Many difficulties may arise during the modeling of the time course of Hamilton Rating Scale for Depression (HAM D) 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 HAM D 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 (HAM D score) and dropout data. We show that dropout is a complex phenomenon that may be influenced by HAM D evolution, dose changes, and occurrence of drug-related adverse effects.
Joint Modeling of Efficacy, Dropout, and Tolerability in Flexible-Dose Trials: A Case Study in Depression
RUSSU, ALBERTO;MAROSTICA, ELEONORA;DE NICOLAO, GIUSEPPE;
2012-01-01
Abstract
Many difficulties may arise during the modeling of the time course of Hamilton Rating Scale for Depression (HAM D) 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 HAM D 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 (HAM D score) and dropout data. We show that dropout is a complex phenomenon that may be influenced by HAM D evolution, dose changes, and occurrence of drug-related adverse effects.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
