Introduction
deaths, tumor, death
REGRESSION, TUMOR, TUMOR GROWTH
In the preclinical phase of oncology drug development, various Moving beyond the classical efficacy study with PDX models to estimate antitumor efficacy for a single tumor model, there is an innovative and recent preclinical experiment design, the mouse clinical trial (MCT) or PDX Clinical Trial (Description of PDX model and MCT design. In preclinical oncology models, antitumor efficacy is usually measured by the dynamic of tumor growth in response to a defined treatment. For each PDX model, after an expansion phase, this dynamic of the subcutaneous tumor size of each mouse is measured with calipers over a given follow-up period or until the death of the mouse if this occurs before the end of the planned follow-up.How to best analyze the data collected in such MCT experiments has not clearly been resolved. In the literature, several criteria have been suggested to summarize this dynamic (Nevertheless, the most genuine approach is the analysis of quantitative tumor growth evaluated at different times during the follow-up. A simple and often used, but naïve method for the analysis of PDX data could be the comparison of tumor volume between the treatment and control groups at each evaluation time. However, this method is flawed as it does not take into account the shape of the tumor growth and the deaths of mice during follow-up. Because of deaths occurring before the end of follow-up, the evaluation of the treatment effect at late follow-up times can be biased, a well-known concept in epidemiology that is called survivor bias (subtype of selection bias). Indeed, in the presence of many deaths, the comparison between the groups based on observed data at a given timepoint is only made on mice that are still alive, that is, mice that have survived up to this timepoint (and are thus observable). These mice correspond in general to those animals with a less rapid tumor growth (Description of tumor growth evolution by subtypes and doses. The average trend (in blue) and its confidence interval (in gray) are estimated by a Loess-type regression method that ignores missing data mechanisms and within-mouse correlations.In addition, some authors (Our objective was thus to apply and contrast these two kinds of longitudinal modeling approaches of tumor growth to estimate the antitumor efficacy in the presence of early dropout due to death, with the perspective of providing recommendations on which statistical approach to favor in MCT designs. We focused on MCT designs including several biomarker subgroups because identification of subgroups with promising treatment effects is an important objective when using MCTs as a translational model towards the clinical development.