Public Health Agency of Canada, Ottawa, Ontario, Canada.
Clinical Workforce Planning, Health Professions Strategy and Practice (HPSP), Alberta Health Services, Edmonton, Alberta, Canada.
Health Promot Chronic Dis Prev Can. 2020 Sep;40(9):267-280. doi: 10.24095/hpcdp.40.9.02.
Cancer 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.
We 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.
Five 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.
The Canproj package was able to provide projections that closely matched the real data for most cancer sites.
癌症预测可以提供关键信息，有助于优先考虑癌症控制策略，分配资源，并评估当前的治疗和干预措施。Canproj 是一个癌症预测工具，它建立在 Nordpred R 包的基础上，增加了一系列预测模型。本项目的目的是验证 Canproj R 包在癌症发病率的短期预测中的有效性。
我们使用了来自国家癌症发病率报告系统和加拿大癌症登记处的 1986 年至 2014 年的全国癌症发病率数据。交叉验证用于估计 Canproj 生成的预测的准确性，并使用相对偏差 (RB) 作为验证指标。还评估了 Canproj 自动模型选择决策树。
六种模型中的五种模型的平均 RB 在 5%至 10%之间，中位数 RB 在 5%左右。对于一些更难预测的癌症部位，较短的时间段提高了可靠性。Nordpred 模型虽然只有 24%的时间具有最小的 RB，但 Canproj 自动模型选择 79%的时间选择了它。
Canproj 包能够提供与大多数癌症部位的实际数据非常匹配的预测。
