Selecting the minimum prediction base of historical data to perform 5-year predictions of the cancer burden: The GoF-optimal method

Abstract Background Predicting the future burden of cancer is a key issue for health services planning, where a method for selecting the predictive model and the prediction base is a challenge. A method, named here Goodness-of-Fit optimal ( GoF-optimal ), is presented to determine the minimum prediction base of historical data to perform 5-year predictions of the number of new cancer cases or deaths. Methods An empirical ex-post evaluation exercise for cancer mortality data in Spain and cancer incidence in Finland using simple linear and log-linear Poisson models was performed. Prediction bases were considered within the time periods 1951–2006 in Spain and 1975–2007 in Finland, and then predictions were made for 37 and 33 single years in these periods, respectively. The performance of three fixed different prediction bases (last 5, 10, and 20 years of historical data) was compared to that of the prediction base determined by the GoF-optimal method. The coverage (COV) of the 95% prediction interval and the discrepancy ratio (DR) were calculated to assess the success of the prediction. Results The results showed that (i) models using the prediction base selected through GoF-optimal method reached the highest COV and the lowest DR and (ii) the best alternative strategy to GoF-optimal was the one using the base of prediction of 5-years. Conclusions The GoF-optimal approach can be used as a selection criterion in order to find an adequate base of prediction.