Sequential modeling for a class of reference‐based imputation methods in clinical trials with quantitative or binary outcomes
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9303
Abstract: We consider treatment effect estimation in a randomized clinical trial with longitudinally measured quantitative or categorical outcomes. To handle missing data, we usually assume missing at random and then conduct sensitivity analysis for missing not… read more here.
Keywords: reference; imputation methods; reference based; treatment ... See more keywords
A Statistical Approach for Identifying the Best Combination of Normalization and Imputation Methods for Label-Free Proteomics Expression Data.
Sign Up to like & getrecommendations! Published in 2024 at "Journal of proteome research"
DOI: 10.1021/acs.jproteome.4c00552
Abstract: Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods. The selection of appropriate normalization and imputation methods is inherently data-specific, and choosing the… read more here.
Keywords: imputation methods; normalization; normalization imputation; approach ... See more keywords
Design based synthetic imputation methods for domain mean
Sign Up to like & getrecommendations! Published in 2024 at "Scientific Reports"
DOI: 10.1038/s41598-024-53909-0
Abstract: In real life, situations may arise when the available data are insufficient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable… read more here.
Keywords: synthetic imputation; imputation methods; design based; based synthetic ... See more keywords
Applying machine learning-based multiple imputation methods to nonparametric multiple comparisons in longitudinal clinical studies.
Sign Up to like & getrecommendations! Published in 2024 at "Journal of biopharmaceutical statistics"
DOI: 10.1080/10543406.2024.2444243
Abstract: Dependent samples, in which repeated measurements are made on the same subjects, eliminate potential differences among the subjects. In k-dependent samples, missing data can occur for various reasons. The Skillings-Mack test is used instead of… read more here.
Keywords: imputation methods; multiple imputation; nonparametric multiple; machine learning ... See more keywords
To fill or not to fill: Comparing imputation methods for improved riverine long‐term biodiversity monitoring
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Applied Ecology"
DOI: 10.1111/1365-2664.70110
Abstract: The preservation of global biodiversity has become challenging due to intensifying anthropogenic pressures. This study addresses the complex challenges associated with long‐term monitoring data (i.e. missing years and gap filling) on the accuracy of temporal… read more here.
Keywords: imputation methods; biodiversity; long term; trend ... See more keywords
Characterizing the effects of missing data and evaluating imputation methods for chemical prioritization applications using ToxPi
Sign Up to like & getrecommendations! Published in 2018 at "BioData Mining"
DOI: 10.1186/s13040-018-0169-5
Abstract: BackgroundThe Toxicological Priority Index (ToxPi) is a method for prioritization and profiling of chemicals that integrates data from diverse sources. However, individual data sources (“assays”), such as in vitro bioassays or in vivo study endpoints,… read more here.
Keywords: imputation; effects missing; prioritization; characterizing effects ... See more keywords
The impact of missing data rates and imputation methods on the assumption of unidimensionality
Sign Up to like & getrecommendations! Published in 2025 at "PLOS One"
DOI: 10.1371/journal.pone.0321344
Abstract: Statistical models are essential tools in data analysis. However, missing data plays a pivotal role in impacting the assumptions and effectiveness of statistical models, especially when there is a significant amount of missing data. This… read more here.
Keywords: assumption unidimensionality; imputation methods; data rates; impact missing ... See more keywords
Assessment of the performance of different imputation methods for low-coverage sequencing in Holstein cattle.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of dairy science"
DOI: 10.3168/jds.2021-21360
Abstract: Low-coverage sequencing (LCS) followed by imputation has been proposed as a cost-effective genotyping approach for obtaining genotypes of whole-genome variants. Imputation performance is essential for the effectiveness of this approach. Several imputation methods have been… read more here.
Keywords: imputation methods; coverage sequencing; holstein cattle; low coverage ... See more keywords
Empirical Comparison of Imputation Methods for Multivariate Missing Data in Public Health
Sign Up to like & getrecommendations! Published in 2023 at "International Journal of Environmental Research and Public Health"
DOI: 10.3390/ijerph20021524
Abstract: Sample estimates derived from data with missing values may be unreliable and may negatively impact the inferences that researchers make about the underlying population due to nonresponse bias. As a result, imputation is often preferred… read more here.
Keywords: imputation methods; health; public health; missing data ... See more keywords
Comprehensive Evaluation of Advanced Imputation Methods for Proteomic Data Acquired via the Label-Free Approach
Sign Up to like & getrecommendations! Published in 2024 at "International Journal of Molecular Sciences"
DOI: 10.3390/ijms252413491
Abstract: Mass-spectrometry-based proteomics frequently utilizes label-free quantification strategies due to their cost-effectiveness, methodological simplicity, and capability to identify large numbers of proteins within a single analytical run. Despite these advantages, the prevalence of missing values (MV),… read more here.
Keywords: imputation methods; performance; imputation; similarity ... See more keywords