Conclusions

Cancer is a global health challenge. It is perhaps the most significant problem which humanity will have to face in the next two or more decades after global warning (World Health Organization, Media Center, 2015). Nowadays lung cancer is the first or second most frequent tumor type among men and third or fourth among women (World Health Organization, Media Center, 2015). Therefore, efforts to reduce and prevent lung cancer are of course essential.

Forecasting the burden of lung cancer incidence and mortality is important for evaluating prevention strategies and for administrative planning at lung cancer facilities. We collect the data of lung cancer incidence and mortality from Saudi Cancer Registry (SCR) and Central Department of Statistics (CDS) in Saudi Arabia (KSA) from 1994 to 2009. Population data were prepared from forecasts made by the United Nations between 2010 and 2020. Our aim is to use forecasting methods to describe the broad picture of the future lung cancer burden in Saudi Arabia and to report baseline incidences against which progress in implementing the National Health Service (NHS) Cancer Plan will be measured.

We study lung cancer incidence and mortality in Saudi Arabia between 1994 and 2009. The first part of this study uses time-series methods in determining and forecasting lung cancer incidence data using Box-Jenkins methodology and dynamic regression models. In the Box-Jenkins analysis, we present Seasonal Autoregressive Integrated Moving Average (SARIMA) models in chapter 4. In dynamic regression, we describe more general autoregressive AR processes such as AR(1), distributed lag models (DLMs), and polynomial distributed lag models (PDLs). We develop, analyze, and perform a one- step ahead forecast of the various models to explore the best-fit model for lung cancer cases in Saudi Arabia. We propose a new approach called autoregressive polynomial distributed lag (ARPDL) model. This approach results in having a model with a lower standard error and more accurate fit. The second part of this study concentrates on the age- period-cohort (APC) models. Natural cubic splines were used in APC models for drawing inference on the impact of lung cancer incidence rates. Using the restriction of the cubic splines being linear beyond the boundary knots, we were able to make better projections in

the magnitude of the rates, the variation by age, and time trends in the rates into the future. Using splines and more finely split data as opposed to the factor models with coarsely split data seems better. Bayesian dynamic APC models were used for modelling and forecasting lung cancer mortality rates between 1994 and 2020. Bayesian approaches assume some sort of smoothness of age, period and cohort effects in order to improve estimation and facilitate prediction. Three models were used: the full APC, AP and AC models. Comparison between nested models was evaluated by the changes in Deviance Information Criterion DIC.

The empirical results of lung cancer incidence show that most of the cases are among males and suggest that lung cancer cases are strongly affected by smoking habits. The overall best one-step-ahead forecast of dynamic regression model is the ARPDL(12,3,26,8) model of the total cases of lung cancer on smoking population separately for males and females. This is confirmed by the value of adjusted R-squared as well as the significance of the F-statistic of the regressions. The overall best Box-Jenkins SARIMA model is the SARIMA(2,1,1)x(0,1,1)12 model. It is best on all three information criteria: AIC, AICc and

BIC. The forecasts generated by ARPDL and SARIMA models both capture the seasonality trends. However, the ARPDL model with a small lag does not capture the seasonality as well as the ARPDL model with large lag. Nonetheless, we prefer the forecast generated from the SARIMA model since it has a fewer parameters to estimate. The preferred SARIMA model suggests that there will be an average of 45 cases of lung cancer per month for the next 24 months. In addition, the estimated yearly lung cancer cases in 2010 and 2011 were 538 and 555 respectively. We conclude from the data that more incident cases are diagnosed in winter.

The estimated incidence rates from age-period-cohort modelling show a sharp decrease in males and a gradual increase in females over the next 10 years. The male age standardised rate of lung cancer incidence is projected to fall from 5.6 to 2.4 per 100,000 by 2020, whereas the female age standardised rate of lung cancer incidence is projected to increase from 2.0 to 2.2 per 100,000 by 2020. The growing and ageing populations will have a substantial impact, therefore the number of cases per year are projected to decrease in males (from 356 to 320) and to increase in females (from 134 to 247) between 2009 and 2020. These results reflect the decrease of smoking prevalence among males and the increase of smoking prevalence in females. The results show that in KSA, males have about a 79% greater incidence of lung cancer than females across the entire dataset when adjusting for the other effects. The p-value for the gender term highlights that the effect for

gender is significant at the 0.1% level. In addition, the p-values for the covariates of race, Southern, Western, and Eastern regions show that the effects for these covariates are statistically significant.

By comparing the trends of lung cancer incidence in Saudi Arabia (KSA) to that of the United Kingdom (UK), we seem to have almost the same pattern. However, the rate of lung cancer incidence is much higher in the UK than in KSA due to the high prevalence of smoking among males and females in the UK (see Appendix B2 and B3). In 1994, the overall age-standardised incidence rates of lung cancer in the UK were 90.5 and 35 per 100,000 for males and females respectively. Over the same period, the overall age- standardised incidence rates in KSA were 7.7 and 2 per 100,000 for males and females respectively. The projection of lung cancer incidence cases from 2009 to 2020 for both countries is expected to decrease sharply in males by 16.28% in UK and 57.14% in KSA. On the other hand, females are expected to have a slight decrease by 8.45% in UK and a slight increase by 10% in KSA. Thus, age-standardised incidence rates are projected to decease in males to 47.8 and to 2.4 per 100,000 in the UK and in KSA respectively. Whereas females age-standardised incidence rates are expected to decrease slightly in the UK to 32.5 and to increase slightly in KSA to 2.2 per 100,000.

The estimated age standardized rate (ASR) of lung cancer mortality within its 95% credible interval is expected to increase from 1.8 (1.61, 1.94) in 2010 to 3.04 (2.13, 5.94) per 100,000 population in 2020. Our results suggest that the expected age-specific standardized rates of lung cancer mortality will increase gradually in all age groups between 2010 and 2020. Mortality risk from lung cancer reaches its peak between ages 65 and 69 years. The posterior mean (age-specific standardized rate) within its 95% credible interval is expected to increase from 4.50 (3.90, 5.09) in 2010 to 5.66 (2.78, 8.54) per 100,000 population in 2020. The trends of lung cancer mortality are mainly due to the age effect and slightly due to the period effect but no obvious cohort effects were observed in the study. The lack of cohort effect may be due to the short time period of the observed mortality data. Age has a strong association with lung cancer mortality, suggesting age- related causes such as cumulative exposures of smoking over time. This may be the main reason of increasing lung cancer mortality in KSA, since the prevalence of smoking is increasing especially in women. Tobacco is responsible for around 70% of lung cancer mortality (World Health Organization, Media Center, 2015). The increase of lung cancer mortality rate in all age groups during the period of our study could also be attributed to the lack of early detection and screening of lung cancer mortality.

In this thesis we have proposed different approaches to model and forecast lung cancer incidence and mortality in Saudi Arabia. We used finite and infinite dynamic regression models and we came up with a new approach called autoregressive polynomial distributed lag (ARPDL) model. This approach results in having a model with a lower standard error and more accurate fit than PDL and OLS models. Also, we used two methodological approaches on modelling age-period-cohort models, namely spline functions and Bayesian dynamic models. Our results show that both APC models using spline functions and Bayesian dynamic models are able to overcome the identification problem and identify the effect of age, period and cohort. However, Bayesian dynamic APC model is preferred in forecasting the incidence or the mortality rates of lung cancer especially when the data are sparse or has zero counts, because the forecast based on Bayesian dynamic APC model does not rely on strong parametric assumptions for future values of subjective cohort and period effects. Additionally, the sparse data and zero counts in Bayesian dynamic APC models do not pose any implementation problems when fitting APC models.