Cox proportional hazards models with left truncation and time-varying coefficient: Application of age at event as outcome in cohort studies.
When analyzing time-to-event cohort data, two different ways of choosing a time scale have been discussed in the literature: time-on-study or age at onset of disease. One advantage of choosing the latter is interpretability of the hazard ratio as a function of age. To handle the analysis of age at onset in a principled manner, we present an analysis of the Cox Proportional Hazards model with time-varying coefficient for left-truncated and right-censored data. In the analysis of Northern Manhattan Study (NOMAS) with age at onset of stroke as outcome, we demonstrate that well-established risk factors may be important only around a certain age span and less established risk factors can have a strong effect in a certain age span.
Department of Statistics, Seoul National University, Seoul, 151-747, Korea.
Minjin
M
Kim
Department of Statistics, Seoul National University, Seoul, 151-747, Korea.
Myunghee Cho
MC
Paik
Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL 60612, USA.
Jiyeong
J
Jang
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
Ying K
YK
Cheung
Department of Neurology, Neurological Institute, Columbia University, New York, NY 10032, USA.
Joshua
J
Willey
Department of Neurology, Neurological Institute, Columbia University, New York, NY 10032, USA.
Mitchell S V
MSV
Elkind
Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA.
Ralph L
RL
Sacco
D000367:Age Factors; D001699:Biometry / Q000379:methods*; D015331:Cohort Studies; D006801:Humans; D016016:Proportional Hazards Models; D012307:Risk Factors; D020521:Stroke / Q000453:epidemiology; D016019:Survival Analysis; D013997:Time Factors
Estimating equation; Local linear fitting; Profile likelihood; Time-to-event cohort data; Time-varying coefficient