Bairu Zhang, Lukasz Magiera, Juliana Candido, Olga Muraeva, Jane Coates Ulrichsen, Jim Eyles, Elena Galvani, Natasha A. Karp
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引用次数: 0
Abstract
In anticancer research, tumor growth measured in mouse models is important for assessing treatment efficacy for a treatment to progress to human clinical trials. Statistical analysis of time-to-event tumor volume data is complex because of heterogeneity in response and welfare-related data loss. Traditional statistical methods of testing the mean difference between groups are not robust because they assume common responses across a population. Heterogeneity in response is also seen in the clinic, and consequently, the assessment of the treatment considers the diversity through classification of the individual’s response using the RECIST (Response Evaluation Criteria in Solid Tumors). To provide a comparable and translatable assessment of in vivo tumor response, we developed a statistical method called INSPECT (IN vivo reSPonsE Classification of Tumors) for analyzing heterogeneous responses through Bayesian modeling. This method can classify individual tumor behaviors into the categories of nonresponder, modest responder, stable responder, and regressing responder. Using both published and simulated data, we show that INSPECT methodology is more accurate and sensitive than existing methods with respect to balancing false-negative and false-positive rates. A case study demonstrates the value of INSPECT in drug projects for supporting the translation of drug efficacy from the preclinical phase into clinical trials. We also provide a package, “INSPECTumours,” that launches a web interface to enable users to conduct the analysis and generate reports.
期刊介绍:
Science Translational Medicine is an online journal that focuses on publishing research at the intersection of science, engineering, and medicine. The goal of the journal is to promote human health by providing a platform for researchers from various disciplines to communicate their latest advancements in biomedical, translational, and clinical research.
The journal aims to address the slow translation of scientific knowledge into effective treatments and health measures. It publishes articles that fill the knowledge gaps between preclinical research and medical applications, with a focus on accelerating the translation of knowledge into new ways of preventing, diagnosing, and treating human diseases.
The scope of Science Translational Medicine includes various areas such as cardiovascular disease, immunology/vaccines, metabolism/diabetes/obesity, neuroscience/neurology/psychiatry, cancer, infectious diseases, policy, behavior, bioengineering, chemical genomics/drug discovery, imaging, applied physical sciences, medical nanotechnology, drug delivery, biomarkers, gene therapy/regenerative medicine, toxicology and pharmacokinetics, data mining, cell culture, animal and human studies, medical informatics, and other interdisciplinary approaches to medicine.
The target audience of the journal includes researchers and management in academia, government, and the biotechnology and pharmaceutical industries. It is also relevant to physician scientists, regulators, policy makers, investors, business developers, and funding agencies.