Forecasting Mortality in the Middle-Aged and Older Population of England: A 1D-CNN Approach

Longitudinal surveys are follow-up studies, in which participants' information is recorded at different time steps, say every two years. The difference between time series analysis and longitudinal studies is that in the former we have records of, say economic variables over a long period, whereas in the latter, we have a few records of participants, and the number of records depends on the number of participants. One problem with such studies is that we may have a large number of drop-outs, known as right-censoring in survival analysis, due to death, illness, immigration, etc. Another difference is that in time-series analysis, we aim to predict, say future stock prices based on past data, whereas in longitudinal study our goal is to predict a target based on some features. Longitudinal studies are used to study life events such as clinical psychology. Generalised linear models (GLMs) (McCullagh and Nelder, 1989), and generalised linear mixed models (GLMMs) (Frees, 2004) are traditional methods used in longitudinal studies. Qazvini (2023) employs GLMM to analyse the survival rate among the English population using the ELSA dataset.