Pages that link to "Item:Q3076052"
From MaRDI portal
The following pages link to Multiple Imputation Approaches for the Analysis of Dichotomized Responses in Longitudinal Studies with Missing Data (Q3076052):
Displaying 15 items.
- Model selection of generalized estimating equations with multiply imputed longitudinal data (Q2857996) (← links)
- A hot-deck multiple imputation procedure for gaps in longitudinal recurrent event histories (Q2893422) (← links)
- On responder analyses when a continuous variable is dichotomized and measurement error is present (Q3084189) (← links)
- Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model (Q3299128) (← links)
- Multiple Imputation for Longitudinal Data Under a Bayesian Multilevel Model (Q3645008) (← links)
- Dual imputation model for incomplete longitudinal data (Q4614647) (← links)
- Multiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studies (Q4670474) (← links)
- A Multiple Imputation Approach for the Evaluation of Surrogate Markers in the Principal Stratification Causal Inference Framework (Q4984852) (← links)
- An R package for model fitting, model selection and the simulation for longitudinal data with dropout missingness (Q5087553) (← links)
- Strategies for Analyzing Missing Item Response Data with an Application to Lung Cancer (Q5122745) (← links)
- Multiple imputation for ordinal longitudinal data with monotone missing data patterns (Q5138532) (← links)
- Multiple imputation for the analysis of incomplete compound variables (Q5256380) (← links)
- A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data (Q5259172) (← links)
- Practical Advice on How to Impute Continuous Data When the Ultimate Interest Centers on Dichotomized Outcomes Through Pre-Specified Thresholds (Q5436403) (← links)
- Multiple imputation of semi-continuous exposure variables that are categorized for analysis (Q6628314) (← links)
