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Two UBC Statistics MSc student presentations

Tuesday, May 4, 2021 - 11:00 to 12:00
Lulu Pei, UBC Statistics MSc student; Lily Xia, UBC Statistics MSc student

To Join Via Zoom: To join this seminar, please request Zoom connection details from headsec [at]

Presentation 1

Time: 11am – 11:30am

Speaker: Lulu Pei, UBC Statistics MSc student

Title: An assessment of the robustness of nonlinear mixed effects models to covariance structure specification

Abstract: Nonlinear mixed effects (NLME) models are widely used with applications in both HIV/AIDS studies and pharmacokinetic/pharmacodynamic studies. In HIV trials, NLME models can be used to describe the virus elimination and production process for a patient on antiretroviral therapy. The estimated viral dynamic parameters can then be used to evaluate the efficacies of the treatments. In practice, there are often substantial variations in viral load and CD4 cell count measurements among patients, and the viral dynamic parameters may vary greatly across patients. Mixed effects models appear appealing in such cases since random effects can be used to characterize individual deviations from population averages. In the case of viral load, it is evident that both the variations of the within-individual repeated measurements and the variations between individuals increase over time. As such, there is a need to consider appropriate specification of covariance structures beyond the default constant variance and independence between repeated measurements over time assumed by common NLME implementation software such as R. We will explore various covariance structures for the within-individual repeated measurements and the between-individual random effects to see if analysis results are sensitive to structure specification.

Presentation 2

Time: 11:30am – 12pm

Speaker: Lily Xia, UBC Statistics MSc student

Title: Quantifying the utility of personalized treatment decision rules: Extending and comparing two metrics for summarizing the heterogeneity of treatment effects

Abstract: The treatment benefit prediction model is a type of clinical prediction model that quantifies the magnitude of treatment benefit given an individual's unique characteristics. As the topic of treatment effect modelling is relatively new, quantifying and summarizing the performance of treatment benefit models are not well studied. The “concordance-statistic for benefit” and the “concentration of benefit index” are two newly developed metrics that evaluate the discriminative ability of the treatment benefit prediction. However, the similarities and differences between these two metrics are not yet explored. We compare and contrast the metrics from conceptual, theoretical, and empirical perspectives and illustrate the application of the metrics. We consider the common scenario of a logistic regression model for a binary response developed based on data from a randomized controlled trial with two treatment arms. This dissertation provides two major contributions: first, the two metrics are expanded into three pairs of metrics, each having a particular scope; second, it provides results of theoretical and simulation studies that compare and contrast the construct and empirical behaviour of these metrics. We found that the heterogeneity of treatment effect appropriately influences these metrics. Metrics related to the “concordance-statistic for benefit” are sensitive to the unobservable correlation between counterfactual outcomes. In a case study, we quantify the metrics in a randomized controlled trial of acute myocardial infarction therapies on 30-day mortality. We conclude that these metrics help understand the heterogeneity of treatment effect and the consequent impact on treatment decision-making.