ArticleStatistics in medicine2026
A Fully-Integrated Bayesian Approach for the Imputation and Analysis of Derived Outcome Variables With Missingness.
Article in Statistics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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Who cites it
1 citing paper in PubMed.
- A Fully-Integrated Bayesian Approach for the Imputation and Analysis of Derived Outcome Variables With Missingness.Statistics in medicine · 2026Article
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Authors and funding
3 authors.
Funding
Abstract
Derived variables are variables that are constructed from one or more source variables through established mathematical operations or algorithms. For example, body mass index (BMI) is a derived variable constructed from two source variables: weight and height. When using a derived variable as the outcome in a statistical model, complications arise when some of the source variables have missing values. In this paper, we propose how one can define a single fully integrated Bayesian model to simultaneously impute missing values and sample from the posterior. We compare our proposed method with alternative approaches that rely on multiple imputation (MI), with examples including an analysis to estimate the risk of microcephaly (a derived variable based on sex, gestational age, and head circumference at birth) in newborns exposed to the ZIKA virus.
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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.