Results 31 to 40 of about 290,361 (291)

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

Multiple Imputation for Bounded Variables [PDF]

Psychometrika, 2018
Missing data are a common issue in statistical analyses. Multiple imputation is a technique that has been applied in countless research studies and has a strong theoretical basis. Most of the statistical literature on multiple imputation has focused on unbounded continuous variables, with mostly ad hoc remedies for variables with bounded support. These
GERACI Marco, MCLAIN Alexander
openaire   +3 more sources

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

Fractional Imputation in Survey Sampling: A Comparative Review [PDF]

, 2015
Fractional imputation (FI) is a relatively new method of imputation for handling item nonresponse in survey sampling. In FI, several imputed values with their fractional weights are created for each missing item.
Kim, Jae Kwang, Kim, Jae Kwang, Yang, Shu   +2 more
core   +4 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

Variable selection with Random Forests for missing data [PDF]

, 2013
Variable selection has been suggested for Random Forests to improve their efficiency of data prediction and interpretation. However, its basic element, i.e.
Hapfelmeier, Alexander, Ulm, Kurt
core   +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

Missing-Data Handling Methods for Lifelogs-Based Wellness Index Estimation: Comparative Analysis With Panel Data

JMIR Medical Informatics, 2020
BackgroundA lifelogs-based wellness index (LWI) is a function for calculating wellness scores based on health behavior lifelogs (eg, daily walking steps and sleep times collected via a smartwatch).
Kim, Ki-Hun, Kim, Kwang-Jae
doaj   +1 more source

MissForest - nonparametric missing value imputation for mixed-type data

, 2011
Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set.
D. J. Stekhoven, Harley, Kurgan, Latal, LITTLE, Oba, P. Buhlmann, Smit, Troyanskaya, van Buuren, Wille, Wu   +11 more
core   +1 more source