ArticleRadiation protection dosimetry2017
THE GROWTH OF BIOSTATISTICS AND ESTIMATION OF CANCER RISK ESTIMATES: PAST, CURRENT, AND FUTURE CHALLENGES.
Article in Radiation protection dosimetry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
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Authors and funding
2 authors.
Funding
Abstract
There has been a long process of growth and development of statistical approaches to the analysis of cancer incidence and mortality data obtained from the follow-up of radiation exposed populations. The challenges of radiation risk analysis provided impetus for innovative statistical methods, including, the inception and continued improvement of hazard rate regression methods. Key statistical contributions that improved cancer risk estimation include statistical advances pertaining to the measurement error problem. Current statistical problems involve extensions of the measurement error methods to account for shared non-independent uncertainties in dose estimation, 'transportability' of risk coefficients for radioprotection and risk estimation world-wide, and extrapolation from high dose rate to low-dose rate exposures or from low LET to high LET. Future problems include quantification of individual sensitivity to radiation-related diseases due to individual genetic differences (or other factors), and in understanding the synergy (additive, multiplicative, etc.) between underlying individual risk and radiation exposure.
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Registered trials
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