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Relationship between reasons for intermittent missing patient-reported outcomes data and missing data mechanisms
PurposeNon-response (NR) to patient-reported outcome (PRO) questionnaires may cause bias if not handled appropriately. Collecting reasons for NR is...
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On the use of multiple imputation to address data missing by design as well as unintended missing data in case-cohort studies with a binary endpoint
BackgroundCase-cohort studies are conducted within cohort studies, with the defining feature that collection of exposure data is limited to a subset...
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A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis
BackgroundMissing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches...
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The impact of electronic versus paper-based data capture on data collection logistics and on missing scores in thyroid cancer patients
PurposeThe purpose of this study was to investigate the impact of the type of data capture on the time and help needed for collecting...
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Missing Data in Sport Science: A Didactic Example Using Wearables in American Football
Data are often recorded from athletes to make decisions regarding the mitigation of injuries or the enhancement of performance. However, data...
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Addressing Systematic Missing Data in the Context of Causally Interpretable Meta-analysis
Evidence synthesis involves drawing conclusions from trial samples that may differ from the target population of interest, and there is often...
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Handling Missing Data in Health Economics and Outcomes Research (HEOR): A Systematic Review and Practical Recommendations
BackgroundMissing data in costs and/or health outcomes and in confounding variables can create bias in the inference of health economics and outcomes...
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Missing Data in Patient-Reported Outcomes Research: Utilizing Multiple Imputation to Address an Unavoidable Problem
BackgroundPatient-reported outcomes (PROs) have become a focus in postoperative surgical care. Unfortunately, studies using PROs can be subject to...
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Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets
BackgroundMissing data is frequently an inevitable issue in cohort studies and it can adversely affect the study's findings. We assess the...
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Implications of missing data on reported breast cancer mortality
BackgroundNational cancer registries are valuable tools to analyze patterns of care and clinical outcomes; yet, missing data may impact the accuracy...
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Predictive models in emergency medicine and their missing data strategies: a systematic review
In the field of emergency medicine (EM), the use of decision support tools based on artificial intelligence has increased markedly in recent years....
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Propensity score analysis with missing data using a multi-task neural network
BackgroundPropensity score analysis is increasingly used to control for confounding factors in observational studies. Unfortunately, unavoidable...
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Sensitivity analyses for data missing at random versus missing not at random using latent growth modelling: a practical guide for randomised controlled trials
BackgroundMissing data are ubiquitous in randomised controlled trials. Although sensitivity analyses for different missing data mechanisms (missing...
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Treatment of missing data in Bayesian network structure learning: an application to linked biomedical and social survey data
BackgroundAvailability of linked biomedical and social science data has risen dramatically in past decades, facilitating holistic and systems-based...
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Missing data imputation techniques for wireless continuous vital signs monitoring
Wireless vital signs sensors are increasingly used for remote patient monitoring, but data analysis is often challenged by missing data periods. This...
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Missing Race and Ethnicity Data among COVID-19 Cases in Massachusetts
Infectious disease surveillance frequently lacks complete information on race and ethnicity, making it difficult to identify health inequities....
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Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy
BackgroundThe handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing...
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Methods for handling missing data in serially sampled sputum specimens for mycobacterial culture conversion calculation
BackgroundThe occurrence and timing of mycobacterial culture conversion is used as a proxy for tuberculosis treatment response. When researchers...
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Accommodating heterogeneous missing data patterns for prostate cancer risk prediction
BackgroundWe compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts,...
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Publicly available data sources in sport-related concussion research: a caution for missing data
BackgroundResearchers often use publicly available data sources to describe injuries occurring in professional athletes, develo** and testing...
