Results 21 to 30 of about 293,301 (286)

Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa. [PDF]

PLoS ONE, 2015
Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing ...
Katya L Masconi, Tandi E Matsha, Rajiv T Erasmus, Andre P Kengne   +3 more
doaj   +1 more source

Recovery of information from multiple imputation: a simulation study

Emerging Themes in Epidemiology, 2012
Background Multiple imputation is becoming increasingly popular for handling missing data. However, it is often implemented without adequate consideration of whether it offers any advantage over complete case analysis for the research question of ...
Lee Katherine J, Carlin John B
doaj   +1 more source

How handling missing data may impact conclusions: A comparison of six different imputation methods for categorical questionnaire data

SAGE Open Medicine, 2019
Objectives: Missing data is a recurrent issue in many fields of medical research, particularly in questionnaires. The aim of this article is to describe and compare six conceptually different multiple imputation methods, alongside the commonly used ...
Marianne Riksheim Stavseth, Thomas Clausen, Jo Røislien   +2 more
doaj   +1 more source

Multiple imputation for handling missing outcome data when estimating the relative risk

BMC Medical Research Methodology, 2017
Background Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk.
Thomas R. Sullivan, Katherine J. Lee, Philip Ryan, Amy B. Salter   +3 more
doaj   +1 more source

Imputation strategies for missing baseline neurological assessment covariates after traumatic brain injury: A CENTER-TBI study.

PLoS ONE, 2021
Statistical models for outcome prediction are central to traumatic brain injury research and critical to baseline risk adjustment. Glasgow coma score (GCS) and pupil reactivity are crucial covariates in all such models but may be measured at multiple ...
Ari Ercole, Abhishek Dixit, David W Nelson, Shubhayu Bhattacharyay, Frederick A Zeiler, Daan Nieboer, Omar Bouamra, David K Menon, Andrew I R Maas, Simone A Dijkland, Hester F Lingsma, Lindsay Wilson, Fiona Lecky, Ewout W Steyerberg, CENTER-TBI Investigators and Participants   +14 more
doaj   +1 more source

A Machine Learning-Based Multiple Imputation Method for the Health and Aging Brain Study–Health Disparities

Informatics, 2023
The Health and Aging Brain Study–Health Disparities (HABS–HD) project seeks to understand the biological, social, and environmental factors that impact brain aging among diverse communities. A common issue for HABS–HD is missing data. It is impossible to
Fan Zhang, Melissa Petersen, Leigh Johnson, James Hall, Raymond F. Palmer, Sid E. O’Bryant, on behalf of the Health and Aging Brain Study (HABS–HD) Study Team   +6 more
doaj   +1 more source

The Multiple Adaptations of Multiple Imputation [PDF]

Journal of the American Statistical Association, 2007
Multiple imputation was first conceived as a tool that statistical agencies could use to handle nonresponse in large-sample public use surveys. In the last two decades, the multiple-imputation framework has been adapted for other statistical contexts.
Reiter, Jerome P., Raghunathan, Trivellore E.   +1 more
openaire   +2 more sources

Multiple Imputation Through XGBoost

Journal of Computational and Graphical Statistics, 2023
The use of multiple imputation (MI) is becoming increasingly popular for addressing missing data. Although some conventional MI approaches have been well studied and have shown empirical validity, they have limitations when processing large datasets with complex data structures.
Yongshi Deng, Thomas Lumley
openaire   +3 more sources

Multiple imputation methods for handling missing values in a longitudinal categorical variable with restrictions on transitions over time: a simulation study

BMC Medical Research Methodology, 2019
Background Longitudinal categorical variables are sometimes restricted in terms of how individuals transition between categories over time. For example, with a time-dependent measure of smoking categorised as never-smoker, ex-smoker, and current-smoker ...
Anurika Priyanjali De Silva, Margarita Moreno-Betancur, Alysha Madhu De Livera, Katherine Jane Lee, Julie Anne Simpson   +4 more
doaj   +1 more source

Comparison of Selected Multiple Imputation Methods for Continuous Variables – Preliminary Simulation Study Results

Acta Universitatis Lodziensis. Folia Oeconomica, 2018
The problem of incomplete data and its implications for drawing valid conclusions from statistical analyses is not related to any particular scientific domain, it arises in economics, sociology, education, behavioural sciences or medicine.
Małgorzata Misztal
doaj   +1 more source