ArticleStatistics in medicine2016
Testing moderation in network meta-analysis with individual participant data.
Article in Statistics in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Project Harmony: A Meta-Analysis With Individual Patient Data on Behavioral and Pharmacologic Trials for Comorbid Posttraumatic Stress and Alcohol or Other Drug Use Disorders.The American journal of psychiatry · 2023Pooled it
- How changes in post-traumatic stress disorder (PTSD) severity mediate substance use disorder (SUD) severity during and after treatment for co-occurring PTSD and SUD: Results from Project Harmony.Addiction (Abingdon, England) · 2025Observational
- The Impact of Sparse Datasets When Harmonizing Data from Studies with Different Measures of the Same Construct.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity.Statistical science : a review journal of the Institute of Mathematical Statistics · 2023Article
- Mitigating Multiple Sources of Bias in a Quasi-Experimental Integrative Data Analysis: Does Treating Childhood Anxiety Prevent Substance Use Disorders in Late Adolescence/Young Adulthood?Prevention science : the official journal of the Society for Prevention Research · 2023Article
- Retrospective Psychometrics and Effect Heterogeneity in Integrated Data Analysis: Commentary on the Special Issue.Prevention science : the official journal of the Society for Prevention Research · 2023Article
- Integrative data analysis of clinical trials network studies to examine the impact of psychosocial treatments for black people who use cocaine: Study protocol.Contemporary clinical trials · 2023Article
- Design and methodology for an integrative data analysis of coping power: Direct and indirect effects on adolescent suicidality.Contemporary clinical trials · 2022Article
- Article
- Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health.Administration and policy in mental health · 2020Article
- Three Flavorings for a Soup to Cure what Ails Mental Health Services.Administration and policy in mental health · 2020Article
- Designs and methods for implementation research: Advancing the mission of the CTSA program.Journal of clinical and translational science · 2020Article
- Mass deworming for improving health and cognition of children in endemic helminth areas: A systematic review and individual participant data network meta-analysis.Campbell systematic reviews · 2019Review
- Evaluating construct equivalence of youth depression measures across multiple measures and multiple studies.Psychological assessment · 2019Article
- Deworming children for soil-transmitted helminths in low and middle-income countries: systematic review and individual participant data network meta-analysis.Journal of development effectiveness · 2019Article
- Addressing Methodologic Challenges and Minimizing Threats to Validity in Synthesizing Findings from Individual-Level Data Across Longitudinal Randomized Trials.Prevention science : the official journal of the Society for Prevention Research · 2018Article
- Limitations in Using Multiple Imputation to Harmonize Individual Participant Data for Meta-Analysis.Prevention science : the official journal of the Society for Prevention Research · 2018Article
- Two-Year Impact of Prevention Programs on Adolescent Depression: an Integrative Data Analysis Approach.Prevention science : the official journal of the Society for Prevention Research · 2018Article
- Multivariate and network meta-analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples.BMJ (Clinical research ed.) · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Meta-analytic methods for combining data from multiple intervention trials are commonly used to estimate the effectiveness of an intervention. They can also be extended to study comparative effectiveness, testing which of several alternative interventions is expected to have the strongest effect. This often requires network meta-analysis (NMA), which combines trials involving direct comparison of two interventions within the same trial and indirect comparisons across trials. In this paper, we extend existing network methods for main effects to examining moderator effects, allowing for tests of whether intervention effects vary for different populations or when employed in different contexts. In addition, we study how the use of individual participant data may increase the sensitivity of NMA for detecting moderator effects, as compared with aggregate data NMA that employs study-level effect sizes in a meta-regression framework. A new NMA diagram is proposed. We also develop a generalized multilevel model for NMA that takes into account within-trial and between-trial heterogeneity and can include participant-level covariates. Within this framework, we present definitions of homogeneity and consistency across trials. A simulation study based on this model is used to assess effects on power to detect both main and moderator effects. Results show that power to detect moderation is substantially greater when applied to individual participant data as compared with study-level effects. We illustrate the use of this method by applying it to data from a classroom-based randomized study that involved two sub-trials, each comparing interventions that were contrasted with separate control groups. Copyright © 2016 John Wiley & Sons, Ltd.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.