Cox Model with Left-truncation, Complex Censoring, and Error-prone Survival Outcomes

Oregon Convention Center 

Time-to-event analysis is one of the most popular tools for modeling disease process data. It may be expensive/invasive to measure the true survival outcome (e.g., time to abnormality of cerebrospinal fluid biomarkers). Thus, the true survival outcome is only available for a small group of participants, which brings limitations in sample size and estimating efficiency. An inexpensive/less invasive auxiliary outcome that measures the true outcome with error may be collected. We propose a likelihood-based method and an EM algorithm for Cox regression models, which incorporate the error-prone auxiliary outcomes and improve efficiency. The proposed method allows left-truncation in the event time of interest and complex censoring, which further complicates the problem. We show that the proposed regression coefficient estimator is consistent and asymptotically normally distributed. We evaluate the finite sample performance of the proposed method through simulation studies. We illustrate the proposed method using the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.