Prediction in the presence of missing values is a complex and still poorly understood problem, particularly when future records also contain missing values.
Mertens, et al. (2020) demonstrate that with non-linear models (such as logistic regression or Cox survival) and when using imputations, averaging of multiple predictions obtained from distinct models fitted on imputed data should be...
In a prospective study of patients with muco-obstructive lung disease, aiming to develop a cough alert system based on nocturnal cough monitoring, to identify patient-individual thresholds at which cough frequency exceeds normal variability, so far 92 of intended 220 patients were included.
From den Brinker et al. in a study with 30 COPD patients it is known, that the day-to-day variation of...
Multiple imputation (MI) continues to be a popular approach to deal with missing at-random covariate data. For MI to perform well, it is advisable to ensure that the imputation model for a given covariate does not make conflicting assumptions with substantive/analysis model. In the case of substantive models that assume proportional hazards (e.g., the standard Cox model for a single...
Handling of missing data is a crucial aspect when preparing data sets for further analyses in several research areas. Previous studies have shown that the choice of imputation method can have a high influence on subsequent analyses, especially in medical research, where missing values often occur due to study design or data collection challenges.
In this study, we conduct a comparative...
Missing covariate data is a significant source of bias in observational studies that use propensity score (PS) analysis to make causal inference. The accuracy of treatment effect estimation is determined not just by how missing data is handled, but also by the method used to calculate propensity scores. A variety of methods for handling missing covariate data in propensity score analyses have...
