Building absolute breast cancer risk prediction models for women treated with chest radiation for Hodgkin lymphoma.

IF 1 4区数学 Q3 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2025-01-03 eCollection Date: 2025-03-01 DOI:10.1093/jrsssc/qlae063

Sander Roberti, Flora E van Leeuwen, Michael Hauptmann, Ruth M Pfeiffer

引用次数: 0

Abstract

We built models to predict absolute breast cancer (BC) risk in women treated with radiotherapy for Hodgkin lymphoma (HL). We first estimated relative risks (RRs) for risk factors, including radiation dose to 10 breast segments to accommodate heterogeneity of treatment effects, using a case-control sample nested in an HL survivor cohort. To estimate RRs of case-control matching factors we developed novel weighting approaches. We then combined RRs with age-specific BC incidence and competing mortality rates from the HL survivor cohort and a population-based registry, accommodating differences between them. We compared the performance of models using segment-specific doses with using mean dose only.

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来源期刊

Journal of the Royal Statistical Society Series C-Applied Statistics 数学-统计学与概率论

CiteScore

2.50

自引率

0.00%

发文量

审稿时长

>12 weeks

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.