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A Novel Metric to Assess the Clinical Utility of a Drug in the Presence of Efficacy and Dropout Information
Author(s) -
Goyal N,
Gomeni R
Publication year - 2012
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1038/clpt.2011.197
Subject(s) - dropout (neural networks) , clinical pharmacology , clinical trial , metric (unit) , medical physics , medicine , intensive care medicine , drug , computer science , pharmacology , psychology , management science , machine learning , economics , operations management
The fact that there are high dropout rates in clinical trials of antipsychotic medications raises critical questions regarding the most appropriate method of designing new trials, analyzing efficacy data, and evaluating the clinical utility (CU) of novel treatments. In this article, we consider the use of a model‐based approach to define an integrated CU criterion for better characterizing the clinical response to a treatment, for optimizing proof‐of‐concept trials, and for providing differentiating criteria for novel medications when complete information is not available. Clinical Pharmacology & Therapeutics (2012); 91 2, 215–219. doi: 10.1038/clpt.2011.197