Statistics in Medicine, 2021 Randomized clinical trials with outcome measured longitudinally are frequently analyzed using either random effect models or generalized estimating equations.
N. Kaciroti, R. Little
semanticscholar +1 more source
Properties of the full random‐effect modeling approach with missing covariate data
Statistics in Medicine, 2023 During drug development, a key step is the identification of relevant covariates predicting between‐subject variations in drug response. The full random effects model (FREM) is one of the full‐covariate approaches used to identify relevant covariates in ...
J. Nyberg +3 more
semanticscholar +1 more source
A new estimator for population total in the presence of missing data under unequal probability sampling without replacement: A case study on fine particulate matter in Northern Thailand [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2023 The issue of fine particulate matter in Thailand, especially in Northern Thailand, is an urgent problem that needs to be solved because of potential harm to human health. Prior estimates of fine particulate matter help planning how to reduce it.
Chugiat Ponkaew, Nuanpan Lawson
doaj
Dealing with Missing Data and Uncertainty in the Context of Data Mining [PDF]
, 2018 Missing data is an issue in many real-world datasets yet robust methods for dealing with missing data appropriately still need development. In this paper we conduct an investigation of how some methods for handling missing data perform when the ...
A Fichman +17 more
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Using item response theory as a methodology to impute categorical missing values
Scientific ReportsMost datasets suffer from partial or complete missing values, which has downstream limitations on the available models on which to test the data and on any statistical inferences that can be made from the data.
Adrienne Kline, Yuan Luo
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A real data-driven simulation strategy to select an imputation method for mixed-type trait data.
PLoS Computational Biology, 2023 Missing observations in trait datasets pose an obstacle for analyses in myriad biological disciplines. Considering the mixed results of imputation, the wide variety of available methods, and the varied structure of real trait datasets, a framework for ...
Jacqueline A May +2 more
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Correcting bias due to missing stage data in the non-parametric estimation of stage-specific net survival for colorectal cancer using multiple imputation. [PDF]
, 2017 BACKGROUND: Population-based net survival by tumour stage at diagnosis is a key measure in cancer surveillance. Unfortunately, data on tumour stage are often missing for a non-negligible proportion of patients and the mechanism giving rise to the ...
Carpenter, James R, Falcaro, Milena
core +2 more sources
An Investigation of Missing Data Methods for Classiffcation Trees [PDF]
, 2006 There are many different missing data methods used by classification tree algorithms, but few studies have been done comparing their appropriateness and performance.
Ding, Yufeng, Simonoff, Jeffrey S.
core
Effect of Missing Data Types and Imputation Methods on Supervised Classifiers: An Evaluation Study
Big Data and Cognitive Computing, 2023 Data completeness is one of the most common challenges that hinder the performance of data analytics platforms. Different studies have assessed the effect of missing values on different classification models based on a single evaluation metric, namely ...
Menna Ibrahim Gabr +2 more
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Missing values: sparse inverse covariance estimation andanextension to sparse regression [PDF]
, 2018 We propose an ℓ 1-regularized likelihood method for estimating the inverse covariance matrix in the high-dimensional multivariate normal model in presence of missing data.
Bühlmann, Peter, Städler, Nicolas
core