Multiple imputation for multilevel data with continuous and binary variables

We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. The comparisons show that these...

Multiple imputation for multilevel data with continuous and binary variables

We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. Simulations are reproducible. The...

Better experiences with quality of care predict well-being of patients with chronic obstructive pulmonary disease in the Netherlands

Objective: This study was conducted to (1) identify improvements in care quality and well-being of patients with chronic obstructive pulmonary disease in the Netherlands and (2) investigate the longitudinal relationship between these factors. Methods: This longitudinal study was conducted among patients diagnosed with chronic obstructive...

Imputation of systematically missing predictors in an individual participant data meta-analysis: A generalized approach using MICE

Individual participant data meta-analyses (IPD-MA) are increasingly used for developing and validating multivariable (diagnostic or prognostic) risk prediction models. Unfortunately, some predictors or even outcomes may not have been measured in each study and are thus systematically missing in some individual studies of the IPD-MA. As a...

Dual imputation model for incomplete longitudinal data

Missing values are a practical issue in the analysis of longitudinal data. Multiple imputation (MI) is a well-known likelihood-based method that has optimal properties in terms of efficiency and consistency if the imputation model is correctly specified. Doubly robust (DR) weighing-based methods protect against misspecification bias if one of...

Doubly Robust Imputation of Incomplete Binary Longitudinal Data

Estimation in binary longitudinal data by using generalized estimating equation (GEE) becomes complicated in the presence of missing data because standard GEEs are only valid under the restrictive missing completely at random assumption. Weighted GEE has therefore been proposed to allow the validity of GEE's under the weaker missing at random...

Combining the complete-data and nonresponse models for drawing imputations under MAR

In multiple imputation (MI), the resulting estimates are consistent if the imputation model is correct. To specify the imputation model, it is recommended to combine two sets of variables: those that are related to the incomplete variable and those that are related to the missingness mechanism. Several possibilities exist, but it is not clear...