Cause-specific hazard regression for competing risks data under interval censoring and left truncation

Inference for cause-specific hazards from competing risks data under interval censoring and possible left truncation has been understudied. Aiming at this target, a penalized likelihood approach for a Cox-type proportional cause-specific hazards model is developed, and the associated asymptotic theory is discussed. Monte Carlo simulations show that the approach performs very well for moderate sample sizes. An application to a longitudinal study of dementia illustrates the practical utility of the method. In the application, the age specific hazards of AD, other dementia and death without dementia are estimated, and risk factors of all competing risks are studied. (C) 2016 Elsevier B.V. All rights reserved.