Conditional regression models for transient state survival analysis.
Survival models are important tools for the analysis of data when a disease event occurs in time and subjects are lost to follow-up. Many models, however, can also be adapted for use when an event is characterized by transitions through intermediate states of disease with increasing severity. In this presentation, such adaptations will be demonstrated for a class of conditional regression models for the analysis of transient state events occurring among grouped event times. The type of conditioning that will be described is useful in providing comparisons of specific disease states and an assessment of transition-dependent risk factor effects. An example will be given based on the Framingham Heart Study.
D000328:Adult; D001794:Blood Pressure; D002784:Cholesterol / Q000097:blood; D003327:Coronary Disease / Q000401:mortality; D004813:Epidemiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011379:Prognosis; D012044:Regression Analysis; D012306:Risk; D012907:Smoking; D013223:Statistics as Topic
D002784:Cholesterol