Prioritized and data-robust estimation strategies for tumor growth studies

Music City Center 

Longitudinal tumor growth studies serve a foundational role in preclinical therapeutic evaluation, acting as precursors to human clinical trials. Despite the prevalence of these experiments, there is little consensus on how best to analyze the resulting data, largely due to underemphasized data challenges such as non-linearity, censoring and correlated errors. We capitalize on common design characteristics to develop a composite, prioritized estimator that is interpretable as well as robust to several of these data challenges. To provide a platform for identifying treatment synergy or dose toxicity, the semi-parametric proportional odds model is proposed to extend our estimator to the regression setting. We develop an algorithm to maximize a quasi-conditional likelihood, allowing us to avoid the estimation of N-1 nuisance parameters. Finally, we show how a time-dependent win ratio can be used to extend our method to the case of clustered data, where one animal may have several tumors under study. Closed form cluster-correct variance calculations are provided. The implementation of the methods are demonstrated on several HPV+ head and neck squamous cell carcinoma xenograft models.

Win ratio

Composite

Semi-parametric

Preclinical

Proportional odds

Growth models 

Biopharmaceutical Section