In cancer drug development, demonstrated efficacy in tumor xenograft models is an important step toward bringing a promising compound to human use. A key outcome variable is tumor volume measured over a period of time, while mice are treated with certain treatment regimens. A constrained parametric model has been proposed to account for special features, such as intrinsic tumor growth, or tumor volume truncations due to tumor size being either too large or too small to detect. However, since the drug concentration in the blood of a mouse or its tissues may be stabilized at a certain level and maintained during a period of time, the treatment may have sustained effects. This article extends the constrained parametric model to account for the sustained drug effects. The ECM algorithm for incomplete data is applied to estimating the dose-response relationship in the proposed model. The model selection based on likelihood functions is given and a simulation study is conducted to investigate the performance of the proposed estimator. A real xenograft study on the antitumor agent temozolomide combined with irinotecan against the rhabdomyosarcoma is analyzed using the proposed methods.
Keywords: ECM algorithm; Gibbs sampling; Longitudinal data; MLE; Missingness; Truncation; Tumor xenograft models.