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Gou J. Reverse graphical approaches for multiple test procedures. J Biopharm Stat 2024; 34:90-110. [PMID: 36757196 DOI: 10.1080/10543406.2023.2171428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 01/17/2023] [Indexed: 02/10/2023]
Abstract
The graphical approach has been proposed as a general framework for clinical trial designs involving multiple hypotheses, where decisions are made only based on the observed marginal p -values. The graphical approach starts from a graph that includes all hypotheses as vertices and gradually removes some vertices when their corresponding hypotheses are rejected. In this paper, we propose a reverse graphical approach, which starts from a set of singleton graphs and gradually adds vertices into graphs until rejection of a set of hypotheses is made. Proofs of familywise error rate control are provided. A simulation study is conducted for statistical power analysis, and a case study is included to illustrate how the proposed approach can be applied to clinical studies.
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Affiliation(s)
- Jiangtao Gou
- Department of Mathematics and Statistics, Villanova University, Villanova, PA, USA
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2
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Gou J. On dependence assumption in p-value based multiple test procedures. J Biopharm Stat 2023; 33:596-610. [PMID: 36607042 DOI: 10.1080/10543406.2022.2162066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 12/19/2022] [Indexed: 01/07/2023]
Abstract
There are various multiple comparison procedures used in confirmatory clinical studies and exploratory research for multiplicity adjustment. Among them are the Hochberg and Benjamini-Hochberg procedures. A common misconception is that these procedures control the type I error rate properly if the test statistics are independent or positively correlated. In fact, a much stronger positive dependence assumption needs to be satisfied to guarantee the type I error rate control. We give a comprehensive review of the dependence conditions used in multiple testing procedures. We show that a weaker positive dependence assumption may result an inflation of type I error rate by a factor of 2 and discuss the type I error rate control under certain negative dependence conditions.
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Affiliation(s)
- Jiangtao Gou
- Department of Mathematics and Statistics, Villanova University, Villanova, Pennsylvania, United States
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Luo X, Quan H. Some multiplicity adjustment procedures for clinical trials with sequential design and multiple endpoints. Stat Biopharm Res 2023. [DOI: 10.1080/19466315.2023.2191989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Affiliation(s)
- Xiaodong Luo
- Biostatistics and Programming, Sanofi US, 55 Corporate Drive, Bridgewater, NJ, USA, 08807
| | - Hui Quan
- Biostatistics and Programming, Sanofi US, 55 Corporate Drive, Bridgewater, NJ, USA, 08807
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Gou J. A test of the dependence assumptions for the Simes-test-based multiple test procedures. Stat Biopharm Res 2023. [DOI: 10.1080/19466315.2023.2190930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Affiliation(s)
- Jiangtao Gou
- Department of Mathematics and Statistics, Villanova University
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Tamhane AC, Xi D, Gou J. Group sequential Holm and Hochberg procedures. Stat Med 2021; 40:5333-5350. [PMID: 34636081 DOI: 10.1002/sim.9128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 06/20/2021] [Accepted: 06/21/2021] [Indexed: 11/11/2022]
Abstract
The problem of testing multiple hypotheses using a group sequential procedure often arises in clinical trials. We review several group sequential Holm (GSHM) type procedures proposed in the literature and clarify the relationships between them. In particular, we show which procedures are equivalent or, if different, which are more powerful and what are their pros and cons. We propose a step-up group sequential Hochberg (GSHC) procedure as a reverse application of a particular step-down GSHM procedure. We conducted an extensive simulation study to evaluate the familywise error rate (FWER) and power properties of that GSHM procedure and the GSHC procedure and found that the GSHC procedure controls FWER more closely and is more powerful. All procedures are illustrated with a common numerical example, the data for which are chosen to bring out the differences between them. A real case study is also presented to illustrate application of these procedures. R programs for applying the proposed procedures, additional simulation results, and the proof of the FWER control of the GSHC procedure in a special case are provided in Supplementary Material.
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Affiliation(s)
- Ajit C Tamhane
- Department of Industrial Engineering & Management Sciences, Northwestern University, Evanston, Illinois, USA
| | - Dong Xi
- Statistical Methodology, Novartis Pharmaceuticals, East Hanover, New Jersey, USA
| | - Jiangtao Gou
- Department of Mathematics and Statistics, Villanova University, Villanova, Pennsylvania, USA
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Öhrn F, Niewczas J, Burman CF. Improved group sequential Holm procedures for testing multiple correlated hypotheses over time. J Biopharm Stat 2021; 32:230-246. [PMID: 34686107 DOI: 10.1080/10543406.2021.1979574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Clinical trials can typically feature two different types of multiple inference: testing of more than one null hypothesis and testing at multiple time points. These modes of multiplicity are closely related mathematically but distinct statistically and philosophically. Regulatory agencies require strong control of the family-wise error rate (FWER), the risk of falsely rejecting any null hypothesis at any analysis. The correlations between test statistics at interim analyses and the final analysis are therefore routinely used in group sequential designs to achieve less conservative critical values. However, the same type of correlations between different comparisons, endpoints or sub-populations are less commonly used. As a result, FWER is in practice often controlled conservatively for commonly applied procedures.Repeated testing of the same null hypothesis may give changing results, when the hypothesis is rejected at an interim but accepted at the final analysis. The mathematically correct overall rejection is at odds with an inference theoretic approach and with common sense. We discuss these two issues, of incorporating correlations and how to interpret time-changing conclusions, and provide case studies where power can be increased while adhering to sound statistical principles.
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Affiliation(s)
- Fredrik Öhrn
- Early Biometrics and Statistical Innovation, Data Science and Artificial Intelligence, R&D, AstraZeneca, Gothenburg, Sweden
| | - Julia Niewczas
- Early Biometrics and Statistical Innovation, Data Science and Artificial Intelligence, R&D, AstraZeneca, Gothenburg, Sweden
| | - Carl-Fredrik Burman
- Early Biometrics and Statistical Innovation, Data Science and Artificial Intelligence, R&D, AstraZeneca, Gothenburg, Sweden
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Gou J. Trigger Strategy in Repeated Tests on Multiple Hypotheses. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1947361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Jiangtao Gou
- Department of Mathematics and Statistics, Villanova University, Villanova, PA
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Gou J. Quick Multiple Test Procedures and p-Value Adjustments. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1927825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Jiangtao Gou
- Department of Mathematics and Statistics, Villanova University, Villanova, PA
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Hamasaki T, Bretz F. Statistics in Biopharmaceutical Research Best Papers Award. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1912479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
| | - Frank Bretz
- Clinical Development & Analytics, Novartis Pharma, Basel, Switzerland
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
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Gou J. Sample size optimization and initial allocation of the significance levels in group sequential trials with multiple endpoints. Biom J 2021; 64:301-311. [PMID: 33751645 DOI: 10.1002/bimj.202000081] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 08/09/2020] [Accepted: 10/08/2020] [Indexed: 11/10/2022]
Abstract
We consider multistage tests of multiple hypotheses under a flexible setting of calendar time and information fraction, focusing on the case where there are two hypotheses under testing. Explicit expressions of statistical powers are derived. With a proof of existence and uniqueness of solution, we develop a numerical method to search the optimal sample size. The proposed method allows us to find the suitable allocation of initial significance level along with the minimum sample size for group sequential designs, with and without hierarchical structures among different endpoints.
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Affiliation(s)
- Jiangtao Gou
- Department of Mathematics and Statistics, Villanova University, Villanova, PA, USA
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Hamasaki T, Hung HMJ, Hsiao CF, Evans SR. On selecting the critical boundary functions in group-sequential trials with two time-to-event outcomes. Contemp Clin Trials 2021; 101:106244. [PMID: 33309946 PMCID: PMC7954908 DOI: 10.1016/j.cct.2020.106244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 11/23/2020] [Accepted: 12/06/2020] [Indexed: 11/22/2022]
Abstract
We investigate selection of critical boundary functions for testing the hypotheses of two time-to-event outcomes as both primary endpoints or a primary and a secondary endpoint in group-sequential clinical trials, where (1) the effect sizes of endpoints are unequal, or (2) one endpoint is for short-term evaluation and the other for long-term evaluation. Bonferroni-Holm and fixed-sequence procedures are considered. We assess the effects of the magnitudes of the hazard ratios and the correlation between the endpoints on statistical powers and provide guidance for consideration.
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Affiliation(s)
- Toshimitsu Hamasaki
- The Biostatistics Center and the Innovations in Design, Education, and Analysis Committee (IDEA), the George Washington University, USA; Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, the George Washington University, USA.
| | - H M James Hung
- Division of Biometrics I, Office of Biostatistics, OTS/CDER, Food and Drug Administration, USA
| | - Chin-Fu Hsiao
- Institute of Population Health Sciences, National Health Research Institutes, Taiwan
| | - Scott R Evans
- The Biostatistics Center and the Innovations in Design, Education, and Analysis Committee (IDEA), the George Washington University, USA; Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, the George Washington University, USA
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Nomura S. Sample Size Determination in Group-Sequential Trials Assessing Interim Futility by Intermediate Composite Endpoints. Stat Biopharm Res 2020. [DOI: 10.1080/19466315.2020.1799852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Shogo Nomura
- Department of Biostatistics and Bioinformatics, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, Japan
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13
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Refined critical boundary with enhanced statistical power for non-directional two-sided tests in group sequential designs with multiple endpoints. Stat Pap (Berl) 2019. [DOI: 10.1007/s00362-019-01134-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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