ONLINE - Right-Censoring Bias Correction for Growth Curve Linear Mixed Models

27.11.2020 11:15 – 12:15

RESEARCH CENTER FOR STATISTICS SEMINAR / ABSTRACT

Tumor growth inhibition studies typically involve analyzing tumor sizes measured regularly over a period of time. The aim is usually to detect differences in growth rate between experimental conditions. Many methods have been considered. Some summarize each growth curve into a single measure and compare the location parameter of these statistics between different experimental conditions by means of Welsh tests. Others consider mixed/longitudinal models, taking into account the time and within tumor dependence of the observations to provide a parametric fit on all collected data. As animals are culled when their tumor size exceeds a legal upper limit or when the discomfort level is considered too high, such data are often right censored, leading to biased growth estimates. Our objective is to develop a method allowing one to correct the bias of growth curve linear mixed models in the presence of right censoring due to a fixed upper tumor size limit. Simulations show that the iterative bootstrap bias corrected estimator we developed for random intercept and slope mixed models allows us to obtain unbiased growth rate estimates as well as confidence intervals showing coverages close to the nominal value.

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