1
|
Gao P, Li Y. Adaptive two-stage seamless sequential design for clinical trials. J Biopharm Stat 2024:1-23. [PMID: 38704845 DOI: 10.1080/10543406.2024.2342518] [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: 04/16/2023] [Accepted: 04/09/2024] [Indexed: 05/07/2024]
Abstract
We propose an adaptive sequential testing procedure for the selection and testing of multiple treatment options, such as dose/regimen, different drugs, sub-populations, endpoints, or a mixture of them in a seamlessly combined phase II/III trial. The selection is to be made at the end of phase 2 stage. Unlike in many of the published literature, the selection rule is not required to be to "select the best", and does not need to be pre-specified, which provides flexibility and allows the trial investigators to use any efficacy and safety information/criteria, or surrogate or intermediate endpoint to make the selection. Sample size and power calculations are provided. The calculations have been confirmed to be accurate by simulations. Interim analysis can be performed after the selection, sample size can be modified if the observed efficacy deviates from the assumed. Inference after the trial, including p-value, median unbiased point estimate and confidence intervals, are provided. By applying a dominance theorem, the procedure can be applied to normal, binary, Poisson, negative binomial distributed endpoints and time-to-event endpoints, and a mixture of these distributions (in trials involving endpoint selection).
Collapse
Affiliation(s)
- Ping Gao
- Biostatistics, Innovatio Statistics, Inc ., Bridgewater, USA
| | - Yingqiu Li
- School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, Hunan, China
| |
Collapse
|
2
|
Research for an Adaptive Classifier Based on Dynamic Graph Learning. Neural Process Lett 2022. [DOI: 10.1007/s11063-021-10452-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
3
|
Posch M, Ristl R, König F. Testing and interpreting the ”right” hypothesis - comment on ”Non-proportional hazards — An evaluation of the MaxCombo Test in cancer clinical trials”. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2090431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Martin Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna
| | - Robin Ristl
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna
| | - Franz König
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna
| |
Collapse
|
4
|
Kunz CU, Jörgens S, Bretz F, Stallard N, Van Lancker K, Xi D, Zohar S, Gerlinger C, Friede T. Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue? Stat Biopharm Res 2020; 12:461-477. [PMID: 34191979 PMCID: PMC8011492 DOI: 10.1080/19466315.2020.1799857] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/17/2020] [Accepted: 07/18/2020] [Indexed: 01/09/2023]
Abstract
Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for ongoing clinical trials in non-COVID-19 conditions. Motivated by four current clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve Type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to use early read-outs; and (v) how to use Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.
Collapse
Affiliation(s)
| | | | - Frank Bretz
- Novartis Pharma AG, Basel, Switzerland
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Nigel Stallard
- Division of Health Sciences, Warwick Medical School, The University of Warwick, Coventry, UK
| | - Kelly Van Lancker
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Dong Xi
- Novartis Pharmaceuticals, East Hanover, NJ
| | - Sarah Zohar
- INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
| | - Christoph Gerlinger
- Statistics and Data Insights, Bayer AG, Berlin, Germany
- Department of Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland, Homburg/Saar, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
| |
Collapse
|
5
|
Xi D, Bretz F. Symmetric graphs for equally weighted tests, with application to the Hochberg procedure. Stat Med 2019; 38:5268-5282. [PMID: 31657025 DOI: 10.1002/sim.8375] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 05/02/2019] [Accepted: 09/02/2019] [Indexed: 01/21/2023]
Abstract
The graphical approach to multiple testing provides a convenient tool for designing, visualizing, and performing multiplicity adjustments in confirmatory clinical trials while controlling the familywise error rate. It assigns a set of weights to each intersection null hypothesis within the closed test framework. These weights form the basis for intersection tests using weighted individual p-values, such as the weighted Bonferroni test. In this paper, we extend the graphical approach to intersection tests that assume equal weights for the elementary null hypotheses associated with any intersection hypothesis, including the Hochberg procedure as well as omnibus tests such as Fisher's combination, O'Brien's, and F tests. More specifically, we introduce symmetric graphs that generate sets of equal weights so that the aforementioned tests can be applied with the graphical approach. In addition, we visualize the Hochberg and the truncated Hochberg procedures in serial and parallel gatekeeping settings using symmetric component graphs. We illustrate the method with two clinical trial examples.
Collapse
Affiliation(s)
- Dong Xi
- Statistical Methodology, Novartis Pharmaceuticals, East Hanover, New Jersey
| | - Frank Bretz
- Statistical Methodology, Novartis Pharma AG, Basel, Switzerland.,Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
6
|
Glimm E, Bezuidenhoudt M, Caputo A, Maurer W. A Testing Strategy With Adaptive Dose Selection and Two Endpoints. Stat Biopharm Res 2018. [DOI: 10.1080/19466315.2018.1497531] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Ekkehard Glimm
- Novartis Pharma AG, Basel, Switzerland
- Otto-von-Guericke-University Magdeburg, Germany
| | | | | | | |
Collapse
|
7
|
Ristl R, Urach S, Rosenkranz G, Posch M. Methods for the analysis of multiple endpoints in small populations: A review. J Biopharm Stat 2018; 29:1-29. [PMID: 29985752 DOI: 10.1080/10543406.2018.1489402] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
While current guidelines generally recommend single endpoints for primary analyses of confirmatory clinical trials, it is recognized that certain settings require inference on multiple endpoints for comprehensive conclusions on treatment effects. Furthermore, combining treatment effect estimates from several outcome measures can increase the statistical power of tests. Such an efficient use of resources is of special relevance for trials in small populations. This paper reviews approaches based on a combination of test statistics or measurements across endpoints as well as multiple testing procedures that allow for confirmatory conclusions on individual endpoints. We especially focus on feasibility in trials with small sample sizes and do not solely rely on asymptotic considerations. A systematic literature search in the Scopus database, supplemented by a manual search, was performed to identify research papers on analysis methods for multiple endpoints with relevance to small populations. The identified methods were grouped into approaches that combine endpoints into a single measure to increase the power of statistical tests and methods to investigate differential treatment effects in several individual endpoints by multiple testing.
Collapse
Affiliation(s)
- Robin Ristl
- a Center for Medical Statistics, Informatics, and Intelligent Systems , Medical University of Vienna , Vienna , Austria
| | - Susanne Urach
- a Center for Medical Statistics, Informatics, and Intelligent Systems , Medical University of Vienna , Vienna , Austria
| | - Gerd Rosenkranz
- a Center for Medical Statistics, Informatics, and Intelligent Systems , Medical University of Vienna , Vienna , Austria
| | - Martin Posch
- a Center for Medical Statistics, Informatics, and Intelligent Systems , Medical University of Vienna , Vienna , Austria
| |
Collapse
|
8
|
Sugitani T, Posch M, Bretz F, Koenig F. Flexible alpha allocation strategies for confirmatory adaptive enrichment clinical trials with a prespecified subgroup. Stat Med 2018; 37:3387-3402. [PMID: 29945304 DOI: 10.1002/sim.7851] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 03/08/2018] [Accepted: 05/25/2018] [Indexed: 02/05/2023]
Abstract
Adaptive enrichment designs have recently received considerable attention as they have the potential to make drug development process for personalized medicine more efficient. Several statistical approaches have been proposed so far in the literature and the operating characteristics of these approaches are extensively investigated using simulation studies. In this paper, we improve on existing adaptive enrichment designs by assigning unequal weights to the significance levels associated with the hypotheses of the overall population and a prespecified subgroup. More specifically, we focus on the standard combination test, a modified combination test, the marginal combination test, and the partial conditional error rate approach and explore the operating characteristics of these approaches by a simulation study. We show that these approaches can lead to power gains, compared to existing approaches, if the weights are chosen carefully.
Collapse
Affiliation(s)
- Toshifumi Sugitani
- Biostatistics Group, Astellas Pharma Inc, Tokyo, Japan.,Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Frank Bretz
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria.,Statistical Methodology, Novartis Pharma AG, Basel, Switzerland
| | - Franz Koenig
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| |
Collapse
|
9
|
Hilgers RD, Bogdan M, Burman CF, Dette H, Karlsson M, König F, Male C, Mentré F, Molenberghs G, Senn S. Lessons learned from IDeAl - 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials. Orphanet J Rare Dis 2018; 13:77. [PMID: 29751809 PMCID: PMC5948846 DOI: 10.1186/s13023-018-0820-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 05/01/2018] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND IDeAl (Integrated designs and analysis of small population clinical trials) is an EU funded project developing new statistical design and analysis methodologies for clinical trials in small population groups. Here we provide an overview of IDeAl findings and give recommendations to applied researchers. METHOD The description of the findings is broken down by the nine scientific IDeAl work packages and summarizes results from the project's more than 60 publications to date in peer reviewed journals. In addition, we applied text mining to evaluate the publications and the IDeAl work packages' output in relation to the design and analysis terms derived from in the IRDiRC task force report on small population clinical trials. RESULTS The results are summarized, describing the developments from an applied viewpoint. The main result presented here are 33 practical recommendations drawn from the work, giving researchers a comprehensive guidance to the improved methodology. In particular, the findings will help design and analyse efficient clinical trials in rare diseases with limited number of patients available. We developed a network representation relating the hot topics developed by the IRDiRC task force on small population clinical trials to IDeAl's work as well as relating important methodologies by IDeAl's definition necessary to consider in design and analysis of small-population clinical trials. These network representation establish a new perspective on design and analysis of small-population clinical trials. CONCLUSION IDeAl has provided a huge number of options to refine the statistical methodology for small-population clinical trials from various perspectives. A total of 33 recommendations developed and related to the work packages help the researcher to design small population clinical trial. The route to improvements is displayed in IDeAl-network representing important statistical methodological skills necessary to design and analysis of small-population clinical trials. The methods are ready for use.
Collapse
Affiliation(s)
- Ralf-Dieter Hilgers
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany.
| | - Malgorzata Bogdan
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Carl-Fredrik Burman
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Holger Dette
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Mats Karlsson
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Franz König
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Christoph Male
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - France Mentré
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Geert Molenberghs
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| | - Stephen Senn
- Department of Medical Statistics, RWTH Aachen University, Pauwelsstr. 19, D-52074, Aachen, Germany
| |
Collapse
|
10
|
Bauer P, Bretz F, Dragalin V, König F, Wassmer G. Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls. Stat Med 2016; 35:325-47. [PMID: 25778935 PMCID: PMC6680191 DOI: 10.1002/sim.6472] [Citation(s) in RCA: 130] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Revised: 02/03/2015] [Accepted: 02/19/2015] [Indexed: 12/26/2022]
Abstract
'Multistage testing with adaptive designs' was the title of an article by Peter Bauer that appeared 1989 in the German journal Biometrie und Informatik in Medizin und Biologie. The journal does not exist anymore but the methodology found widespread interest in the scientific community over the past 25 years. The use of such multistage adaptive designs raised many controversial discussions from the beginning on, especially after the publication by Bauer and Köhne 1994 in Biometrics: Broad enthusiasm about potential applications of such designs faced critical positions regarding their statistical efficiency. Despite, or possibly because of, this controversy, the methodology and its areas of applications grew steadily over the years, with significant contributions from statisticians working in academia, industry and agencies around the world. In the meantime, such type of adaptive designs have become the subject of two major regulatory guidance documents in the US and Europe and the field is still evolving. Developments are particularly noteworthy in the most important applications of adaptive designs, including sample size reassessment, treatment selection procedures, and population enrichment designs. In this article, we summarize the developments over the past 25 years from different perspectives. We provide a historical overview of the early days, review the key methodological concepts and summarize regulatory and industry perspectives on such designs. Then, we illustrate the application of adaptive designs with three case studies, including unblinded sample size reassessment, adaptive treatment selection, and adaptive endpoint selection. We also discuss the availability of software for evaluating and performing such designs. We conclude with a critical review of how expectations from the beginning were fulfilled, and - if not - discuss potential reasons why this did not happen.
Collapse
Affiliation(s)
- Peter Bauer
- Section of Medical StatisticsMedical University of ViennaSpitalgasse 231090 WienAustria
| | - Frank Bretz
- Novartis Pharma AGLichtstrasse 354002BaselSwitzerland
- Shanghai University of Finance and EconomicsChina
| | | | - Franz König
- Section of Medical StatisticsMedical University of ViennaSpitalgasse 231090 WienAustria
| | - Gernot Wassmer
- Aptiv Solutions, an ICON plc companyRobert‐Perthel‐Str. 77a50739KölnGermany
- Institute for Medical Statistics, Informatics and EpidemiologyUniversity of Cologne50924KölnGermany
| |
Collapse
|
11
|
Wang D, Li Y, Wang X, Liu X, Fu B, Lin Y, Larsen L, Offen W. Overview of multiple testing methodology and recent development in clinical trials. Contemp Clin Trials 2015. [PMID: 26210511 DOI: 10.1016/j.cct.2015.07.014] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Multiplicity control is an important statistical issue in clinical trials where strong control of the type I error rate is required. Many multiple testing methods have been proposed and applied to address multiplicity issues in clinical trials. This paper provides an application oriented and comprehensive overview of commonly used multiple testing procedures and recent developments in statistical methodology in multiple testing in clinical trials. Commonly used multiple testing procedures are applied to test non-hierarchical hypotheses and gatekeeping procedures can be used to test hierarchically ordered hypotheses while controlling the overall type I error rate. The recently developed graphical approach has the flexibility to integrate hierarchical and non-hierarchical procedures into one framework. A graphical multiple testing procedure with "no-dead-end" provides an opportunity to fully recycle α across hypothesis families. Two hypothetical clinical trial examples are used to illustrate applications of these procedures. The advantages and disadvantages of the different procedures are briefly discussed.
Collapse
Affiliation(s)
- Deli Wang
- Data and Statistical Science, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064-6075, USA.
| | - Yihan Li
- Data and Statistical Science, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064-6075, USA
| | - Xin Wang
- Data and Statistical Science, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064-6075, USA
| | - Xuan Liu
- Data and Statistical Science, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064-6075, USA
| | - Bo Fu
- Data and Statistical Science, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064-6075, USA
| | - Yunzhi Lin
- Data and Statistical Science, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064-6075, USA
| | - Lois Larsen
- Data and Statistical Science, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064-6075, USA
| | - Walter Offen
- Data and Statistical Science, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064-6075, USA
| |
Collapse
|
12
|
Sugitani T, Bretz F, Maurer W. A simple and flexible graphical approach for adaptive group-sequential clinical trials. J Biopharm Stat 2014; 26:202-16. [PMID: 25372071 DOI: 10.1080/10543406.2014.972509] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
In this article, we introduce a graphical approach to testing multiple hypotheses in group-sequential clinical trials allowing for midterm design modifications. It is intended for structured study objectives in adaptive clinical trials and extends the graphical group-sequential designs from Maurer and Bretz (Statistics in Biopharmaceutical Research 2013; 5: 311-320) to adaptive trial designs. The resulting test strategies can be visualized graphically and performed iteratively. We illustrate the methodology with two examples from our clinical trial practice. First, we consider a three-armed gold-standard trial with the option to reallocate patients to either the test drug or the active control group, while stopping the recruitment of patients to placebo, after having demonstrated superiority of the test drug over placebo at an interim analysis. Second, we consider a confirmatory two-stage adaptive design with treatment selection at interim.
Collapse
Affiliation(s)
- Toshifumi Sugitani
- a Section for Medical Statistics, Medical University of Vienna , Vienna , Austria
| | - Frank Bretz
- b Novartis Pharma AG , Basel , Switzerland.,c Shanghai University of Finance and Economics , Shanghai , Peoples Republic of China
| | | |
Collapse
|