arXiv subjects

Yujing Gao

Publications and source records attributed to Yujing Gao.

4 recordsLinked to original sources

arXiv2025

Designing Efficient Hybrid and Single-Arm Trials: External Control Borrowing and Sample Size Calculation

External controls (ECs) from historical trials or real-world data have gained increasing attention as a way to augment hybrid and single-arm trials, especially when balanced randomization is infeasible. While most existing work has focused on post-trial inference using ECs, their role in prospective trial design remains less explored. We address this gap by focusing on the sample size determination and power analysis for an experimental design problem that encompasses standard randomized controlled trials (RCTs), hybrid trials, and single-arm trials. Building on estimators derived from the efficient influence function, we develop hybrid and single-arm design strategies that leverage comparable EC data to reduce the required sample size of the current study. We derive asymptotic variance expressions for these estimators in terms of interpretable, population-level quantities and introduce a pre-experimental variance estimation procedure to guide sample size calculation, ensuring prespecified type I error and power for the relevant hypothesis test. Simulation studies demonstrate that the proposed hybrid and single-arm designs maintain valid type I error and achieve target power across diverse scenarios while requiring substantially fewer subjects in the current study than RCT designs. A real data application further illustrates the practical utility and advantages of the proposed hybrid and single-arm designs.

stat.ME↗

arXiv2025

SensIAT: An R Package for Conducting Sensitivity Analysis of Randomized Trials with Irregular Assessment Times

This paper introduces an R package SensIAT that implements a sensitivity analysis methodology, based on augmented inverse intensity weighting, for randomized trials with irregular and potentially informative assessment times. Targets of inference involve the population mean outcome in each treatment arm as well as the difference in these means (i.e., treatment effect) at specified times after randomization. This methodology is useful in settings where there is concern that study participants are either more, or less, likely to have assessments at times when their outcomes are worse. In such settings, unadjusted estimates can be biased. The methodology allows researchers to see how inferences are impacted by a range of assumptions about the strength and direction of informative timing in each arm, while incorporating flexible semi-parametric modeling. We describe the functions implemented in SensIAT and illustrate them through an analysis of a synthetic dataset motivated by the HAP2 asthma randomized clinical trial.

stat.ME↗

arXiv2022

Semi-Parametric Sensitivity Analysis for Trials with Irregular and Informative Assessment Times

Many trials are designed to collect outcomes at or around pre-specified times after randomization. If there is variability in the times when participants are actually assessed, this can pose a challenge to learning the effect of treatment, since not all participants have outcome assessments at the times of interest. Furthermore, observed outcome values may not be representative of all participants' outcomes at a given time. Methods have been developed that account for some types of such irregular and informative assessment times; however, since these methods rely on untestable assumptions, sensitivity analyses are needed. We develop a methodology that is benchmarked at the explainable assessmen (EA) assumption, under which assessment and outcomes at each time are related only through data collected prior to that time. Our method uses an exponential tilting assumption, governed by a sensitivity analysis parameter, that posits deviations from the EA assumption. Our inferential strategy is based on a new influence function-based, augmented inverse intensity-weighted estimator. Our approach allows for flexible semiparametric modeling of the observed data, which is separated from specification of the sensitivity parameter. We apply our method to a randomized trial of low-income individuals with uncontrolled asthma, and we illustrate implementation of our estimation procedure in detail.

stat.ME↗

arXiv2020

Weighted Lasso Estimates for Sparse Logistic Regression: Non-asymptotic Properties with Measurement Error

When we are interested in high-dimensional system and focus on classification performance, the $\ell_{1}$-penalized logistic regression is becoming important and popular. However, the Lasso estimates could be problematic when penalties of different coefficients are all the same and not related to the data. We proposed two types of weighted Lasso estimates depending on covariates by the McDiarmid inequality. Given sample size $n$ and dimension of covariates $p$, the finite sample behavior of our proposed methods with a diverging number of predictors is illustrated by non-asymptotic oracle inequalities such as $\ell_{1}$-estimation error and squared prediction error of the unknown parameters. We compare the performance of our methods with former weighted estimates on simulated data, then apply these methods to do real data analysis.

stat.ML↗