ArticleStatistics in medicine2015
Multiple imputation for harmonizing longitudinal non-commensurate measures in individual participant data meta-analysis.
Article in Statistics in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Addressing Systematic Missing Data in the Context of Causally Interpretable Meta-analysis.Prevention science : the official journal of the Society for Prevention Research · 2023Pooled it
- The contribution of depressive 'disorder characteristics' to determinations of prognosis for adults with depression: an individual patient data meta-analysis.Psychological medicine · 2021Pooled it
- Overcoming Data Gaps in Life Course Epidemiology by Matching Across Cohorts.Epidemiology (Cambridge, Mass.) · 2024Article
- Approaches to protocol standardization and data harmonization in the ECHO-wide cohort study.Pediatric research · 2024Review
- Psychosis Symptom Trajectories Across Childhood and Adolescence in Three Longitudinal Studies: An Integrative Data Analysis with Mixture Modeling.Prevention science : the official journal of the Society for Prevention Research · 2023Article
- Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI.Scientific reports · 2021Article
- Translating RDoC to Real-World Impact in Developmental Psychopathology: A Neurodevelopmental Framework for Application of Mental Health Risk Calculators.Development and psychopathology · 2021Article
- Development and Validation of a Large Synthetic Cohort for the Study of Cardiovascular Health Across the Life Span.American journal of epidemiology · 2021Article
- EEG/ERP as a pragmatic method to expand the reach of infant-toddler neuroimaging in HBCD: Promises and challenges.Developmental cognitive neuroscience · 2021Review
- Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets.BMC bioinformatics · 2021Article
- A CD-based mapping method for combining multiple related parameters from heterogeneous intervention trials.Statistics and its interface · 2020Article
- Mapping infant neurodevelopmental precursors of mental disorders: How synthetic cohorts & computational approaches can be used to enhance prediction of early childhood psychopathology.Behaviour research and therapy · 2019Article
- Article
- A Bayesian approach for correcting exposure misclassification in meta-analysis.Statistics in medicine · 2019Article
- Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes.Frontiers in genetics · 2019Article
- Measurement error and misclassification in electronic medical records: methods to mitigate bias.Current epidemiology reports · 2018Article
- A comparison of one-stage vs two-stage individual patient data meta-analysis methods: A simulation study.Research synthesis methods · 2018Article
- 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
- A comparison of existing methods for multiple imputation in individual participant data meta-analysis.Statistics in medicine · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
There are many advantages to individual participant data meta-analysis for combining data from multiple studies. These advantages include greater power to detect effects, increased sample heterogeneity, and the ability to perform more sophisticated analyses than meta-analyses that rely on published results. However, a fundamental challenge is that it is unlikely that variables of interest are measured the same way in all of the studies to be combined. We propose that this situation can be viewed as a missing data problem in which some outcomes are entirely missing within some trials and use multiple imputation to fill in missing measurements. We apply our method to five longitudinal adolescent depression trials where four studies used one depression measure and the fifth study used a different depression measure. None of the five studies contained both depression measures. We describe a multiple imputation approach for filling in missing depression measures that makes use of external calibration studies in which both depression measures were used. We discuss some practical issues in developing the imputation model including taking into account treatment group and study. We present diagnostics for checking the fit of the imputation model and investigate whether external information is appropriately incorporated into the imputed values.
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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.