ArticleHealth promotion and chronic disease prevention in Canada : research, policy and practice2020
Validation of Canproj for projecting Canadian cancer incidence data.
Article in Health promotion and chronic disease prevention in Canada : research, policy and practice, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Evaluation and forecasting methods to estimate number of patients with non-Hodgkin lymphoma: a systematic literature review.Population health metrics · 2025Pooled it
- Workforce Trends Among Canadian Medical Oncologists and Medical Oncology Trainees over Two Decades.Current oncology (Toronto, Ont.) · 2025Article
- Article
- Projected estimates of cancer in Canada in 2024.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2024Article
- Exploring the Future of Cancer Impact in Alberta: Projections and Trends 2020-2040.Current oncology (Toronto, Ont.) · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionCancer projections can provide key information to help prioritize cancer control strategies, allocate resources and evaluate current treatments and interventions. Canproj is a cancer-projection tool that builds on the Nordpred R-package by adding a selection of projection models. The objective of this project was to validate the Canproj R-package for the short-term projection of cancer rates.
methodsWe used national cancer incidence data from 1986 to 2014 from the National Cancer Incidence Reporting System and Canadian Cancer Registry. Cross-validation was used to estimate the accuracy of the projections generated by Canproj and relative bias (RB) was used as validation measure. The Canproj automatic model selection decision tree was also assessed.
resultsFive of the six models had mean RB between 5% and 10% and median RB around 5%. For some of the cancer sites that were more difficult to project, a shorter time period improved reliability. The Nordpred model was selected 79% of the time by Canproj automatic model selection although it had the smallest RB only 24% of the time.
conclusionsThe Canproj package was able to provide projections that closely matched the real data for most cancer sites.
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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.