2025/12/22 by Ujjwal Das, Ranojoy Basu · 2 citations
Mathematics · #Clinical trial #Feature (linguistics) #Feature selection #Proportional hazards model #Selection (genetic algorithm) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistical Methods in Clinical Trials #Survival analysis
paper · doi:10.1080/00031305.2025.2606077
published in The American Statistician 80(3), 395-404 (Taylor & Francis)
openalex created_date 2025/12/22 · openalex publication_date 2025/12/22 · openalex updated_date 2026/07/29
In many real-life experiments with human subjects, missing data are common. Multiple imputation is widely used to handle unobserved data points. In statistical research, selecting important variables from multiple imputed datasets can be challenging, as each imputed dataset may yield different sets of variables. Over the last decade, stacking imputed datasets and analyzing the resulting integrated data has gained attention. In this article, we consider both horizontal and vertical stacking approaches. The horizontal stacking approach in conjunction with different group penalties is discussed alongside the recently proposed vertical appending method, for identifying predominant variables under time-to-event data. The proposed methods are investigated numerically. Finally, the methods are illustrated in two real-world oncology experiments.