1
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Zimmer F, Henninger M, Debelak R. Sample size planning for complex study designs: A tutorial for the mlpwr package. Behav Res Methods 2024; 56:5246-5263. [PMID: 38030925 PMCID: PMC11289223 DOI: 10.3758/s13428-023-02269-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/09/2023] [Indexed: 12/01/2023]
Abstract
A common challenge in designing empirical studies is determining an appropriate sample size. When more complex models are used, estimates of power can only be obtained using Monte Carlo simulations. In this tutorial, we introduce the R package mlpwr to perform simulation-based power analysis based on surrogate modeling. Surrogate modeling is a powerful tool in guiding the search for study design parameters that imply a desired power or meet a cost threshold (e.g., in terms of monetary cost). mlpwr can be used to search for the optimal allocation when there are multiple design parameters, e.g., when balancing the number of participants and the number of groups in multilevel modeling. At the same time, the approach can take into account the cost of each design parameter, and aims to find a cost-efficient design. We introduce the basic functionality of the package, which can be applied to a wide range of statistical models and study designs. Additionally, we provide two examples based on empirical studies for illustration: one for sample size planning when using an item response theory model, and one for assigning the number of participants and the number of countries for a study using multilevel modeling.
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Affiliation(s)
- Felix Zimmer
- Psychological Methods, Evaluation and Statistics, Department of Psychology, University of Zurich, Binzmuehlestrasse 14, Box 27, 8050, Zurich, Switzerland.
| | - Mirka Henninger
- Psychological Methods, Evaluation and Statistics, Department of Psychology, University of Zurich, Binzmuehlestrasse 14, Box 27, 8050, Zurich, Switzerland
| | - Rudolf Debelak
- Psychological Methods, Evaluation and Statistics, Department of Psychology, University of Zurich, Binzmuehlestrasse 14, Box 27, 8050, Zurich, Switzerland
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2
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Wu L, Lin J. An adaptive seamless 2-in-1 design with biomarker-driven subgroup enrichment. J Biopharm Stat 2024:1-15. [PMID: 38651758 DOI: 10.1080/10543406.2024.2341683] [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: 10/02/2023] [Accepted: 04/05/2024] [Indexed: 04/25/2024]
Abstract
Adaptive seamless phase 2/3 subgroup enrichment design plays a pivotal role in streamlining efficient drug development within a competitive landscape, while also enhancing patient access to promising treatments. This design approach identifies biomarker subgroups with the highest potential to benefit from investigational regimens. The seamless integration of Phase 2 and Phase 3 ensures a timely confirmation of clinical benefits. One significant challenge in adaptive enrichment decisions is determining the optimal timing and maturity of the primary endpoint. In this paper, we propose an adaptive seamless 2-in-1 biomarker-driven subgroup enrichment design that addresses this challenge by allowing subgroup selection using an early intermediate endpoint that predicts clinical benefits (i.e. a surrogate endpoint). The proposed design initiates with a Phase 2 stage involving all participants and can potentially expand into a Phase 3 study focused on the subgroup demonstrating the most favorable clinical outcomes. We will show that, under certain correlation assumptions, the overall type I error may not be inflated at the end of the study. In scenarios where the assumptions may not hold, we present a general framework to control the multiplicity. The flexibility and efficacy of the proposed design are highlighted through an extensive simulation study and illustrated in a case study in multiple myeloma.
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Affiliation(s)
- Liwen Wu
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Jianchang Lin
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
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3
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Kita R, Yanagimoto Y, Imazeki H, Booka E, Tsushima T, Mizusawa J, Sasaki K, Fukuda H, Kurokawa Y, Takeuchi H, Kato K, Kitagawa Y, Boku N, Yoshikawa T, Terashima M. Protocol digest of a randomized controlled adaptive Phase II/III trial of neoadjuvant chemotherapy for Japanese patients with oesophagogastric junction adenocarcinoma: Japan Clinical Oncology Group Study JCOG2203 (NEO-JPEG). Jpn J Clin Oncol 2024; 54:206-211. [PMID: 37952093 DOI: 10.1093/jjco/hyad149] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/10/2023] [Indexed: 11/14/2023] Open
Abstract
Treatment strategies for oesophagogastric junction adenocarcinoma have not been standardized despite its poor prognosis due to differences in the incidence rates between Western countries and Asia. This randomized Phase II/III trial was initiated in June 2023 to determine which neoadjuvant chemotherapy regimen, docetaxel, oxaliplatin and S-1 or fluorouracil, oxaliplatin and docetaxel, is a more promising treatment in Phase II and confirm the superiority of neoadjuvant chemotherapy with docetaxel, oxaliplatin and S-1 or fluorouracil, oxaliplatin and docetaxel followed by surgery and postoperative chemotherapy over upfront surgery and postoperative chemotherapy in terms of overall survival in patients with Clinical Stage III or IVA oesophagogastric junction adenocarcinoma in Phase III. A total of 460 patients, including 150 patients in Phase II and 310 patients in Phase III, are planned to be enrolled from 85 hospitals in Japan over 5 years. This trial has been registered in the Japan Registry of Clinical Trials as jRCTs031230182 (https://jrct.niph.go.jp/latest-detail/jRCTs031230182).
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Affiliation(s)
- Ryosuke Kita
- Japan Clinical Oncology Group Data Center/Operations Office, National Cancer Center Hospital, Tokyo, Japan
| | | | - Hiroshi Imazeki
- Department of Gastroenterology, Chiba Cancer Center, Chiba, Japan
| | - Eisuke Booka
- Department of Surgery, Hamamatsu University School of Medicine, Shizuoka, Japan
| | - Takahiro Tsushima
- Division of Gastrointestinal Oncology, Shizuoka Cancer Center, Shizuoka, Japan
| | - Junki Mizusawa
- Japan Clinical Oncology Group Data Center/Operations Office, National Cancer Center Hospital, Tokyo, Japan
| | - Keita Sasaki
- Japan Clinical Oncology Group Data Center/Operations Office, National Cancer Center Hospital, Tokyo, Japan
| | - Haruhiko Fukuda
- Japan Clinical Oncology Group Data Center/Operations Office, National Cancer Center Hospital, Tokyo, Japan
| | - Yukinori Kurokawa
- Department of Gastroenterological Surgery, Osaka University, Osaka, Japan
| | - Hiroya Takeuchi
- Department of Surgery, Hamamatsu University School of Medicine, Shizuoka, Japan
| | - Ken Kato
- Department of Head and Neck, Esophageal Medical Oncology, National Cancer Center Hospital, Tokyo, Japan
| | - Yuko Kitagawa
- Department of Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Narikazu Boku
- Department of Oncology and General Medicine, Institute of Medical Science, University of Tokyo, Tokyo, Japan
| | - Takaki Yoshikawa
- Gastrointestinal Medical Oncology Division, National Cancer Center Hospital, Tokyo, Japan
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4
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Friedrich S, Friede T. On the role of benchmarking data sets and simulations in method comparison studies. Biom J 2024; 66:e2200212. [PMID: 36810737 DOI: 10.1002/bimj.202200212] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 01/26/2023] [Accepted: 02/01/2023] [Indexed: 02/24/2023]
Abstract
Method comparisons are essential to provide recommendations and guidance for applied researchers, who often have to choose from a plethora of available approaches. While many comparisons exist in the literature, these are often not neutral but favor a novel method. Apart from the choice of design and a proper reporting of the findings, there are different approaches concerning the underlying data for such method comparison studies. Most manuscripts on statistical methodology rely on simulation studies and provide a single real-world data set as an example to motivate and illustrate the methodology investigated. In the context of supervised learning, in contrast, methods are often evaluated using so-called benchmarking data sets, that is, real-world data that serve as gold standard in the community. Simulation studies, on the other hand, are much less common in this context. The aim of this paper is to investigate differences and similarities between these approaches, to discuss their advantages and disadvantages, and ultimately to develop new approaches to the evaluation of methods picking the best of both worlds. To this aim, we borrow ideas from different contexts such as mixed methods research and Clinical Scenario Evaluation.
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Affiliation(s)
- Sarah Friedrich
- Institute of Mathematics, University of Augsburg, Augsburg, Germany
- Centre for Advanced Analytics and Predictive Sciences, University of Augsburg, Augsburg, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Humboldtallee, Göttingen, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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5
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Gera RG, Friede T. Blinded sample size recalculation in multiple composite population designs with normal data and baseline adjustments. Biom J 2023; 65:e2000326. [PMID: 37309256 DOI: 10.1002/bimj.202000326] [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: 10/30/2020] [Revised: 12/13/2022] [Accepted: 03/07/2023] [Indexed: 06/14/2023]
Abstract
The increasing interest in subpopulation analysis has led to the development of various new trial designs and analysis methods in the fields of personalized medicine and targeted therapies. In this paper, subpopulations are defined in terms of an accumulation of disjoint population subsets and will therefore be called composite populations. The proposed trial design is applicable to any set of composite populations, considering normally distributed endpoints and random baseline covariates. Treatment effects for composite populations are tested by combining p-values, calculated on the subset levels, using the inverse normal combination function to generate test statistics for those composite populations while the closed testing procedure accounts for multiple testing. Critical boundaries for intersection hypothesis tests are derived using multivariate normal distributions, reflecting the joint distribution of composite population test statistics given no treatment effect exists. For sample size calculation and sample size, recalculation multivariate normal distributions are derived which describe the joint distribution of composite population test statistics under an assumed alternative hypothesis. Simulations demonstrate the absence of any practical relevant inflation of the type I error rate. The target power after sample size recalculation is typically met or close to being met.
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Affiliation(s)
- Roland G Gera
- Department of Medical Statistics, University Medical Centre Göttingen, Göttingen, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Centre Göttingen, Göttingen, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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6
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Duputel B, Stallard N, Montestruc F, Zohar S, Ursino M. Using dichotomized survival data to construct a prior distribution for a Bayesian seamless Phase II/III clinical trial. Stat Methods Med Res 2023; 32:963-977. [PMID: 36919403 PMCID: PMC10521165 DOI: 10.1177/09622802231160554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Master protocol designs allow for simultaneous comparison of multiple treatments or disease subgroups. Master protocols can also be designed as seamless studies, in which two or more clinical phases are considered within the same trial. They can be divided into two categories: operationally seamless, in which the two phases are separated into two independent studies, and inferentially seamless, in which the interim analysis is considered an adaptation of the study. Bayesian designs are scarcely studied. Our aim is to propose and compare Bayesian operationally seamless Phase II/III designs using a binary endpoint for the first stage and a time-to-event endpoint for the second stage. At the end of Phase II, arm selection is based on posterior (futility) and predictive (selection) probabilities. The results of the first phase are then incorporated into prior distributions of a time-to-event model. Simulation studies showed that Bayesian operationally seamless designs can approach the inferentially seamless counterpart, allowing for an increasing simulated power with respect to the operationally frequentist design.
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Affiliation(s)
- Benjamin Duputel
- Universitè Paris Citè, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, Paris, France
- Inria, HeKA, Paris, France
- eXYSTAT, Malakoff, France
| | - Nigel Stallard
- Warwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK
| | | | - Sarah Zohar
- Universitè Paris Citè, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, Paris, France
- Inria, HeKA, Paris, France
| | - Moreno Ursino
- Universitè Paris Citè, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, Paris, France
- Inria, HeKA, Paris, France
- Unit of Clinical Epidemiology, Assistance Publique-Hôpitaux de Paris, CHU Robert Debrè, Paris, France
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7
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Baldi Antognini A, Frieri R, Zagoraiou M. New insights into adaptive enrichment designs. Stat Pap (Berl) 2023. [DOI: 10.1007/s00362-023-01433-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
AbstractThe transition towards personalized medicine is happening and the new experimental framework is raising several challenges, from a clinical, ethical, logistical, regulatory, and statistical perspective. To face these challenges, innovative study designs with increasing complexity have been proposed. In particular, adaptive enrichment designs are becoming more attractive for their flexibility. However, these procedures rely on an increasing number of parameters that are unknown at the planning stage of the clinical trial, so the study design requires particular care. This review is dedicated to adaptive enrichment studies with a focus on design aspects. While many papers deal with methods for the analysis, the sample size determination and the optimal allocation problem have been overlooked. We discuss the multiple aspects involved in adaptive enrichment designs that contribute to their advantages and disadvantages. The decision-making process of whether or not it is worth enriching should be driven by clinical and ethical considerations as well as scientific and statistical concerns.
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8
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Placzek M, Friede T. Blinded sample size recalculation in adaptive enrichment designs. Biom J 2023; 65:e2000345. [PMID: 35983952 DOI: 10.1002/bimj.202000345] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 09/24/2021] [Accepted: 11/07/2021] [Indexed: 12/17/2022]
Abstract
In the precision medicine era, (prespecified) subgroup analyses are an integral part of clinical trials. Incorporating multiple populations and hypotheses in the design and analysis plan, adaptive designs promise flexibility and efficiency in such trials. Adaptations include (unblinded) interim analyses (IAs) or blinded sample size reviews. An IA offers the possibility to select promising subgroups and reallocate sample size in further stages. Trials with these features are known as adaptive enrichment designs. Such complex designs comprise many nuisance parameters, such as prevalences of the subgroups and variances of the outcomes in the subgroups. Additionally, a number of design options including the timepoint of the sample size review and timepoint of the IA have to be selected. Here, for normally distributed endpoints, we propose a strategy combining blinded sample size recalculation and adaptive enrichment at an IA, that is, at an early timepoint nuisance parameters are reestimated and the sample size is adjusted while subgroup selection and enrichment is performed later. We discuss implications of different scenarios concerning the variances as well as the timepoints of blinded review and IA and investigate the design characteristics in simulations. The proposed method maintains the desired power if planning assumptions were inaccurate and reduces the sample size and variability of the final sample size when an enrichment is performed. Having two separate timepoints for blinded sample size review and IA improves the timing of the latter and increases the probability to correctly enrich a subgroup.
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Affiliation(s)
- Marius Placzek
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.,DZHK (German Center for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
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9
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Granholm A, Kaas-Hansen BS, Lange T, Schjørring OL, Andersen LW, Perner A, Jensen AKG, Møller MH. An overview of methodological considerations regarding adaptive stopping, arm dropping, and randomization in clinical trials. J Clin Epidemiol 2023; 153:45-54. [PMID: 36400262 DOI: 10.1016/j.jclinepi.2022.11.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/17/2022] [Accepted: 11/02/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND AND OBJECTIVES Adaptive features may increase flexibility and efficiency of clinical trials, and improve participants' chances of being allocated to better interventions. Our objective is to provide thorough guidance on key methodological considerations for adaptive clinical trials. METHODS We provide an overview of key methodological considerations for clinical trials employing adaptive stopping, adaptive arm dropping, and response-adaptive randomization. We cover pros and cons of different decisions and provide guidance on using simulation to compare different adaptive trial designs. We focus on Bayesian multi-arm adaptive trials, although the same general considerations apply to frequentist adaptive trials. RESULTS We provide guidance on 1) interventions and possible common control, 2) outcome selection, follow-up duration and model choice, 3) timing of adaptive analyses, 4) decision rules for adaptive stopping and arm dropping, 5) randomization strategies, 6) performance metrics, their prioritization, and arm selection strategies, and 7) simulations, assessment of performance under different scenarios, and reporting. Finally, we provide an example using a newly developed R simulation engine that may be used to evaluate and compare different adaptive trial designs. CONCLUSION This overview may help trialists design better and more transparent adaptive clinical trials and to adequately compare them before initiation.
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Affiliation(s)
- Anders Granholm
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
| | - Benjamin Skov Kaas-Hansen
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Theis Lange
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Olav Lilleholt Schjørring
- Department of Anaesthesia and Intensive Care, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Lars W Andersen
- Research Center for Emergency Medicine, Department of Clinical Medicine, Aarhus University and Aarhus University Hospital, Aarhus, Denmark; Department of Anesthesiology and Intensive Care, Aarhus University Hospital, Aarhus, Denmark; Prehospital Emergency Medical Services, Central Denmark Region, Aarhus, Denmark
| | - Anders Perner
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Aksel Karl Georg Jensen
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Morten Hylander Møller
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
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10
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Zhong C, Li Q, Wu L, Lin J. Using surrogate information to improve confirmatory platform trial with sample size re-estimation. J Biopharm Stat 2022; 32:547-566. [PMID: 35714331 DOI: 10.1080/10543406.2022.2080693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Platform design which allows exploring multiple arms with a common control simultaneously is becoming essential for efficient drug development. However, one of the critical challenges for confirmatory platform trials is immature data for interim decisions, particularly for the treatment arm selection and sample size determination with limited data available. We use a modified conditional power (CP) for both treatment arm selection and sample size determination at interim analysis for the proposed platform trial. The modified CP uses the available data from both primary and surrogate endpoints. We also demonstrated the application in a case study of a lung cancer trial.
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Affiliation(s)
- Chengxue Zhong
- Department of Biostatistics and Data Science, Biostatistics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Qing Li
- Biostatistics and data management, MorphoSys US Inc, Boston, Massachusetts, USA
| | - Liwen Wu
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts
| | - Jianchang Lin
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts
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11
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Richter J, Friede T, Rahnenführer J. Improving adaptive seamless designs through Bayesian optimization. Biom J 2022; 64:948-963. [PMID: 35212423 DOI: 10.1002/bimj.202000389] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 08/29/2021] [Accepted: 10/01/2021] [Indexed: 11/07/2022]
Abstract
We propose to use Bayesian optimization (BO) to improve the efficiency of the design selection process in clinical trials. BO is a method to optimize expensive black-box functions, by using a regression as a surrogate to guide the search. In clinical trials, planning test procedures and sample sizes is a crucial task. A common goal is to maximize the test power, given a set of treatments, corresponding effect sizes, and a total number of samples. From a wide range of possible designs, we aim to select the best one in a short time to allow quick decisions. The standard approach to simulate the power for each single design can become too time consuming. When the number of possible designs becomes very large, either large computational resources are required or an exhaustive exploration of all possible designs takes too long. Here, we propose to use BO to quickly find a clinical trial design with high power from a large number of candidate designs. We demonstrate the effectiveness of our approach by optimizing the power of adaptive seamless designs for different sets of treatment effect sizes. Comparing BO with an exhaustive evaluation of all candidate designs shows that BO finds competitive designs in a fraction of the time.
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Affiliation(s)
- Jakob Richter
- Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany
| | - Tim Friede
- Institut für Medizinische Statistik, Universitätsmedizin Göttingen, Göttingen, Germany.,Deutsches Zentrum für Herz-Kreislauf-Forschung (DZHK), Standort Göttingen, Göttingen, Germany
| | - Jörg Rahnenführer
- Fakultät Statistik, Technische Universität Dortmund, Dortmund, Germany
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12
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Wu L, Li Q, Liu M, Lin J. Incorporating Surrogate Information for Adaptive Subgroup Enrichment Design with Sample Size Re-estimation. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2046150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Liwen Wu
- Takeda Pharmaceuticals, 40 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Qing Li
- MorphoSys US Inc., 470 Atlantic Ave 14th Floor, Boston, MA, 02210, USA
| | - Mengya Liu
- Takeda Pharmaceuticals, 40 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Jianchang Lin
- Takeda Pharmaceuticals, 40 Landsdowne Street, Cambridge, MA, 02139, USA
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13
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Wilkinson J, Stocking K. Study design flaws and statistical challenges in evaluating fertility treatments. REPRODUCTION AND FERTILITY 2022; 2:C9-C21. [PMID: 35128452 PMCID: PMC8812412 DOI: 10.1530/raf-21-0015] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 05/21/2021] [Indexed: 12/16/2022] Open
Abstract
Health interventions should be tested before being introduced into clinical practice, to find out whether they work and whether they are harmful. However, research studies will only provide reliable answers to these questions if they are appropriately designed and analysed. But these are not trivial tasks. We review some methodological challenges that arise when evaluating fertility interventions and explain the implications for a non-statistical audience. These include flexibility in outcomes and analyses; use of surrogate outcomes instead of live birth; use of inappropriate denominators; evaluating cumulative outcomes and time to live birth; allowing each patient or couple to contribute to a research study more than once. We highlight recurring errors and present solutions. We conclude by highlighting the importance of collaboration between clinical and methodological experts, as well as people with experience of subfertility, for realising high-quality research. Lay summary We do research to find out whether fertility treatments are beneficial and to make sure they don't cause harm. However, research will only provide reliable answers if it is done properly. It is not unusual for researchers to make mistakes when they are designing research studies and analysing the data that we get from them. In this review, we describe some of the mistakes people make when they do research about fertility treatments and explain how to avoid them. These include challenges which arise due to the large number of things that can be measured and reported when looking to see if fertility treatments work; failure to check whether the treatment increases the number of live births; failing to include all study participants in calculations;challenges in studies where participants may have more than one treatment attempt. We conclude by highlighting the importance of collaboration between clinical and methodological experts, as well as people with experience of fertility problems.
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Affiliation(s)
- Jack Wilkinson
- Centre for Biostatistics, Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK
| | - Katie Stocking
- Centre for Biostatistics, Division of Population Health, Health Services Research and Primary Care, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK
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14
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Li Q, Lin J, Liu M, Wu L, Liu Y. Using Surrogate Endpoints in Adaptive Designs with Delayed Treatment Effect. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1938203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Qing Li
- Takeda Pharmaceuticals, Cambridge, MA
| | | | | | - Liwen Wu
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA
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15
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Meyer EL, Mesenbrink P, Mielke T, Parke T, Evans D, König F. Systematic review of available software for multi-arm multi-stage and platform clinical trial design. Trials 2021; 22:183. [PMID: 33663579 PMCID: PMC7931508 DOI: 10.1186/s13063-021-05130-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 02/13/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND In recent years, the popularity of multi-arm multi-stage, seamless adaptive, and platform trials has increased. However, many design-related questions and questions regarding which operating characteristics should be evaluated to determine the potential performance of a specific trial design remain and are often further complicated by the complexity of such trial designs. METHODS A systematic search was conducted to review existing software for the design of platform trials, whereby multi-arm multi-stage trials were also included. The results of this search are reported both on the literature level and the software level, highlighting the software judged to be particularly useful. RESULTS In recent years, many highly specialized software packages targeting single design elements on platform studies have been released. Only a few of the developed software packages provide extensive design flexibility, at the cost of limited access due to being commercial or not being usable as out-of-the-box solutions. CONCLUSIONS We believe that both an open-source modular software similar to OCTOPUS and a collaborative effort will be necessary to create software that takes advantage of and investigates the impact of all the flexibility that platform trials potentially provide.
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Affiliation(s)
- Elias Laurin Meyer
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Peter Mesenbrink
- Novartis Pharmaceuticals Corporation, One Health Plaza, East Hanover, NJ, USA
| | | | | | | | - Franz König
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
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16
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Mütze T, Friede T. Data monitoring committees for clinical trials evaluating treatments of COVID-19. Contemp Clin Trials 2020; 98:106154. [PMID: 32961361 PMCID: PMC7833551 DOI: 10.1016/j.cct.2020.106154] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 09/15/2020] [Indexed: 12/15/2022]
Abstract
The first cases of coronavirus disease 2019 (COVID-19) were reported in December 2019 and the outbreak of SARS-CoV-2 was declared a pandemic in March 2020 by the World Health Organization. This sparked a plethora of investigations into diagnostics and vaccination for SARS-CoV-2, as well as treatments for COVID-19. Since COVID-19 is a severe disease associated with a high mortality, clinical trials in this disease should be monitored by a data monitoring committee (DMC), also known as data safety monitoring board (DSMB). DMCs in this indication face a number of challenges including fast recruitment requiring an unusually high frequency of safety reviews, more frequent use of complex designs and virtually no prior experience with the disease. In this paper, we provide a perspective on the work of DMCs for clinical trials of treatments for COVID-19. More specifically, we discuss organizational aspects of setting up and running DMCs for COVID-19 trials, in particular for trials with more complex designs such as platform trials or adaptive designs. Furthermore, statistical aspects of monitoring clinical trials of treatments for COVID-19 are considered. Some recommendations are made regarding the presentation of the data, stopping rules for safety monitoring and the use of external data. The proposed stopping boundaries are assessed in a simulation study motivated by clinical trials in COVID-19.
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Affiliation(s)
- Tobias Mütze
- Statistical Methodology, Novartis Pharma AG, Basel, Switzerland
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany; DZHK (German Center for Cardiovascular Research), partner site Göttingen, Göttingen, Germany.
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17
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First international meeting of early career investigators: Current opportunities, challenges and horizon in critical care nutrition research. Clin Nutr ESPEN 2020; 40:92-100. [PMID: 33183579 DOI: 10.1016/j.clnesp.2020.09.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 09/09/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND Appropriate nutritional support is a key component of care for critically ill patients. While malnutrition increases complications, impacting long term outcomes and healthcare-related costs, uncertainties persist regarding optimal provision of nutritional support in this setting. METHODS An international group of healthcare providers (HCPs) from critical care specialties and nutrition researchers convened to identify knowledge gaps and learnings from studies in critical care nutrition. Clinical research needs were identified in order to better inform future nutrition practices. RESULTS Challenges in critical care nutrition arise, in part, from inconsistent outcomes in several large-scale studies regarding the optimal amount of calories and protein to prescribe, the optimal time to initiate nutritional support and the role of parental nutrition to support critically ill patients. Furthermore, there is uncertainty on how best to identify patients at nutritional risk, and the appropriate outcome measures for ICU nutrition studies. Given HCPs have a suboptimal evidence base to inform the nutritional management of critically ill patients, further well-designed clinical trials capturing clinically relevant endpoints are needed to address these knowledge gaps. CONCLUSIONS The identified aspects for future research could be addressed in studies designed and conducted in collaboration with an international team of interdisciplinary nutrition experts. The aim of this collaboration is to address the unmet need for robust clinical data needed to develop high-quality evidence-based nutritional intervention recommendations to better inform the future management of critically ill patients.
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18
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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.
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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
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19
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Stallard N, Hampson L, Benda N, Brannath W, Burnett T, Friede T, Kimani PK, Koenig F, Krisam J, Mozgunov P, Posch M, Wason J, Wassmer G, Whitehead J, Williamson SF, Zohar S, Jaki T. Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19. Stat Biopharm Res 2020; 12:483-497. [PMID: 34191981 PMCID: PMC8011600 DOI: 10.1080/19466315.2020.1790415] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/23/2020] [Accepted: 06/24/2020] [Indexed: 02/06/2023]
Abstract
The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.
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Affiliation(s)
- Nigel Stallard
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Lisa Hampson
- Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland
| | - Norbert Benda
- The Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany
| | - Werner Brannath
- Institute for Statistics, University of Bremen, Bremen, Germany
| | - Thomas Burnett
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Peter K. Kimani
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
| | - Franz Koenig
- Section for Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | - Johannes Krisam
- Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany
| | - Pavel Mozgunov
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Martin Posch
- Section for Medical Statistics, CeMSIIS, Medical University of Vienna, Vienna, Austria
| | - James Wason
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | | | - John Whitehead
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - S. Faye Williamson
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Sarah Zohar
- INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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