title: Some drop-the-loser designs for monitoring multiple doses.
abstract: A scenario not uncommon at the end of a Phase II clinical development is that although choices are narrowed down to two to three doses, the project team cannot make a recommendation of one single dose for the Phase III confirmatory study based upon the available data. Several 'drop-the-loser' designs to monitor multiple doses of an experimental treatment compared with a control in a pivotal Phase III study are considered. Ineffective and/or toxic doses compared with the control may be dropped at the interim analyses as the study continues, and when the accumulated data have demonstrated convincing efficacy and an acceptable safety profile for one dose, the corresponding dose or the study may be stopped to make the experimental treatment available to patients. A decision to drop a toxic dose is usually based upon a comprehensive review of all the available safety data and also a risk/benefit assessment. For dropping ineffective doses, a non-binding futility boundary may be used as guidance. The desired futility boundary can be derived by using an appropriate combination of risk level (i.e. error rate for accepting null hypothesis when the dose is truly efficacious) and spending strategy (dropping a dose aggressively in early analyses versus late). For establishing convincing evidence of the treatment efficacy, three methods for calculating the efficacy boundary are discussed: the Joint Monitoring (JM) approach, the Marginal Monitoring method with Bonferroni correction (MMB), and the Marginal Monitoring method with Adjustment for correlation (MMA). The JM approach requires intensive computation especially when there are several doses and multiple interim analyses. The marginal monitoring methods are computationally more attractive and also more flexible since each dose is monitored separately by its own alpha-spending function. The JM and MMB methods control the false positive rate. The MMA method tends to protect the false positive rate and is more powerful than the Bonferroni-based MMB method. The MMA method offers a practical and flexible solution when there are several doses and multiple interim looks.
authors.affiliation: Merck Research Laboratories, North Wales, PA 19454, USA. joshua_chen@merck.com
authors.forename: Y H
authors.initials: YH
authors.lastname: Joshua Chen
authors.forename: David L
authors.initials: DL
authors.lastname: Demets
authors.forename: K K
authors.initials: KK
authors.lastname: Gordon Lan
mesh_terms: D017326:Clinical Trials, Phase III as Topic / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D004364:Pharmaceutical Preparations / Q000008:administration & dosage*; D013269:Stochastic Processes
chemical_list: D004364:Pharmaceutical Preparations