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Joint Modeling of Efficacy, Dropout, and Tolerability in Flexible‐Dose Trials: A Case Study in Depression
Author(s) -
Russu A,
Marostica E,
De Nicolao G,
Hooker A C,
Poggesi I,
Gomeni R,
Zamuner S
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.2011.322
Subject(s) - tolerability , hamd , dropout (neural networks) , placebo , clinical trial , rating scale , clinical pharmacology , adverse effect , psychology , antidepressant , medicine , pharmacology , psychiatry , computer science , alternative medicine , developmental psychology , machine learning , anxiety , pathology
Many difficulties may arise during the modeling of the time course of Hamilton Rating Scale for Depression (HAMD) scores in clinical trials for the evaluation of antidepressant drugs: (i) flexible designs, used to increase the chance of selecting more efficacious doses, (ii) dropout events, and (iii) adverse effects related to the experimental compound. It is crucial to take into account all these factors when designing an appropriate model of the HAMD time course and to obtain a realistic description of the dropout process. In this work, we propose an integrated approach to the modeling of a double‐blind, flexible‐dose, placebo‐controlled, phase II depression trial that comprises response, tolerability, and dropout. We investigate three different dropout mechanisms in terms of informativeness. Goodness of fit is quantitatively assessed with respect to response (HAMD score) and dropout data. We show that dropout is a complex phenomenon that may be influenced by HAMD evolution, dose changes, and occurrence of drug‐related adverse effects. Clinical Pharmacology & Therapeutics (2012); 91 5, 863–871. doi: 10.1038/clpt.2011.322