Joint Bayesian models link longitudinal creatinine trajectories to time-to-event kidney disease risk in pediatric autoimmune patients and enable dynamic risk predictions based on observed data.
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A review summarizing parametric, nonparametric, Bayesian, and machine learning methods for efficacy analysis in clinical trials and identifying gaps such as high-dimensional data and missingness.
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Bayesian Joint Modelling of Longitudinal Creatinine Trajectories in Children with Auto-Immune Disorders to Predict Paediatric Kidney Disease Risk in a Single Centre Study
Joint Bayesian models link longitudinal creatinine trajectories to time-to-event kidney disease risk in pediatric autoimmune patients and enable dynamic risk predictions based on observed data.
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Efficacy Analysis in Clinical Trials: A Comprehensive Review of Statistical and Machine Learning Approaches
A review summarizing parametric, nonparametric, Bayesian, and machine learning methods for efficacy analysis in clinical trials and identifying gaps such as high-dimensional data and missingness.