INTRODUCTION
Cancer patients may face elevated mortality not only from their malignancy but also from other factors including multi-morbidity and the impact of treatment-related complications. This study analyses mortality from all causes other than the index cancer among cancer patients compared to the general population.
METHODS
We analysed EUROCARE-6 data on 1,894,057 cancers diagnosed in 13 European countries and collected by 17 cancer registries with complete official cause-of-death information. Included were first-primary head and neck (H&N), stomach, colorectal, lung, breast, cervical, endometrial, ovarian, prostatic, kidney, and bladder cancers diagnosed between 1998 and 2013 in patients aged 15-79, with follow-up until 31st December 2014. We estimated the relative risk (RR) of death from all other causes for patients relative to population mortality rates. Cause-specific survival (CSS) was compared to relative survival (RS) at 5 and 10 years.
RESULTS
RR were > 2 for H&N and female lung cancers, between 1 and 2 for stomach, male lung, cervix, endometrial, ovary, kidney and bladder, and < 1 for prostate and female breast cancers. Relative risk declined with increasing age for almost all cancer types. Ten-year CSS exceeded RS by 7%-11% points (pp) in H&N cancers, with smaller differences in bladder (2-6 pp), stomach, lung, cervix, ovary and kidney (2-3 pp). CSS was lower than RS in breast and prostate cancers.
CONCLUSION
Differentiation between deaths due to the index cancer and to all other causes is crucial for patient follow-up and to improve quality of life. Cause-of-death data should be a core component of population-based cancer registries to inform interpretation of survivorship data.
Gemma Gatta, R. Capocaccia, S. Rossi et al.· Cancer Medicine· 0 citations
Abstract Background Synthetic data hold substantial potential to address practical challenges in epidemiology due to restricted data access and privacy concerns. However, many current methods suffer from limited quality, high computational demands, and complexity for non-experts. Furthermore, common evaluation strategies for synthetic data often fail to directly reflect statistical utility and measure privacy risks sufficiently. Against this background, a critical underexplored question is whether synthetic data can reliably reproduce key findings from epidemiological research while preserving privacy. Methods We propose adversarial random forests (ARF) as an efficient and convenient method for synthesizing tabular epidemiological data. To evaluate its performance, we replicated statistical analyses from six epidemiological publications covering blood pressure, anthropometry, myocardial infarction, accelerometry, loneliness, and diabetes, from the German National Cohort (NAKO Gesundheitsstudie), the Bremen STEMI Registry U45 Study, and the Guelph Family Health Study. We further assessed how dataset dimensionality and variable complexity affect the quality of synthetic data, and contextualized ARF’s performance by comparison with commonly used tabular data synthesizers in terms of utility, privacy, generalization, and runtime. Results Across all replicated studies, results on ARF-generated synthetic data consistently aligned with original findings. Even for datasets with relatively low sample size-to-dimensionality ratios, replication outcomes closely matched the original results across descriptive and inferential analyses. Reduced dimensionality and variable complexity further enhanced synthesis quality. ARF demonstrated favourable performance regarding utility, privacy preservation, and generalization relative to other synthesizers and superior computational efficiency. Conclusions In summary, ARF reliably generates high-quality synthetic data that replicate diverse epidemiological analyses while offering a competitive privacy–utility trade-off.
J. Kapar, Kathrin Günther, L. Vallis et al.· International Journal of Epi...· 1 citation
P-tau217, NfL and GFAP showed strong associations with AD risk, especially within the first 9 years of follow-up, outperforming p-tau181, and over the later years of follow-up, the predictive accuracy of p-tau217 was significantly reduced.
K. Trares, D. Duman, L. Beyer et al.· EMBO Molecular Medicine· 0 citations
This pooled analysis of 15,731 cases showed that nulliparity, age at menopause, MHT, and BMI have independent, dose-response associations with ER, PR, and grade, clarifying patterns of etiologic heterogeneity.
Daniel Adams, Amber N. Hurson, T. Ahearn et al.· Journal of the National Canc...· 0 citations
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