participant data meta-analysis on source data from community-dwelling participants aged 80 years and older from the Netherlands, New Zealand, United Kingdom, and Japan. Outcome measures included disability in daily living (disability in activities of daily living [ADL] questionnaires), cognitive function (Mini-Mental State Examination [MMSE]), depressive symptoms (Geriatric Depression Scale [GDS]), physical function (grip strength) at baseline and after 5 years of follow-up, and all-cause five-year mortality. Results: Of the total 2116 participants at baseline (mean age 87 years, range 80–109 years), 105 participants (5.0%) were overtly hypothyroid, 136 (6.4%) subclinically hypothyroid, 1811 (85.6%) euthyroid, 60 (2.8%) subclinically hyperthyroid, and 4 (0.2%) overtly hyperthyroid. Participants with thyroid dysfunction at baseline had nonsignificantly different ADL scores compared with euthyroid participants at baseline and had similar MMSE scores, GDS scores, and grip strength. There was no difference in the change of any of these functional measures in participants with thyroid dysfunction during five years of follow-up. Compared with the euthyroid participants, no 5-year survival differences were identified in participants with overt hypothyroidism (hazard ratio [HR] 1.0, 95% confidence interval [CI 0.6–1.6]), subclinical hypothyroidism (HR 0.9 [CI 0.7–1.2]), subclinical hyperthyroidism (HR 1.1 [CI 0.8–1.7]), and overt hyperthyroidism (HR 1.5 [CI 0.4–5.9]). Results did not differ after excluding participants using thyroid-influencing medication. Conclusions: In community-dwelling people aged 80 years and older, (sub-)clinical thyroid dysfunction was not associated with functional outcomes or mortality and may therefore be of limited clinical significance. [ABSTRACT FROM AUTHOR]

Introduction: Aggregate data meta-analyses have shown heterogeneous treatment effects for cognitive-behavioural therapy (CBT) for patients with schizophrenia spectrum diagnoses. This heterogeneity could stem from specific intervention or patient characteristics that could influence the clinical effectiveness of CBT, termed treatment effect modifiers. This individual participant data meta-analysis will investigate a range of potential treatment effect modifiers of the efficacy of CBT., Methods and Analysis: We will perform a systematic review and meta-analysis of studies investigating CBT versus treatment as usual, or CBT versus other psychosocial interventions, for patients with schizophrenia spectrum diagnoses. The Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, EMBASE and the online clinical trials registers of the US government, European Union, WHO and Current Controlled Trials will be searched. Two researchers will screen titles and abstracts identified by the search. Individual participant data will be requested for any eligible study, for the primary outcome (overall psychotic symptoms), secondary outcomes and treatment effect modifiers. Data will be checked and recoded according to an established statistical analysis plan. One-stage and two-stage random effects meta-analyses investigating potential treatment effect modifiers will be conducted. A list of potential treatment effect modifiers for CBT will be produced, motivating future research into particular modifiers., Ethics and Dissemination: This study does not require ethical approval as it is based on data from existing studies, although best ethical practice for secondary analysis of clinical data will be followed. The findings will be submitted for publication in peer-reviewed journals, and promoted to relevant stakeholders., Prospero Registration Number: CRD42017060068., Competing Interests: Competing interests: We recognise that one member of our team may be regarded as having a vested interest in CBT (AM is the only coapplicant actively involved in CBT training, leading of trial grants and receiving royalties from CBT texts or books)., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)