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Showing 2 results for Evaluation
Shohre Jalaei, Soghrat Faghihzadeh, Farzad Eskandari, Touba Ghazanfari,
Volume 2, Issue 1 (8-2008)
Abstract
Part of the recent literature on the evaluation of surrogate endpoints is started by a definition of validity in terms of both trial-level and individual-level association between a potential surrogate and a true endpoint. In another part, we review the main considerable statistical methods being proposed for the evaluation of a biomarker as surrogate endpoints, which have developed and consider how the validation process might be arranged within the regulatory and practical constraints evaluation. In the present work, we propose a new. Bayesian approach to evaluate individual level surrogacy. Deferent variations to prior distributions were implemented for responses with binomial distribution. Then these methods are compared in a simulation study. Finally, we apply and compare the previous and new methodology using a clinical study.
Dr Alireza Chaji,
Volume 16, Issue 2 (3-2023)
Abstract
High interpretability and ease of understanding decision trees have made
them one of the most widely used machine learning algorithms. The key to building
efficient and effective decision trees is to use the suitable splitting method. This
paper proposes a new splitting approach to produce a tree based on the T-entropy criterion
for the splitting method. The method presented on three data sets is examined
by 11 evaluation criteria. The results show that the introduced method in making
the decision tree has a more accurate performance than the well-known methods of
Gini index, Shannon, Tisalis, and Renny entropies and can be used as an alternative
method in producing the decision tree.
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