Duo Yu, Meilin Huang, Michael J Kane, Brian P Hobbs
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引用次数: 0
Abstract
This paper introduces a novel statistical methodology integrating machine learning (ML) and Bayesian modelling to facilitate personalized prognostic predictions with application to oncology. Utilizing power priors, we construct 'patient-similarity embeddings' that identify localized patterns of prognosis. The methodology is applied to study the prognostic value of markers of anticancer immunity within the tumour microenvironment of nonsmall cell lung cancer while adjusting for established clinical characteristics. The method outperforms traditional regression and ML models, while accurately identifying subgroup patterns, thereby enhancing statistical inference and hypothesis testing.
期刊介绍：
The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies).
A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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