Results 11 to 20 of about 293,301 (286)

A Multiple Imputation Workflow for Handling Missing Covariate Data in Pharmacometrics Modeling [PDF]

CPT: Pharmacometrics & Systems Pharmacology
Covariate missingness is a prevalent issue in pharmacometrics modeling. Incorrect handling of missing covariates can lead to biased parameter estimates, adversely affecting clinical practice and drug development dosing decisions.
My‐Luong Vuong, Geert Verbeke, Erwin Dreesen   +2 more
doaj   +2 more sources

Multiple Imputation for Longitudinal Data: A Tutorial [PDF]

Statistics in Medicine
ABSTRACTLongitudinal studies are frequently used in medical research and involve collecting repeated measures on individuals over time. Observations from the same individual are invariably correlated and thus an analytic approach that accounts for this clustering by individual is required.
Rushani Wijesuriya, Margarita Moreno‐Betancur, John B. Carlin, Ian R. White, Matteo Quartagno, Katherine J. Lee   +5 more
openaire   +4 more sources

Model-based standardization using multiple imputation [PDF]

BMC Medical Research Methodology
Background When studying the association between treatment and a clinical outcome, a parametric multivariable model of the conditional outcome expectation is often used to adjust for covariates.
Antonio Remiro-Azócar, Anna Heath, Gianluca Baio   +2 more
doaj   +2 more sources

Multiple Imputation [PDF]

JAMA, 2015
In this issue of JAMA, Asch et al1 report results of a cluster-randomized clinical trial designed to evaluate the effects of physician financial incentives, patient incentives, or shared physician and patient incentives on low density lipoprotein cholesterol (LDL-C) levels among patients with high cardiovascular risk.
Peng, Li, Elizabeth A, Stuart, David B, Allison   +2 more
openaire   +3 more sources

The effect of high prevalence of missing data on estimation of the coefficients of a logistic regression model when using multiple imputation

BMC Medical Research Methodology, 2022
Background Multiple imputation is frequently used to address missing data when conducting statistical analyses. There is a paucity of research into the performance of multiple imputation when the prevalence of missing data is very high. Our objective was
Peter C. Austin, Stef van Buuren
doaj   +1 more source

The Use of Multiple Imputation to Handle Missing Data in Secondary Datasets: Suggested Approaches when Missing Data Results from the Survey Structure

Inquiry: The Journal of Health Care Organization, Provision, and Financing, 2022
Secondary datasets are used in healthcare research because of its cost advantages, its convenience, and the size of the datasets. However, missing data can cause problems that are difficult to resolve.
Soojung Jo PhD, RN
doaj   +1 more source

A comparison of imputation methods for categorical data

Informatics in Medicine Unlocked, 2023
Objectives: Missing data is commonplace in clinical databases, which are being increasingly used for research. Without giving any regard to missing data, results from analysis may become biased and unrepresentative.
Shaheen MZ. Memon, Robert Wamala, Ignace H. Kabano   +2 more
doaj   +1 more source

A novel scaling methodology to reduce the biases associated with missing data from commercial activity monitors.

PLoS ONE, 2020
BackgroundCommercial physical activity monitors have wide utility in the assessment of physical activity in research and clinical settings, however, the removal of devices results in missing data and has the potential to bias study conclusions.
R O'Driscoll, J Turicchi, C Duarte, J Michalowska, S C Larsen, A L Palmeira, B L Heitmann, G W Horgan, R J Stubbs   +8 more
doaj   +1 more source

A note on multiple imputation for method of moments estimation [PDF]

, 2015
Multiple imputation is a popular imputation method for general purpose estimation. Rubin(1987) provided an easily applicable formula for the variance estimation of multiple imputation.
Kim, Jae Kwang, Kim, Jae Kwang, Yang, Shu   +2 more
core   +4 more sources

The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond

Journal of Statistical Software, 2020
Applications of multiple imputation have long outgrown the traditional context of dealing with item nonresponse in cross-sectional data sets. Nowadays multiple imputation is also applied to impute missing values in hierarchical data sets, address ...
Matthias Speidel, Jörg Drechsler, Shahab Jolani   +2 more
doaj   +1 more source