Events Calendar

The Department of Statistics and Actuarial Science Fall Colloquium Series presents:

Shu Yang, Associate Professor of Statistics, University Faculty Scholar, Department of Statistics, North Carolina State University

"Enhancing Statistical Validity and Power in Hybrid Controlled Trials: A Randomization Inference Approach with Conformal Selective Borrowing"

Abstract: Randomized controlled trials (RCTs) are the gold standard for causal inference on treatment effects, but they can be limited by small sample sizes due to the indications associated with rare diseases and small patient populations, where ethical concerns or patient reluctance may limit control group assignment. Hybrid controlled trials use external controls (ECs) from historical studies or large observational databases to enhance statistical efficiency. However, non-randomized ECs can introduce biases that compromise validity and inflate Type I errors for treatment discovery, particularly in small samples. To address this, we extend the Fisher randomization test to hybrid controlled trials. Our approach involves a test statistic combining RCT and EC data and is based solely on randomization in the RCT. This method strictly controls the Type I error rate, even with biased ECs, and improves power by incorporating unbiased ECs. To mitigate the power loss caused by biased ECs, we introduce Conformal Selective Borrowing, which uses individual conformal p-values to selectively incorporate unbiased ECs, offering the flexibility to use either computationally efficient parametric models or off-the-shelf machine learning models to construct the score function, along with model-agnostic reliability. We identify a risk-benefit trade-off in the power of FRT, associated with different selection thresholds for conformal p-values, analogous to the mean squared error trade-offs observed in the data integrative estimators. We propose a data-driven selection of the threshold value to achieve robust performance across different levels of hidden bias. The advantages of our method are demonstrated through simulations and an application to a small-sized lung cancer trial with ECs from the National Cancer Database.

This virtual presentation begins at 3:15 p.m.

Topic: UIowa Department of Statistics & Actuarial Science Colloquium

Time: Nov 21, 2024 03:15 PM Central Time (US and Canada)

Zoom link

Meeting ID: 912 7594 8742

Passcode: iowa