ArticlePrevention science : the official journal of the Society for Prevention Research2018
Limitations in Using Multiple Imputation to Harmonize Individual Participant Data for Meta-Analysis.
Article in Prevention science : the official journal of the Society for Prevention Research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Introduction to the Special Issue on Innovations and Applications of Integrative Data Analysis (IDA) and Related Data Harmonization Procedures in Prevention Science.Prevention science : the official journal of the Society for Prevention Research · 2023Pooled 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
- One- and two-stage surgical revision of peri-prosthetic joint infection of the hip: a pooled individual participant data analysis of 44 cohort studies.European journal of epidemiology · 2018Pooled it
- Structured missingness in biomedical research.Communications AI & computing · 2026Article
- Gender, Racial, and Socioeconomic Disparities on Social and Behavioral Skills for K-8 Students With and Without Interventions: An Integrative Data Analysis of Eight Cluster Randomized Trials.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
- Methodological Strategies for Prospective Harmonization of Studies: Application to 10 Distinct Outcomes Studies of Preventive Interventions Targeting Opioid Misuse.Prevention science : the official journal of the Society for Prevention Research · 2023Article
- Common Data Elements to Facilitate Sharing and Re-use of Participant-Level Data: Assessment of Psychiatric Comorbidity Across Brain Disorders.Frontiers in psychiatry · 2022Article
- Article
- Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI.Scientific reports · 2021Article
- Development and Validation of a Large Synthetic Cohort for the Study of Cardiovascular Health Across the Life Span.American journal of epidemiology · 2021Article
- A CD-based mapping method for combining multiple related parameters from heterogeneous intervention trials.Statistics and its interface · 2020Article
- Multivariate Higher-Order IRT Model and MCMC Algorithm for Linking Individual Participant Data From Multiple Studies.Frontiers in psychology · 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
- Strength in Numbers.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
- A comparison of existing methods for multiple imputation in individual participant data meta-analysis.Statistics in medicine · 2017Article
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Authors and funding
5 authors.
Funding
Abstract
Individual participant data (IPD) meta-analysis is a meta-analysis in which the individual-level data for each study are obtained and used for synthesis. A common challenge in IPD meta-analysis is when variables of interest are measured differently in different studies. The term harmonization has been coined to describe the procedure of placing variables on the same scale in order to permit pooling of data from a large number of studies. Using data from an IPD meta-analysis of 19 adolescent depression trials, we describe a multiple imputation approach for harmonizing 10 depression measures across the 19 trials by treating those depression measures that were not used in a study as missing data. We then apply diagnostics to address the fit of our imputation model. Even after reducing the scale of our application, we were still unable to produce accurate imputations of the missing values. We describe those features of the data that made it difficult to harmonize the depression measures and provide some guidelines for using multiple imputation for harmonization in IPD meta-analysis.
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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.