ArticleJournal of radiological protection : official journal of the Society for Radiological Protection2025
Colossus: software for radiation epidemiological studies with big data.
Article in Journal of radiological protection : official journal of the Society for Radiological Protection, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Colossus is designed to meet a growing need for survival analysis software capable of analyzing tens of millions of rows of radiation epidemiological data. Colossus is an R package devised to offer scalable survival analysis for the Million Person Study. The total and relative rate equations available in Colossus are outlined in this article, which are used in conjunction with Cox proportional hazards, Poisson, and Fine-Grey regression models. Following a comparison with existing software, validation with epidemiological cohort data is described. Exposure data and specific causes of death among workers at Los Alamos National Laboratory and U.S. nuclear power plants were analyzed by Colossus and 32-bit Epicure and compared with published results. Colossus results agreed with the results of existing software and previous publications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.