Work

A maximum Likelihood Approach to Analyzing Incomplete Longitudinal Data in Mammary Tumor Development Experiments with Mice

Year: 2013

Type: article

Abstract: Longitudinal mammary tumor development studies using mice as experimental units are affected by i) missing data towards the end of the study by natural death or euthanasia, and ii) the presence of censored data caused by the detection limits of instrumental sensitivity. To accommodate these characteristics, we investigate a test to carry out K-group comparisons based on maximum likelihood methodology. We derive a relevant likelihood ratio test based on general distributions, investigate its properties of based on theoretical propositions, and evaluate the performance of the test via a simulation study. We apply the results to data extracted from a study designed to investigate the development of breast cancer in mice.

Source: Sri Lankan Journal of Applied Statistics

DOI: 10.4038/sljastats.v13i0.5124

Authors: Jihnhee Yu, Albert Vexler, Alan D Hutson

Institution: University at Buffalo, State University of New York

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: National Institutes of Health, National Cancer Institute, National Institute of Dental and Craniofacial Research