ArticleStatistics in medicine2011
Gaussian-based routines to impute categorical variables in health surveys.
Article in Statistics in medicine, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A computationally efficient sequential regression imputation algorithm for multilevel data.Journal of applied statistics · 2024Article
- Repeated measures discriminant analysis using multivariate generalized estimation equations.Statistical methods in medical research · 2022Article
- ROBUST INFERENCE WHEN COMBINING INVERSE-PROBABILITY WEIGHTING AND MULTIPLE IMPUTATION TO ADDRESS MISSING DATA WITH APPLICATION TO AN ELECTRONIC HEALTH RECORDS-BASED STUDY OF BARIATRIC SURGERY.The annals of applied statistics · 2021Article
- Three Sample Estimates of Fraction of Missing Information From Full Information Maximum Likelihood.Frontiers in psychology · 2021Article
- Evaluation of approaches for multiple imputation of three-level data.BMC medical research methodology · 2020Article
- Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study.BMC medical research methodology · 2019Article
- ICARUS: Minimizing Human Effort in Iterative Data Completion.Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases · 2018Article
- A Colorimetric and Fluorescent Chemosensor for the Selective Detection of CuJournal of fluorescence · 2017Article
- Model checking in multiple imputation: an overview and case study.Emerging themes in epidemiology · 2017Article
- Disclosure control using partially synthetic data for large-scale health surveys, with applications to CanCORS.Statistics in medicine · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The multivariate normal (MVN) distribution is arguably the most popular parametric model used in imputation and is available in most software packages (e.g., SAS PROC MI, R package norm). When it is applied to categorical variables as an approximation, practitioners often either apply simple rounding techniques for ordinal variables or create a distinct 'missing' category and/or disregard the nominal variable from the imputation phase. All of these practices can potentially lead to biased and/or uninterpretable inferences. In this work, we develop a new rounding methodology calibrated to preserve observed distributions to multiply impute missing categorical covariates. The major attractiveness of this method is its flexibility to use any 'working' imputation software, particularly those based on MVN, allowing practitioners to obtain usable imputations with small biases. A simulation study demonstrates the clear advantage of the proposed method in rounding ordinal variables and, in some scenarios, its plausibility in imputing nominal variables. We illustrate our methods on a widely used National Survey of Children with Special Health Care Needs where incomplete values on race posed a valid threat on inferences pertaining to disparities.
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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.