16 papers · ranked by Valyu relevance
Shiyu Zhang, Yajuan Si, John J. Dziak
Background When analyzing randomized controlled trials (RCTs) data, covariate adjustment is often employed to increase the precision of estimated treatment effects. Missing data in covariates, if not handled properly, can result in biased and inefficient estimates. However, the existing literature on handling missing…
Molly Ehrig, Garrett S Bullock, Xiaoyan Iris Leng, Nicholas M Pajewski + 2 more
'Nicholas M Pajewski' 'Jaime Lynn Speiser' 'Christian Lovis'] Title: Abstract Background Missing data in electronic health records are highly prevalent and result in analytical concerns such as heterogeneous sources of bias and loss of statistical power. One simple analytic method for addressing missing or unknown…
Mutamba T. Kayembe, Shahab Jolani, Frans E. S. Tan, Gerard J. P. van Breukelen
'Gerard J. P. van Breukelen'] Title: Summary In this article, we first review the literature on dealing with missing values on a covariate in randomized studies and summarize what has been done and what is lacking to date. We then investigate the situation with a continuous outcome and a missing binary covariate in…
Matthew Sperrin, Glen P. Martin
Background Within routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for example, in the case of electronic health records, where the presence or absence of data is informative. While the naive use of missing indicators to try to exploit such…
Tingyan Yue, Tao Zhang
Background Traditional approaches to identify missing mechanisms are usually based on the hypothesis test and confronted with both theoretical and practical challenges. It has been proved that the Bayesian network is powerful in integrating, analyzing and visualizing information, and some previous researches have…
Morten Wærsted, Taran Svenssen Børnick, Jos W. R. Twisk, Kaj Bo Veiersted
'Kaj Bo Veiersted'] Objective Missing data in longitudinal studies may constitute a source of bias. We suggest three simple missing data indicators for the initial phase of getting an overview of the missingness pattern in a dataset with a high number of follow-ups. Possible use of the indicators is exemplified in two…
Alireza Zamanian, Henrik von Kleist, Octavia-Andreea Ciora, Marta Piperno + 3 more
Simple Summary This paper argues the importance of considering domain knowledge when dealing with missing data in healthcare. We identify fundamental missingness scenarios in healthcare facilities and show how they impact the missing data analysis methods. Abstract Despite the extensive literature on missing data…
Rolf H. H. Groenwold
Electronic health records provide a potentially valuable data source of information for developing clinical prediction models. However, missing data are common in routinely collected health data and often missingness is informative. Informative missingness can be incorporated in a clinical prediction model, for example…
Xuewen Yu, Jim Q. Smith, Kateřina Hlaváčková-Schindler
Graph-based causal inference has recently been successfully applied to explore system reliability and to predict failures in order to improve systems. One popular causal analysis following Pearl and Spirtes et al. to study causal relationships embedded in a system is to use a Bayesian network (BN). However, certain…
Machelle D. Wilson, Matthew D. Ponzini, Sandra L. Taylor, Kyoungmi Kim + 3 more
'Kyoungmi Kim' 'António Ferreira' 'Marta Sousa Silva' 'Carlos Cordeiro'] The analysis of high-throughput metabolomics mass spectrometry data across multiple biological sample types (biospecimens) poses challenges due to missing data. During differential abundance analysis, dropping samples with missing values can lead…
Huiting Ou, Anuradha Surendra, Graeme S V McDowell, Emily Hashimoto-Roth + 4 more
If the user’s dataset has at least 3 features and 6 samples with no missing values, or a minimum of 18 non-missing values across minimum of 3 features and 6 samples, ImpLiMet further offers an optimization option wherein the error of each imputation method is evaluated by simulating the three different sources of…
Jiwei Zhao, Chi Chen
We study how to conduct statistical inference in a regression model where the outcome variable is prone to missing values and the missingness mechanism is unknown. The model we consider might be a traditional setting or a modern high-dimensional setting where the sparsity assumption is usually imposed and the…
Ayman Omar Baniamer, Henri Tilga
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 study addresses one of the core assumptions supporting many statistical models…
Laila Mousafi Alasal, Emma U Hammarlund, Kenneth J Pienta, Lars Rönnstrand + 2 more
The XeroGraph library provides a straightforward workflow, enabling users to analyze and manage missing data through intuitive steps. The process involves initializing the library with the dataset, performing exploratory data analysis, applying imputation methods, and evaluating the results. Comprehensive documentation…
Valter Cesar de Souza, Sergio Augusto Rodrigues, Luís Roberto Almeida Gabriel Filho, Salim Heddam
'Luís Roberto Almeida Gabriel Filho' 'Salim Heddam'] Meteorological data acquired with precision, quality, and reliability are crucial in various agronomy fields, especially in studies related to reference evapotranspiration (ETo). ETo plays a fundamental role in the hydrological cycle, irrigation system planning and…
M. Templ, Markus Ulmer
Many imputation methods have been developed over the years and tested mostly under ideal settings. Surprisingly, there is no detailed research on how imputation methods perform when the idealized assumptions about the distribution of data and/or model assumptions are partly not fulfilled. This research looks into the…