A New Approach in Survival Analysis with Longitudinal Covariates
Survival Analysis , Longitudinal Covariates , Joint Model , State Space Model , Hidden Markov Model , Kalman Filter , Event History Chart , Conditional Expected Remaining Lifespan
In this study we look at the problem of analysing survival data in the presence of longitudinally collected covariates. New methodology for analysing such data has been developed through the use of hidden Markov modeling. Special attention has been given to the case of large information volume, where a preliminary data reduction is necessary. Novel graphical diagnostics have been proposed to assess goodness of fit and significance of covariates. The methodology developed has been applied to the data collected on behaviors of Mexican fruit flies, which were monitored throughout their lives. It has been found that certain patterns in eating behavior may serve as an aging marker. In particular it has been established that the frequency of eating is positively correlated with survival times.