A0473
Title: Evaluating biomarkers for treatment selection from reproducibility studies
Authors: Xiao Song - University of Georgia (United States) [presenting]
Kevin K Dobbin - University of Georgia (United States)
Abstract: Evaluating new or more accurately measured predictive biomarkers for treatment selection based on a previous clinical trial involving standard biomarkers is considered. Instead of rerunning the clinical trial with the new biomarkers, a more efficient approach requires only either conducting a reproducibility study in which the new biomarkers and standard biomarkers are both measured on a set of patient samples or adopting replicated measures of the error-contaminated standard biomarkers in the original study is proposed. This approach is easier to conduct and much less expensive than studies that require new samples from patients randomized to the intervention. In addition, it makes it possible to perform the estimation of the clinical performance quickly since there will be no requirement to wait for events to occur, as would be the case with prospective validation. The treatment selection is assessed via a working model, but the proposed estimator of the mean restricted lifetime is valid even if the working model is misspecified. The proposed approach is assessed through simulation studies and applied to a cancer study
