Assessing the performance of different outcomes for tumor growth studies with animal models

• Published in: Animal models and experimental medicine 2022-09, Vol.5 (3), p.248-257
• Main Authors: Patten, Luke W., Blatchford, Patrick, Strand, Matthew, Kaizer, Alexander M.
• Format: Article
• Language: English
• Subjects: Animal models; mouse model; oncology; Original; outcome selection; patient‐derived xenograft (PDX); Performance evaluation; Regular; statistical analysis; TGII; translational science; Tumors; Xenografts
• ISSN: 2576-2095; 2096-5451; 2576-2095
• DOI: 10.1002/ame2.12250

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 treatment conditions. Currently, a wide array of statistical methods (e.g., t‐test, regression, 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 variations 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 10 000 iterations. Comparisons between different outcomes were made with respect to average bias, variance, type‐1 error, and power. A total of 18 continuous, categorical, and time‐to‐event outcomes were evaluated, with ultimately 2 outcomes outperforming 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. The choice of outcome in any animal study is important since it affects the appropriate statistical analysis and the ability to detect a meaningful difference if it exists. In this paper, we introduce various outcomes used in oncology animal models for tumor models and demonstrate the optimal choices based on simulation studies and real world application.