Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials

Ye, Ting; Shao, Jun; Yi, Yanyao; Zhao, Qingyuan

doi:10.1080/01621459.2022.2049278

Cited by 27 publications

(18 citation statements)

References 40 publications

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“…Before we end the section on covariate adaptive design, we found that the R package carat can be used to conduct statistical inference on the ATE. Following Ye et al (2022), simple estimators, including the ordinary least squares estimator (which can be immediately implemented in R), can also provide unbiased estimates of the ATE.…”

Section: Covariate Adaptive Designmentioning

confidence: 99%

“…Ma et al (2020) and Wang et al (2021) further studied the statistical properties of the covariate adaptive designs under more general setups incorporating continuous covariates. Recent work by Ye et al (2022) provided a unified theoretical framework for analyzing various ATE estimators without requiring a linear model to be correctly specified, and demonstrated that the regression adjustment estimator incorporating the covariates and treatment‐covariate interactions could be asymptotically optimal. In particular, Ye et al (2022) showed that this regression adjustment estimator incorporating the covariates and treatment‐covariate interactions in covariate adaptive designs has exactly the same asymptotic variance as in complete randomization (Lin, 2013).…”

Section: Data Collection Mechanism In Randomized Experimentsmentioning

confidence: 99%

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Subgroup analysis and adaptive experiments crave for debiasing

Wang

2023

WIREs Computational Stats

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Results obtained from reliably designed randomized experiments are often considered to be evidence of the highest grade for assessing the effectiveness of biomedical or behavioral interventions. Nevertheless, even with randomized experiments, statistical bias can arise in post hoc analysis of the data or through adaptive data collection. In this article, we discuss the need for and review some of the recent developments in statistical methodologies to address the issue of potential bias in adaptive experiments and in subgroup analysis. For adaptive experiments, we focus on adaptive treatment assignments. For subgroup analysis, we focus on post hoc subgroup selection and review several frequentist approaches for debiased inference on the best‐selected subgroup effects.This article is categorized under: Applications of Computational Statistics > Clinical Trials

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Section: Covariate Adaptive Designmentioning

confidence: 99%

Section: Data Collection Mechanism In Randomized Experimentsmentioning

confidence: 99%

Subgroup analysis and adaptive experiments crave for debiasing

Wang

2023

WIREs Computational Stats

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“…Remark The use of stratum‐common coefficients

\beta (a)

was recommended by Ye et al. (2022a). In their work, the coefficients were achieved by the analysis of covariance using a heterogeneous working model (ANHECOVA) and the estimator derived by these coefficients for potential outcomes was referred to ANHECOVA estimator.…”

Section: Regression‐based Multiple Treatment Effect Estimationmentioning

confidence: 99%

“…This approach produces a valid inference by using a working model between responses and covariates, regardless of whether the working model is correct or not. To consider the regression adjustment for baseline covariates in addition to stratification covariates, stratum-common estimators and stratum-specific estimators have been developed, mainly for the case in which the allocation ratios are the same across strata (Liu et al, 2023;Ma et al, 2022;Ye et al, 2022aYe et al, , 2022b. However, little attention has been paid to the case in which the allocation ratios are different across strata, especially when additional baseline covariates are included, although different allocation ratios are commonly used in practice and are more flexible (Angrist et al, 2014;Chong et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Regression-Based Multiple Treatment Effect Estimation under Covariate-Adaptive Randomization

Gu,

Liu,

2023

Biometrics

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Covariate‐adaptive randomization methods are widely used in clinical trials to balance baseline covariates. Recent studies have shown the validity of using regression‐based estimators for treatment effects without imposing functional form requirements on the true data generation model. These studies have had limitations in certain scenarios; for example, in the case of multiple treatment groups, these studies did not consider additional covariates or assumed that the allocation ratios were the same across strata. To address these limitations, we develop a stratum‐common estimator and a stratum‐specific estimator under multiple treatments. We derive the asymptotic behaviors of these estimators and propose consistent nonparametric estimators for asymptotic variances. To determine their efficiency, we compare the estimators with the stratified difference‐in‐means estimator as the benchmark. We find that the stratum‐specific estimator guarantees efficiency gains, regardless of whether the allocation ratios across strata are the same or different. Our conclusions were also validated by simulation studies and a real clinical trial example.

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“…Hence, following their suggestions, students were randomly assigned to experimental or control conditions following the standards of a randomized control trial. Block randomization method was used in this research (Ye et al, 2022). This method is designed to randomize subjects into groups that result in equal sample sizes (Xia et al, 2021).…”

Section: Experimental Designmentioning

confidence: 99%

Peer tutoring in middle school mathematics: academic and psychological effects and moderators

Miravet

2022

Educational Psychology

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In this study reciprocal peer tutoring was implemented in middle school mathematics classes for a six-month period. The effects of this methodology on students' mathematics achievements, mathematics anxiety levels, attitudes toward mathematics, and mathematics self-concepts were examined. Study participants included 768 students in grades 7 to 9 (12 to 15 years old). Statistically significant differences and positive effect sizes were reported for mathematics achievement, mathematics anxiety level, and mathematics self-concept (Hedge's g average value of 0.28). No significant statistical differences were reported for mathematics attitude. Students' repeating condition acted as significant moderators for mathematics anxiety. The main conclusion is that reciprocal peer tutoring can be very beneficial academically and psychologically for middle school mathematics students.

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Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials

Cited by 27 publications

References 40 publications

Subgroup analysis and adaptive experiments crave for debiasing

Subgroup analysis and adaptive experiments crave for debiasing

Regression-Based Multiple Treatment Effect Estimation under Covariate-Adaptive Randomization

Peer tutoring in middle school mathematics: academic and psychological effects and moderators

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