Results 1 to 10 of about 293,301 (286)

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

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 checking in multiple imputation: an overview and case study

Emerging Themes in Epidemiology, 2017
Background Multiple imputation has become very popular as a general-purpose method for handling missing data. The validity of multiple-imputation-based analyses relies on the use of an appropriate model to impute the missing values.
Cattram D. Nguyen, John B. Carlin, Katherine J. Lee   +2 more
doaj   +3 more sources

Multiple Imputation: Theory and Method

International Statistical Review, 2003
SummaryIn this review paper, we discuss the theoretical background of multiple imputation, describe how to build an imputation model and how to create proper imputations. We also present the rules for making repeated imputation inferences. Three widely used multiple imputation methods, the propensity score method, the predictive model method and the ...
exaly   +4 more sources

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

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