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A dose finding design for seizure reduction in neonates
Author(s) -
Ursino Moreno,
Yuan Ying,
Alberti Corinne,
Comets Emmanuelle,
Favrais Geraldine,
Friede Tim,
Lentz Frederike,
Stallard Nigel,
Zohar Sarah
Publication year - 2019
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12289
Subject(s) - levetiracetam , medicine , inference , clinical trial , bayesian inference , bayesian probability , logistic regression , pediatrics , intensive care medicine , emergency medicine , epilepsy , statistics , computer science , artificial intelligence , psychiatry , mathematics
Summary Clinical trials in vulnerable populations are extremely difficult to conduct. A sequential phase I–II trial aimed at finding the appropriate dose of levetiracetam for treating neonatal seizures was planned with a maximum sample size of 50 newborns. Three primary outcomes are considered: efficacy and two types of toxicity that occur at the same time but are measured at different time points. In the case of failure, physicians could add a second agent as a rescue medication. The primary outcomes were modelled via a logistic model for efficacy and a weighted likelihood with pseudo‐outcomes for the two toxicities taking into account the dependences under Bayesian inference. Simulations were conducted to assess the design properties.