Title: A New Method of Predicting US and State-level Cancer Mortality Counts for the Current Calendar Year

 

Speaker: Ram Tiwari, Mathematical Statistician, National cancer Institute

 

Accurate prediction of cancer mortality figures for the current and upcoming year are extremely essential for public health planning and evaluation. Due to delay in reporting cause-specific mortality for the US, there is a 3-year lag between the latest year for which such figures are available and the current year. Until recently, the American cancer Society (ACS) has been predicting cancer mortality counts by first fitting a time series model with quadratic trend and autoregressive error to the past data and then projecting this model into the future. The proposed method uses a quadratic trend with random time-varying coefficients to model the mortality counts, and is called a state space model (SSM). Since the SSM has time-varying random coefficients, it is able to quickly adjust to sudden changes in the observed trend, and, as a result, generally provides predictions of mortality counts that are closer to their observed values compared to the corresponding predictions obtained from the previous ACS method. The ACS has implemented the SSM methodology to produce the estimates of cancer deaths for US and its States in its annual publication Cancer facts & Figures, 2004.