Implementation of a two-group likelihood time-to-event continual reassessment method using SAS

Amber Salter, Charity Morgan, Inmaculada B. Aban

Research output: Contribution to journalArticlepeer-review

Abstract

Background and objectives: Dose finding trials using model-based methods have the ability to handle the increasingly complex landscape being seen in clinical trials. Issues such as patient heterogeneity in trial populations are important to address in the designing of a trial in addition to the inclusion/exclusion criteria. Designs accommodating patient heterogeneity have been described using the continual reassessment method (CRM) and time-to-event CRM (TITE-CRM), yet, the implementation of these trials in practice have been limited. These methods and other model-based methods generally need statisticians to help design and conduct these trials. However, the statistical programs which facilitate the use of these methods, currently available focus on estimation in the one-sample case. Methods: A SAS program to accommodate two groups using the TITE-CRM and likelihood estimation has been developed. The program consists of macros that assist with the planning and implementation of a trial accounting for patient heterogeneity. Results: Description of the program is given as well as examples using the programs. For planning purposes, an example will be provided showing how the program can be used to guide sample size estimates for the trial. Conclusions: This program provides researchers with a valuable tool for designing dose-finding studies to account for the presence of patient heterogeneity and conduct a trial using a hypothetical example.

Original languageEnglish (US)
Pages (from-to)189-196
Number of pages8
JournalComputer Methods and Programs in Biomedicine
Volume121
Issue number3
DOIs
StatePublished - Oct 1 2015
Externally publishedYes

Keywords

  • Adaptive designs
  • Continual reassessment method
  • Dose finding
  • Maximum likelihood
  • Phase I trial
  • Sample size

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics

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