Over time, clinical trials have increasingly incorporated complex design and analysis elements such as interim analyses, adaptations, multiple endpoints, and sophisticated multiplicity schemes for multiple endpoints and/or treatment arms following the paradigm of frequentist inference. In frequentist clinical trials multiplicity can come from (at least) four sources: multiple looks at the data, multiple endpoints, multiple populations, or multiple treatment comparisons. Normally, Type 1 error control across the multiple hypotheses is implemented to control chance of false positive decisions. To achieve this advanced techniques such as adaptive designs or graphical multiple testing procedures have been developed and are used in the design of clinical trials. However, these methods focus on hypothesis testing while subsequent estimation remains crucial to allow for a benefit-risk assessment and further use of the results by various stakeholders. Through examples, we illustrate challenges in estimation and transparent communication. In general, there are no simple solutions to this conceptual and communicational challenge. The purpose of this paper is to generate awareness of these issues and initiate a discussion about how to address them moving forward.

Abstract Clostridioides difficile (C. difficile) is a major cause of severe diarrhea and colitis usually affecting individuals with dysbiosis. Currently, there is no licensed vaccine nor prophylaxis for C. difficile infection (CDI), while treatment remains challenging. To support immunogenicity assessment and vaccine development programs, we analyzed data from two Sanofi vaccine trials (Phase II and III) with 1,096 participants. We evaluated baseline predictors of 16 seroresponse outcomes, measured 30 days after the third vaccine dose, using five statistical models. Relevant predictors of positive seroresponse after vaccination for both Toxin A and B included lower comorbidity index, age < 65 years, and future CDI risk exposure (i.e., impending hospitalization/nursing facility admission were more relevant than past-exposure history). Higher baseline antibody levels, North American study region, and female sex were mainly related with Toxin B seroresponse. These findings highlight the need for further research to optimize vaccine trial design and personalized vaccination approaches.

Platform trials evaluate multiple treatments within a single trial infrastructure. Such designs have gained a lot of attraction in clinical research. If information gained from platform trials should provide confirmatory evidence for regulatory decisions, control of the Type I error rate is key. One critical issue is information leakage, for example, if any information of the ongoing trial is available, especially if it may impact the further conduct of treatments still in the platform trial and bias their results. This paper evaluates the potential impact of information leakage on the control of the Type I error rate in platform trials with time-to-event endpoints such as overall survival. We explore different strategies how information on the treatment effect of an still ongoing treatment could be obtained if a pre-planned analysis for another arm is conducted. This (leaked) information might be used to decide whether to continue the other arm as planned or conduct its final analysis immediately. By means of clinical trial simulations we evaluate the impact of different levels of information leakage on the Type I error rate. We show how the conditional error principle can be applied to estimate worst case Type I error rate inflation for the different forms of information leakage. We do not aim to quantify the exact maximum Type I error rate inflation but rather to raise awareness of the potential risk for estimation of comparative results. Finally, we discuss the regulatory implications of information leakage and propose strategies to mitigate these risks.

Abstract Background Complex clinical trials offer flexibility in evaluating multiple treatments or diseases simultaneously. These trials often feature adaptive designs, common controls, and the potential to add new arms. However, the increased complexity raises methodological challenges particularly regarding multiple testing or the adequate choice of control groups. Despite guidance from regulatory bodies like the European Medicines Agency and the U.S. Food and Drug Administration, uncertainties remain about the regulatory acceptance of these trials. Methods This systematic review examines scientific advice procedures of products in the remit of the Paul-Ehrlich-Institut for complex clinical trials to highlight key concerns and regulatory feedback. We identified 30 scientific advice procedures corresponding to 29 different complex clinical trials. Results Our findings reveal an increasing number of complex trial designs proposed by applicants. A lack of multiplicity control due to multiple arms was generally considered acceptable by regulators in exploratory trials and if cohorts can be considered independent. Additionally, the use of common control groups is frequently proposed by applicants. The review underscores the importance of pre-planning for new treatment arms, appropriate multiplicity control, and the definition of control groups in trial designs. Conclusion Overall, our findings suggest that regulatory concerns regarding complex trials largely align with those in traditional trial designs, though their complexity requires careful, case-by-case consideration. Early engagement with regulatory agencies can be crucial to ensure the successful design and implementation of these trials.

Interim analyses for group-sequential decision making are prevalent in clinical trials. Methodology is well established and has been routinely implemented over the last decades. Still, confusions and uncertainties on aspects of how to operationalize and interpret interim analyses exist for many stakeholders. In this paper, a team of statisticians from the pharmaceutical industry, academia, and regulatory agencies provide a multi-stakeholder perspective on the key concepts behind interim analyses, with the aim to introduce standard terminology to mitigate misunderstandings and facilitate clearer discussions.

Although platform trials have many benefits, the complexity of these designs may result not only in increased methodological but also regulatory and ethical challenges. These aspects were addressed as part of the IMI project EU Patient-Centric Clinical Trial Platforms (EU-PEARL). We reviewed the available guidelines on platform trials in the European Union and the United States. This is supported and complemented by feedback received from regulatory interactions with the European Medicines Agency and the US Food and Drug Administration. Throughout the project we collected the needs of all relevant stakeholders including ethics committees, regulators, and health technology assessment bodies through active dialog and dedicated stakeholder workshops. Furthermore, we focused on methodological aspects and where applicable identified the corresponding guidance. Learnings from the guideline review, regulatory interactions, and workshops are provided. Based on these, a master protocol template was developed. Issues that still need harmonization or clarification in guidelines or where further methodological research is needed are also presented. These include questions around clinical trial submissions in Europe, the need for multiplicity control across the whole master protocol, the use of non-concurrent controls, and the impact of different randomization schemes. Master protocols are an efficient and patient-centered clinical trial design that can expedite drug development. However, they can also introduce additional operational and regulatory complexities. It is important to understand the different requirements of stakeholders upfront and address them in the trial. While relevant guidance is increasing, early dialog with relevant stakeholders can help to further support such designs.

Technological and scientific innovations in the area of gene and cell therapies, so-called advanced therapy medicinal products (ATMPs), have contributed to the steep increase in treatment options for patients with rare diseases. They offer opportunities to address the underlying genetic defect by gene addition, i.e., the delivery of the gene of interest to the target cells, or by genome editing approaches through direct repair of disease-causing mutations. This paper outlines clinical evidence requirements in the context of marketing authorisations for rare diseases. Two out of fifteen gene therapies that have been approved in the European Union since 2018 are used as case studies: Libmeldy (atidarsagen autotemcel) for the treatment of patients with metachromatic leukodystrophy, and Roctavian (valoctocogen roxaparvovec) for the treatment of patients with haemophilia A. Special aspects of the evaluation of single-arm trials with small sample size and requirements with regard to the isolation and causal attribution of the treatment effect are discussed. The role of clinical data obtained under everyday conditions (real world data) to support the generation of evidence in the pre- and post authorisation phase is critically examined. Furthermore, the paper outlines aspects related to conditional versus standard marketing authorisations as well as aspects related to registry-based non-interventional studies in the context of market and patient access to urgently needed drugs.

The COVID-19 pandemic triggered an unprecedented research effort to develop vaccines and therapeutics. Urgency dictated that development and regulatory assessment were accelerated, while maintaining all standards for quality, safety and efficacy. To speed up evaluation the European Medicines Agency (EMA) implemented "rolling reviews" allowing developers to submit data for assessment as they became available. We discuss the clinical trial designs and the applied statistical approaches in vaccine efficacy trials, focusing on aspects such as multiple testing, interim and updated analyses, and reporting of results for the first four vaccines recommended for approval by the EMA. The fast accrual of COVID-19 cases in the clinical vaccine efficacy trials led to multiple data updates within a short time frame, which had consequences for the evaluation and interpretation of results. Key trial results are discussed in the light of these aspects. Notably, the aspects discussed did not affect the benefit/risk relationship in a meaningful way, which was clearly positive for all four vaccines. Assessment of the development and evaluation of the four vaccine trials during the pandemic has led to a proposal for standardized terminology for trials with multiple analyses and a recommendation to appropriately preplan the timing of primary and updated analyses. For the reporting of updated estimates of vaccine efficacy, we discuss how to best describe the uncertainty around estimates of vaccine efficacy (e.g., via confidence intervals). Finally, we briefly highlight the benefit of a comprehensive discussion on estimands for vaccine efficacy trials. for this article are available online.

Das Spektrum der Behandlungsmöglichkeiten für genetisch bedingte Seltene Erkrankungen hat sich durch technologischen und wissenschaftlichen Fortschritt im Bereich der Gen- und Zelltherapeutika, sogenannter Arzneimittel für neuartige Therapien, wesentlich erweitert. Gentherapeutika zielen darauf ab, die genetische Grundursache der Erkrankung zu behandeln. Neben dem zurzeit noch vorrangigen Konzept der Genaddition, des Einbringens funktionsfähiger Genkopien in die Zielzellen über Genfähren, kann mit der neuen Methode der Genom-Editierung die krankmachende Mutation direkt im Erbgut des betroffenen Patienten korrigiert werden. In diesem Beitrag werden regulatorische Anforderungen an die klinische Evidenz bei Zulassung erläutert und anhand der Zulassung von zwei der fünfzehn seit 2018 in der Europäischen Union zugelassenen Gentherapeutika für Seltene Erkrankungen exemplarisch dargestellt. Am Beispiel der Gentherapeutika Libmeldy (Atidarsagen autotemcel) zur Behandlung von Patienten mit metachromatischer Leukodystrophie und Roctavian (Valoctocogen roxaparvovec) zur Behandlung von Patienten mit Hämophilie A werden Aspekte der Nutzen-Risiko-Bewertung in der Zulassung aufgezeigt. Besonderheiten in der Bewertung von einarmigen Studien mit kleinen Fallzahlen und Anforderungen im Hinblick auf die Isolierung und kausale Zuordnung des Behandlungseffekts werden diskutiert. Die Rolle von unter Alltagsbedingungen gewonnenen klinischen Daten (Real World Data) zur unterstützenden Wissensgenerierung vor und nach Zulassung wird kritisch beleuchtet. Die bedingte Zulassung wird im Hinblick auf das Inverkehrbringen von dringend benötigten Arzneimitteln und den Patientenzugang beleuchtet, und es werden Erfahrungen mit registerbasierten Daten im Hinblick auf regulatorische Anforderungen dargestellt.

Platform trials offer a framework to study multiple interventions in one trial with the opportunity of opening and closing arms. The use of common controls can increase efficiency as compared to individual controls. The need for multiplicity adjustment because of common controls is currently a debate among researchers, pharmaceutical companies, and regulators. The impact of common controls on the type one error in a fixed platform trial, i.e. when all treatments start and end recruitment at the same time, has been discussed in the literature before. We complement these findings by investigating the impact of a common control on the type one error and power in a flexible platform trial, i.e. when one arm joins the platform later. We derived the correlation of test statistics to assess the impact of the overlap and compared the results to a trial with individual controls. Furthermore, we evaluate the power, and the impact of multiplicity adjustment on the power in fixed and flexible platform trials. These methodological considerations are complemented by a regulatory guideline review. With multiple arms, the FWER is inflated when no multiplicity adjustment is applied. However, the FWER inflation is smaller with common controls than with individual controls. Even after multiplicity adjustment, a trial with common controls is often beneficial in terms of sample size and power. However, in some cases, the trial with common controls loses the efficiency gain and it might be advisable to run a separate trial rather than joining a platform trial.

The specification of a particular type of effect (e.g., linear or non-linear) of a covariate in a regression model can be either based on graphical assessment, subject matter knowledge or also on data-driven model choice procedures. For the latter variant, we present a boosting approach that is available for a huge number of different model classes. Boosting is an indirect regularization technique that leads to variable selection and can easily incorporate also non-linear or smooth effects. Furthermore, the algorithm can be adapted in a way to automatically select whether to model a continuous variable with a smooth or a linear effect. We enhance this model choice procedure by trying to compensate the inherent bias towards the more complex effect by incorporating a pragmatic and simple deselection technique that was originally implemented for enhanced variable selection. We illustrate our approach in the analysis of T3 thyroid hormone levels from a larger Galician cohort and investigate its performance in a simulation study.

1 Department of Mathematics, Carl von Ossietzky University Oldenburg, 26111 Oldenburg, Germany, fabian.sobotka@uni-oldenburg.de 2 Department of Mathematics, Humboldt-University Berlin, 12489 Berlin, Germany, mirkov@math.hu-berlin.de 3 Institute for Medical Informatics, Biometrics and Epidemiology, Friedrich Alexander University Erlangen-Nürnberg, 91054 Erlangen, Germany, benjamin.hofner@ imbe.med.uni-erlangen.de 4 Erasmus Medical Center, University Rotterdam, 3015 GE Rotterdam, Netherlands, p.eilers@erasmusmc.nl 5 Department of Economics, Georg August University Göttingen, 37073 Göttingen, Germany, tkneib@uni-goettingen.de

AbstractOn June 28, 2018, the Committee for Advanced Therapies and the Committee for Medicinal Products for Human Use adopted a positive opinion, recommending the granting of a marketing authorization for the medicinal product Yescarta for the treatment of adult patients with relapsed or refractory diffuse large B-cell lymphoma and primary mediastinal large B-cell lymphoma, after two or more lines of systemic therapy. Yescarta, which was designated as an orphan medicinal product and included in the European Medicines Agency's Priority Medicines scheme, was granted an accelerated review timetable.The active substance of Yescarta is axicabtagene ciloleucel, an engineered autologous T-cell immunotherapy product whereby a patient's own T cells are harvested and genetically modified ex vivo by retroviral transduction using a retroviral vector to express a chimeric antigen receptor (CAR) comprising an anti-CD19 single chain variable fragment linked to CD28 costimulatory domain and CD3-zeta signaling domain. The transduced anti-CD19 CAR T cells are expanded ex vivo and infused back into the patient, where they can recognize and eliminate CD19-expressing cells.The benefits of Yescarta as studied in ZUMA-1 phase II (NCT02348216) were an overall response rate per central review of 66% (95% confidence interval, 56%–75%) at a median follow-up of 15.1 months in the intention to treat population and a complete response rate of 47% with a significant duration. The most common adverse events were cytokine release syndrome, neurological adverse events, infections, pyrexia, diarrhea, nausea, hypotension, and fatigue.

Spatial and temporal processes shaping microbial communities are inseparably linked but rarely studied together. By Illumina 16S rRNA sequencing, we monitored soil bacteria in 360 stations on a 100 square meter plot distributed across six intra-annual samplings in a rarely managed, temperate grassland. Using a multi-tiered approach, we tested the extent to which stochastic or deterministic processes influenced the composition of local communities. A combination of phylogenetic turnover analysis and null modeling demonstrated that either homogenization by unlimited stochastic dispersal or scenarios, in which neither stochastic processes nor deterministic forces dominated, explained local assembly processes. Thus, the majority of all sampled communities (82%) was rather homogeneous with no significant changes in abundance-weighted composition. However, we detected strong and uniform taxonomic shifts within just nine samples in early summer. Thus, community snapshots sampled from single points in time or space do not necessarily reflect a representative community state. The potential for change despite the overall homogeneity was further demonstrated when the focus shifted to the rare biosphere. Rare OTU turnover, rather than nestedness, characterized abundance-independent β-diversity. Accordingly, boosted generalized additive models encompassing spatial, temporal and environmental variables revealed strong and highly diverse effects of space on OTU abundance, even within the same genus. This pure spatial effect increased with decreasing OTU abundance and frequency, whereas soil moisture - the most important environmental variable - had an opposite effect by impacting abundant OTUs more than the rare ones. These results indicate that - despite considerable oscillation in space and time - the abundant and resident OTUs provide a community backbone that supports much higher β-diversity of a dynamic rare biosphere. Our findings reveal complex interactions among space, time, and environmental filters within bacterial communities in a long-established temperate grassland.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Heterologous expression of genes requires their adaptation to the host organism to achieve adequate protein synthesis rates. Typically codons are adjusted to resemble those seen in highly expressed genes of the host organism which lacks a deeper understanding of codon optimality. The codon-specific elongation model (COSEM) identifies optimal codon choices by simulating ribosome dynamics during mRNA translation. COSEM is used in combination with machine learning techniques to predict protein abundance and to optimize codon usage.

Background and purpose: This multicenter, phase 3 trial investigates whether the incorporation of concurrent paclitaxel and cisplatin together with a reduced total dose of radiotherapy is superior to standard fluorouracil-cisplatin based CRT. Materials and methods: Patients with SCCHN, stage III-IVB, were randomized to receive paclitaxel/cisplatin (PacCis)-CRT (arm A; paclitaxel 20 mg/m(2) on days 2, 5, 8, 11 and 25, 30, 33, 36; cisplatin 20 mg/m(2), days 1-4 and 29-32; RT to a total dose of 63.6 Gy) or fluorouracil/cisplatin (CisFU)-CRT (arm B; fluorouracil 600 mg/m(2); cisplatin 20 mg/m(2), days 1-5 and 29-33; RT: 70.6 Gy). Endpoint was 3-year-disease free survival (3y-DFS). Results: A total of 221 patients were enrolled between 2010 and 2015. With a median follow-up of 3.7 years, 3y-DFS in the CisFU arm and PacCis arm was 58.2% and 48.4%, respectively (HR 0.82, 95% CI 0.56-1.21, p = 0.52). The 3y-OS amounted to 64.6% in the CisFU arm, and to 59.2% in the PacCis arm (HR 0.82, 95% CI 0.54-1.24, p = 0.43). In the subgroup of p16-positive oropharyngeal carcinomas, 3yDFS and 3y-OS was 84.6% vs 83.9% (p = 0.653), and 92.3% vs. 83.5% (p = 0.76) in arm A and B, respectively. Grade 3-4 hematological toxicities were significantly reduced in arm A (anemia, p = 0.01; leukocytopenia, p = 0.003), whereas grade 3 infections were reduced in arm B (p = 0.01). Conclusion: Paclitaxel/cisplatin-CRT with a reduced RT-dose is not superior to standard fluorouracil/cis platin-CRT. Subgroup analyses indicate that a reduced radiation dose seems to be sufficient for p16+ oropharyngeal cancer or non-smokers. (C) 2020 Elsevier B.V. All rights reserved.

Master protocols have received a growing interest during the last years. By assigning patients to specific substudies, they aim at targeting and accelerating clinical development. Given their complexity, basket, umbrella, and platform designs have raised challenging regulatory and statistical questions, especially the control of multiplicity in confirmatory trials. In basket trials, regulatory assessment of the benefit/risk in pooled populations and choice of the treatment indication is challenging. We provide here our perspectives on these topics. In master protocols, as long as the statistical hypotheses tested between the different substudies are independent, no supplementary adjustment for multiplicity over the different substudies should be required. Moreover, sharing a control arm within an umbrella or a platform trial investigating different drugs would not require a correction for the type I error rate, whereas the chance of multiple false positive regulatory decisions should be recognized. In basket trials, pooling across substudies requires a rationale supporting the intended indication and should be preplanned. Assessment of the benefit/risk in pooled target populations can be complicated by differences in design or in efficacy/safety signals between the substudies. While trials governed by a master protocol can offer logistic and financial advantages, more experience is needed to gain a deeper insight into this novel framework.

Modeling organism distributions from survey data involves numerous statistical challenges, including accounting for zero-inflation, overdispersion, and selection and incorporation of environmental covariates. In environments with high spatial and temporal variability, addressing these challenges often requires numerous assumptions regarding organism distributions and their relationships to biophysical features. These assumptions may limit the resolution or accuracy of predictions resulting from survey-based distribution models. We propose an iterative modeling approach that incorporates a negative binomial hurdle, followed by modeling of the relationship of organism distribution and abundance to environmental covariates using generalized additive models (GAM) and generalized additive models for location, scale, and shape (GAMLSS). Our approach accounts for key features of survey data by separating binary (presence-absence) from count (abundance) data, separately modeling the mean and dispersion of count data, and incorporating selection of appropriate covariates and response functions from a suite of potential covariates while avoiding overfitting. We apply our modeling approach to surveys of sea duck abundance and distribution in Nantucket Sound (Massachusetts, USA), which has been proposed as a location for offshore wind energy development. Our model results highlight the importance of spatiotemporal variation in this system, as well as identifying key habitat features including distance to shore, sediment grain size, and seafloor topographic variation. Our work provides a powerful, flexible, and highly repeatable modeling framework with minimal assumptions that can be broadly applied to the modeling of survey data with high spatiotemporal variability. Applying GAMLSS models to the count portion of survey data allows us to incorporate potential overdispersion, which can dramatically affect model results in highly dynamic systems. Our approach is particularly relevant to systems in which little a priori knowledge is available regarding relationships between organism distributions and biophysical features, since it incorporates simultaneous selection of covariates and their functional relationships with organism responses.