Assessing the Performance of Different Outcomes for Tumor Growth Studies with Animal Models

• Activity Number: 355 - Contributed Poster Presentations: Biopharmaceutical Section
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Luke William Patten* and Alexander Kaizer and Patrick Blatchford
• Companies: Center for Innovative Design and Analysis, University of Colorado, Anschutz Medical Campus and University of Colorado Anschutz Medical Campus and University of Colorado
• Keywords: preclinical; tumor growth; xenograft; outcome choice; statistical reporting; simulations

Abstract

A common way to assess the efficacy of a given anti-tumor treatment is to analyze tumor growth from patient-derived xenografts (PDX) in mouse models. The typical PDX method involves the implantation of cloned tumors from a specific human patient into the mice. There are currently a wide array of statistical methods (e.g., t-test, chi-square test, regression models) utilized to analyze this data, which depend on the outcome chosen (e.g., tumor volume, relative tumor growth, categorical tumor growth). Here, a novel variation of a common outcome used to explain relative tumor growth, tumor growth inhibition index (TGII), is developed and evaluated. We compare continuous, categorical, and time-to-event outcomes with respect to bias, power, and type-1 error rates in addition to other considerations such as missing data. The strengths and weaknesses of each approach are evaluated using simulations, real data application, and comparisons of asymptotic properties where applicable. Multiple scenarios are investigated and evidence-based suggestions are provided for more consistent reporting in this field of study.