Balancing continuous covariates based on Kernel densities

Ma, Zhenjun; Hu, Feifang

doi:10.1016/j.cct.2012.12.004

Cited by 27 publications

(37 citation statements)

References 34 publications

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“…First, all covariate-adaptive designs considered in this article are based on discrete covariates. We may consider covariate-adaptive designs (Lin and Su 2012;Ma and Hu 2013) that directly use continuous covariates without discretization. However, related theoretical work is limited in the literature.…”

Section: Discussionmentioning

confidence: 99%

Testing Hypotheses of Covariate-Adaptive Randomized Clinical Trials

Ma¹,

Hu²,

Zhang³

2015

Journal of the American Statistical Association

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Covariate-adaptive designs are often implemented to balance important covariates in clinical trials. However, the theoretical properties of conventional testing hypotheses are usually unknown under covariate-adaptive randomized clinical trials. In the literature, most studies are based on simulations. In this article, we provide theoretical foundation of hypothesis testing under covariate-adaptive designs based on linear models. We derive the asymptotic distributions of the test statistics of testing both treatment effects and the significance of covariates under null and alternative hypotheses. Under a large class of covariate-adaptive designs, (i) the hypothesis testing to compare treatment effects is usually conservative in terms of small Type I error; (ii) the hypothesis testing to compare treatment effects is usually more powerful than complete randomization; and (iii) the hypothesis testing for significance of covariates is still valid. The class includes most of the covariate-adaptive designs in the literature; for example, Pocock and Simon's marginal procedure, stratified permuted block design, etc. Numerical studies are also performed to assess their corresponding finite sample properties. Supplementary material for this article is available online.

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Section: Discussionmentioning

confidence: 99%

Testing Hypotheses of Covariate-Adaptive Randomized Clinical Trials

Ma¹,

Hu²,

Zhang³

2015

Journal of the American Statistical Association

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“…The treatment group with a larger p ‐value is then favored using a bias probability that varies with the degree of imbalance that exists at the time each patient is randomized. As expected, Frane's procedure performs very well in maintaining similarity in covariate means among treatment groups . But the method does not ensure good balance of the trial in other aspects .…”

Section: Balance On Continuous Covariatesmentioning

confidence: 92%

“…Hoehler and Stigsby and Taves use covariate ranks to replace the numerical values. Although the above methods have their own strengths, we advocate the approaches of Lin and Su and Ma and Hu, which tend to achieve distributional balance. The pros and cons of these methods are discussed in greater detail below.…”

Section: Balance On Continuous Covariatesmentioning

confidence: 99%

Adaptive randomization for balancing over covariates

et al. 2014

WIREs Computational Stats

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In controlled clinical trials, balanced allocation over covariates is often viewed as an essential component in ensuring valid treatment comparisons. Minimization, sometimes called 'dynamic allocation', or 'covariate-adaptive randomization' has an advantage over stratified randomization, in that it is able to achieve balance over a large number of covariates when the sample size is small to medium. Despite its effectiveness, minimization has been questioned by regulatory agencies, mainly because of its increased complexity in practice and its potential impact on subsequent analysis. In recent years, however, with developments in clinical trials information technology, as well as advances in statistical theory, the attitudes toward minimization have evolved. In its 2013 draft guidelines, the European Medicines Agency (EMA) provided instructive guidelines for the implementation of minimization. In this paper we review the broad class of methods that belong to minimization, including its original forms for balancing over covariate margins and its generalization to balancing over other subgroups of interest or over continuous covariates. Moreover, we review the theoretical development in recent years, including the large-sample properties of balance under minimization, the impact of minimization on inference for different data types, and on suitable randomization tests.

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“…Stigsby & Taves (2010) considered the rank-sum based covariate adaptive procedure, and Su (2011) discussed a method using quantiles of the covariate differences. Ma & Hu (2013) proposed a randomization procedure by defining the imbalance of the covariates through kernel density estimators, which summarize all the information in the covariate distributions.…”

Section: Such a Problem Has Arisen In Many Clinical Trials Which Is mentioning

confidence: 99%

“…Despite their popularity, the main drawbacks of the these designs are that continuous covariates must be categorized into several groups, while clinical trials often collect a large number of continuous covariates and different ways of categorization may lead to different imbalanced structures. In addition, breaking down continuous covariates into sub-categories often changes the nature of the covariates and makes distributional balance unattainable (Ma & Hu 2013). If the sub-categories are not appropriately defined, it can even lead to error and loss of efficiency in the randomization procedure (Stigsby & Taves 2010).…”

Section: Introductionmentioning

confidence: 99%

Kernel-Based Adaptive Randomization Toward Balance in Continuous and Discrete Covariates

Jiang

Yin

2018

STAT SINICA

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Covariate balance among different treatment arms is critical in clinical trials, as confounding effects can be effectively eliminated when patients in different arms are alike. To balance the prognostic factors across different arms, we propose a new dynamic scheme for patient allocation. Our approach does not require discretizing continuous covariates to multiple categories, and can handle both continuous and discrete covariates naturally. This is achieved through devising a statistical measure to characterize the similarity between a new patient and all the existing patients in the trial. Under the similarity weighting scheme, we develop a covariate-adaptive biased coin design and establish its theoretical properties, as well as improving the original Pocock-Simon design. We conduct extensive simulation studies to examine the design operating characteristics and illustrate our method with a real data example. The new approach is demonstrated to be superior to other existing methods in terms of performance.

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Balancing continuous covariates based on Kernel densities

Cited by 27 publications

References 34 publications

Testing Hypotheses of Covariate-Adaptive Randomized Clinical Trials

Testing Hypotheses of Covariate-Adaptive Randomized Clinical Trials

Adaptive randomization for balancing over covariates

Kernel-Based Adaptive Randomization Toward Balance in Continuous and Discrete Covariates

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