ArticleStatistics in medicine2014
Binary variable multiple-model multiple imputation to address missing data mechanism uncertainty: application to a smoking cessation trial.
Article in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Mechanisms underlying mindfulness-based addiction treatment versus cognitive behavioral therapy and usual care for smoking cessation.Journal of consulting and clinical psychology · 2017Trial
- Using Momentary Measures to Understand Physical Activity Adoption and Maintenance: Protocol for a Longitudinal Study.JMIR research protocols · 2026Article
- Two-stage multiple imputation with a longitudinal composite variable.BMC medical research methodology · 2025Article
- Do Substances Used in Adolescence Predict the Persistence of Substance Use Disorders in Adulthood? A 15-Year Study of Youth After Detention.The Journal of adolescent health : official publication of the Society for Adolescent Medicine · 2025Article
- Randomized controlled trial protocol of health coaching for veterans with complex chronic pain.Trials · 2023Article
- Mindfulness-Based Smoking Cessation Delivered Through Telehealth and Text Messaging for Low-Income Smokers: Protocol for a Randomized Controlled Trial.JMIR research protocols · 2022Article
- Article
- Development and Validation of a Large Synthetic Cohort for the Study of Cardiovascular Health Across the Life Span.American journal of epidemiology · 2021Article
- Simulation model of disease incidence driven by diagnostic activity.Statistics in medicine · 2021Article
- A general method for elicitation, imputation, and sensitivity analysis for incomplete repeated binary data.Statistics in medicine · 2020Article
- Incomplete data analysis of non-inferiority clinical trials: Difference between binomial proportions case.Contemporary clinical trials communications · 2020Article
- Multiple Imputation for Incomplete Data in Epidemiologic Studies.American journal of epidemiology · 2018Article
- Improved methods for estimating fraction of missing information in multiple imputation.Cogent mathematics & statistics · 2018Article
- Analyses of Sensitivity to the Missing-at-Random Assumption Using Multiple Imputation With Delta Adjustment: Application to a Tuberculosis/HIV Prevalence Survey With Incomplete HIV-Status Data.American journal of epidemiology · 2017Article
- Health Disparities in Drug- and Alcohol-Use Disorders: A 12-Year Longitudinal Study of Youths After Detention.American journal of public health · 2016Article
- Evaluation of a weighting approach for performing sensitivity analysis after multiple imputation.BMC medical research methodology · 2015Article
- Association of an Objective Structural and Functional Reference Standard for Glaucoma with Quality of Life Outcomes.Ophthalmology. GlaucomaArticle
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Authors and funding
4 authors.
Funding
Abstract
The true missing data mechanism is never known in practice. We present a method for generating multiple imputations for binary variables, which formally incorporates missing data mechanism uncertainty. Imputations are generated from a distribution of imputation models rather than a single model, with the distribution reflecting subjective notions of missing data mechanism uncertainty. Parameter estimates and standard errors are obtained using rules for nested multiple imputation. Using simulation, we investigate the impact of missing data mechanism uncertainty on post-imputation inferences and show that incorporating this uncertainty can increase the coverage of parameter estimates. We apply our method to a longitudinal smoking cessation trial where nonignorably missing data were a concern. Our method provides a simple approach for formalizing subjective notions regarding nonresponse and can be implemented using existing imputation software.
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