A Cluster-Adjusted Rank-Based Test for a Clinical Trial Concerning Multiple Endpoints With Application to Dietary Intervention Assessment

Zhang, Wei Emma; Liu, Aiyi; Tang, Larry; Li, Qizhai

doi:10.1111/biom.13029

Cited by 4 publications

(8 citation statements)

References 37 publications

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“…The CHEF study 1,2 is an 18-month clinical trial where a total of 136 youth aged 8–18 years with type 1 diabetes were randomly divided into two groups: the behavioral intervention group with 66 individuals and the control group with 70 individuals. For the behavioral intervention group, the children’s dietary intake was measured at baseline (months = 0) and every three months after the intervention, and thus there are six observation time points: months= {0,3,6,9,12,18}.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…These 10 dietary outcomes were used to assess the dietary quality, but here, just like Zhang et al. 2 did, we divided each of them by the total calorie and multiplied by 1000.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…The investigators need to make a judgement whether there are significant differences between the treatment and the control groups, or whether the treatment is more significantly effective than a placebo. For example, in the dietary intervention clinical trial, 1,2 researchers are concerned with whether the family-based behavioral intervention affects the outcomes of dietary intake and glycemic control among youth aged 8–18 years with type 1 diabetes. They designed the cultivating healthful eating in families of children with type 1 diabetes study (abbreviated as CHEF study), which is a 18-month clinical trial involving 66 individuals in the intervention group and 70 individuals in the control group.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al. 2 came up with a cluster-adjusted rank-based test for clustered data. Both tests are developed under a specific experiment design.…”

Section: Introductionmentioning

confidence: 99%

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Directional-sum test for nonparametric Behrens-Fisher problem with applications to the dietary intervention trial

Meng

Yang

et al. 2021

Stat Methods Med Res

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For a nonparametric Behrens-Fisher problem, a directional-sum test is proposed based on division-combination strategy. A one-layer wild bootstrap procedure is given to calculate its statistical significance. We conduct simulation studies with data generated from lognormal, t and Laplace distributions to show that the proposed test can control the type I error rates properly and is more powerful than the existing rank-sum and maximum-type tests under most of the considered scenarios. Applications to the dietary intervention trial further show the performance of the proposed test.

show abstract

Section: Simulation Studiesmentioning

confidence: 99%

“…These 10 dietary outcomes were used to assess the dietary quality, but here, just like Zhang et al. 2 did, we divided each of them by the total calorie and multiplied by 1000.…”

Section: Simulation Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Zhang et al. 2 came up with a cluster-adjusted rank-based test for clustered data. Both tests are developed under a specific experiment design.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Directional-sum test for nonparametric Behrens-Fisher problem with applications to the dietary intervention trial

Meng

Yang

et al. 2021

Stat Methods Med Res

Self Cite

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“…They also considered the general nonparametric Behrens-Fisher hypothesis problem. The global rank-sum test was extended to censored outcomes by Ramchandani et al (2016), and extension to clustered multiple endpoints were considered in Zhang et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Longitudinal Modeling of Rank-based Global Outcome

Ding,

Ning,

et al. 2026

STAT SINICA

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Many chronic diseases exhibit multifaceted symptoms that cannot be comprehensively characterized by one outcome. To address this, researchers often adopt a global outcome to combine information from multiple individual outcomes. The global rank-sum facilitates robust integration of multiple outcomes and has been applied in many clinical studies. We consider longitudinal settings and devise a global percentile outcome for depicting patients' time-varying global disease burden. We develop useful regression strategies for the longitudinal global percentile outcome based on a flexible regression framework of the monotonic index model. Posing minimal restrictions, we propose a maximum rank correlation type estimator and show that it entails desirable asymptotic properties.The methods are also extended to accommodate the common missing at random dropout scenarios. We propose a computationally stable and efficient procedure for parameter estimation, as well as a perturbation scheme for consistent variance estimation. Numerical studies show that our method performs well under realistic settings. We apply the proposed method to data from a Parkinson's 1

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A novel longitudinal rank-sum test for multiple primary endpoints in clinical trials: Applications to neurodegenerative disorders

Luo

2023

Preprint

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Clinical trials of many chronic diseases such as Parkinsons disease often collect multiple health outcomes to monitor the disease severity and progression. It is of scientific interest to test whether the experimental treatment has an overall efficacy on the multiple outcomes across time, as compared to placebo or an active control. To compare the multivariate longitudinal outcomes between two groups, the rank-sum test and the variance-adjusted rank-sum test can be used to test the treatment efficacy. But these two rank-based tests, by utilizing only the change from baseline to the last time point, do not fully take advantage of the multivariate longitudinal outcome data, and thus may not objectively evaluate the global treatment effect over the entire therapeutic period. In this paper, we develop rank-based test procedures to detect global treatment efficacy in clinical trials with multiple longitudinal outcomes. We first conduct an interaction test to determine whether treatment effect varies over time, and then propose a longitudinal rank-sum test to assess the main treatment effect either with or without the interaction. Asymptotic properties of the proposed test procedures are derived and thoroughly examined. Simulation studies under various scenarios are performed. The test statistic is motivated by and applied to a recently-completed randomized controlled trial of Parkinsons disease.

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A Cluster-Adjusted Rank-Based Test for a Clinical Trial Concerning Multiple Endpoints With Application to Dietary Intervention Assessment

Cited by 4 publications

References 37 publications

Directional-sum test for nonparametric Behrens-Fisher problem with applications to the dietary intervention trial

Directional-sum test for nonparametric Behrens-Fisher problem with applications to the dietary intervention trial

Longitudinal Modeling of Rank-based Global Outcome

A novel longitudinal rank-sum test for multiple primary endpoints in clinical trials: Applications to neurodegenerative disorders

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