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Abstract Purpose In the context of clinical research, there is an increasing need for new study designs that help to incorporate already available data. With the help of historical controls, the existing information can be utilized to support the new study design, but of course, inclusion also carries the risk of bias in the study results. Methods To combine historical and randomized controls we investigate the Fill-it-up-design, which in the first step checks the comparability of the historical and randomized controls performing an equivalence pre-test. If equivalence is confirmed, the historical control data will be included in the new RCT. If equivalence cannot be confirmed, the historical controls will not be considered at all and the randomization of the original study will be extended. We are investigating the performance of this study design in terms of type I error rate and power. Results We demonstrate how many patients need to be recruited in each of the two steps in the Fill-it-up-design and show that the family wise error rate of the design is kept at 5 $$\%$$ % . The maximum sample size of the Fill-it-up-design is larger than that of the single-stage design without historical controls and increases as the heterogeneity between the historical controls and the concurrent controls increases. Conclusion The two-stage Fill-it-up-design represents a frequentist method for including historical control data for various study designs. As the maximum sample size of the design is larger, a robust prior belief is essential for its use. The design should therefore be seen as a way out in exceptional situations where a hybrid design is considered necessary.

IntroductionA clear immune correlate of protection from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has not been defined. We explored antibody, B-cell, and T-cell responses to the third-dose vaccine and relationship to incident SARS-CoV-2 infection.MethodsAdults in a prospective cohort provided blood samples at day 0, day 14, and 10 months after the third-dose SARS-CoV-2 vaccine. Participants self-reported incident SARS-CoV-2 infection. Plasma anti–SARS-CoV-2 receptor-binding domain (RBD) and spike-subunit-1 and spike-subunit-2 antibodies were measured. A sub-study assessed SARS-CoV-2–specific plasma and memory B-cell and memory T-cell responses in peripheral blood mononuclear cells by enzyme-linked immunospot. Comparative analysis between participants who developed incident infection and uninfected participants utilised non-parametric t-tests, Kaplan–Meier survival analysis, and Cox proportional hazard ratios.ResultsOf the 132 participants, 47 (36%) reported incident SARS-CoV-2 infection at a median 16.5 (16.25–21) weeks after the third-dose vaccination. RBD titres and B-cell responses, but not T-cell responses, increased after the third-dose vaccine. Whereas no significant difference in day 14 antibody titres or T-cell responses was observed between participants with and without incident SARS-CoV-2 infection, RBD memory B-cell frequencies were significantly higher in those who did not develop infection [10.0% (4.5%–16.0%) versus 4.9% (1.6%–9.3%), p = 0.01]. RBD titres and memory B-cell frequencies remained significantly higher at 10 months than day 0 levels (p < 0.01).DiscussionRobust antibody and B-cell responses persisted at 10 months following the third-dose vaccination. Higher memory B-cell frequencies, rather than antibody titres or T-cell responses, predicted protection from subsequent infection, identifying memory B cells as a correlate of protection.

Objectives: To assess the safety and immunogenicity of a fourth vaccination (second booster) in individuals aged ≥75 years. Methods: Participants were randomized to BNT162b2 (Comirnaty, 30 µg) or messenger RNA (mRNA)-1273 (Spikevax, 100 µg). The primary end point was the rate of two-fold antibody titer increase 14 days after vaccination, targeting the receptor binding domain (RBD) region of wild-type SARS-CoV-2. The secondary end points included changes in neutralizing activity against wild-type and 25 variants. Safety was assessed by monitoring solicited adverse events (AEs) for 7 days. Results: A total of 269 participants (mean age 81 years, mRNA-1273 n = 135/BNT162b2 n = 134) were included. Two-fold anti-RBD immunoglobulin (Ig) G titer increase was achieved by 101 of 129 (78%) and 116 of 133 (87%) subjects in the BNT162b2 and the mRNA-1273 group, respectively (P = 0.054). A second booster of mRNA-1273 provided higher anti-RBD IgG geometric mean titer: 21.326 IU/mL (95% confidence interval: 18.235-24.940) vs BNT162b2: 15.181 IU/mL (95% confidence interval: 13.172-17.497). A higher neutralizing activity was noted for the mRNA-1273 group. The most frequent AE was pain at the injection site (51% in mRNA-1273 and 48% in BNT162b2). Participants in the mRNA-1273 group had less vaccine-related AEs (30% vs 39%). Conclusions: A second booster of either BNT162b2 or mRNA-1273 provided substantial IgG increase. Full-dose mRNA-1273 provided higher IgG levels and neutralizing capacity against SARS-CoV-2, with similar safety profile for subjects of advanced age.

Abstract Extracellular vesicles (EVs) accumulate during packed red blood cell (PRBC) storage. To date, the involvement of EVs in transfusion-related immunomodulation (TRIM) has not been prospectively evaluated in intensive care unit (ICU) patients. This was a prospective subanalysis of a recent observational feasibility study in postoperative ICU patients after: (1) open aortic surgery (Aorta), (2) bilateral lung transplantation (LuTx), and (3) other types of surgery (Comparison). Patient plasma was collected three times each before and after leukoreduced PRBC transfusion at 30-min intervals. The total number of EVs and EVs derived from erythrocytes (EryEVs), total platelets (total PEVs), activated platelets, granulocytes (GEVs), monocytes, and myeloid cells in PRBC samples and patient plasma were analyzed by flow cytometry. Statistical analysis was performed by Spearman’s correlation test, linear mixed models and pairwise comparisons by Wilcoxon matched-pairs test. Twenty-three patients (Aorta n = 5, LuTx n = 9, Comparison n = 9) were included in the final analysis. All EV subgroups analyzed were detectable in all PRBCs samples (n = 23), but concentrations did not correlate with storage time. Moreover, all EVs analyzed were detectable in all plasma samples (n = 138), and EV counts were consistent before transfusion. Concentrations of total EVs, EryEVs, total PEVs, and GEVs increased after transfusion compared with baseline in the entire cohort but not in specific study groups. Furthermore, the change in plasma EV counts (total EVs and EryEVs) after transfusion correlated with PRBC storage time in the entire cohort. Extracellular vesicles were detectable in all PRBC and plasma samples. Individual EV subtypes increased after transfusion in the entire cohort, and in part correlated with storage duration. Future clinical studies to investigate the role of EVs in TRIM are warranted and should anticipate a larger sample size. Trial registration: Clinicaltrials.gov: NCT03782623.

Abstract Background Platform trials gained popularity during the last few years as they increase flexibility compared to multi-arm trials by allowing new experimental arms entering when the trial already started. Using a shared control group in platform trials increases the trial efficiency compared to separate trials. Because of the later entry of some of the experimental treatment arms, the shared control group includes concurrent and non-concurrent control data. For a given experimental arm, non-concurrent controls refer to patients allocated to the control arm before the arm enters the trial, while concurrent controls refer to control patients that are randomised concurrently to the experimental arm. Using non-concurrent controls can result in bias in the estimate in case of time trends if the appropriate methodology is not used and the assumptions are not met. Methods We conducted two reviews on the use of non-concurrent controls in platform trials: one on statistical methodology and one on regulatory guidance. We broadened our searches to the use of external and historical control data. We conducted our review on the statistical methodology in 43 articles identified through a systematic search in PubMed and performed a review on regulatory guidance on the use of non-concurrent controls in 37 guidelines published on the EMA and FDA websites. Results Only 7/43 of the methodological articles and 4/37 guidelines focused on platform trials. With respect to the statistical methodology, in 28/43 articles, a Bayesian approach was used to incorporate external/non-concurrent controls while 7/43 used a frequentist approach and 8/43 considered both. The majority of the articles considered a method that downweights the non-concurrent control in favour of concurrent control data (34/43), using for instance meta-analytic or propensity score approaches, and 11/43 considered a modelling-based approach, using regression models to incorporate non-concurrent control data. In regulatory guidelines, the use of non-concurrent control data was considered critical but was deemed acceptable for rare diseases in 12/37 guidelines or was accepted in specific indications (12/37). Non-comparability (30/37) and bias (16/37) were raised most often as the general concerns with non-concurrent controls. Indication specific guidelines were found to be most instructive. Conclusions Statistical methods for incorporating non-concurrent controls are available in the literature, either by means of methods originally proposed for the incorporation of external controls or non-concurrent controls in platform trials. Methods mainly differ with respect to how the concurrent and non-concurrent data are combined and temporary changes handled. Regulatory guidance for non-concurrent controls in platform trials are currently still limited.

Abstract This retrospective study evaluated temporal and regional trends of patient admissions to hospitals, intensive care units (ICU), and intermediate care units (IMCU) as well as outcomes during the COVID-19 pandemic in Austria. We analysed anonymous data from patients admitted to Austrian hospitals with COVID-19 between January 1st, 2020 and December 31st, 2021. We performed descriptive analyses and logistic regression analyses for in-hospital mortality, IMCU or ICU admission, and in-hospital mortality following ICU admission. 68,193 patients were included, 8304 (12.3%) were primarily admitted to ICU, 3592 (5.3%) to IMCU. Hospital mortality was 17.3%; risk factors were male sex (OR 1.67, 95% CI 1.60–1.75, p

Summary: Platform trials bring the promise of making clinical research more efficient and more patient centric. While their use has become more widespread, including their prominent role during the COVID-19 pandemic response, broader adoption of platform trials has been limited by the lack of experience and tools to navigate the critical upfront planning required to launch such collaborative studies. The European Union-Patient-cEntric clinicAl tRial pLatform (EU-PEARL) initiative has produced new methodologies to expand the use of platform trials with an overarching infrastructure and services embedded into Integrated Research Platforms (IRPs), in collaboration with patient representatives and through consultation with U.S. Food and Drug Administration and European Medicines Agency stakeholders. In this narrative review, we discuss the outlook for platform trials in Europe, including challenges related to infrastructure, design, adaptations, data sharing and regulation. Documents derived from the EU-PEARL project, alongside a literature search including PubMed and relevant grey literature (e.g., guidance from regulatory agencies and health technology agencies) were used as sources for a multi-stage collaborative process through which the 10 more important points based on lessons drawn from the EU-PEARL project were developed and summarised as guidance for the setup of platform trials. We conclude that early involvement of critical stakeholder such as regulatory agencies or patients are critical steps in the implementation and later acceptance of platform trials. Addressing these gaps will be critical for attaining the full potential of platform trials for patients. Funding: Innovative Medicines Initiative 2 Joint Undertaking with support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.

Abstract Background In the ongoing COVID-19 pandemic, advanced age is a risk factor for a severe clinical course of SARS-CoV-2 infection. Thus, older people may benefit in particular from booster doses with potent vaccines and research should focus on optimal vaccination schedules. In addition to each individual’s medical history, immunosenescence warrants further research in this population. This study investigates vaccine-induced immune response over 1 year. Methods/design EU-COVAT-1-AGED is a randomised controlled, adaptive, multicentre phase II protocol evaluating different booster strategies in individuals aged ≥75 years (n=600) already vaccinated against SARS-CoV-2. The initial protocol foresaw a 3rd vaccination (1st booster) as study intervention. The present modified Part B of this trial foresees testing of mRNA-1273 (Spikevax®) vs. BNT162b2 (Comirnaty®) as 4th vaccination dose (2nd booster) for comparative assessment of their immunogenicity and safety against SARS-CoV-2 wild-type and variants. The primary endpoint of the trial is to assess the rate of 2-fold antibody titre increase 14 days after vaccination measured by quantitative enzyme-linked immunosorbent assay (Anti-RBD-ELISA) against wild-type virus. Secondary endpoints include the changes in neutralising antibody titres (Virus Neutralisation Assay) against wild-type as well as against Variants of Concern (VOC) at 14 days and up to 12 months. T cell response measured by qPCR will be performed in subgroups at 14 days as exploratory endpoint. Biobanking samples are being collected for neutralising antibody titres against potential future VOC. Furthermore, potential correlates between humoral immune response, T cell response and neutralising capacity will be assessed. The primary endpoint analysis will be triggered as soon as for all patients the primary endpoint (14 days after the 4th vaccination dose) has been observed. Discussion The EU-COVAT-1-AGED trial Part B compares immunogenicity and safety of mRNA-1273 (Spikevax®) and BNT162b2 (Comirnaty®) as 4th SARS-CoV-2 vaccine dose in adults ≥75 years of age. The findings of this trial have the potential to optimise the COVID-19 vaccination strategy for this at-risk population. Trial registration ClinicalTrials.gov NCT05160766 . Registered on 16 December 2021. Protocol version: V06_0: 27 July 2022

Abstract Background In RNA-sequencing studies a large number of hypothesis tests are performed to compare the differential expression of genes between several conditions. Filtering has been proposed to remove candidate genes with a low expression level which may not be relevant and have little or no chance of showing a difference between conditions. This step may reduce the multiple testing burden and increase power. Results We show in a simulation study that filtering can lead to some increase in power for RNA-sequencing data, too aggressive filtering, however, can lead to a decline. No uniformly optimal filter in terms of power exists. Depending on the scenario different filters may be optimal. We propose an adaptive filtering strategy which selects one of several filters to maximise the number of rejections. No additional adjustment for multiplicity has to be included, but a rule has to be considered if the number of rejections is too small. Conclusions For a large range of simulation scenarios, the adaptive filter maximises the power while the simulated False Discovery Rate is bounded by the pre-defined significance level. Using the adaptive filter, it is not necessary to pre-specify a single individual filtering method optimised for a specific scenario.

Abstract Background Platform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using shared control groups. However, for a treatment entering the trial at a later time point, the control group is divided into concurrent controls, consisting of patients randomised to control when that treatment arm is in the platform, and non-concurrent controls, patients randomised before. Using non-concurrent controls in addition to concurrent controls can improve the trial’s efficiency by increasing power and reducing the required sample size, but can introduce bias due to time trends. Methods We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added at a later time, we assess the robustness of recently proposed model-based approaches to adjust for time trends when utilizing non-concurrent controls. In particular, we consider approaches where time trends are modeled either as linear in time or as a step function, with steps at time points where treatments enter or leave the platform trial. For trials with continuous or binary outcomes, we investigate the type 1 error rate and power of testing the efficacy of the newly added arm, as well as the bias and root mean squared error of treatment effect estimates under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with different time trends or time trends that are not additive in the scale of the model. Results A step function model, fitted on data from all treatment arms, gives increased power while controlling the type 1 error, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the shape of the time trend deviates from a step function when patients are allocated to arms by block randomisation. However, if time trends differ between arms or are not additive to treatment effects in the scale of the model, the type 1 error rate may be inflated. Conclusions The efficiency gained by using step function models to incorporate non-concurrent controls can outweigh potential risks of biases, especially in settings with small sample sizes. Such biases may arise if the model assumptions of equality and additivity of time trends are not satisfied. However, the specifics of the trial, scientific plausibility of different time trends, and robustness of results should be carefully considered.

Platform trials evaluate the efficacy of multiple treatments, allowing for late entry of the experimental arms and enabling efficiency gains by sharing controls. The power of individual treatment-control comparisons in such trials can be improved by utilizing non-concurrent controls (NCC) in the analysis. We present the R-package NCC for the design and analysis of platform trials using non-concurrent controls. NCC allows for simulating platform trials and evaluating the properties of analysis methods that make use of non-concurrent controls in a variety of settings. We describe the main NCC functions and show how to use the package to simulate and analyse platform trials by means of specific examples.

Introduction: Eicosanoids are bioactive lipids present in packed red blood cells (PRBCs), and might play a role in transfusion-related immunomodulation (TRIM). We tested the feasibility of analyzing eicosanoid profiles in PRBC supernatant and in plasma samples of postoperative intensive care unit (ICU) patients transfused with one unit of PRBCs.Methods: We conducted a prospective, observational feasibility study enrolling postoperative ICU patients: 1) patients treated with acetylsalicylic acid following abdominal aortic surgery (Aorta); 2) patients on immunosuppressants after bilateral lung transplantation (LuTx); and 3) patients undergoing other types of major surgery (Comparison). Abundances of arachidonic acid (AA) and seven pre-defined eicosanoids were assessed by liquid chromatography and tandem mass spectrometry. PRBC supernatant was sampled directly from the unit immediately prior to transfusion. Spearman’s correlations between eicosanoid abundance in PRBCs and storage duration were assessed. Patient plasma was collected at 30-min intervals: Three times each before and after transfusion. To investigate temporal changes in eicosanoid abundances, we fitted linear mixed models.Results: Of 128 patients screened, 21 were included in the final analysis (Aorta n = 4, LuTx n = 8, Comparison n = 9). In total, 21 PRBC and 125 plasma samples were analyzed. Except for 20-hydroxyeicosatetraenoic acid (HETE), all analyzed eicosanoids were detectable in PRBCs, and their abundance positively correlated with storage duration of PRBCs. While 5-HETE, 12-HETE/8-HETE, 15-HETE, 20-HETE, and AA were detectable in virtually all plasma samples, 9-HETE and 11-HETE were detectable in only 57% and 23% of plasma samples, respectively.Conclusions: Recruitment of ICU patients into this transfusion study was challenging but feasible. Eicosanoid abundances increased in PRBC supernatants during storage. In plasma of ICU patients, eicosanoid abundances were ubiquitously detectable and showed limited fluctuations over time prior to transfusion. Taken together, larger clinical studies seem warranted and feasible to further investigate the role of PRBC-derived eicosanoids in TRIM.

Aim of this studyThis study seeks to investigate, whether extubation of tracheally intubated patients admitted to intensive care units (ICU) postoperatively either immediately at the day of admission (day 1) or delayed at the first postoperative day (day 2) is associated with differences in outcomes.Materials and methodsWe performed a retrospective analysis of data from an Austrian ICU registry. Adult patients admitted between January 1st, 2012 and December 31st, 2019 following elective and emergency surgery, who were intubated at the day 1 and were extubated at day 1 or day 2, were included. We performed logistic regression analyses for in-hospital mortality and over-sedation or agitation following extubation.Results52 982 patients constituted the main study population. 1 231 (3.3%) patients extubated at day 1 and 958 (5.9%) at day 2 died in hospital, 464 (1.3%) patients extubated at day 1 and 613 (3.8%) at day 2 demonstrated agitation or over-sedation after extubation during ICU stay; OR (95% CI) for in-hospital mortality were OR 1.17 (1.01-1.35, p = 0.031) and OR 2.15 (1.75-2.65, pConclusionsWe conclude that immediate extubation as soon as deemed feasible by clinicians is associated with favourable outcomes and may thus be considered preferable in tracheally intubated patients admitted to ICU postoperatively.

Abstract The number of reported coronavirus disease (COVID-19) deaths per 100,000 persons observed so far in 2020 is described in 15 European countries and the USA as dependent on age groups and sex. It is compared with the corresponding historic all-cause mortality per year depending on age and sex observed in these countries. Some common features exist although substantial differences in age and sex dependency of COVID-19 mortality were noted between countries. An exponential increase with age is a good model to describe and analyze both COVID-19 and all-cause mortality above 40 years old, where almost all COVID-19 deaths occur. Moreover, age dependency is stronger for COVID-19 mortality than for all-cause mortality, and males have an excess risk compared with women, which is less pronounced in the higher age groups. Additionally, concerning calendar time, differences in the age and sex dependency between countries were noted with the common tendency that male excess risk for COVID-19 mortality was smaller in the second half of the year.

Abstract This study seeks to identify factors that are associated with decisions of prehospital physicians to start (continue, if ongoing) or withhold (terminate, if ongoing) CPR in patients with OHCA. We conducted a retrospective study using anonymised data from a prehospital physician response system. Data on patients attended for cardiac arrest between January 1st, 2010 and December 31st, 2018 except babies at birth were included. Logistic regression analysis with start of CPR by physicians as the dependent variable and possible associated factors as independent variables adjusted for anonymised physician identifiers was conducted. 1525 patient data sets were analysed. Obvious signs of death were present in 278 cases; in the remaining 1247, resuscitation was attempted in 920 (74%) and were withheld in 327 (26%). Factors significantly associated with higher likelihood of CPR by physicians (OR 95% CI) were resuscitation efforts by EMS before physician arrival (60.45, 19.89–184.29), first monitored heart rhythm (3.07, 1.21–7.79 for PEA; 29.25, 1.93–442. 51 for VF / pVT compared to asystole); advanced patient age (modelled using cubic splines), physician response time (0.92, 0.87–0.97 per minute) and malignancy (0.22, 0.05–0.92) were significantly associated with lower odds of CPR. We thus conclude that prehospital physicians make decisions to start or withhold resuscitation routinely and base those mostly on situational information and immediately available patient information known to impact outcomes.

BackgroundSystematic analyses about sex differences in wait-listing and kidney transplantation after dialysis initiation are scarce. We aimed at identifying sex-specific disparities along the path of kidney disease treatment, comparing two countries with distinctive health care systems, the US and Austria, over time.MethodsWe analyzed subjects who initiated dialysis from 1979–2018, in observational cohort studies from the US and Austria. We used Cox regression to model male-to-female cause-specific hazard ratios (csHRs, 95% confidence intervals) for transitions along the consecutive states dialysis initiation, wait-listing, kidney transplantation and death, adjusted for age and stratified by country and decade of dialysis initiation.ResultsAmong 3,053,206 US and 36,608 Austrian patients starting dialysis, men had higher chances to enter the wait-list, which however decreased over time [male-to-female csHRs for wait-listing, 1978–1987: US 1.94 (1.71, 2.20), AUT 1.61 (1.20, 2.17); 2008–2018: US 1.35 (1.32, 1.38), AUT 1.11 (0.94, 1.32)]. Once wait-listed, the advantage of the men became smaller, but persisted in the US [male-to-female csHR for transplantation after wait-listing, 2008–2018: 1.08 (1.05, 1.11)]. The greatest disparity between men and women occurred in older age groups in both countries [male-to-female csHR for wait-listing after dialysis, adjusted to 75% age quantile, 2008–2018: US 1.83 (1.74, 1.92), AUT 1.48 (1.02, 2.13)]. Male-to-female csHRs for death were close to one, but higher after transplantation than after dialysis.ConclusionsWe found evidence for sex disparities in both countries. Historically, men in the US and Austria had 90%, respectively, 60% higher chances of being wait-listed for kidney transplantation, although these gaps decreased over time. Efforts should be continued to render kidney transplantation equally accessible for both sexes, especially for older women.

Abstract Background To assess uncertainty in regulatory decision-making for orphan medicinal products (OMP), a summary of the current basis for approval is required; a systematic grouping of medical conditions may be useful in summarizing information and issuing recommendations for practice. Methods A grouping of medical conditions with similar characteristics regarding the potential applicability of methods and designs was created using a consensus approach. The 125 dossiers for authorised OMP published between 1999 and 2014 on the EMA webpage were grouped accordingly and data was extracted from European Public Assessment Reports (EPARs) to assess the extent and robustness of the pivotal evidence supporting regulatory decisions. Results 88% (110/125) of OMP authorizations were based on clinical trials, with 35% (38/110) including replicated pivotal trials. The mean (SD) number of pivotal trials per indication was 1.4 (0.7), and the EPARs included a median of three additional non-pivotal supportive studies. 10% of OMPs (13/125) were authorised despite only negative pivotal trials. One-third of trials (53/159) did not include a control arm, one-third (50/159) did not use randomisation, half the trials (75/159) were open-label and 75% (119/159) used intermediate or surrogate variables as the main outcome. Chronic progressive conditions led by multiple system/organs, conditions with single acute episodes and progressive conditions led by one organ/system were the groups where the evidence deviated most from conventional standards. Conditions with recurrent acute episodes had the most robust datasets. The overall size of the exposed population at the time of authorisation of OMP − mean(SD) 190.5 (202.5) − was lower than that required for the qualification of clinically-relevant adverse reactions. Conclusions The regulatory evidence supporting OMP authorization showed substantial uncertainties, including weak protection against errors, substantial use of designs unsuited for conclusions on causality, use of intermediate variables, lack of a priorism and insufficient safety data to quantify risks of relevant magnitude. Grouping medical conditions based on clinical features and their methodological requirements may facilitate specific methodological and regulatory recommendations for the study of OMP to strengthen the evidence base.

Abstract Background In recent years, experience on the application of adaptive designs in confirmatory clinical trials has accumulated. Although planning such trials comes at the cost of additional operational complexity, adaptive designs offer the benefit of flexibility to update trial design and objectives as data accrue. In 2007, the European Medicines Agency (EMA) provided guidance on confirmatory clinical trials with adaptive (or flexible) designs. In order to better understand how adaptive trials are implemented in practice and how they may impact medicine approval within the EMA centralised procedure, we followed on 59 medicines for which an adaptive clinical trial had been submitted to the EMA Scientific Advice (SA) and analysed previously in a dedicated EMA survey of scientific advice letters. We scrutinized in particular the submission of the corresponding medicines for a marketing authorisation application (MAA). We also discuss the current regulatory perspective as regards the implementation of adaptive designs in confirmatory clinical trials. Methods Using the internal EMA MAA database, the AdisInsight database and related trial registries, we analysed how many of these 59 trials actually started, the completion status, results, the time to trial start, the adaptive elements finally implemented after SA, their possible influence on the success of the trial and corresponding product approval. Results Overall 31 trials out of 59 (53%) were retrieved. Thirty of them (97%) have been started and 23 (74%) concluded. Nine of these trials (39% out of 23) demonstrated a significant treatment effect on their primary endpoint and 4 (17% out of 23) supported a marketing authorisation (MA). An additional two trials were stopped using pre-defined criteria for futility, efficiently identifying trials on which further resources should not be spent. Median time to trial start after SA letter was given by EMA was 5 months. In the investigated trial registries, at least 18 trial (58% of 31 retrieved trials) designs were implemented with adaptive elements, which were predominantly dose selection, sample size reassessment (SSR) and stopping for futility (SFF). Among the 11 completed trials including adaptive elements, 6 demonstrated a significant treatment effect on their primary endpoint (55%). Conclusions Adaptive designs are now well established in the drug development landscape. If properly pre-planned, adaptations can play a key role in the success of some of these trials, for example to help successfully select the most promising dose regimens for phase II/III trials. Interim analyses can also enable stopping of trials for futility when they do not hold their promises. Type I error rate control, trial integrity and results consistency between the different stages of the analyses are fundamental aspects to be discussed thoroughly. Engaging early dialogue with regulators and implementing the scientific advice received is strongly recommended, since much experience in discussing adaptive designs and assessing their results has been accumulated.

Abstract Background The ASTERIX project developed a number of novel methods suited to study small populations. The objective of this exercise was to evaluate the applicability and added value of novel methods to improve drug development in small populations, using real world drug development programmes as reported in European Public Assessment Reports. Methods The applicability and added value of thirteen novel methods developed within ASTERIX were evaluated using data from 26 European Public Assessment Reports (EPARs) for orphan medicinal products, representative of rare medical conditions as predefined through six clusters. The novel methods included were ‘innovative trial designs’ (six methods), ‘level of evidence’ (one method), ‘study endpoints and statistical analysis’ (four methods), and ‘meta-analysis’ (two methods) and they were selected from the methods developed within ASTERIX based on their novelty; methods that discussed already available and applied strategies were not included for the purpose of this validation exercise. Pre-requisites for application in a study were systematized for each method, and for each main study in the selected EPARs it was assessed if all pre-requisites were met. This direct applicability using the actual study design was firstly assessed. Secondary, applicability and added value were explored allowing changes to study objectives and design, but without deviating from the context of the drug development plan. We evaluated whether differences in applicability and added value could be observed between the six predefined condition clusters. Results and discussion Direct applicability of novel methods appeared to be limited to specific selected cases. The applicability and added value of novel methods increased substantially when changes to the study setting within the context of drug development were allowed. In this setting, novel methods for extrapolation, sample size re-assessment, multi-armed trials, optimal sequential design for small sample sizes, Bayesian sample size re-estimation, dynamic borrowing through power priors and fall-back tests for co-primary endpoints showed most promise - applicable in more than 40% of evaluated EPARs in all clusters. Most of the novel methods were applicable to conditions in the cluster of chronic and progressive conditions, involving multiple systems/organs. Relatively fewer methods were applicable to acute conditions with single episodes. For the chronic clusters, Goal Attainment Scaling was found to be particularly applicable as opposed to other (non-chronic) clusters. Conclusion Novel methods as developed in ASTERIX can improve drug development programs. Achieving optimal added value of these novel methods often requires consideration of the entire drug development program, rather than reconsideration of methods for a specific trial. The novel methods tested were mostly applicable in chronic conditions, and acute conditions with recurrent episodes.

AimsMass antigen testing programs have been challenged because of an alleged insufficient specificity, leading to a large number of false positives. The objective of this study is to derive a lower bound of the specificity of the SD Biosensor Standard Q Ag-Test in large scale practical use.MethodsBased on county data from the nationwide tests for SARS-CoV-2 in Slovakia between 31.10.-1.11. 2020 we calculate a lower confidence bound for the specificity. As positive test results were not systematically verified by PCR tests, we base the lower bound on a worst case assumption, assuming all positives to be false positives.Results3,625,332 persons from 79 counties were tested. The lowest positivity rate was observed in the county of Rožňava where 100 out of 34307 (0.29%) tests were positive. This implies a test specificity of at least 99.6% (97.5% one-sided lower confidence bound, adjusted for multiplicity).ConclusionThe obtained lower bound suggests a higher specificity compared to earlier studies in spite of the underlying worst case assumption and the application in a mass testing setting. The actual specificity is expected to exceed 99.6% if the prevalence in the respective regions was non-negligible at the time of testing. To our knowledge, this estimate constitutes the first bound obtained from large scale practical use of an antigen test.

Abstract Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017. The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods.

Abstract Background Most confirmatory randomised controlled clinical trials (RCTs) are designed with specified power, usually 80% or 90%, for a hypothesis test conducted at a given significance level, usually 2.5% for a one-sided test. Approval of the experimental treatment by regulatory agencies is then based on the result of such a significance test with other information to balance the risk of adverse events against the benefit of the treatment to future patients. In the setting of a rare disease, recruiting sufficient patients to achieve conventional error rates for clinically reasonable effect sizes may be infeasible, suggesting that the decision-making process should reflect the size of the target population. Methods We considered the use of a decision-theoretic value of information (VOI) method to obtain the optimal sample size and significance level for confirmatory RCTs in a range of settings. We assume the decision maker represents society. For simplicity we assume the primary endpoint to be normally distributed with unknown mean following some normal prior distribution representing information on the anticipated effectiveness of the therapy available before the trial. The method is illustrated by an application in an RCT in haemophilia A. We explicitly specify the utility in terms of improvement in primary outcome and compare this with the costs of treating patients, both financial and in terms of potential harm, during the trial and in the future. Results The optimal sample size for the clinical trial decreases as the size of the population decreases. For non-zero cost of treating future patients, either monetary or in terms of potential harmful effects, stronger evidence is required for approval as the population size increases, though this is not the case if the costs of treating future patients are ignored. Conclusions Decision-theoretic VOI methods offer a flexible approach with both type I error rate and power (or equivalently trial sample size) depending on the size of the future population for whom the treatment under investigation is intended. This might be particularly suitable for small populations when there is considerable information about the patient population.

Abstract Background In this study, we primarily investigated whether ICU admission or ICU stay at weekends (Saturday and Sunday) is associated with a different risk of ICU mortality or chance of ICU discharge than ICU admission or ICU stay on weekdays (Monday to Friday). Secondarily, we analysed whether weekend ICU admission or ICU stay influences risk of hospital mortality or chance of hospital discharge. Methods A retrospective study was performed for all adult patients admitted to 119 ICUs participating in the benchmarking project of the Austrian Centre for Documentation and Quality Assurance in Intensive Care (ASDI) between 2012 and 2015. Readmissions to the ICU during the same hospital stay were excluded. Results In a multivariable competing risk analysis, a strong weekend effect was observed. Patients admitted to ICUs on Saturday or Sunday had a higher mortality risk after adjustment for severity of illness by Simplified Acute Physiology Score (SAPS) 3, year, month of the year, type of admission, ICU, and weekday of death or discharge. Hazard ratios (95% confidence interval) for death in the ICU following admission on a Saturday or Sunday compared with Wednesday were 1.15 (1.08–1.23) and 1.11 (1.03–1.18), respectively. Lower hazard ratios were observed for dying on a Saturday (0.93 (0.87–1.00)) or Sunday (0.85 (0.80–0.91)) compared with Wednesday. This is probably related to the reduced chance of being discharged from the ICU at the weekend (0.63 (0.62–064) for Saturday and 0.56 (0.55–0.57) for Sunday). Similar results were found for hospital mortality and hospital discharge following ICU admission. Conclusions Patients admitted to ICUs at weekends are at increased risk of death in both the ICU and the hospital even after rigorous adjustment for severity of illness. Conversely, death in the ICU and discharge from the ICU are significantly less likely at weekends.

Identifying subgroups of treatment responders through the different phases of clinical trials has the potential to increase success in drug development. Recent developments in subgroup analysis consider subgroups that are defined in terms of the predicted individual treatment effect, i.e. the difference between the predicted outcome under treatment and the predicted outcome under control for each individual, which in turn may depend on multiple biomarkers. In this work, we study the properties of different modelling strategies to estimate the predicted individual treatment effect. We explore linear models and compare different estimation methods, such as maximum likelihood and the Lasso with and without randomized response. For the latter, we implement confidence intervals based on the selective inference framework to account for the model selection stage. We illustrate the methods in a dataset of a treatment for Alzheimer disease (normal response) and in a dataset of a treatment for prostate cancer (survival outcome). We also evaluate via simulations the performance of using the predicted individual treatment effect to identify subgroups where a novel treatment leads to better outcomes compared to a control treatment.

An important objective in the development of targeted therapies is to identify the populations where the treatment under consideration has positive benefit risk balance. We consider pivotal clinical trials, where the efficacy of a treatment is tested in an overall population and/or in a pre-specified subpopulation. Based on a decision theoretic framework we derive optimized trial designs by maximizing utility functions. Features to be optimized include the sample size and the population in which the trial is performed (the full population or the targeted subgroup only) as well as the underlying multiple test procedure. The approach accounts for prior knowledge of the efficacy of the drug in the considered populations using a two dimensional prior distribution. The considered utility functions account for the costs of the clinical trial as well as the expected benefit when demonstrating efficacy in the different subpopulations. We model utility functions from a sponsor's as well as from a public health perspective, reflecting actual civil interests. Examples of optimized trial designs obtained by numerical optimization are presented for both perspectives.

Mid-study design modifications are becoming increasingly accepted in confirmatory clinical trials, so long as appropriate methods are applied such that error rates are controlled. It is therefore unfortunate that the important case of time-to-event endpoints is not easily handled by the standard theory. We analyze current methods that allow design modifications to be based on the full interim data, i.e., not only the observed event times but also secondary endpoint and safety data from patients who are yet to have an event. We show that the final test statistic may ignore a substantial subset of the observed event times. An alternative test incorporating all event times is found, where a conservative assumption must be made in order to guarantee type I error control. We examine the power of this approach using the example of a clinical trial comparing two cancer therapies.