Tag: Hidimensional data

Enhancing Survival Analysis: Innovative Approaches for Small Sample Sizes and Unequal Censoring

Exploring New Imputation and Permutation Methods to Improve Statistical Validity in Clinical Trials The article “Testing and interval estimation for two-sample survival comparisons with small sample sizes and unequal censoring” by Rui Wang, Stephen W. Lagakos, and Robert J. Gray, published in Biostatistics, presents innovative methodologies aimed at addressing significant challenges in survival analysis, particularly when

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EBD vs MTD: An Overview of Bayesian Effective Biological Dose Determination in Immunotherapy Response Trials

The article “Bayesian Effective Biological Dose Determination in Immunotherapy Response Trial” by Souvik Banerjee et al. presents a novel statistical approach to determine the effective biological dose (EBD) for immunotherapy, particularly focusing on checkpoint inhibitors. This research is significant for statisticians, clinicians, and clinical researchers as it addresses the limitations of conventional dose-finding methods in

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SurvHiDim — High dimensional time to events data analysis with variable selection techniques.

We created this amazing R package to analyze high-dimensional time-to-events data. Thanks to machine learning we now know various variable selection techniques. Thus using Python and R we can implement variable sections easily. Most of the time the outcome variable types are dichotomous and continuous. Principle component analysis, factor analysis, LASSO, and ridge regression are

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