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Article: Personalized Risk-Based Screening Design for Comparative Two-Arm Group Sequential Clinical Trials.

Park, Yeonhee

2022 Volume 12, Issue 3

Abstract: Personalized medicine has been emerging to take into account individual variability in genes and environment. In the era of personalized medicine, it is critical to incorporate the patients' characteristics and improve the clinical benefit for patients. ... ...

Abstract	Personalized medicine has been emerging to take into account individual variability in genes and environment. In the era of personalized medicine, it is critical to incorporate the patients' characteristics and improve the clinical benefit for patients. The patients' characteristics are incorporated in adaptive randomization to identify patients who are expected to get more benefit from the treatment and optimize the treatment allocation. However, it is challenging to control potential selection bias from using observed efficacy data and the effect of prognostic covariates in adaptive randomization. This paper proposes a personalized risk-based screening design using Bayesian covariate-adjusted response-adaptive randomization that compares the experimental screening method to a standard screening method based on indicators of having a disease. Personalized risk-based allocation probability is built for adaptive randomization, and Bayesian adaptive decision rules are calibrated to preserve error rates. A simulation study shows that the proposed design controls error rates and yields a much smaller number of failures and a larger number of patients allocated to a better intervention compared to existing randomized controlled trial designs. Therefore, the proposed design performs well for randomized controlled clinical trials under personalized medicine.
Language	English
Publishing date	2022-03-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662248-8
ISSN	2075-4426
ISSN	2075-4426
DOI	10.3390/jpm12030448
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Interval design to identify the optimal biological dose for immunotherapy.

Park, Yeonhee

Contemporary clinical trials communications

2022 Volume 30, Page(s) 101005

Abstract: Immunotherapeutics have revolutionized the treatment of metastatic cancers and are expected to play an increasingly prominent role in the treatment of cancer patients. Recent advances in checkpoint inhibition show promising early results in a number of ... ...

Abstract	Immunotherapeutics have revolutionized the treatment of metastatic cancers and are expected to play an increasingly prominent role in the treatment of cancer patients. Recent advances in checkpoint inhibition show promising early results in a number of malignancies, and several treatments have been approved for use. However, the immunotherapeutic agents have been shown to have different mechanisms of antitumor activity from cytotoxic agents, and many limitations and challenges encountered in the traditional paradigm were recently pointed out for immunotherapy. I propose a desirability-based method to determine the optimal biological dose of immunotherapeutics by effectively using toxicity, immune response, and tumor response. Moreover, a new dose allocation algorithm of interval designs is proposed to incorporate immune response in addition to toxicity and tumor response. Simulation studies show that the proposed design has desirable operating characteristics compared to existing dose-finding designs. It also inherits the strengths of interval designs for dose-finding trials, yielding good performance with ease of implementation.
Language	English
Publishing date	2022-09-24
Publishing country	Netherlands
Document type	Journal Article
ISSN	2451-8654
ISSN (online)	2451-8654
DOI	10.1016/j.conctc.2022.101005
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Article: Challenges and opportunities in biomarker-driven trials: adaptive randomization.

Park, Yeonhee

Annals of translational medicine

2022 Volume 10, Issue 18, Page(s) 1035

Abstract: In an era of precision medicine, as advanced technology such as molecular profiling at individual patient level has been developed and become increasingly accessible and affordable, biomarker-driven trials have been received a lot of attention and are ... ...

Abstract	In an era of precision medicine, as advanced technology such as molecular profiling at individual patient level has been developed and become increasingly accessible and affordable, biomarker-driven trials have been received a lot of attention and are expected to receive more attention in order to integrate clinical practice with clinical research. Biomarkers play a critical role to identify patients who are expected to get benefit from a treatment, and it is important to effectively incorporate the biomarkers into clinical trials to understand the biomarker-treatment relationship and increase the efficiency. We investigate incorporating biomarkers in adaptive randomization to identify patients who would respond better to the treatment and optimize the treatment allocation. The covariate-adjusted variants of the existing response-adaptive randomization are used to implement biomarker-driven randomization, and the performance of the biomarker-driven randomization is compared with the existing randomization methods, such as traditional fixed randomization with equal probability and response-adaptive randomization without incorporating biomarkers, under the group sequential design allowing early stopping due to superiority and futility. Various scenarios are taken into account to see the impact of the biomarker-driven randomization in the simulation study. It shows that the overall type I error rate is likely to be inflated by the effect of prognostic biomarkers. Several suggestions and considerations for the challenges are discussed to maintain the type I error rate at the nominal level.
Language	English
Publishing date	2022-10-20
Publishing country	China
Document type	Clinical Trial ; Journal Article ; Review
ZDB-ID	2893931-1
ISSN	2305-5847 ; 2305-5839
ISSN (online)	2305-5847
ISSN	2305-5839
DOI	10.21037/atm-21-6027
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Article ; Online: Optimal two-stage design of single arm Phase II clinical trials based on median event time test.

Park, Yeonhee

PloS one

2021 Volume 16, Issue 2, Page(s) e0246448

Abstract: The Phase II clinical trials aim to assess the therapeutic efficacy of a new drug. The therapeutic efficacy has been often quantified by response rate such as overall response rate or survival probability in the Phase II setting. However, there is a ... ...

Abstract	The Phase II clinical trials aim to assess the therapeutic efficacy of a new drug. The therapeutic efficacy has been often quantified by response rate such as overall response rate or survival probability in the Phase II setting. However, there is a strong desire to use survival time, which is the gold standard endpoint for the confirmatory Phase III study, when investigators set the primary objective of the Phase II study and test hypotheses based on the median survivals. We propose a method for median event time test to provide the sample size calculation and decision rule of testing. The decision rule is simple and straightforward in that it compares the observed median event time to the identified threshold. Moreover, it is extended to optimal two-stage design for practice, which extends the idea of Simon's optimal two-stage design for survival endpoint. We investigate the performance of the proposed methods through simulation studies. The proposed methods are applied to redesign a trial based on median event time for trial illustration, and practical strategies are given for application of proposed methods.
MeSH term(s)	Clinical Trials, Phase II as Topic/methods ; Computer Simulation ; Data Interpretation, Statistical ; Endpoint Determination ; Humans ; Probability ; Research Design ; Sample Size
Language	English
Publishing date	2021-02-08
Publishing country	United States
Document type	Journal Article
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0246448
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Article ; Online: Data-driven monitoring for phase II clinical trial designs based on percentile event time test.

Park, Yeonhee / Xu, Zhanpeng

Journal of biopharmaceutical statistics

2023 , Page(s) 1–20

Abstract: The goal of phase II clinical trials is to evaluate the therapeutic efficacy of a new drug. Some investigators want to use the time-to-event endpoint as the primary endpoint of the phase II study to see the improvement of the therapeutic efficacy of a ... ...

Abstract	The goal of phase II clinical trials is to evaluate the therapeutic efficacy of a new drug. Some investigators want to use the time-to-event endpoint as the primary endpoint of the phase II study to see the improvement of the therapeutic efficacy of a new drug in median survival time. Recently, median event time test (METT) has been proposed to provide a simple and straightforward rule which compares the observed median survival time with the prespecified threshold. However, median survival time would not be observed during the trial if the drug performs well and indeed cures most patients or if the accrual rate is so fast. To address the issues in clinical practice, we first propose a percentile event time test (PETT), which generalizes METT to any percentile of the survival time, and develop data-driven monitoring for phase II clinical trial designs based on PETT. We evaluate the performance of the method through simulations and illustrate the proposed method with a trial example.
Language	English
Publishing date	2023-12-22
Publishing country	England
Document type	Journal Article
ZDB-ID	1131763-2
ISSN	1520-5711 ; 1054-3406
ISSN (online)	1520-5711
ISSN	1054-3406
DOI	10.1080/10543406.2023.2292209
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Article ; Online: Sample size determination and evaluation for two-stage adaptive designs of single arm clinical trials based on median event time test.

Park, Yeonhee / Chen, Yi

Contemporary clinical trials communications

2023 Volume 36, Page(s) 101225

Abstract: Clinical trials play a critical role in drug development which involves a series of phases and requires a significant amount of time and effort. Efficient clinical trial designs are necessary to investigate a new drug. Investigators strongly desire to ... ...

Abstract	Clinical trials play a critical role in drug development which involves a series of phases and requires a significant amount of time and effort. Efficient clinical trial designs are necessary to investigate a new drug. Investigators strongly desire to use the time-to-event endpoint as the primary endpoint for Phase II studies, which evaluates the therapeutic efficacy of the new drug, with the hypothesis that the new drug improves the median survival time. The one-sample log-rank test has been used for single-arm Phase II trials, but it generally requires more samples. Recently, the median event time test was proposed to provide a simple, straightforward decision rule, which compares the observed median survival time for the new drug with the threshold, which is determined through the numerical search. We improve the computation of the method for the two-stage design of single-arm clinical trials based on the median event time test. By utilizing the large sample theory of order statistics, we provide the explicit formulas to calculate the sample size for the first and second stages and propose the testing procedure. The performance of the proposed method is evaluated through simulations and a trial example.
Language	English
Publishing date	2023-10-29
Publishing country	Netherlands
Document type	Journal Article
ISSN	2451-8654
ISSN (online)	2451-8654
DOI	10.1016/j.conctc.2023.101225
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Article: Immune Evasion of G-CSF and GM-CSF in Lung Cancer.

Park, Yeonhee / Chung, Chaeuk

Tuberculosis and respiratory diseases

2023 Volume 87, Issue 1, Page(s) 22–30

Abstract: Tumor immune evasion is a complex process that involves various mechanisms, such as antigen recognition restriction, immune system suppression, and T cell exhaustion. The tumor microenvironment contains various immune cells involved in immune evasion. ... ...

Abstract	Tumor immune evasion is a complex process that involves various mechanisms, such as antigen recognition restriction, immune system suppression, and T cell exhaustion. The tumor microenvironment contains various immune cells involved in immune evasion. Recent studies have demonstrated that granulocyte colony-stimulating factor (G-CSF) and granulocyte-macrophage colony-stimulating factor (GM-CSF) induce immune evasion in lung cancer by modulating neutrophils and myeloid-derived suppressor cells. Here we describe the origin and function of G-CSF and GM-CSF, particularly their role in immune evasion in lung cancer. In addition, their effects on programmed death-ligand 1 expression and clinical implications are discussed.
Language	English
Publishing date	2023-09-20
Publishing country	Korea (South)
Document type	Journal Article
ZDB-ID	2161256-0
ISSN	1738-3536 ; 0378-0066
ISSN	1738-3536 ; 0378-0066
DOI	10.4046/trd.2023.0037
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Zs.A 5937: Show issues

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Article ; Online: A randomized group sequential enrichment design for immunotherapy and targeted therapy.

Park, Yeonhee / Liu, Suyu

Contemporary clinical trials

2022 Volume 116, Page(s) 106742

Abstract: In targeted therapy or immunotherapy, it is common that only a subpopulation of patients are sensitive to and thus may benefit from the therapy. In practice, based on pre-clinical data, it is often assumed that the sensitive subpopulation is known. ... ...

Abstract	In targeted therapy or immunotherapy, it is common that only a subpopulation of patients are sensitive to and thus may benefit from the therapy. In practice, based on pre-clinical data, it is often assumed that the sensitive subpopulation is known. Subsequent clinical trial data, however, often show that this assumptions is false. We propose a randomized, group sequential enrichment (GSE) design to evaluate an experimental treatment against a control. The GSE design starts by enrolling patients under broad eligibility criteria and then alters the entry criteria to restrict enrollment to treatment-sensitive patients based on accumulating data of short-term and long-term survival endpoints. The short-term endpoint is used to facilitate enrichment when a limited number of survival events are observed, while the final determination of treatment efficacy and sensitive subpopulation is based on the survival. The group sequential approach is adopted to implement the adaptive enrichment strategy. The proposed design simultaneously achieves two primary goals of precision medicine: to determine whether the experimental drug is superior to the control and to identify the target population that is sensitive to the treatment. A simulation study shows that the proposed design well controls the type I error rate and yields substantially higher power than the conventional group sequential design and existing two-stage enrichment design.
MeSH term(s)	Computer Simulation ; Humans ; Immunotherapy ; Research Design ; Treatment Outcome
Language	English
Publishing date	2022-04-08
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2182176-8
ISSN	1559-2030 ; 1551-7144
ISSN (online)	1559-2030
ISSN	1551-7144
DOI	10.1016/j.cct.2022.106742
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Zs.A 1581: Show issues

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Article: A Markov decision process for response-adaptive randomization in clinical trials

Merrell, David / Chandereng, Thevaa / Park, Yeonhee

Computational statistics & data analysis. 2023 Feb., v. 178

2023

Abstract: In clinical trials, response-adaptive randomization (RAR) has the appealing ability to assign more subjects to better-performing treatments based on interim results. Traditional RAR strategies alter the randomization ratio on a patient-by-patient basis. ... ...

Abstract	In clinical trials, response-adaptive randomization (RAR) has the appealing ability to assign more subjects to better-performing treatments based on interim results. Traditional RAR strategies alter the randomization ratio on a patient-by-patient basis. An alternate approach is blocked RAR, which groups patients together in blocks and recomputes the randomization ratio in a block-wise fashion; past works show that this provides robustness against time-trend bias. However, blocked RAR poses additional questions: how many blocks should there be, and how many patients should each block contain? TrialMDP is an algorithm that designs two-armed blocked RAR clinical trials. It differs from other trial design approaches in that it optimizes the size and number of blocks in addition to their treatment allocations. More precisely, the algorithm yields an adaptive policy that chooses the size and allocation ratio of the next block, based on results seen up to that point in the trial. TrialMDP is related to past works that compute optimal trial designs via dynamic programming. The algorithm maximizes a utility function balancing (i) statistical power, (ii) patient outcomes, and (iii) the number of blocks. It attains significant improvements in utility over a suite of baseline designs, and gives useful control over the tradeoff between statistical power and patient outcomes. It is well suited for small trials that assign high cost to patient failures.
Keywords	algorithms ; data analysis ; issues and policy ; patients ; statistics ; utility functions
Language	English
Dates of publication	2023-02
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	1478763-5
ISSN	0167-9473
ISSN	0167-9473
DOI	10.1016/j.csda.2022.107599
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Envelope-based partial partial least squares with application to cytokine-based biomarker analysis for COVID-19.

Park, Yeonhee / Su, Zhihua / Chung, Dongjun

Statistics in medicine

2022 Volume 41, Issue 23, Page(s) 4578–4592

Abstract: Partial least squares (PLS) regression is a popular alternative to ordinary least squares regression because of its superior prediction performance demonstrated in many cases. In various contemporary applications, the predictors include both continuous ... ...

Abstract	Partial least squares (PLS) regression is a popular alternative to ordinary least squares regression because of its superior prediction performance demonstrated in many cases. In various contemporary applications, the predictors include both continuous and categorical variables. A common practice in PLS regression is to treat the categorical variable as continuous. However, studies find that this practice may lead to biased estimates and invalid inferences (Schuberth et al., 2018). Based on a connection between the envelope model and PLS, we develop an envelope-based partial PLS estimator that considers the PLS regression on the conditional distributions of the response(s) and continuous predictors on the categorical predictors. Root-n consistency and asymptotic normality are established for this estimator. Numerical study shows that this approach can achieve more efficiency gains in estimation and produce better predictions. The method is applied for the identification of cytokine-based biomarkers for COVID-19 patients, which reveals the association between the cytokine-based biomarkers and patients' clinical information including disease status at admission and demographical characteristics. The efficient estimation leads to a clear scientific interpretation of the results.
MeSH term(s)	Biomarkers ; COVID-19/diagnosis ; Cytokines ; Humans ; Least-Squares Analysis
Chemical Substances	Biomarkers ; Cytokines
Language	English
Publishing date	2022-07-15
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.9526
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