Pages that link to "Item:Q5259172"

The following pages link to A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data (Q5259172):

Displaying 20 items.

• Accuracy versus convenience: a simulation-based comparison of two continuous imputation models for incomplete ordinal longitudinal clinical trials data (Q440145) (← links)
• Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout (Q1731257) (← links)
• 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 (Q2039155) (← links)
• Comparing different planned missingness designs in longitudinal studies (Q2049561) (← links)
• An evaluation of methods to handle missing data in the context of latent variable interaction analysis: multiple imputation, maximum likelihood, and random forest algorithm (Q2103281) (← links)
• Analyzing data with missing continuous covariates by multiple imputation using proper impu\-tation (Q2904354) (← links)
• Simulation study: Introduction of imputation methods for missing data in longitudinal analysis (Q2908223) (← links)
• Comparison of alternative imputation methods for ordinal data (Q2965574) (← links)
• On the Performance of Sequential Regression Multiple Imputation Methods with Non Normal Error Distributions (Q3625355) (← links)
• Multiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studies (Q4670474) (← links)
• Multiple imputation of longitudinal categorical data through bayesian mixture latent Markov models (Q5037018) (← links)
• Validity and efficiency in analyzing ordinal responses with missing observations (Q5094325) (← links)
• Testing the proportional odds assumption in multiply imputed ordinal longitudinal data (Q5130336) (← links)
• Multiple imputation for ordinal longitudinal data with monotone missing data patterns (Q5138532) (← links)
• Joint GEEs for multivariate correlated data with incomplete binary outcomes (Q5138678) (← links)
• A general GEE framework for the analysis of longitudinal ordinal missing data and related issues (Q5142235) (← links)
• A multiple imputation method for incomplete correlated ordinal data using multivariate probit models (Q5267924) (← links)
• Imputation of repeatedly observed multinomial variables in longitudinal surveys (Q5358376) (← links)
• Estimating longitudinal change in latent variable means: a comparison of non-negative matrix factorization and other item non-response methods (Q5887971) (← links)
• A monotone data augmentation algorithm for multivariate nonnormal data: with applications to controlled imputations for longitudinal trials (Q6625580) (← links)