Analysis of Potential Time-to-Event Data with Competing Risks

• Activity Number: 130
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Liang Li*+ and Bo Hu and Michael Kattan
• Companies: Cleveland Clinic and Cleveland Clinic and Cleveland Clinic
• Keywords: Causal inference ; Fine-Gray model ; Inverse probability censoring weighting (IPCW) ; Landmark analysis ; Potential outcomes ; Prediction model

Abstract

Patients receiving radical prostatectomy are at risk of metastasis or prostate cancer related death, and often need repeated clinical evaluations to determine whether additional adjuvant or salvage therapies are needed. Since the prostate cancer is a slow progressing disease, and these additional therapies come with significant side effects, it is important for clinical decision making purposes to estimate a patient's risk of cancer metastasis, in the presence of a competing risk by death, under the hypothetical condition that the patient does not receive any additional therapy. In observational studies, patients may receive additional therapy by choice; the time to metastasis without any therapy is often a potential outcome and not always observed. We study the competing risks model of Fine and Gray (1999) with adjustment for treatment choice by inverse probability censoring weighting (IPCW). The model can be fit using standard software for partial likelihood with double IPCW weights. The proposed methodology is used in a prostate cancer study to estimate the post-prostatectomy cumulative incidence probability of cancer metastasis without additional adjuvant or salvage therapies.