ArticleBMJ open2019
Forecasting annual incidence and mortality rate for prostate cancer in Australia until 2022 using autoregressive integrated moving average (ARIMA) models.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Development and interpretability of a machine learning-derived model for the identification of latent prostate cancer in patients initially diagnosed with benign prostatic hyperplasia: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- Incremental diagnostic value of MRI PI-RADS classification combined with PSA density for prostate cancer.Frontiers in oncology · 2026Article
- National trends in bone-metastatic prostate cancer mortality in the United States, 1999-2024: a multiple-cause-of-death analysis with projections to 2040.Frontiers in oncology · 2026Article
- Global burden of thyroid cancer among adolescents and young adults, 1990-2021, and projections to 2050: an analysis based on the GBD 2021.Frontiers in endocrinology · 2025Article
- A SEER-based analysis of trends in HPV-associated oropharyngeal squamous cell carcinoma.Infectious agents and cancer · 2024Article
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- Assessment of the epidemiological trends for prostate cancer using administrative data in Ontario.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2023Article
- Robotic-assisted radical prostatectomy: learning curves and outcomes from an Australian perspective.Prostate international · 2023Article
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- Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes.International journal of environmental research and public health · 2021Article
- Predicting Diabetes and Estimating Its Economic Burden in China Using Autoregressive Integrated Moving Average Model.International journal of public health · 2021Article
- Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework.Infection and drug resistance · 2020Article
- Time series analysis of cumulative incidences of typhoid and paratyphoid fevers in China using both Grey and SARIMA models.PloS one · 2020Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesProstate cancer is the second most common cause of cancer-related death in males after lung cancer, imposing a significant burden on the healthcare system in Australia. We propose the use of autoregressive integrated moving average (ARIMA) models in conjunction with population forecasts to provide for robust annual projections of prostate cancer.
designData on the incidence and mortality from prostate cancer was obtained from the Australian Institute of Health and Welfare. We formulated several ARIMA models with different autocorrelation terms and chose one which provided for an accurate fit of the data based on the mean absolute percentage error (MAPE). We also assessed the model for external validity. A similar process was used to model age-standardised incidence and mortality rate for prostate cancer in Australia during the same time period.
resultsThe annual number of prostate cancer cases diagnosed in Australia increased from 3606 in 1982 to 20 065 in 2012. There were two peaks observed around 1994 and 2009. Among the various models evaluated, we found that the model with an autoregressive term of 1 (coefficient=0.45, p=0.028) as well as differencing the series provided the best fit, with a MAPE of 5.2%. External validation showed a good MAPE of 5.8% as well. We project prostate cancer incident cases in 2022 to rise to 25 283 cases (95% CI: 23 233 to 27 333).
conclusionOur study has accurately characterised the trend of prostate cancer incidence and mortality in Australia, and this information will prove useful for resource planning and manpower allocation.
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