[HTML][HTML] Missing data in clinical research: a tutorial on multiple imputation
Missing data is a common occurrence in clinical research. Missing data occurs when the
value of the variables of interest are not measured or recorded for all subjects in the sample …
value of the variables of interest are not measured or recorded for all subjects in the sample …
[图书][B] Flexible imputation of missing data
S Van Buuren - 2018 - books.google.com
Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or
mean imputation, only work under highly restrictive conditions, which are often not met in …
mean imputation, only work under highly restrictive conditions, which are often not met in …
Tuning multiple imputation by predictive mean matching and local residual draws
Background Multiple imputation is a commonly used method for handling incomplete
covariates as it can provide valid inference when data are missing at random. This depends …
covariates as it can provide valid inference when data are missing at random. This depends …
Propensity score analysis with missing data.
Propensity score analysis is a method that equates treatment and control groups on a
comprehensive set of measured confounders in observational (nonrandomized) studies. A …
comprehensive set of measured confounders in observational (nonrandomized) studies. A …
[图书][B] Handbook of missing data methodology
Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …
data. But since the 1990s, there have been important developments in the statistical …
A model-based imputation procedure for multilevel regression models with random coefficients, interaction effects, and nonlinear terms.
Despite the broad appeal of missing data handling approaches that assume a missing at
random (MAR) mechanism (eg, multiple imputation and maximum likelihood estimation) …
random (MAR) mechanism (eg, multiple imputation and maximum likelihood estimation) …
Multiple imputation of missing data for multilevel models: Simulations and recommendations
S Grund, O Lüdtke, A Robitzsch - Organizational Research …, 2018 - journals.sagepub.com
Multiple imputation (MI) is one of the principled methods for dealing with missing data. In
addition, multilevel models have become a standard tool for analyzing the nested data …
addition, multilevel models have become a standard tool for analyzing the nested data …
Generating missing values for simulation purposes: a multivariate amputation procedure
Missing data form a ubiquitous problem in scientific research, especially since most
statistical analyses require complete data. To evaluate the performance of methods dealing …
statistical analyses require complete data. To evaluate the performance of methods dealing …
[HTML][HTML] The effectiveness of game-based literacy app learning in preschool children from diverse backgrounds
Family background factors like socio-economic status (SES) and migration background,
along with child characteristics such as gender and intelligence, significantly influence early …
along with child characteristics such as gender and intelligence, significantly influence early …
Combining fractional polynomial model building with multiple imputation
Multivariable fractional polynomial (MFP) models are commonly used in medical research.
The datasets in which MFP models are applied often contain covariates with missing values …
The datasets in which MFP models are applied often contain covariates with missing values …