Mark Conaway1,2. 1. 1 University of Virginia Health System, Charlottesville, VA, USA. 2. 2 Division of Translational Research & Applied Statistics, Department of Public Health Sciences, University of Virginia School of Medicine, University of Virginia, Charlottesville, VA, USA.
Abstract
BACKGROUND/AIMS: Dose-finding trials can be conducted such that patients are first stratified into multiple risk groups before doses are allocated. The risk groups are often completely ordered in that, for a fixed dose, the probability of toxicity is monotonically increasing across groups. In some trials, the groups are only partially ordered. For example, one of several groups in a trial may be known to have the least risk of toxicity for a given dose, but the ordering of the risk among the remaining groups may not be known. The aim of the article is to introduce a method for designing dose-finding trials of cytotoxic agents in completely or partially ordered groups of patients. METHODS: This article presents a method for dose-finding that combines previously proposed mathematical models, augmented with results using order restricted inference. The resulting method is computationally convenient and allows for dose-finding in trials with completely or partially ordered groups. Extensive simulations are done to evaluate the performance of the method, using randomly generated dose-toxicity curves where, within each group, the risk of toxicity is an increasing function of dose. RESULTS: Our simulations show that the hybrid method, in which order-restricted estimation is applied to parameters of a parsimonious mathematical model, gives results that are similar to previously proposed methods for completely ordered groups. Our method generalizes to a wide range of partial orders among the groups. CONCLUSION: The problem of dose-finding in partially ordered groups has not been extensively studied in the statistical literature. The proposed method is computationally feasible, and provides a potential solution to the design of dose-finding studies in completely or partially ordered groups.
BACKGROUND/AIMS: Dose-finding trials can be conducted such that patients are first stratified into multiple risk groups before doses are allocated. The risk groups are often completely ordered in that, for a fixed dose, the probability of toxicity is monotonically increasing across groups. In some trials, the groups are only partially ordered. For example, one of several groups in a trial may be known to have the least risk of toxicity for a given dose, but the ordering of the risk among the remaining groups may not be known. The aim of the article is to introduce a method for designing dose-finding trials of cytotoxic agents in completely or partially ordered groups of patients. METHODS: This article presents a method for dose-finding that combines previously proposed mathematical models, augmented with results using order restricted inference. The resulting method is computationally convenient and allows for dose-finding in trials with completely or partially ordered groups. Extensive simulations are done to evaluate the performance of the method, using randomly generated dose-toxicity curves where, within each group, the risk of toxicity is an increasing function of dose. RESULTS: Our simulations show that the hybrid method, in which order-restricted estimation is applied to parameters of a parsimonious mathematical model, gives results that are similar to previously proposed methods for completely ordered groups. Our method generalizes to a wide range of partial orders among the groups. CONCLUSION: The problem of dose-finding in partially ordered groups has not been extensively studied in the statistical literature. The proposed method is computationally feasible, and provides a potential solution to the design of dose-finding studies in completely or partially ordered groups.
Entities:
Keywords:
Partial order; dose-finding; multiple risk groups
Authors: Federico Innocenti; Richard L Schilsky; Jacqueline Ramírez; Linda Janisch; Samir Undevia; Larry K House; Soma Das; Kehua Wu; Michelle Turcich; Robert Marsh; Theodore Karrison; Michael L Maitland; Ravi Salgia; Mark J Ratain Journal: J Clin Oncol Date: 2014-06-23 Impact factor: 44.544
Authors: Ticiana B Leal; Scot C Remick; Chris H Takimoto; Ramesh K Ramanathan; Angela Davies; Merrill J Egorin; Anne Hamilton; Patricia A LoRusso; Stephen Shibata; Heinz-Josef Lenz; James Mier; John Sarantopoulos; Sridhar Mani; John J Wright; S Percy Ivy; Rachel Neuwirth; Lisa von Moltke; Karthik Venkatakrishnan; Daniel Mulkerin Journal: Cancer Chemother Pharmacol Date: 2011-04-09 Impact factor: 3.333
Authors: Ramesh K Ramanathan; Merrill J Egorin; Chris H M Takimoto; Scot C Remick; James H Doroshow; Patricia A LoRusso; Daniel L Mulkerin; Jean L Grem; Anne Hamilton; Anthony J Murgo; Douglas M Potter; Chandra P Belani; Michael J Hayes; Bin Peng; S Percy Ivy Journal: J Clin Oncol Date: 2008-02-01 Impact factor: 44.544