Results 11 to 20 of about 290,361 (291)

Outcome-sensitive multiple imputation: a simulation study [PDF]

BMC Medical Research Methodology, 2017
Background Multiple imputation is frequently used to deal with missing data in healthcare research. Although it is known that the outcome should be included in the imputation model when imputing missing covariate values, it is not known whether it should
Evangelos Kontopantelis, Ian R. White, Matthew Sperrin, Iain Buchan   +3 more
doaj   +5 more sources

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

Statistical inference for Hardy-Weinberg proportions in the presence of missing genotype information. [PDF]

PLoS ONE, 2013
In genetic association studies, tests for Hardy-Weinberg proportions are often employed as a quality control checking procedure. Missing genotypes are typically discarded prior to testing.
Jan Graffelman, Milagros Sánchez, Samantha Cook, Victor Moreno   +3 more
doaj   +3 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

Methods to Handle Incomplete Data

MAMC Journal of Medical Sciences, 2020
Context: The major question for data analysis is determining the appropriate analytic approach in the presence of incomplete observations. The most common solution to handle missing data in a data set is imputation, where missing values are estimated and
Vinny Johny, Mariamma Philip, Swathi Augustine   +2 more
doaj   +1 more source

Multiple Imputation Ensembles (MIE) for dealing with missing data [PDF]

, 2020
Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation ...
A Farhangfar, AM Sefidian, B Schölkopf, C Cortes, CT Tran, DA Newman, DB Rubin, DB Rubin, DH Wolpert, EL Silva-Ramírez, GE Batista, GJ van der Heijden, H Gao, IH Witten, J Demšar, J Honaker, J Honaker, J Scheffer, JA Sterne, JL Schafer, JL Schafer, JR Quinlan, K Abayomi, KM Ting, L Breiman, L Breiman, L Rokach, M Fichman, M Khalilia, M Spratt, MA Klebanoff, MJ Azur, NJ Horton, PJ García-Laencina, PJ Kelly, PN Tan, RJ Little, S García, S Van Buuren, S Van Buuren, SS Chae, SS Choi, U Garciarena, V Vapnik, X Chen, Y Dong, Y Freund, Y He, Z Che, Z Liu   +49 more
core   +1 more source

Practical strategies for handling breakdown of multiple imputation procedures

Emerging Themes in Epidemiology, 2021
Multiple imputation is a recommended method for handling incomplete data problems. One of the barriers to its successful use is the breakdown of the multiple imputation procedure, often due to numerical problems with the algorithms used within the ...
Cattram D. Nguyen, John B. Carlin, Katherine J. Lee   +2 more
doaj   +1 more source

Practical guidance to handle missing values in the 25-question Geriatric Locomotive Function Scale (GLFS-25): a simulation study

BMJ Open, 2022
Objectives Despite the possible large number of missing values on the 25-question Geriatric Locomotive Function Scale (GLFS-25), how we should treat them is unknown.
Sakae Tanaka, Keiko Yamada, Toru Ogata, Hirotaka Chikuda, Kozo Nakamura, Takuya Kawahara, Ryohei Terashima, Ikumi Takashima, Hiromasa Miura, Takashi Ohe   +9 more
doaj   +1 more source

A Noise-Aware Multiple Imputation Algorithm for Missing Data

Mathematics, 2022
Missing data is a common and inevitable phenomenon. In practical applications, the datasets usually contain noises for various reasons. Most of the existing missing data imputing algorithms are affected by noises which reduce the accuracy of the ...
Fangfang Li, Hui Sun, Yu Gu, Ge Yu
doaj   +1 more source

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