Nature

Variable selection for inferential models with relatively high-dimensional data: Between method heterogeneity and covariate stability as adjuncts to robust selection

Variable selection in inferential modelling is problematic when the number of variables is large relative to the number of data points, especially when multicollinearity is present. A variety of ...
Nature

A simulation study on missing data imputation for dichotomous variables using statistical and machine learning methods

Missing data is a common issue in medical research, and is often caused by human factors during data collection in epidemiology and clinical studies, which mainly involve individuals as subjects.
ascopubs.org

Causal Inference in Oncology Comparative Effectiveness Research Using Observational Data: Are Instrumental Variables Underutilized?

In the article that accompanies this editorial, Lu et al 5 conducted a systematic review on the use of instrumental variable (IV) methods in oncology comparative effectiveness research. The main ...
EurekAlert!

Hybrid methods improve key variable prediction in process industry using small noisy datasets

Soft measurement based on data-driven models is widely used to predict key variables in process industry due to low cost and real-time capability. However, these models struggle with noisy datasets ...