The analysis of survival data with a non-susceptible fraction and dual censoring mechanisms.
It is known that the ages of onset of many diseases are determined by both a genetic predisposition to disease as well as environmental risk factors that are capable of either triggering or hastening the onset of disease. Difficulties in modelling onset ages arise when a large fraction fail to inherit the disease-causing gene, and multiple reasons for censoring result in unobserved onset ages. We present a parametric Bayesian model that includes subjects with missing age information, non-susceptible subjects and allows for regression on risk factor information. The model is fit using Markov chain Monte Carlo simulation from the posterior distribution, and allows the simultaneous estimation of the proportion of the population at risk of disease, the mean onset age of disease, survival after disease onset, and the association of risk factors with susceptibility, onset age and survival after onset. An example employing Huntington's disease data is presented.
D017668:Age of Onset; D001499:Bayes Theorem; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D006816:Huntington Disease / Q000235:genetics; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D011336:Probability; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D014481:United States