arXiv subjects

Yongwu Shao

Publications and source records attributed to Yongwu Shao.

4 recordsLinked to original sources

arXiv2026

Toward Efficient Estimation of Regional Treatment Effects in Multi-Regional Clinical Trials

A multi-regional clinical trial (MRCT) is a single clinical trial conducted in multiple regions simultaneously under a common protocol, which may be used to support parallel submissions to multiple regulatory authorities. For a regional regulatory authority, treatment effects defined specifically for its own region are more relevant to consider than overall treatment effects based on all regions included in an MRCT. A regional treatment effect can be estimated consistently using local data from the region of interest; however, this approach is generally inefficient as it excludes data from other regions and ignores possible similarities between regions. On the other hand, simply pooling data across regions requires strong assumptions and may introduce bias when the required assumptions are not met. Here, we propose a simple and robust approach to estimating a regional treatment effect in a two-arm randomized MRCT. The proposed approach uses a working regression model to incorporate information from baseline covariates as well as data from other regions for improved efficiency. The model accounts for residual regional differences (after adjusting for measured covariates) using interaction terms that describe how the dependence of outcome on treatment and covariates may vary across regions. The adaptive lasso is used to identify null interactions and thus achieve selective borrowing of information from other regions. The resulting regional treatment effect estimator is consistent and asymptotically normal even when the working model is misspecified, and able to improve efficiency over local estimation when there are similarities between regions in the form of null interactions.

stat.ME↗

arXiv2025

Variance Estimation for the Inverse Probability of Treatment Weighted Kaplan Meier Estimator

In a widely cited paper, Xie and Liu (henceforth XL) proposed to use inverse probability of treatment weighting (IPTW) to account for possible confounding in observational studies with survival endpoints subject to right censoring. Their proposal includes an IPTW Kaplan-Meier (KM) estimator for the survival function of a treatment-specific potential failure time, which can be used to evaluate the causal effect of one treatment versus another. The IPTW KM estimator is remarkably simple and highly effective for confounding bias correction. The method has been implemented in SAS's popular procedure LIFETEST for analyzing survival data and has seen widespread use. This letter is concerned with variance estimation for the IPTW KM estimator. The variance estimator provided by XL does not account for the variability of the IPTW weight when the propensity score is estimated from data, as is usually the case in observational studies. In this letter, we provide a rigorous asymptotic analysis of the IPTW KM estimator based on an estimated propensity score. Our analysis indicates that estimating the propensity score does tend to result in a smaller asymptotic variance, which can be estimated consistently using a plug-in variance estimator. We also present a simulation study comparing the variance estimator we propose with the XL variance estimator. Our simulation results confirm that the proposed variance estimator is more accurate than the XL variance estimator, which tends to over-estimate the sampling variance of the IPTW KM estimator.

stat.ME↗

arXiv2025

Likelihood confidence intervals for misspecified Cox models

The robust Wald confidence interval (CI) for the Cox model is commonly used when the model may be misspecified or when weights are applied. However it can perform poorly when there are few events in one or both treatment groups, as may occur when the event of interest is rare or when the experimental arm is highly efficacious. For instance, if we artificially remove events (assuming more events are unfavorable) from the experimental group, the resulting upper CI may increase. This is clearly counter-intuitive as a small number of events in the experimental arm represents stronger evidence for efficacy. It is well known that, when the sample size is small to moderate, likelihood CIs are better than Wald CIs in terms of actual coverage probabilities closely matching nominal levels. However, a robust version of the likelihood CI for the Cox model remains an open problem. For example, in the SAS procedure PHREG, the likelihood CI provided in the outputs is still the regular version, even when the robust option is specified. This is obviously undesirable as a user may mistakenly assume that the CI is the robust version. In this article we demonstrate that the likelihood ratio test statistic of the Cox model converges to a weighted chi-square distribution when the model is misspecified. The robust likelihood CI is then obtained by inverting the robust likelihood ratio test. The proposed CIs are evaluated through simulation studies and illustrated using real data from an HIV prevention trial.

stat.ME↗

arXiv2010

The linearity condition and adaptive estimation in single-index regressions

We show that under a linearity condition on the distribution of the predictors, the coefficient in single-index regression can be estimated with the same efficiency as in the case when the link function is known. Thus, the linearity condition seems to substitute for knowing the exact conditional distribution of the response given the linear combinations of the predictors.

math.ST↗