Speaker: Eric Sanders, UBC Statistics M.Sc. student
Title: Incorporating Partial Adherence Into the Principal Stratification Analysis Framework
Abstract: Participants in pragmatic clinical trials often partially adhere to treatment. Simple statistical analyses of binary adherence (receiving either full or no treatment) introduce biases in the presence of partial adherence. We developed a framework which expands the principal stratification approach to allow partial adherers to have their own principal stratum and treatment level. We derived consistent estimates for bounds on population values of interest. A Monte Carlo posterior sampling method was derived that is computationally faster than Markov Chain Monte Carlo sampling, with confirmed equivalent results. Simulations indicate that the two methods agree with each other and are superior in most cases to the biased estimators created through standard principal stratification. The results suggest that these new methods may lead to increased accuracy of inference in settings where study participants only partially adhere to assigned treatment.
Speaker: Nikolas Krstic, UBC Statistics M.Sc. student
Title: Prediction of renal transplant rejection in pediatric patients using urinary metabolite data
Abstract: T-cell mediated rejection (TCMR) is a form of organ transplant rejection that can develop in renal transplant pediatric patients. Due to difficulties of correctly diagnosing TCMR, we use urinary metabolite data to predict the presence of TCMR in these patients. We use multiple different estimation methods to handle the high dimensionality and correlation present within the metabolite data, such as regularized regression and partial least squares. We also investigate how eliminating low quality samples (using sample quality metrics) or normalizing the metabolites by creatinine can affect predictive performance. Of the estimation methods used, PLS seems to be the best method for predicting TCMR when only using metabolite data. However, the composite LASSO model that incorporates both metabolite data and medical history data achieves similar predictive performance. We also observe that not normalizing metabolites by creatinine yields an equivalent or slightly improved predictive performance when compared to results obtained from metabolite normalization. Removal of low quality samples in the data (even at differing quality thresholds) does not seem to improve predictive performance, likely due to information loss.