Thresholding of a Continuous Companion Diagnostic Test Confident of Efficacy in Targeted Population

Liu, Yi; Tang, Szu-Yu; Man, Michael; Li, Ying Grace; Ruberg, Stephen J.; Kaizar, Eloise; Hsu, Jason C.

doi:10.1080/19466315.2016.1206486

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…The current version of our method only handles discrete markers and more work is required to generalize it for continuous markers. In doing that, one may borrow the idea from Liu et al 50 which considers all candidate thresholds for a continuous marker when deriving simultaneous confidence intervals. Note that, unlike machine-learning-based methods, our CE4 method is not designed to test multiple markers simultaneously in a joint model.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Identification and inference for subgroups with differential treatment efficacy from randomized controlled trials with survival outcomes through multiple testing

Yue

Hsu

Chen

et al. 2021

Statistics in Medicine

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With the uptake of targeted therapies, instead of the "one-fits-all" approach, modern randomized controlled trials (RCTs) often aim to develop treatments that target a subgroup of patients. Motivated by analyzing the Age-Related Eye Disease Study (AREDS) data, a large RCT to study the efficacy of nutritional supplements in delaying the progression of an eye disease, age-related macular degeneration (AMD), we develop a simultaneous inference procedure to identify and infer subgroups with differential treatment efficacy in RCTs with time-to-event outcomes. Specifically, we formulate the multiple testing problem through contrasts and construct their simultaneous confidence intervals, which appropriately control both within-and across-marker multiplicity. Realistic simulations are conducted using real genotype data to evaluate the method performance under various scenarios. The method is then applied to AREDS to assess the efficacy of antioxidants and zinc combination in delaying AMD progression. Multiple gene regions including ESRRB-VASH1 on chromosome 14 have been identified with subgroups showing differential efficacy. We further validate our findings in an independent subsequent RCT, AREDS2, by discovering consistent differential treatment responses in the targeted and non-targeted subgroups identified from AREDS. This multiple-testing-based simultaneous inference approach provides a step forward to confidently identify and infer subgroups in modern drug development.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Identification and inference for subgroups with differential treatment efficacy from randomized controlled trials with survival outcomes through multiple testing

Yue

Hsu

Chen

et al. 2021

Statistics in Medicine

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show abstract

“…For three cut‐points, at the

95\%

confidence level, the Liu et al. (2016) multiplicity‐adjusted quantile is about 2.4 to 2.5. At the

95\%

confidence level, the multiplicity‐adjusted quantile of this article, adjusting for the multiplicity of simultaneous confidence intervals for efficacy

\eta _{g^+_c}

\eta _{g^-_c}

, and

\eta _{\lbrace all\rbrace }

for each of infinitely many cut‐point values

c \in [a,b]

, is about 2.2 to 2.3.…”

Section: A Qualitative Comparison With Previous Approachesmentioning

confidence: 89%

“…The method in Liu et al. (2016), which fits an analogous two‐way ANOVA model but does not make the confidence intervals have equal width across all cut‐points, is not target monotone either, as one can deduce from table 2 in Liu et al. (2016).…”

Section: Deriving Confidence Intervals For η{All}$\eta _{\lbrace All ...mentioning

confidence: 99%

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Exact simultaneous confidence intervals for logical selection of a biomarker cut‐point

et al. 2021

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This article proposes four new principles for logical biomarker cut‐point selection methods to adhere to: subgroup sensibility, sensitivity, specificity, and target monotonicity. At every cut‐point value, our method gives confidence intervals not only for the efficacy at that cut‐point value, but also efficacies in the marker‐positive and marker‐negative subgroups defined by that cut‐point. These confidence intervals are given simultaneously for all possible cut‐point values. Using Alzheimer's disease (AD) and type 2 diabetes (T2DM) as examples, we show our method achieves the four principles. Our method strongly controls familywise type I error rate (FWER) across both levels of multiplicity: the multiplicity of having marker‐positive and marker‐negative subgroups at each cut‐point, and the multiplicity of searching through infinitely many cut‐points. This is in contrast to other available methods. The confidence level of our simultaneous confidence intervals is in fact exact (not conservative). An application (app) is available, which implements the method we propose.

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“…The current version of our method only handles discrete markers and more work is required to generalize it for continuous markers. In doing that, one may borrow the idea from Liu et al (2016) which considers all candidate thresholds for a continuous marker when deriving simultaneous confidence intervals.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A Simultaneous Inference Procedure to Identify Subgroups from RCTs with Survival Outcomes: Application to Analysis of AMD Progression Studies

Wei,

Hsu,

Chen

et al. 2020

Preprint

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With the uptake of targeted therapies, instead of the "one-fits-all" approach, modern randomized clinical trials (RCTs) often aim to develop treatments that target a subgroup of patients. Motivated by analyzing the Age-Related Eye Disease Study (AREDS) data, a large RCT to study the efficacy of nutritional supplements in delaying the progression of an eye disease, age-related macular degeneration (AMD), we develop a simultaneous inference procedure to identify and infer subgroups with differential treatment efficacy in RCTs with survival outcome. Specifically, we formulate the multiple testing problem through contrasts and construct their simultaneous confidence intervals, which control both within-and across-marker multiplicity appropriately. Realistic simulations are conducted using real genotype data to evaluate the method performance under various scenarios. The method is then applied to AREDS to assess the efficacy of antioxidants and zinc combination in delaying AMD progression. Multiple gene regions including ESRRB-VASH1 on chromosome 14 have been identified with subgroups showing differential efficacy. We further validate our findings in an independent subsequent RCT, AREDS2, by discovering consistent differential treatment responses in the targeted and nontargeted subgroups been identified from AREDS. This simultaneous inference approach provides a step forward to confidently identify and infer subgroups in modern drug development.

show abstract

Thresholding of a Continuous Companion Diagnostic Test Confident of Efficacy in Targeted Population

Cited by 5 publications

References 13 publications

Identification and inference for subgroups with differential treatment efficacy from randomized controlled trials with survival outcomes through multiple testing

Identification and inference for subgroups with differential treatment efficacy from randomized controlled trials with survival outcomes through multiple testing

Exact simultaneous confidence intervals for logical selection of a biomarker cut‐point

A Simultaneous Inference Procedure to Identify Subgroups from RCTs with Survival Outcomes: Application to Analysis of AMD Progression Studies

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