248 – Contributed Oral Poster Presentations: Section on Statistical Computing
Estimating Adverse Outcome Incidence Rate Imputing Partial Status with Application to a Phase IV Cancer Trial
Shesh N. Rai
University of Louisville
Jianmin Pan
University of Louisville JG Brown Cancer Center
Xiaoyong Wu
University of Louisville
Pradeep Singh
Southeast Missouri State University
Melissa M. Hudson
St. Jude Children��s Research Hospital
Deo K. Srivastava
St. Jude Children��s Research Hospital,
Phase IV clinical trials are designed to monitor the long-term toxic effects of drugs in cancer survivors. Evaluations to study the long-term effects of the cancer treatment are often made in cross-sectional surveys. In addition to finding prognostic factors for log-term survival outcome, estimating the cumulative incidence rates of adverse outcomes is also desired. Such data pose many issues: incomplete data, competing risks and selection bias. For example, one such study was designed to study the effect of anthracyclines exposure, received as part of treatment for childhood cancer, to cardiotoxicity.1 In this paper, we resort to imputing the missing current status using regression method, under some parametric assumptions, and then combining with methods previously described in Rai et al.2 to estimate the cumulative incidence rates in an illness-death/failure model. A comprehensive simulation study suggests that the results obtained using the imputation approach is significantly more efficient than those obtained without imputation. We further apply the proposed approach to the data reported in Rai et al.2 and compare the results reported there to our approach that utilizes imputation.