ArticleJournal of the National Cancer Institute2020
The Value of All-Cause Mortality as a Metric for Assessing Breast Cancer Screening.
Article in Journal of the National Cancer Institute, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Screening for breast cancer: a systematic review update to inform the Canadian Task Force on Preventive Health Care guideline.Systematic reviews · 2024Pooled it
- Supplemental Screening as an Adjunct to Mammography for Breast Cancer Screening in People With Dense Breasts: A Health Technology Assessment.Ontario health technology assessment series · 2023Pooled it
- Quality-adjusted life years in the presence and absence of organized mammographic screening using data from BreastScreen Norway.International journal of cancer · 2026Article
- Breast cancer mortality among women who have attended BreastScreen Norway, 1996-2023.Breast cancer research : BCR · 2026Article
- Deceptive Measures of "Success" in Early Cancer Detection.Current oncology (Toronto, Ont.) · 2024Review
- Overdetection of Breast Cancer.Current oncology (Toronto, Ont.) · 2022Article
- Intervention-related Deaths in the European Randomized Study of Screening for Prostate Cancer.European urology open science · 2021Article
- The impact of episodic screening interruption: COVID-19 and population-based cancer screening in Canada.Journal of medical screening · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although screening mammography has been demonstrated to contribute to reducing mortality due to breast cancer, some have suggested that reduced all-cause mortality should constitute the burden of proof for effectiveness. Using a microsimulation model of the development, detection, and treatment of breast cancer, it is straightforward to demonstrate that this is an unrealistic expectation for trials of practical size and period of observation, even where the reduction of breast cancer mortality is substantial. Estimates of all-cause mortality will depend not only on the efficacy of the screening intervention but also on the alignment between the age distribution of the effect of screening on reduction of deaths and that of the other major causes of death. The size of a randomized trial required to demonstrate a reduction in all-cause mortality will, therefore, depend on the length and timing of the observation period and will typically be at least 10 times larger than the size of a trial powered to test for a reduction in deaths due to breast cancer. For breast cancer, which represents a small fraction of overall deaths, all-cause mortality is neither a practical nor informative metric for assessing the effectiveness of screening.
Indexed as
Identifiers
What OpenQuestion holds
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.