ArticleBiometrics2021
A stacked approach for chained equations multiple imputation incorporating the substantive model.
Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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
18 citing papers in PubMed.
- Associations of combined lifestyle and genetic risk with incident head and neck cancer: a prospective study in the UK Biobank.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Intraoperative supraglottic airway device, anesthetic, and mechanical factors associated with acute postoperative sore throat in elective tympanoplasty: a retrospective cohort study.BMC anesthesiology · 2026Article
- Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model: Performance Under Penalized Likelihood.Statistics in medicine · 2026Article
- Biobanking knowledge and donation willingness among musculoskeletal patients in England: a multisite cross-sectional study.BMJ open · 2026Article
- Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU.Intensive care medicine experimental · 2026Article
- Research on coal and gas outburst risk prediction based on improved search algorithm optimized deep learning network.Scientific reports · 2025Article
- Exploring the Association Between Behavioral Determinants and Intention to Use a Chatbot-Led Parenting Intervention by Caregivers of Adolescent Girls in South Africa: Cross-Sectional Study.JMIR pediatrics and parenting · 2025Article
- High Pan-Immune Inflammation Values are Associated with Prolonged Length of Hospital Stay in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.International journal of general medicine · 2025Article
- Gaps in the usage and reporting of multiple imputation for incomplete data: findings from a scoping review of observational studies addressing causal questions.BMC medical research methodology · 2024Article
- Long-term retention and positive deviant practices in Uganda's community client-led antiretroviral distribution groups (CCLADs): a mixed-methods study.BMJ global health · 2024Article
- Multiple imputation strategies for missing event times in a multi-state model analysis.Statistics in medicine · 2024Article
- A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.Biometrics · 2023Article
- Trends and Comparisons of Blood Pressure and Fasting Plasma Glucose in Patients with Hypertension, Diabetes, and Comorbidity: 4-Year Follow-Up Data.Risk management and healthcare policy · 2022Article
- On the Treatment of Missing Item Responses in Educational Large-Scale Assessment Data: An Illustrative Simulation Study and a Case Study Using PISA 2018 Mathematics Data.European journal of investigation in health, psychology and education · 2021Article
- Accounting for not-at-random missingness through imputation stacking.Statistics in medicine · 2021Article
- Explore association of genes in PDL1/PD1 pathway to radiotherapy survival benefit based on interaction model strategy.Radiation oncology (London, England) · 2021Article
- A machine learning-based model for "In-time" prediction of periprosthetic joint infection.Digital healthArticle
- Beyond Linear Risk: A Machine Learning Approach to Understanding Perinatal Depression in Context.Multivariate behavioral researchArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Multiple imputation by chained equations (MICE) has emerged as a popular approach for handling missing data. A central challenge for applying MICE is determining how to incorporate outcome information into covariate imputation models, particularly for complicated outcomes. Often, we have a particular analysis model in mind, and we would like to ensure congeniality between the imputation and analysis models. We propose a novel strategy for directly incorporating the analysis model into the handling of missing data. In our proposed approach, multiple imputations of missing covariates are obtained without using outcome information. We then utilize the strategy of imputation stacking, where multiple imputations are stacked on top of each other to create a large data set. The analysis model is then incorporated through weights. Instead of applying Rubin's combining rules, we obtain parameter estimates by fitting a weighted version of the analysis model on the stacked data set. We propose a novel estimator for obtaining standard errors for this stacked and weighted analysis. Our estimator is based on the observed data information principle in Louis' work and can be applied for analyzing stacked multiple imputations more generally. Our approach for analyzing stacked multiple imputations is the first method that can be easily applied (using R package StackImpute) for a wide variety of standard analysis models and missing data settings.
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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.