Current state-of-the-art and gaps in platform trials: 10 things you should know, insights from EU-PEARL.

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.

On the use of non-concurrent controls in platform trials: a scoping review.

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.

Review article: The need for more efficient and patient-oriented drug development pathways in NASH-setting the scene for platform trials.

BACKGROUND AND AIMS: Non-alcoholic steatohepatitis (NASH) constitutes a significant unmet medical need with a burgeoning field of clinical research and drug development. Platform trials (PT) might help accelerate drug development while lowering overall costs and creating a more patient-centric environment. This review provides a comprehensive and nuanced assessment of the NASH clinical development landscape. METHODS: Narrative review and expert opinion with insight gained during the EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) project. RESULTS: Although NASH represents an opportunity to use adaptive trial designs, including master protocols for PT, there are barriers that might be encountered owing to distinct and sometimes opposing priorities held by these stakeholders and potential ways to overcome them. The following aspects are critical for the feasibility of a future PT in NASH: readiness of the drug pipeline, mainly from large drug companies, while there is not yet an FDA/EMA-approved treatment; the most suitable design (trial Phase and type of population, e.g., Phase 2b for non-cirrhotic NASH patients); the operational requirements such as the scope of the clinical network, the use of concurrent versus non-concurrent control arms, or the re-allocation of participants upon trial adaptations; the methodological appraisal (i.e. Bayesian vs. frequentist approach); patients' needs and patient-centred outcomes; main regulatory considerations and the funding and sustainability scenarios. CONCLUSIONS: PT represent a promising avenue in NASH but there are a number of conundrums that need addressing. It is likely that before a global NASH PT becomes a reality, 'proof-of-platform' at a smaller scale needs to be provided.

Platform trials to overcome major shortcomings of traditional clinical trials in non-alcoholic steatohepatitis? Pros and cons.

Non-alcoholic fatty liver disease is a condition that affects 25% of the population. Non-alcoholic steatohepatitis (NASH) is a progressive form of the disease that can lead to severe complications such as cirrhosis and hepatocellular carcinoma. Despite its high prevalence, no drugs are currently approved for the treatment of NASH. The drug development pipeline in NASH is very active, yet most assets do not progress to phase III trials and those that do reach phase III often fail to achieve the endpoints necessary for approval by regulatory agencies. Amongst other reasons, the methodological and operational features of traditional clinical trials in NASH might impede optimal drug development. In this regard, platform trials might be an attractive complement or alternative to conventional clinical trials. Platform trials use a master protocol which enables evaluation of multiple investigational medicinal products concurrently or sequentially with a single, shared control arm. Through Bayesian interim analyses, these trials allow for early exit of drugs from the trial based on success or futility, while providing participants better chances of receiving active compounds through adaptive randomisation. Overall, platform trials represent an alternative for patients, pharmaceutical companies, and clinicians in the quest to accelerate the approval of pharmacologic treatments for NASH.

The von Willebrand factor-binding aptamer rondaptivon pegol as a treatment for severe and nonsevere hemophilia A.

Factor VIII (FVIII) circulates in a noncovalent complex with von Willebrand Factor (VWF), the latter determining FVIII half-life. The VWF-binding aptamer rondaptivon pegol (BT200) increases plasma levels of VWF/FVIII in healthy volunteers. This trial assessed its safety, pharmacokinetics, and pharmacodynamics in hemophilia A. Nineteen adult patients (ages 20-62 years, 4 women) with hemophilia A (8 mild, 2 moderate, and 9 severe) received subcutaneous injections of rondaptivon pegol. After an initial fixed dose of 3 mg on days 0 and 4, patients received weekly doses of 2 to 9 mg until day 28. Severe hemophilia A patients underwent sparse-sampling population pharmacokinetics individual profiling after the final dose of rondaptivon pegol. Adverse events, pharmacokinetics, and pharmacodynamics were assessed. FVIII activity and VWF levels were measured. All patients tolerated rondaptivon pegol well. The geometric mean half-life of rondaptivon pegol was 5.4 days and rondaptivon pegol significantly increased VWF levels. In severe hemophilia A, 6 doses of rondaptivon pegol increased the half-lives of 5 different FVIII products from a median of 10.4 hours to 31.1 hours (range, 20.8-56.0 hours). Median FVIII increased from 22% to 48% in mild hemophilia A and from 3% to 7.5% in moderate hemophilia A. Rondaptivon pegol is a first-in-class prohemostatic molecule that extended the half-life of substituted FVIII approximately 3-fold and increased endogenous FVIII levels approximately 2-fold in hemophilia patients. This trial was registered at www.clinicaltrials.gov as #NCT04677803.

Online control of the False Discovery Rate in group-sequential platform trials.

When testing multiple hypotheses, a suitable error rate should be controlled even in exploratory trials. Conventional methods to control the False Discovery Rate assume that all p-values are available at the time point of test decision. In platform trials, however, treatment arms enter and leave the trial at different times during its conduct. Therefore, the actual number of treatments and hypothesis tests is not fixed in advance and hypotheses are not tested at once, but sequentially. Recently, for such a setting the concept of online control of the False Discovery Rate was introduced. We propose several heuristic variations of the LOND procedure (significance Levels based On Number of Discoveries) that incorporate interim analyses for platform trials, and study their online False Discovery Rate via simulations. To adjust for the interim looks spending functions are applied with O'Brien-Fleming or Pocock type group-sequential boundaries. The power depends on the prior distribution of effect sizes, for example, whether true alternatives are uniformly distributed over time or not. We consider the choice of design parameters for the LOND procedure to maximize the overall power and investigate the impact on the False Discovery Rate by including both concurrent and non-concurrent control data.

Early administration of remdesivir may reduce mortality in hospitalized COVID-19 patients : A propensity score matched analysis.

BACKGROUND: Remdesivir is the only antiviral agent approved for the treatment of hospitalized coronavirus disease 2019 (COVID-19) patients requiring supplemental oxygen. Studies show conflicting results regarding its effect on mortality. METHODS: In this single center observational study, we included adult hospitalized COVID-19 patients. Patients who were treated with remdesivir were compared to controls. Remdesivir was administered for 5 days. To adjust for any imbalances in our cohort, a propensity score matched analysis was performed. The aim of our study was to analyze the effect of remdesivir on in-hospital mortality and length of stay (LOS). RESULTS: After propensity score matching, 350 patients (175 remdesivir, 175 controls) were included in our analysis. Overall, in-hospital mortality was not significantly different between groups remdesivir 5.7% [10/175] vs. control 8.6% [15/175], hazard ratio 0.50, 95% confidence interval (CI) 0.22-1.12, p = 0.091. Subgroup analysis showed a significant reduction of in-hospital mortality in patients who were treated with remdesivir ≤ 7 days of symptom onset remdesivir 4.2% [5/121] vs. control 10.4% [13/125], hazard ratio 0.26, 95% CI 0.09 to 0.75, p = 0.012 and in female patients remdesivir 2.9% [2/69] vs. control 12.2% [9/74], hazard ratio 0.18 95%CI 0.04 to 0.85, p = 0.03. Patients in the remdesivir group had a significantly longer LOS (11 days vs. 9 days, p = 0.046). CONCLUSION: Remdesivir did not reduce in-hospital mortality in our whole propensity score matched cohort, but subgroup analysis showed a significant mortality reduction in female patients and in patients treated within ≤ 7 days of symptom onset. Remdesivir may reduce mortality in patients who are treated in the early stages of illness.

Adaptive clinical trial designs with blinded selection of binary composite endpoints and sample size reassessment.

For randomized clinical trials where a single, primary, binary endpoint would require unfeasibly large sample sizes, composite endpoints (CEs) are widely chosen as the primary endpoint. Despite being commonly used, CEs entail challenges in designing and interpreting results. Given that the components may be of different relevance and have different effect sizes, the choice of components must be made carefully. Especially, sample size calculations for composite binary endpoints depend not only on the anticipated effect sizes and event probabilities of the composite components but also on the correlation between them. However, information on the correlation between endpoints is usually not reported in the literature which can be an obstacle for designing future sound trials. We consider two-arm randomized controlled trials with a primary composite binary endpoint and an endpoint that consists only of the clinically more important component of the CE. We propose a trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. Especially, we consider a decision rule to select between a CE and its most relevant component as primary endpoint. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation, and the expected effect sizes of the composite components. We investigate the statistical power and significance level under the proposed design through simulations. We show that the adaptive design is equally or more powerful than designs without adaptive modification on the primary endpoint. Besides, the targeted power is achieved even if the correlation is misspecified at the planning stage while maintaining the type 1 error. All the computations are implemented in R and illustrated by means of a peritoneal dialysis trial.

On model-based time trend adjustments in platform trials with non-concurrent controls.

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.

COVID-19 is not "just another flu": a real-life comparison of severe COVID-19 and influenza in hospitalized patients in Vienna, Austria.

BACKGROUND: COVID-19 is regularly compared to influenza. Mortality and case-fatality rates vary widely depending on incidence of COVID-19 and the testing policy in affected countries. To date, data comparing hospitalized patients with COVID-19 and influenza is scarce. METHODS: Data from patients with COVID-19 were compared to patients infected with influenza A (InfA) and B (InfB) virus during the 2017/18 and 2018/19 seasons. All patients were ≥ 18 years old, had PCR-confirmed infection and needed hospital treatment. Demographic data, medical history, length-of-stay (LOS), complications including in-hospital mortality were analyzed. RESULTS: In total, 142 patients with COVID-19 were compared to 266 patients with InfA and 300 with InfB. Differences in median age (COVID-19 70.5 years vs InfA 70 years and InfB 77 years, p < 0.001) and laboratory results were observed. COVID-19 patients had fewer comorbidities, but complications (respiratory insufficiency, pneumonia, acute kidney injury, acute heart failure and death) occurred more frequently. Median length-of-stay (LOS) was longer in COVID-19 patients (12 days vs InfA 7 days vs. InfB 7 days, p < 0.001). There was a fourfold higher in-hospital mortality in COVID-19 patients (23.2%) when compared with InfA (5.6%) or InfB (4.7%; p < 0.001). CONCLUSION: In hospitalized patients, COVID-19 is associated with longer LOS, a higher number of complications and higher in-hospital mortality compared to influenza, even in a population with fewer co-morbidities. This data, a high reproduction number and limited treatment options, alongside excess mortality during the SARS-CoV-2 pandemic, support the containment strategies implemented by most authorities.

Endpoints for randomized controlled clinical trials for COVID-19 treatments.

BACKGROUND: Endpoint choice for randomized controlled trials of treatments for novel coronavirus-induced disease (COVID-19) is complex. Trials must start rapidly to identify treatments that can be used as part of the outbreak response, in the midst of considerable uncertainty and limited information. COVID-19 presentation is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks to over a month and can end in death. While improvement in mortality would provide unquestionable evidence about the clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical, particularly given a multitude of putative therapies to evaluate. Furthermore, patient states in between "cure" and "death" represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly given the variable time course of COVID-19. Outcomes measured at fixed time points, such as a comparison of severity scores between treatment and control at day 14, may risk missing the time of clinical benefit. An endpoint such as time to improvement (or recovery) avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of "recovered" versus "not recovered." METHODS: We evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials. RESULTS: Power for fixed time-point methods depends heavily on the time selected for evaluation. Time-to-event approaches have reasonable statistical power, even when compared with a fixed time-point method evaluated at the optimal time. DISCUSSION: Time-to-event analysis methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses.

Hypothyroidism correlates with favourable survival prognosis in patients with brain metastatic cancer.

BACKGROUND: Several preclinical and epidemiologic studies have indicated tumour-promoting effects of thyroid hormones (THs). However, very limited knowledge exists on the prognostic impact of thyroid function in metastatic cancer. METHODS: We compiled a discovery cohort of 1692 patients with newly diagnosed brain metastases (BMs) of solid cancers treated at the Medical University of Vienna and an independent validation cohort of 191 patients with newly diagnosed BMs treated at the University Hospital Zurich. RESULTS: Hypothyroidism before diagnosis of cancer was evident in 133 of 1692 (7.9%) patients of the discovery, and in 18 of 191 (9.4%) patients of the validation cohort. In the discovery cohort, hypothyroidism was statistically significantly associated with favourable survival prognosis from diagnosis of cancer (31 vs. 21 months; p = 0.0026) and with survival prognosis from diagnosis of BMs (12 vs. 7 months; p = 0.0079). In multivariate analysis including the diagnosis-specific graded prognostic assessment score, primary tumour type and sex, hypothyroidism was an independent factor associated with survival after diagnosis of BMs (hazard ratio: 0.76; 95% confidence interval [CI]: (0.63; 0.91; p = 0.0034). In the validation cohort, the association of hypothyroidism and favourable survival prognosis from diagnosis of cancer (55 vs. 11 months; p = 0.00058), as well as from diagnosis of BMs (40 vs. 10 months; p = 0.0036) was confirmed. CONCLUSION: Pre-existing hypothyroidism was strongly and independently associated with prognosis in patients with newly diagnosed BMs, supporting the evidence from preclinical data that THs may indeed have a tumour-promoting effect. Further investigation of the underlying pathobiological mechanism and potential therapeutic implications are required.

The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design.

Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits. In order to encourage its wide dissemination this article is freely accessible on the BMJ and Trials journal websites."To maximise the benefit to society, you need to not just do research but do it well" Douglas G Altman.

The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design.

Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits.

Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.

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.

Group sequential adaptive designs in series of time-to-event randomised trials in rare diseases: A simulation study.

In rare diseases, fully powered large trials may not be doable in a reasonable time frame even with international collaborations. In a previous work, we proposed an approach based on a series of smaller parallel group two-arm randomised controlled trials (RCT) performed over a long research horizon. Within the series of trials, the treatment selected after each trial becomes the control treatment of the next one. We concluded that running more trials with smaller sample sizes and relaxed α-levels leads in the long term and under reasonable assumptions to larger survival benefits with a moderate increase of risk as compared to traditional designs based on larger but fewer trials designed to meet stringent evidence criteria. We now extend this quantitative framework with more 'flexible' designs including interim analyses for futility and/or efficacy, and three-arm adaptive designs with treatment selection at interim. In the simulation study, we considered different disease severities, accrual rates, and hypotheses of how treatments improve over time. For each design, we estimated the long-term survival benefit as the relative difference in hazard rates between the end and the start of the research horizon, and the risk defined as the probability of selecting at the end of the research horizon a treatment inferior to the initial control. We assessed the impact of the α-level and the choice of the stopping rule on the operating characteristics. We also compared the performance of series based on two- vs. three-arm trials. We show that relaxing α-levels within the limit of 0.1 is associated with larger survival gains and moderate increase of risk which remains within acceptable ranges. Including an interim analysis with a futility rule is associated with an additional survival gain and a better risk control as compared to series with no interim analysis, when the α-level is below or equal to 0.1, whereas the benefit of including an interim analysis is rather small for higher α-levels. Including an interim analysis for efficacy yields almost no additional gain. Series based on three-arm trials are associated with a systematic improvement in terms of survival gain and risk control as compared to series of two-arm trials.

Are Novel, Nonrandomized Analytic Methods Fit for Decision Making? The Need for Prospective, Controlled, and Transparent Validation.

Real-world data and patient-level data from completed randomized controlled trials are becoming available for secondary analysis on an unprecedented scale. A range of novel methodologies and study designs have been proposed for their analysis or combination. However, to make novel analytical methods acceptable for regulators and other decision makers will require their testing and validation in broadly the same way one would evaluate a new drug: prospectively, well-controlled, and according to a pre-agreed plan. From a European regulators' perspective, the established methods qualification advice procedure with active participation of patient groups and other decision makers is an efficient and transparent platform for the development and validation of novel study designs.