DOI: 10.1002/ame2.12250
Assessing the performance of different outcomes for tumor growth studies with animal models
Luke W.Patten
Patrick Blatchford
Matthew Strand
Alexander M.Kaizer
Department of Biostatistics and Informatics,University of Colorado, Aurora,Colorado,USA
摘要：The consistency of reporting results for patient- derived xenograft (PDX) studies is an area of concern. The PDX method commonly starts by implanting a derivative of a human tumor into a mouse, then comparing the tumor growth under different treat-ment conditions. Currently, a wide array of statistical methods (e.g., t - test, regres-sion, chi- squared test) are used to analyze these data, which ultimately depend on the outcome chosen (e.g., tumor volume, relative growth, categorical growth). In this simulation study, we provide empirical evidence for the outcome selection process by comparing the performance of both commonly used outcomes and novel varia-tions of common outcomes used in PDX studies. Data were simulated to mimic tumor growth under multiple scenarios, then each outcome of interest was evaluated for 10000 iterations. Comparisons between different outcomes were made with respect to average bias, variance, type- 1 error, and power. A total of 18 continuous, categori-cal, and time- to- event outcomes were evaluated, with ultimately 2 outcomes outper-forming the others: final tumor volume and change in tumor volume from baseline. Notably, the novel variations of the tumor growth inhibition index (TGII)— a commonly used outcome in PDX studies— was found to perform poorly in several scenarios with inflated type- 1 error rates and a relatively large bias. Finally, all outcomes of interest were applied to a real- world dataset.
机标关键词：differentstudiesgrowthmodelstumorwithanimalassessing
论文发表日期：2022-06-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：10( 248-257 )
英文信息