Nested multiple imputation of NMES via partially incompatible MCMC
Abstract
The multiple imputation of the National Medical Expenditure Survey (NMES) involved the use of two new techniques, both having potentially broad applicability. The first is to use distributionally incompatible MCMC (Markov Chain Monte Carlo), but to apply it only partially, to impute the missing values that destroy a monotone pattern, thereby limiting the extent of incompatibility. The second technique is to split the missing data into two parts, one that is much more computationally expensive to impute than the other, and create several imputations of the second part for each of the first part, thereby creating nested multiple imputations with their increased inferential efficiency.
Citing Literature
Number of times cited according to CrossRef: 52
- Angelina Hammon, Sabine Zinn, Multiple imputation of binary multilevel missing not at random data, Journal of the Royal Statistical Society: Series C (Applied Statistics), 10.1111/rssc.12401, 69, 3, (547-564), (2020).
- Alexander M. Franks, Edoardo M. Airoldi, Donald B. Rubin, Nonstandard conditionally specified models for nonignorable missing data, Proceedings of the National Academy of Sciences, 10.1073/pnas.1815563117, (201815563), (2020).
- Khaled Khatab, Maruf A. Raheem, Benn Sartorius, Mubarak Ismail, Prevalence and risk factors for child labour and violence against children in Egypt using Bayesian geospatial modelling with multiple imputation, PLOS ONE, 10.1371/journal.pone.0212715, 14, 5, (e0212715), (2019).
- Faisal Maqbool Zahid, Shahla Faisal, Christian Heumann, Variable selection techniques after multiple imputation in high-dimensional data, Statistical Methods & Applications, 10.1007/s10260-019-00493-7, (2019).
- Faisal M Zahid, Christian Heumann, Multiple imputation with sequential penalized regression, Statistical Methods in Medical Research, 10.1177/0962280218755574, 28, 5, (1311-1327), (2018).
- Edwin de Jonge, Mark van der Loo, Imputation and Adjustment, Statistical Data Cleaning with Applications in R, 10.1002/9781118897126, (219-264), (2018).
- David Kaplan, Dan Su, On imputation for planned missing data in context questionnaires using plausible values: a comparison of three designs, Large-scale Assessments in Education, 10.1186/s40536-018-0059-9, 6, 1, (2018).
- Edwin de Jonge, Mark van der Loo, References, Statistical Data Cleaning with Applications in R, 10.1002/9781118897126, (287-296), (2018).
- Simon Grund, Oliver Lüdtke, Alexander Robitzsch, Multiple Imputation of Missing Data at Level 2: A Comparison of Fully Conditional and Joint Modeling in Multilevel Designs, Journal of Educational and Behavioral Statistics, 10.3102/1076998617738087, 43, 3, (316-353), (2017).
- Achilleas Vassilopoulos, Andreas C. Drichoutis, Rodolfo M. Nayga, Panagiotis Lazaridis, Does the supplemental nutrition assistance program really increase obesity? The importance of accounting for misclassification errors, Journal of Applied Statistics, 10.1080/02664763.2017.1414165, 45, 12, (2269-2278), (2017).
- Bronwyn Loong, Donald B. Rubin, Multiply-Imputed Synthetic Data: Advice to the Imputer, Journal of Official Statistics, 10.1515/jos-2017-0047, 33, 4, (1005-1019), (2017).
- Anne Bichteler, Daniele S. Wikoff, Francis Loko, Mark A. Harris, Estimating serum concentrations of dioxin-like compounds in the U.S. population effective 2005–2006 and 2007–2008: A multiple imputation and trending approach incorporating NHANES pooled sample data, Environment International, 10.1016/j.envint.2017.05.003, 105, (112-125), (2017).
- Olanrewaju Akande, Fan Li, Jerome Reiter, An Empirical Comparison of Multiple Imputation Methods for Categorical Data, The American Statistician, 10.1080/00031305.2016.1277158, 71, 2, (162-170), (2017).
- S. Jiao, F. Tiezzi, Y. Huang, K. A. Gray, C. Maltecca, The use of multiple imputation for the accurate measurements of individual feed intake by electronic feeders, Journal of Animal Science, 10.2527/jas.2015-9667, 94, 2, (824-832), (2016).
- J. McGinniss, O. Harel, Multiple imputation in three or more stages, Journal of Statistical Planning and Inference, 10.1016/j.jspi.2016.04.001, 176, (33-51), (2016).
- Min Cherng Lee, Robin Mitra, Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models, Computational Statistics & Data Analysis, 10.1016/j.csda.2015.08.004, 95, (24-38), (2016).
- undefined Xiaowei Yang, Steven Shoptaw, undefined Kun Nie, undefined Juanmei Liu, Thomas R. Belin, Markov transition models for binary repeated measures with ignorable and nonignorable missing values, Statistical Methods in Medical Research, 10.1177/0962280206071843, 16, 4, (347-364), (2016).
- Stef van Buuren, Multiple imputation of discrete and continuous data by fully conditional specification, Statistical Methods in Medical Research, 10.1177/0962280206074463, 16, 3, (219-242), (2016).
- Thomas Klausch, Barry Schouten, Joop J. Hox, Evaluating Bias of Sequential Mixed-mode Designs Against Benchmark Surveys, Sociological Methods & Research, 10.1177/0049124115585362, 46, 3, (456-489), (2015).
- Ronald Wesonga, On multivariate imputation and forecasting of decadal wind speed missing data, SpringerPlus, 10.1186/s40064-014-0774-9, 4, 1, (2015).
- Shyh-Huei Chen, Edward H. Ip, Behaviour of the Gibbs sampler when conditional distributions are potentially incompatible, Journal of Statistical Computation and Simulation, 10.1080/00949655.2014.968159, 85, 16, (3266-3275), (2014).
- Fan Li, Michela Baccini, Fabrizia Mealli, Elizabeth R. Zell, Constantine E. Frangakis, Donald B. Rubin, Multiple Imputation by Ordered Monotone Blocks With Application to the Anthrax Vaccine Research Program, Journal of Computational and Graphical Statistics, 10.1080/10618600.2013.826583, 23, 3, (877-892), (2014).
- Florian Meinfelder, Multiple Imputation: an attempt to retell the evolutionary process, AStA Wirtschafts- und Sozialstatistisches Archiv, 10.1007/s11943-014-0151-8, 8, 4, (249-267), (2014).
- Samantha R. Cook, Donald B. Rubin, Imputation, Wiley StatsRef: Statistics Reference Online, 10.1002/9781118445112, (2014).
- Sebastian Weirich, Nicole Haag, Martin Hecht, Katrin Böhme, Thilo Siegle, Oliver Lüdtke, Nested multiple imputation in large-scale assessments, Large-scale Assessments in Education, 10.1186/s40536-014-0009-0, 2, 1, (2014).
- Susan M. Shortreed, Eric Laber, T. Scott Stroup, Joelle Pineau, Susan A. Murphy, A multiple imputation strategy for sequential multiple assignment randomized trials, Statistics in Medicine, 10.1002/sim.6223, 33, 24, (4202-4214), (2014).
- Victoria Liublinska, Donald B. Rubin, Sensitivity analysis for a partially missing binary outcome in a two‐arm randomized clinical trial, Statistics in Medicine, 10.1002/sim.6197, 33, 24, (4170-4185), (2014).
- Kristian Kleinke, Jost Reinecke, Multiple imputation of incomplete zero‐inflated count data, Statistica Neerlandica, 10.1111/stan.12009, 67, 3, (311-336), (2013).
- Ofer Harel, Hwan Chung, Diana Miglioretti, Latent class regression: Inference and estimation with two‐stage multiple imputation, Biometrical Journal, 10.1002/bimj.201200020, 55, 4, (541-553), (2013).
- Michael W. Robbins, Sujit K. Ghosh, Joshua D. Habiger, Imputation in High-Dimensional Economic Data as Applied to the Agricultural Resource Management Survey, Journal of the American Statistical Association, 10.1080/01621459.2012.734158, 108, 501, (81-95), (2013).
- Michela Baccini, Samantha Cook, Constantine E. Frangakis, Fan Li, Fabrizia Mealli, Donald B. Rubin, Elizabeth R. Zell, Multiple Imputation in the Anthrax Vaccine Research Program, CHANCE, 10.1080/09332480.2010.10739786, 23, 1, (16-23), (2013).
- Susanne Rässler, Donald B. Rubin, Elizabeth R. Zell, Imputation, Wiley Interdisciplinary Reviews: Computational Statistics, 10.1002/wics.1240, 5, 1, (20-29), (2012).
- Jerome P. Reiter, Protecting Data Confidentiality in Publicly Released Datasets: Approaches Based on Multiple Imputation, Handbook of Statistics Volume 28, 10.1016/B978-0-44-451875-0.00020-8, (533-545), (2012).
- Stef Buuren, References, Flexible Imputation of Missing Data, 10.1201/b11826-16, (2012).
- Alessandra Mattei, Fabrizia Mealli, Donald B. Rubin, Missing Data and Imputation Methods, Modern Analysis of Customer Surveys, 10.1002/9781119961154, (129-154), (2011).
- Ton de Waal, Jeroen Pannekoek, Sander Scholtus, Imputation Under Edit Constraints, Handbook of Statistical Data Editing and Imputation, 10.1002/9780470904848, (299-360), (2011).
- Nathaniel Schenker, Lori G. Borrud, Vicki L. Burt, Lester R. Curtin, Katherine M. Flegal, Jeffery Hughes, Clifford L. Johnson, Anne C. Looker, Lisa Mirel, Multiple imputation of missing dual‐energy X‐ray absorptiometry data in the National Health and Nutrition Examination Survey, Statistics in Medicine, 10.1002/sim.4080, 30, 3, (260-276), (2010).
- Michela Baccini, Samantha Cook, Constantine E. Frangakis, Fan Li, Fabrizia Mealli, Donald B. Rubin, Elizabeth R. Zell, Multiple imputation in the anthrax vaccine research program, CHANCE, 10.1007/s00144-010-0004-3, 23, 1, (16-23), (2010).
- Ian R. White, Rhian Daniel, Patrick Royston, Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables, Computational Statistics & Data Analysis, 10.1016/j.csda.2010.04.005, 54, 10, (2267-2275), (2010).
- Edward H. Ip, Yuchung J. Wang, Canonical representation of conditionally specified multivariate discrete distributions, Journal of Multivariate Analysis, 10.1016/j.jmva.2008.11.010, 100, 6, (1282-1290), (2009).
- G. Cottrell, M. Cot, J.-Y. Mary, L’imputation multiple des données manquantes aléatoirement : concepts généraux et présentation d’une méthode Monte-Carlo, Revue d'Épidémiologie et de Santé Publique, 10.1016/j.respe.2009.04.011, 57, 5, (361-372), (2009).
- S. K. Kinney, J. P. Reiter, Inferences for Two-Stage Multiple Imputation for Nonresponse, Journal of Statistical Theory and Practice, 10.1080/15598608.2009.10411927, 3, 2, (307-318), (2009).
- Xiaowei Yang, Jinhui Li, Steven Shoptaw, Imputation‐based strategies for clinical trial longitudinal data with nonignorable missing values, Statistics in Medicine, 10.1002/sim.3111, 27, 15, (2826-2849), (2008).
- J. P. Reiter, Multiple imputation when records used for imputation are not used or disseminated for analysis, Biometrika, 10.1093/biomet/asn042, 95, 4, (933-946), (2008).
- Ofer Harel, Scott M Hofer, Lesa Hoffman, Nancy L Pedersen, Boo Johansson, Population Inference with Mortality and Attrition in Longitudinal Studies on Aging: A Two-Stage Multiple Imputation Method, Experimental Aging Research, 10.1080/03610730701239004, 33, 2, (187-203), (2007).
- Susanne Rässler, Donald B. Rubin, Elizabeth R. Zell, 19 Incomplete Data in Epidemiology and Medical Statistics, Epidemiology and Medical Statistics, 10.1016/S0169-7161(07)27019-1, (569-601), (2007).
- Samantha R. Cook, Donald B. Rubin, Imputation, Wiley Encyclopedia of Clinical Trials, 10.1002/9780471462422, (2007).
- Ofer Harel, Inferences on missing information under multiple imputation and two-stage multiple imputation, Statistical Methodology, 10.1016/j.stamet.2006.03.002, 4, 1, (75-89), (2007).
- Oliver Lüdtke, Alexander Robitzsch, Ulrich Trautwein, Olaf Köller, Umgang mit fehlenden Werten in der psychologischen Forschung, Psychologische Rundschau, 10.1026/0033-3042.58.2.103, 58, 2, (103-117), (2007).
- Donald B. Rubin, Discussion on Multiple Imputation, International Statistical Review, 10.1111/j.1751-5823.2003.tb00216.x, 71, 3, (619-625), (2007).
- Donald B. Rubin, Samantha R. Cook, Use of Multiple Imputation Models in Medical Device Trials, Clinical Evaluation of Medical Devices, 10.1007/978-1-59745-004-1, (241-251), (2006).
- S. Van Buuren, J. P.L. Brand, C. G.M. Groothuis-Oudshoorn, D. B. Rubin, Fully conditional specification in multivariate imputation, Journal of Statistical Computation and Simulation, 10.1080/10629360600810434, 76, 12, (1049-1064), (2006).
