Effect of Censoring Due to Progressive Disease on Tumor Size Kinetic Parameter Estimates

Abstract

Abstract. Tumor growth profiles were simulated for 2 years using the Wang and Claret models under a phase 3 clinical trial design. Profiles were censored when tumor size increased >20% from nadir similar to clinical practice. The percent of patients censored varied from 0% (perfect case) to 100% (real-life case). The model used to generate the data was then fit to the censored data using FOCE in NONMEM. The percent bias in the estimated model parameters determined with censored data was compared to the true values. A total of 100 simulation replicates was used. For the Wang model, under clinical conditions (100% censoring), the parameter related to tumor reduction SR was underpredicted by 30% and the parameter related to tumor growth PR was underpredicted by ∼45%. Most of the variance components in the model were within ±20% of the true values. However, biased parameter estimates in the Wang model did not translate to biased tumor size predictions as the mean percent prediction error between true and model predicted tumor size never exceeded 10%. For the Claret model, at 100% censoring, the tumor growth parameter KL was unaffected by censoring. Both tumor shrinkage parameters, KD and λ, were overestimated by ∼20% in both cases. Future research needs to be directed to develop less empirically based models and to use simulation as a way to improve clinical oncology trials designs.

Establishing the Quantitative Relationship Between Lanreotide Autogel®, Chromogranin A, and Progression-Free Survival in Patients with Nonfunctioning Gastroenteropancreatic Neuroendocrine Tumors

“…However, this can be explained by informative dropout: patients with CgA levels higher than baseline are more likely to drop out of the study; therefore, those patients remaining in the study at later time points will be those with smaller increases in CgA. In addition, it has been shown that ignoring informative dropout can potentially bias biomarker parameters (35).…”

Population pharmacokinetic–pharmacodynamic modelling in oncology: a tool for predicting clinical response

“…It also has the consequence that growth‐related parameters can be difficult to estimate precisely and thereby the possibilities to describe accurately changes related to the drug effect may be limited. Noteworthy, neglecting informative dropout due to disease progression can potentially bias tumour growth and/or tumour shrinkage parameter estimates . To be accurate, simulation‐based tumour model evaluation or simulation of tumour SLD for new trials should take into account the frequency and time course of dropout, which is dependent on tumour SLD, and can be described, e.g.…”

The Use of Model-Based Tumor-Size Metrics to Predict Survival

“…In conclusion, mixed‐effect models of tumor‐size dynamics may be useful for leveraging the available tumor‐size data so as to improve assessment of drug effectiveness in phases I and II of drug development. However, when using these approaches to derive individual model‐based metrics used in survival analysis, investigators should be aware of the potential for misleading results such as biased population parameters 8 and inaccurate type I errors arising from limited individual tumor‐size data. We assumed survival as the only event determining shrinkage and did not include dropout events due to other causes and, in particular, disease progression.…”