ArticleStatistics in medicine2021
Accounting for not-at-random missingness through imputation stacking.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identify the most appropriate imputation method for handling missing values in clinical structured datasets: a systematic review.BMC medical research methodology · 2024Pooled it
- A comparison of missing data approaches for linear regression with missing not at random outcome and predictors.Statistical methods in medical research · 2026Article
- Enhancing propensity score analysis with data missing not at random: Introducing dual-forest proximity imputation.Behavior research methods · 2026Article
- Conceptual framework as a guide to choose appropriate imputation method for missing values in a clinical structured dataset.BMC medical research methodology · 2025Article
- A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.Biometrics · 2023Article
- How to apply variable selection machine learning algorithms with multiply imputed data: A missing discussion.Psychological methods · 2023Article
- Assessing Alternative Imputation Strategies for Infrequently Missing Items on Multi-item Scales.Communications in statistics. Case studies, data analysis and applications · 2022Article
- Multiple imputation with missing data indicators.Statistical methods in medical research · 2021Article
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Authors and funding
2 authors.
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Abstract
Not-at-random missingness presents a challenge in addressing missing data in many health research applications. In this article, we propose a new approach to account for not-at-random missingness after multiple imputation through weighted analysis of stacked multiple imputations. The weights are easily calculated as a function of the imputed data and assumptions about the not-at-random missingness. We demonstrate through simulation that the proposed method has excellent performance when the missingness model is correctly specified. In practice, the missingness mechanism will not be known. We show how we can use our approach in a sensitivity analysis framework to evaluate the robustness of model inference to different assumptions about the missingness mechanism, and we provide R package StackImpute to facilitate implementation as part of routine sensitivity analyses. We apply the proposed method to account for not-at-random missingness in human papillomavirus test results in a study of survival for patients diagnosed with oropharyngeal cancer.
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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.