arXiv subjects

Dong Xi

Publications and source records attributed to Dong Xi.

7 recordsLinked to original sources

arXiv2026

The RobinCar Family: R Tools for Robust Covariate Adjustment in Randomized Clinical Trials

Purpose: Covariate adjustment is a powerful statistical technique that can increase efficiency in clinical trials. Recent guidance from the U.S. FDA provided recommendations and best practices for using covariate adjustment. However, there has existed a gap between the extensive statistical literature on covariate adjustment and software that is easy to use and abides by these best practices. Methods: We have developed the RobinCar Family, which is comprised of RobinCar and RobinCar2. These two R packages enable covariate-adjusted analyses for continuous, discrete, and time-to-event outcomes that follow best practices. For continuous and discrete outcomes, the functions in the RobinCar Family facilitate traditional forms of covariate adjustment such as ANCOVA as well as more recent approaches like ANHECOVA, G-computation with generalized linear models and machine learning models, and adjustment for a super-covariate (as in PROCOVA(TM)). Functions for time-to-event outcomes implement the covariate-adjusted log-rank test, the stratified covariate-adjusted log-rank test, and the marginal covariate-adjusted hazard ratio. The RobinCar Family is supported by the ASA Biopharmaceutical Section Covariate Adjustment Scientific Working Group. Results: We provide an accessible overview of the covariate-adjusted statistical methods, and describe how they are implemented in RobinCar and RobinCar2. We highlight important usage notes for clinical trial practitioners. Conclusion: We apply RobinCar and RobinCar2 functions by analyzing data from the AIDS Clinical Trials Group Study 175, demonstrating that they are straightforward and user-friendly.

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arXiv2025

A Unified Approach to Covariate Adjustment for Survival Endpoints in Randomized Clinical Trials

Covariate adjustment aims to improve the statistical efficiency of randomized trials by incorporating information from baseline covariates. Popular methods for covariate adjustment include analysis of covariance for continuous endpoints and standardized logistic regression for binary endpoints. For survival endpoints, while some covariate adjustment methods have been developed for specific effect measures, they are not commonly used in practice for various reasons, including high demands for theoretical and methodological sophistication as well as computational skills. This article describes an augmentation approach to covariate adjustment for survival endpoints that is relatively easy to understand and widely applicable to different effect measures. This approach involves augmenting a given treatment effect estimator in a way that preserves interpretation, consistency, and asymptotic normality. The optimal augmentation term, which minimizes asymptotic variance, can be estimated using various statistical and machine learning methods. Simulation results demonstrate that the augmentation approach can bring substantial gains in statistical efficiency. This approach has been implemented in an R package named \texttt{sleete}, which is described in detail and illustrated with real data.

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arXiv2024

Futility analyses for the MCP-Mod methodology based on longitudinal models

This article discusses futility analyses for the MCP-Mod methodology. Formulas are derived for calculating predictive and conditional power for MCP-Mod, which also cover the case when longitudinal models are used allowing to utilize incomplete data from patients at interim. A simulation study is conducted to evaluate the repeated sampling properties of the proposed decision rules and to assess the benefit of using a longitudinal versus a completer only model for decision making at interim. The results suggest that the proposed methods perform adequately and a longitudinal analysis outperforms a completer only analysis, particularly when the recruitment speed is higher and the correlation over time is larger. The proposed methodology is illustrated using real data from a dose-finding study for severe uncontrolled asthma.

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arXiv2023

Covariate adjustment and estimation of difference in proportions in randomized clinical trials

Difference in proportions is frequently used to measure treatment effect for binary outcomes in randomized clinical trials. The estimation of difference in proportions can be assisted by adjusting for prognostic baseline covariates to enhance precision and bolster statistical power. Standardization or G-computation is a widely used method for covariate adjustment in estimating unconditional difference in proportions, because of its robustness to model misspecification. Various inference methods have been proposed to quantify the uncertainty and confidence intervals based on large-sample theories. However, their performances under small sample sizes and model misspecification have not been comprehensively evaluated. We propose an alternative approach to estimate the unconditional variance of the standardization estimator based on the robust sandwich estimator to further enhance the finite sample performance. Extensive simulations are provided to demonstrate the performances of the proposed method, spanning a wide range of sample sizes, randomization ratios, and model misspecification. We apply the proposed method in a real data example to illustrate the practical utility.

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arXiv2020

Clinical trials impacted by the COVID-19 pandemic: Adaptive designs to the rescue?

Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for the ongoing clinical trials in non-COVID-19 conditions. Motivated by four currently ongoing clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to utilize early readouts; and (v) how to utilize Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.

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arXiv2016

Optimal exact tests for multiple binary endpoints

In confirmatory clinical trials with small sample sizes, hypothesis tests based on asymptotic distributions are often not valid and exact non-parametric procedures are applied instead. However, the latter are based on discrete test statistics and can become very conservative, even more so, if adjustments for multiple testing as the Bonferroni correction are applied. We propose improved exact multiple testing procedures for the setting where two parallel groups are compared in multiple binary endpoints. Based on the joint conditional distribution of test statistics of Fisher's exact tests, optimal rejection regions for intersection hypotheses tests are constructed. To efficiently search the large space of possible rejection regions, we propose an optimization algorithm based on constrained optimization and integer linear programming. Depending on the optimization objective, the optimal test yields maximal power under a specific alternative, maximal exhaustion of the nominal type I error rate, or the largest possible rejection region controlling the type I error rate. Applying the closed testing principle, we construct optimized multiple testing procedures with strong familywise error rate control. Furthermore, we propose a greedy algorithm for nearly optimal tests, which is computationally more efficient. We numerically compare the unconditional power of the optimized procedure with alternative approaches and illustrate the optimal tests with a clinical trial example in a rare disease.

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arXiv2016

On weighted parametric tests

We describe a general framework for weighted parametric multiple test procedures based on the closure principle. We utilize general weighting strategies that can reflect complex study objectives and include many procedures in the literature as special cases. The proposed weighted parametric tests bridge the gap between rejection rules using either adjusted significance levels or adjusted $p$-values. This connection is possible by allowing intersection hypotheses to be tested at level smaller than $\alpha$, which may be needed for certain study considerations. For such cases we introduce a subclass of exact $\alpha$-level parametric tests which satisfy the consonance property. When only subsets of test statistics are correlated, a new procedure is proposed to fully utilize the parametric assumptions within each subset. We illustrate the proposed weighted parametric tests using a clinical trial example.

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