PT  - JOURNAL ARTICLE
AU  - Arul Earnest
AU  - Sue M Evans
AU  - Fanny Sampurno
AU  - Jeremy Millar
TI  - Forecasting annual incidence and mortality rate for prostate cancer in Australia until 2022 using autoregressive integrated moving average (ARIMA) models
AID  - 10.1136/bmjopen-2019-031331
DP  - 2019 Aug 01
TA  - BMJ Open
PG  - e031331
VI  - 9
IP  - 8
4099  - http://bmjopen.bmj.com/content/9/8/e031331.short
4100  - http://bmjopen.bmj.com/content/9/8/e031331.full
SO  - BMJ Open2019 Aug 01; 9
AB  - Objectives Prostate 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.Design Data 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.Results The 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).Conclusion Our 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.