An imputation-based method to reduce bias in model parameter estimates due to non-random censoring in oncology trials.
10.12793/tcp.2016.24.4.189
- Author:
Dongwoo CHAE
1
;
Kyungsoo PARK
Author Information
1. Department of Pharmacology, Yonsei University College of Medicine, Seoul 03722, Korea. kspark@yuhs.ac
- Publication Type:Original Article
- Keywords:
Tumor size model;
Non-random censoring;
Oncology trials;
Biased parameter estimates
- MeSH:
Bays;
Bias (Epidemiology)*;
Dataset;
Diagnosis;
Humans;
Methods*
- From:Translational and Clinical Pharmacology
2016;24(4):189-193
- CountryRepublic of Korea
- Language:English
-
Abstract:
In oncology trials, patients are withdrawn from study at the time when progressive disease (PD) is diagnosed, which is defined as 20% increase of tumor size from the minimum. Such informative censoring can lead to biased parameter estimates when nonlinear mixed effects models are fitted using NONMEM. In this work, we investigated how empirical Bayes estimates (EBE) could be exploited to impute missing tumor size observations and partially correct biases in the parameter estimates. 50 simulated datasets, each consisting of 100 patients, were generated based on the published model. From the simulated dataset, censoring due to PD diagnosis has been implemented. Using the post-hoc EBEs acquired from fitting the censored datasets using NONMEM, imputed values were generated from the tumor size model. Model fitting was carried out using censored and imputed datasets. Parameter estimates using both datasets were compared with true values. Tumor growth rate and cell kill rate were approximately 28% and 16% underestimated when fitted using the censored dataset, respectively. With the imputed datasets, relative biases of tumor growth rate and cell kill rate decreased to about 6% and 0%, respectively. Our work demonstrates that using EBEs acquired from fitting the model to the censored dataset and imputing the unknown tumor size observations with individual predictions beyond the PD time point is a viable option to solve the bias associated with structural parameter estimates. This approach, however, would not be helpful in getting better estimates of variance parameters.
