Multiple imputation of discrete and continuous data by fully conditional specification

DOI10.1177/0962280206074463zbMath1122.62382OpenAlexW2096391232WikidataQ31118138 ScholiaQ31118138MaRDI QIDQ5425040

Stef van Buuren

Publication date: 7 November 2007

Published in: Statistical Methods in Medical Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1177/0962280206074463

Mathematics Subject Classification ID

Related Items (68)

Nested case-control studies: should one break the matching? ⋮ Bias Introduced by Rounding in Multiple Imputation for Ordered Categorical Variables ⋮ An Empirical Comparison of Multiple Imputation Methods for Categorical Data ⋮ Estimating longitudinal change in latent variable means: a comparison of non-negative matrix factorization and other item non-response methods ⋮ Recursive partitioning for missing data imputation in the presence of interaction effects ⋮ Variable selection by random forests using data with missing values ⋮ Updating risk prediction tools: A case study in prostate cancer ⋮ Combining Multiple Imputation and Inverse‐Probability Weighting ⋮ Posterior predictive checking of multiple imputation models ⋮ Compatibility results for conditional distributions ⋮ A simple algorithm for checking compatibility among discrete conditional distributions ⋮ Should we impute or should we weight? Examining the performance of two CART-based techniques for addressing missing data in small sample research with nonnormal variables ⋮ Variable selection for multiply-imputed data with penalized generalized estimating equations ⋮ Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes ⋮ Semiparametric Bayesian multiple imputation for regression models with missing mixed continuous-discrete covariates ⋮ A principal component method to impute missing values for mixed data ⋮ Combining Item Response Theory with Multiple Imputation to Equate Health Assessment Questionnaires ⋮ A joint normal‐binary (probit) model ⋮ A multiple robust propensity score method for longitudinal analysis with intermittent missing data ⋮ Vine Copulas for Imputation of Monotone Non‐response ⋮ Missing data: A statistical framework for practice ⋮ Improved generalized raking estimators to address dependent covariate and failure‐time outcome error ⋮ Multiple imputation of ordinal missing not at random data ⋮ Addressing missing data mechanism uncertainty using multiple-model multiple imputation: application to a longitudinal clinical trial ⋮ Determining the number of components in PLS regression on incomplete data set ⋮ A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching ⋮ Application of iterative hybrid MI approach to household survey data with complex dependence structures ⋮ Recursive partitioning on incomplete data using surrogate decisions and multiple imputation ⋮ A method for increasing the robustness of multiple imputation ⋮ The effect of the mechanism and amount of missingness on subset correspondence analysis ⋮ A distance-based rounding strategy for post-imputation ordinal data ⋮ Multiple imputation using multivariateghtransformations ⋮ Multiple imputation of censored survival data in the presence of missing covariates using restricted mean survival time ⋮ Testing the proportional odds assumption in multiply imputed ordinal longitudinal data ⋮ Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables ⋮ Variable selection techniques after multiple imputation in high-dimensional data ⋮ Missing value imputation method for disaster decision-making using K nearest neighbor ⋮ Missing data methods for arbitrary missingness with small samples ⋮ Multiple imputation for ordinal longitudinal data with monotone missing data patterns ⋮ A general GEE framework for the analysis of longitudinal ordinal missing data and related issues ⋮ Navigating choices when applying multiple imputation in the presence of multi-level categorical interaction effects ⋮ Support vector regression for polyhedral and missing data ⋮ Imputation techniques for incomplete data in quadratic discriminant analysis ⋮ A numerical study of multiple imputation methods using nonparametric multivariate outlier identifiers and depth-based performance criteria with clinical laboratory data ⋮ Adjustment for Missing Confounders Using External Validation Data and Propensity Scores ⋮ Compatibility of conditionally specified models ⋮ Multiple imputation in principal component analysis ⋮ The case for the use of multiple imputation missing data methods in stochastic frontier analysis with illustration using English local highway data ⋮ Unnamed Item ⋮ On Inverse Probability Weighting for Nonmonotone Missing at Random Data ⋮ Evaluation of four multiple imputation methods for handling missing binary outcome data in the presence of an interaction between a dummy and a continuous variable ⋮ Multiple imputation: a review of practical and theoretical findings ⋮ Multiple imputation for multilevel data with continuous and binary variables ⋮ Strategies for handling missing data in longitudinal studies with questionnaires ⋮ Doubly robust multiple imputation using kernel-based techniques ⋮ On the Performance of Sequential Regression Multiple Imputation Methods with Non Normal Error Distributions ⋮ Canonical representation of conditionally specified multivariate discrete distributions ⋮ Comments on: Missing data methods in longitudinal studies: a review ⋮ Multilevel models with multivariate mixed response types ⋮ Predictive spreadsheet autocompletion with constraints ⋮ On imputing continuous data when the eventual interest pertains to ordinalized outcomes via threshold concept ⋮ Using multiple imputation with GEE with non-monotone missing longitudinal binary outcomes ⋮ Sequential imputation for models with latent variables assuming latent ignorability ⋮ Multiple Imputation for Missing Values through Conditional Semiparametric Odds Ratio Models ⋮ Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates ⋮ Compatibility of discrete conditional distributions with structural zeros ⋮ An Examination of Discrepancies in Multiple Imputation Procedures Between SAS® and SPSS® ⋮ Iterative Multiple Imputation: A Framework to Determine the Number of Imputed Datasets

Uses Software

• R
• rms
• Stata

Cites Work

• Unnamed Item
• Unnamed Item
• Partially parametric techniques for multiple imputation
• The comparative efficacy of imputation methods for missing data in structural equation model\-ing.
• Conditionally specified distributions: An introduction. (With comments and a rejoinder).
• Inference from iterative simulation using multiple sequences
• Comparison theorems for infinite systems of parabolic functional-differential equations
• Multiple Imputation for Multivariate Data with Missing and Below‐Threshold Measurements: Time‐Series Concentrations of Pollutants in the Arctic
• Fully conditional specification in multivariate imputation
• A note on reducing the bias of the approximate Bayesian bootstrap imputation variance estimator
• Diagnostics for Multivariate Imputations
• Intent-to-Treat Analysis for Longitudinal Studies with Drop-Outs
• Bayesian nonparametric multiple imputation of partially observed data with ignorable nonresponse
• A Multiple Imputation Approach to Cox Regression with Interval‐Censored Data
• Semiparametric Regression Analysis of Interval‐Censored Data
• A Potential for Bias When Rounding in Multiple Imputation
• Multilevel analysis with messy data
• Parameterization and Bayesian Modeling
• Missing-Data Methods for Generalized Linear Models
• Imputation of Binary Treatment Variables With Measurement Error in Administrative Data
• Regression modeling strategies. With applications to linear models, logistic regression and survival analysis

This page was built for publication: Multiple imputation of discrete and continuous data by fully conditional specification