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Published on: September 24, 2014
Repeated-measures models with constrained parameters for incomplete data in tumour xenograft experiments
Ming Tan1, Hong-Bin Fang, Guo-Liang Tian
1Division of Biostatistics, University of Maryland Greenebaum Cancer Center, 22 South Greene Street, Baltimore, MD 21201, USA. mtan@umm.edu
Abstract:
In cancer drug development, xenograft experiments (models) where mice are grafted with human cancer cells are used to elucidate the mechanism of action and/or to assess efficacy of a promising compound. Demonstrated activity in this model is an important step to bring a promising compound to humans. A key outcome variable in these experiments is tumour volumes measured over a period of time, while mice are treated with an anticancer agent following certain schedules. However, a mouse may die during the experiment or may be sacrificed when its tumour volume quadruples and then incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumour shrinkage (<0.01 cm3) or random truncation. In addition, if no treatment were given to the tumour-bearing mice, the tumours would keep growing until the mice die or are sacrificed. This intrinsic growth of tumour in the absence of treatment constrains the parameters in the regression and causes further difficulties in statistical analysis. We develop a maximum likelihood method based on the expectation/conditional maximization (ECM) algorithm to estimate the dose-response relationship while accounting for the informative censoring and the constraints of model parameters. A real xenograft study on a new anti-tumour agent temozolomide combined with irinotecan is analysed using the proposed method.
Insights
This study introduces a new statistical method for analyzing cancer xenograft experiments, addressing missing data from mouse deaths or significant tumor changes. The method improves the estimation of drug efficacy, crucial for advancing cancer treatments.
Area of Science:
- Pharmacology
- Biostatistics
- Oncology
Background:
- Xenograft models are vital in cancer drug development for assessing compound efficacy.
- Tumor volume measurements in xenografts are key outcome variables.
- Incomplete data due to mouse death, sacrifice, or drastic tumor changes complicates analysis.
Purpose of the Study:
- To develop a statistical method for analyzing xenograft data with informative censoring.
- To accurately estimate dose-response relationships in cancer drug development.
- To address challenges posed by intrinsic tumor growth and incomplete measurements.
Main Methods:
- A maximum likelihood method utilizing the expectation/conditional maximization (ECM) algorithm.
- Accounting for informative censoring and model parameter constraints.
- Application to a real xenograft study of temozolomide and irinotecan.
Main Results:
- The proposed ECM-based method provides robust estimation of dose-response relationships.
- The analysis successfully handled missing data and informative censoring in xenograft experiments.
- Accurate efficacy assessment of anti-tumor agents is facilitated.
Conclusions:
- The developed statistical approach enhances the reliability of xenograft model data analysis.
- This method is crucial for advancing promising cancer drug candidates to clinical trials.
- Improved statistical methodologies are essential for modern oncology research.
