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Ling Y, Tariq MB, Tang K, Aronowski J, Fann Y, Savitz SI, Jiang X, Kim Y. An interpretable framework to identify responsive subgroups from clinical trials regarding treatment effects: Application to treatment of intracerebral hemorrhage. PLOS DIGITAL HEALTH 2024; 3:e0000493. [PMID: 38713647 DOI: 10.1371/journal.pdig.0000493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 03/26/2024] [Indexed: 05/09/2024]
Abstract
Randomized Clinical trials (RCT) suffer from a high failure rate which could be caused by heterogeneous responses to treatment. Despite many models being developed to estimate heterogeneous treatment effects (HTE), there remains a lack of interpretable methods to identify responsive subgroups. This work aims to develop a framework to identify subgroups based on treatment effects that prioritize model interpretability. The proposed framework leverages an ensemble uplift tree method to generate descriptive decision rules that separate samples given estimated responses to the treatment. Subsequently, we select a complementary set of these decision rules and rank them using a sparse linear model. To address the trial's limited sample size problem, we proposed a data augmentation strategy by borrowing control patients from external studies and generating synthetic data. We apply the proposed framework to a failed randomized clinical trial for investigating an intracerebral hemorrhage therapy plan. The Qini-scores show that the proposed data augmentation strategy plan can boost the model's performance and the framework achieves greater interpretability by selecting complementary descriptive rules without compromising estimation quality. Our model derives clinically meaningful subgroups. Specifically, we find those patients with Diastolic Blood Pressure≥70 mm hg and Systolic Blood Pressure<215 mm hg benefit more from intensive blood pressure reduction therapy. The proposed interpretable HTE analysis framework offers a promising potential for extracting meaningful insight from RCTs with neutral treatment effects. By identifying responsive subgroups, our framework can contribute to developing personalized treatment strategies for patients more efficiently.
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Affiliation(s)
- Yaobin Ling
- D.Bradley Mc.Williams School of Biomedical Informatics, UTHealth at Houston, Houston, Texas, United States of America
| | - Muhammad Bilal Tariq
- Department of Neurology, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Kaichen Tang
- D.Bradley Mc.Williams School of Biomedical Informatics, UTHealth at Houston, Houston, Texas, United States of America
| | - Jaroslaw Aronowski
- Institute for Stroke and Cerebrovascular Disease, UTHealth at Houston, Houston, Texas, United States of America
| | - Yang Fann
- Intramural Research Program, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Sean I Savitz
- Institute for Stroke and Cerebrovascular Disease, UTHealth at Houston, Houston, Texas, United States of America
| | - Xiaoqian Jiang
- D.Bradley Mc.Williams School of Biomedical Informatics, UTHealth at Houston, Houston, Texas, United States of America
| | - Yejin Kim
- D.Bradley Mc.Williams School of Biomedical Informatics, UTHealth at Houston, Houston, Texas, United States of America
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Kang YK, Morita S, Satoh T, Ryu MH, Chao Y, Kato K, Chung HC, Chen JS, Muro K, Kang WK, Yeh KH, Yoshikawa T, Oh SC, Bai LY, Tamura T, Lee KW, Hamamoto Y, Kim JG, Chin K, Oh DY, Minashi K, Cho JY, Tsuda M, Sameshima H, Chen LT, Boku N. Exploration of predictors of benefit from nivolumab monotherapy for patients with pretreated advanced gastric and gastroesophageal junction cancer: post hoc subanalysis from the ATTRACTION-2 study. Gastric Cancer 2022; 25:207-217. [PMID: 34480657 PMCID: PMC8732926 DOI: 10.1007/s10120-021-01230-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/04/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND The phase 3 ATTRACTION-2 study demonstrated that nivolumab monotherapy was superior to placebo for patients with pretreated advanced gastric or gastroesophageal junction cancer, but early progression of tumors in some patients was of concern. METHODS This post hoc analysis statistically explored the baseline characteristics of the ATTRACTION-2 patients and extracted a single-factor and double-factor combinations associated with early disease progression or early death. In the extracted patient subgroups, the 3-year restricted mean survival times of progression-free survival and overall survival were compared between the nivolumab and placebo arms. RESULTS Two single factors (age and peritoneal metastasis) were extracted as independent predictors of early progression, but none of them, as a single factor, stratified patients into two subgroups with significant differences in restricted mean survival time. In contrast, two double-factor combinations (serum sodium level and white blood cell count; serum sodium level and neutrophil-lymphocyte ratio) stratifying patients into two subgroups with significant differences in the restricted mean survival time were extracted. Additional exploratory analysis of a triple-factor combination showed that patients aged < 60 years with peritoneal metastasis and low serum sodium levels (approximately 7% of all patients) might receive less benefit from nivolumab, and patients aged ≥ 60 years with no peritoneal metastasis and normal serum sodium levels might receive higher benefit. CONCLUSIONS A combination of age, peritoneal metastasis, and serum sodium level might predict benefit from nivolumab as salvage therapy in advanced gastric or gastroesophageal junction cancer patients, especially less benefit for patients having all three risk factors.
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Affiliation(s)
- Yoon-Koo Kang
- grid.267370.70000 0004 0533 4667Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Satoshi Morita
- grid.258799.80000 0004 0372 2033Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Taroh Satoh
- grid.136593.b0000 0004 0373 3971Frontier Science for Cancer and Chemotherapy, Osaka University Graduate School of Medicine, Suita, Japan
| | - Min-Hee Ryu
- grid.267370.70000 0004 0533 4667Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Yee Chao
- grid.278247.c0000 0004 0604 5314Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ken Kato
- grid.272242.30000 0001 2168 5385Division of Gastrointestinal Medical Oncology, National Cancer Center Hospital, Tokyo, Japan
| | - Hyun Cheol Chung
- grid.15444.300000 0004 0470 5454Department of Internal Medicine, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea
| | - Jen-Shi Chen
- grid.145695.a0000 0004 1798 0922Division of Hematology and Oncology, Department of Internal Medicine, Linkou Chang Gung Memorial Hospital, Chang Gung University, Taoyuan, Taiwan
| | - Kei Muro
- grid.410800.d0000 0001 0722 8444Department of Clinical Oncology, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Won Ki Kang
- grid.264381.a0000 0001 2181 989XDivision of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Kun-Huei Yeh
- grid.412094.a0000 0004 0572 7815Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan
| | - Takaki Yoshikawa
- grid.414944.80000 0004 0629 2905Department of Gastrointestinal Surgery, Kanagawa Cancer Center, Yokohama, Japan ,grid.272242.30000 0001 2168 5385Present Address: Department of Gastric Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Sang Cheul Oh
- grid.222754.40000 0001 0840 2678Division of Hematology and Oncology, Department of Internal Medicine, College of Medicine, Korea University, Seoul, South Korea
| | - Li-Yuan Bai
- grid.254145.30000 0001 0083 6092Division of Hematology and Oncology, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Takao Tamura
- grid.258622.90000 0004 1936 9967Department of Medical Oncology, Faculty of Medicine, Kindai University, Osakasayama, Japan ,grid.258622.90000 0004 1936 9967Present Address: Department of Medical Oncology, Kindai University Nara Hospital, Ikoma, Japan
| | - Keun-Wook Lee
- grid.31501.360000 0004 0470 5905Division of Hematology and Oncology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea
| | - Yasuo Hamamoto
- grid.26091.3c0000 0004 1936 9959Keio Cancer Center, Keio University School of Medicine, Tokyo, Japan
| | - Jong Gwang Kim
- grid.258803.40000 0001 0661 1556Kyungpook National University School of Medicine, Daegu, South Korea
| | - Keisho Chin
- grid.410807.a0000 0001 0037 4131Department of Gastroenterology, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Do-Youn Oh
- grid.31501.360000 0004 0470 5905Seoul National University Hospital, Cancer Research Institute, Seoul National University College of Medicine, Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, South Korea
| | - Keiko Minashi
- grid.418490.00000 0004 1764 921XDepartment of Clinical Trial Promotion, Chiba Cancer Center, Chiba, Japan
| | - Jae Yong Cho
- grid.15444.300000 0004 0470 5454Department of Medical Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Masahiro Tsuda
- grid.417755.50000 0004 0378 375XDepartment of Gastroenterological Oncology, Hyogo Cancer Center, Akashi, Japan
| | - Hiroki Sameshima
- grid.459873.40000 0004 0376 2510Medical Affairs, Ono Pharmaceutical, Co. Ltd., Osaka, Japan
| | - Li-Tzong Chen
- grid.59784.370000000406229172National Institute of Cancer Research, National Health Research Institutes, Tainan, Taiwan ,grid.412019.f0000 0000 9476 5696Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Narikazu Boku
- grid.272242.30000 0001 2168 5385Division of Gastrointestinal Medical Oncology, National Cancer Center Hospital, Tokyo, Japan
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Wang Z, Wang F, Wang C, Zhang J, Wang H, Shi L, Tang Z, Rosner GL. A Bayesian Decision-Theoretic Design for Simultaneous Biomarker-Based Subgroup Selection and Efficacy Evaluation. Stat Biopharm Res 2021; 14:568-579. [PMID: 37197312 PMCID: PMC10187767 DOI: 10.1080/19466315.2021.1873843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
The success of drug development of targeted therapy often hinges on an appropriate selection of the sensitive patient population, mostly based on patients' biomarker levels. At the planning stage of a phase II study, although a potential biomarker may have been identified, a threshold value for defining sensitive patient population is often unavailable for adopting many existing biomarker-guided designs. To address this issue, we propose a two-stage design that allows for simultaneous biomarker threshold selection and efficacy evaluation while accommodating situations where the drug is efficacious in the entire patient population. The design uses a Bayesian decision-theoretic approach and incorporates the benefit and cost considerations of the study into a utility function. The operating characteristics of the proposed design under different scenarios are investigated via simulations. We also provide a discussion on the choice of the benefit and cost parameters in practice.
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Affiliation(s)
- Zheyu Wang
- Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | | | - Chenguang Wang
- Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | | | - Hao Wang
- Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Li Shi
- Alpha Biometrics Consulting, San Diego, CA
| | - Zhuojun Tang
- Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Gary L. Rosner
- Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
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