Adding experimental treatment arms to multi-arm multi-stage platform trials in progress.

Multi-arm multi-stage (MAMS) platform trials efficiently compare several treatments with a common control arm. Crucially MAMS designs allow for adjustment for multiplicity if required. If for example, the active treatment arms in a clinical trial relate to different dose levels or different routes of administration of a drug, the strict control of the family-wise error rate (FWER) is paramount. Suppose a further treatment becomes available, it is desirable to add this to the trial already in progress; to access both the practical and statistical benefits of the MAMS design. In any setting where control of the error rate is required, we must add corresponding hypotheses without compromising the validity of the testing procedure.To strongly control the FWER, MAMS designs use pre-planned decision rules that determine the recruitment of the next stage of the trial based on the available data. The addition of a treatment arm presents an unplanned change to the design that we must account for in the testing procedure. We demonstrate the use of the conditional error approach to add hypotheses to any testing procedure that strongly controls the FWER. We use this framework to add treatments to a MAMS trial in progress. Simulations illustrate the possible characteristics of such procedures.

Innovative approaches for vaccine trials as a key component of pandemic preparedness - a white paper.

BACKGROUND: WHO postulates the application of adaptive design features in the global clinical trial ecosystem. However, the adaptive platform trial (APT) methodology has not been widely adopted in clinical research on vaccines. METHODS: The VACCELERATE Consortium organized a two-day workshop to discuss the applicability of APT methodology in vaccine trials under non-pandemic as well as pandemic conditions. Core aspects of the discussions are summarized in this article. RESULTS: An "ever-warm" APT appears ideally suited to improve efficiency and speed of vaccine research. Continuous learning based on accumulating APT trial data allows for pre-planned adaptations during its course. Given the relative design complexity, alignment of all stakeholders at all stages of an APT is central. Vaccine trial modelling is crucial, both before and in a pandemic emergency. Various inferential paradigms are possible (frequentist, likelihood, or Bayesian). The focus in the interpandemic interval may be on research gaps left by industry trials. For activation in emergency, template Disease X protocols of syndromal design for pathogens yet unknown need to be stockpiled and updated regularly. Governance of a vaccine APT should be fully integrated into supranational pandemic response mechanisms. DISCUSSION: A broad range of adaptive features can be applied in platform trials on vaccines. Faster knowledge generation comes with increased complexity of trial design. Design complexity should not preclude simple execution at trial sites. Continuously generated evidence represents a return on investment that will garner societal support for sustainable funding. Adaptive design features will naturally find their way into platform trials on vaccines.

Online error rate control for platform trials.

Platform trials evaluate multiple experimental treatments under a single master protocol, where new treatment arms are added to the trial over time. Given the multiple treatment comparisons, there is the potential for inflation of the overall type I error rate, which is complicated by the fact that the hypotheses are tested at different times and are not necessarily pre-specified. Online error rate control methodology provides a possible solution to the problem of multiplicity for platform trials where a relatively large number of hypotheses are expected to be tested over time. In the online multiple hypothesis testing framework, hypotheses are tested one-by-one over time, where at each time-step an analyst decides whether to reject the current null hypothesis without knowledge of future tests but based solely on past decisions. Methodology has recently been developed for online control of the false discovery rate as well as the familywise error rate (FWER). In this article, we describe how to apply online error rate control to the platform trial setting, present extensive simulation results, and give some recommendations for the use of this new methodology in practice. We show that the algorithms for online error rate control can have a substantially lower FWER than uncorrected testing, while still achieving noticeable gains in power when compared with the use of a Bonferroni correction. We also illustrate how online error rate control would have impacted a currently ongoing platform trial.

Comparison of pharmacokinetics and stability of generics of cefepime, linezolid and piperacillin/tazobactam with their respective originator drugs: an intravenous bioequivalence study in healthy volunteers.

OBJECTIVES: The efficacy and quality of generic antibacterial drug formulations are often questioned by both healthcare specialists and patients. Therefore, the present study investigated the interchangeability of generic drugs with their originators by comparing bioequivalence parameters and stability data of generic cefepime, linezolid and piperacillin/tazobactam with their respective originator drugs. METHODS: In this open-label, randomized, crossover bioequivalence study, three groups of 12 healthy volunteers each received a single intravenous infusion of either 2 g of cefepime or 4.5 g of piperacillin/tazobactam and two generic formulations, or 600 mg of linezolid and one generic formulation. Plasma sampling was performed, with a 5 day washout period between study days. Stability was tested by storing reconstituted generic and originator products according to their own storage specifications and those of the comparator products. All concentrations were measured by LC-MS. RESULTS: Similar ratios of generic/originator (90% CI) Cmax were observed for Cefepime-MIP/Maxipime [93.7 (88.4-99.4)], Cefepime Sandoz/Maxipime [95.9 (89.1-103.2)], Linezolid Kabi/Zyvoxid [104.5 (91.1-119.9)], Piperacillin Kabi/Tazobac [95.9 (90.4-101.7)], Piperacillin Aurobindo/Tazobac [99.7 (84.9-104.7)], Tazobactam Kabi/Tazobac [93.4 (87.4-99.8)] and Tazobactam Aurobindo/Tazobac [97.4 (89.7-105.8)]. Accordingly, similar ratios of AUC0-t were observed for Cefepime-MIP/Maxipime [91.1 (87.6-94.8)], Cefepime Sandoz/Maxipime [97.9 (92.5-103.5)], Linezolid Kabi/Zyvoxid [99.7 (93.3-106.6)], Piperacillin Kabi/Tazobac [92.2 (88.3-96.3)], Piperacillin Aurobindo/Tazobac [99.9 (97.0-102.8)], Tazobactam Kabi/Tazobac [91.4 (86.4-96.7)] and Tazobactam Aurobindo/Tazobac [98.8 (94.3-103.6)]. Stable and similar concentrations were measured for all contiguous substances, regardless of storage conditions. CONCLUSIONS: Compared with their respective originator drugs, generic cefepime, linezolid and piperacillin/tazobactam met the predetermined bioequivalence criteria. All formulations were stable under the storage conditions of their respective comparators.

Decision rules for identifying combination therapies in open-entry, randomized controlled platform trials.

Platform trials have become increasingly popular for drug development programs, attracting interest from statisticians, clinicians and regulatory agencies. Many statistical questions related to designing platform trials-such as the impact of decision rules, sharing of information across cohorts, and allocation ratios on operating characteristics and error rates-remain unanswered. In many platform trials, the definition of error rates is not straightforward as classical error rate concepts are not applicable. For an open-entry, exploratory platform trial design comparing combination therapies to the respective monotherapies and standard-of-care, we define a set of error rates and operating characteristics and then use these to compare a set of design parameters under a range of simulation assumptions. When setting up the simulations, we aimed for realistic trial trajectories, such that for example, a priori we do not know the exact number of treatments that will be included over time in a specific simulation run as this follows a stochastic mechanism. Our results indicate that the method of data sharing, exact specification of decision rules and a priori assumptions regarding the treatment efficacy all strongly contribute to the operating characteristics of the platform trial. Furthermore, different operating characteristics might be of importance to different stakeholders. Together with the potential flexibility and complexity of a platform trial, which also impact the achieved operating characteristics via, for example, the degree of efficiency of data sharing this implies that utmost care needs to be given to evaluation of different assumptions and design parameters at the design stage.

Robust group sequential designs for trials with survival endpoints and delayed response.

Randomized clinical trials in oncology typically utilize time-to-event endpoints such as progression-free survival or overall survival as their primary efficacy endpoints, and the most commonly used statistical test to analyze these endpoints is the log-rank test. The power of the log-rank test depends on the behavior of the hazard ratio of the treatment arm to the control arm. Under the assumption of proportional hazards, the log-rank test is asymptotically fully efficient. However, this proportionality assumption does not hold true if there is a delayed treatment effect. Cancer immunology has evolved over time and several cancer vaccines are available in the market for treating existing cancers. This includes sipuleucel-T for metastatic hormone-refractory prostate cancer, nivolumab for metastatic melanoma, and pembrolizumab for advanced nonsmall-cell lung cancer. As cancer vaccines require some time to elicit an immune response, a delayed treatment effect is observed, resulting in a violation of the proportional hazards assumption. Thus, the traditional log-rank test may not be optimal for testing immuno-oncology drugs in randomized clinical trials. Moreover, the new immuno-oncology compounds have been shown to be very effective in prolonging overall survival. Therefore, it is desirable to implement a group sequential design with the possibility of early stopping for overwhelming efficacy. In this paper, we investigate the max-combo test, which utilizes the maximum of two weighted log-rank statistics, as a robust alternative to the log-rank test. The new test is implemented for two-stage designs with possible early stopping at the interim analysis time point. Two classes of weights are investigated for the max-combo test: the Fleming and Harrington (1981) Gρ,γ$G^{\rho , \gamma }$ weights and the Magirr and Burman (2019) modest (τ∗)$ (\tau ^{*})$  weights.

Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs.

We design two-stage confirmatory clinical trials that use adaptation to find the subgroup of patients who will benefit from a new treatment, testing for a treatment effect in each of two disjoint subgroups. Our proposal allows aspects of the trial, such as recruitment probabilities of each group, to be altered at an interim analysis. We use the conditional error rate approach to implement these adaptations with protection of overall error rates. Applying a Bayesian decision-theoretic framework, we optimize design parameters by maximizing a utility function that takes the population prevalence of the subgroups into account. We show results for traditional trials with familywise error rate control (using a closed testing procedure) as well as for umbrella trials in which only the per-comparison type 1 error rate is controlled. We present numerical examples to illustrate the optimization process and the effectiveness of the proposed designs.

Analysis of Inflammatory Mediator Profiles in Sepsis Patients Reveals That Extracellular Histones Are Strongly Elevated in Nonsurvivors.

The timely recognition of sepsis and the prediction of its clinical course are challenging due to the complex molecular mechanisms leading to organ failure and to the heterogeneity of sepsis patients. Treatment strategies relying on a "one-fits-all" approach have failed to reduce mortality, suggesting that therapeutic targets differ between patient subgroups and highlighting the need for accurate analysis of the molecular cascades to assess the highly variable host response. Here, we characterized a panel of 44 inflammatory mediators, including cytokines, chemokines, damage-associated molecular patterns, and coagulation-related factors, as well as markers of endothelial activation in 30 patients suffering from renal failure in the course of sepsis. All patients received continuous veno-venous hemodialysis with either high cut-off filters or with standard filters, and mediators were quantified for all patients at the initiation of dialysis and after 24 h and 48 h. Mediator concentrations in individual patients ranged widely, demonstrating the heterogeneity of sepsis patients. None of the mediators correlated with SAPS III or TISS scores. The overall in-hospital mortality of the study population was 56.7% (57.1% vs. 56.3% for high cut-off vs. standard filter). The two filter groups differed regarding most of the mediator levels at baseline, prohibiting conclusions regarding the effect of standard filters versus high cut-off filters on mediator depletion. The elevation and correlation of damage-associated molecular patterns and markers of endothelial activation gave evidence of severe tissue damage. In particular, extracellular histones were strongly increased and were almost 30-fold higher in nonsurvivors as compared to survivors, indicating their diagnostic and prognostic potential.

Delayed treatment effects, treatment switching and heterogeneous patient populations: How to design and analyze RCTs in oncology.

In the analysis of survival times, the logrank test and the Cox model have been established as key tools, which do not require specific distributional assumptions. Under the assumption of proportional hazards, they are efficient and their results can be interpreted unambiguously. However, delayed treatment effects, disease progression, treatment switchers or the presence of subgroups with differential treatment effects may challenge the assumption of proportional hazards. In practice, weighted logrank tests emphasizing either early, intermediate or late event times via an appropriate weighting function may be used to accommodate for an expected pattern of non-proportionality. We model these sources of non-proportional hazards via a mixture of survival functions with piecewise constant hazard. The model is then applied to study the power of unweighted and weighted log-rank tests, as well as maximum tests allowing different time dependent weights. Simulation results suggest a robust performance of maximum tests across different scenarios, with little loss in power compared to the most powerful among the considered weighting schemes and huge power gain compared to unfavorable weights. The actual sources of non-proportional hazards are not obvious from resulting populationwise survival functions, highlighting the importance of detailed simulations in the planning phase of a trial when assuming non-proportional hazards.We provide the required tools in a software package, allowing to model data generating processes under complex non-proportional hazard scenarios, to simulate data from these models and to perform the weighted logrank tests.

A critical review of graphics for subgroup analyses in clinical trials.

Subgroup analyses are a routine part of clinical trials to investigate whether treatment effects are homogeneous across the study population. Graphical approaches play a key role in subgroup analyses to visualise effect sizes of subgroups, to aid the identification of groups that respond differentially, and to communicate the results to a wider audience. Many existing approaches do not capture the core information and are prone to lead to a misinterpretation of the subgroup effects. In this work, we critically appraise existing visualisation techniques, propose useful extensions to increase their utility and attempt to develop an effective visualisation approach. We focus on forest plots, UpSet plots, Galbraith plots, subpopulation treatment effect pattern plot, and contour plots, and comment on other approaches whose utility is more limited. We illustrate the methods using data from a prostate cancer study.

A multiple comparison procedure for dose-finding trials with subpopulations.

Identifying subgroups of patients with an enhanced response to a new treatment has become an area of increased interest in the last few years. When there is knowledge about possible subpopulations with an enhanced treatment effect before the start of a trial it might be beneficial to set up a testing strategy, which tests for a significant treatment effect not only in the full population, but also in these prespecified subpopulations. In this paper, we present a parametric multiple testing approach for tests in multiple populations for dose-finding trials. Our approach is based on the MCP-Mod methodology, which uses multiple comparison procedures (MCPs) to test for a dose-response signal, while considering multiple possible candidate dose-response shapes. Our proposed methods allow for heteroscedastic error variances between populations and control the family-wise error rate over tests in multiple populations and for multiple candidate models. We show in simulations that the proposed multipopulation testing approaches can increase the power to detect a significant dose-response signal over the standard single-population MCP-Mod, when the specified subpopulation has an enhanced treatment effect.

Nested combination tests with a time-to-event endpoint using a short-term endpoint for design adaptations.

Adaptive trial methodology for multiarmed trials and enrichment designs has been extensively discussed in the past. A general principle to construct test procedures that control the family-wise Type I error rate in the strong sense is based on combination tests within a closed test. Using survival data, a problem arises when using information of patients for adaptive decision making, which are under risk at interim. With the currently available testing procedures, either no testing of hypotheses in interim analyses is possible or there are restrictions on the interim data that can be used in the adaptation decisions as, essentially, only the interim test statistics of the primary endpoint may be used. We propose a general adaptive testing procedure, covering multiarmed and enrichment designs, which does not have these restrictions. An important application are clinical trials, where short-term surrogate endpoints are used as basis for trial adaptations, and we illustrate how such trials can be designed. We propose statistical models to assess the impact of effect sizes, the correlation structure between the short-term and the primary endpoint, the sample size, the timing of interim analyses, and the selection rule on the operating characteristics.

Interim analysis incorporating short- and long-term binary endpoints.

Designs incorporating more than one endpoint have become popular in drug development. One of such designs allows for incorporation of short-term information in an interim analysis if the long-term primary endpoint has not been yet observed for some of the patients. At first we consider a two-stage design with binary endpoints allowing for futility stopping only based on conditional power under both fixed and observed effects. Design characteristics of three estimators: using primary long-term endpoint only, short-term endpoint only, and combining data from both are compared. For each approach, equivalent cut-off point values for fixed and observed effect conditional power calculations can be derived resulting in the same overall power. While in trials stopping for futility the type I error rate cannot get inflated (it usually decreases), there is loss of power. In this study, we consider different scenarios, including different thresholds for conditional power, different amount of information available at the interim, different correlations and probabilities of success. We further extend the methods to adaptive designs with unblinded sample size reassessments based on conditional power with inverse normal method as the combination function. Two different futility stopping rules are considered: one based on the conditional power, and one from P-values based on Z-statistics of the estimators. Average sample size, probability to stop for futility and overall power of the trial are compared and the influence of the choice of weights is investigated.

A randomized controlled trial of alanyl-glutamine supplementation in peritoneal dialysis fluid to assess impact on biomarkers of peritoneal health.

In early clinical testing, acute addition of alanyl-glutamine (AlaGln) to glucose-based peritoneal dialysis (PD) fluids restored peritoneal cellular stress responses and leukocyte function. This study was designed to test the effect of extended treatment with AlaGln-supplemented PD fluid on biomarkers of peritoneal health. In a double-blinded, randomized crossover design, stable PD patients were treated with AlaGln (8 mM) or placebo added to PD fluid for eight weeks. As primary outcome measures, dialysate cancer-antigen 125 (CA-125) appearance rate and ex vivo stimulated interleukin-6 (IL-6) release were assessed in peritoneal equilibration tests. In 8 Austrian centers, 54 patients were screened, 50 randomized, and 41 included in the full analysis set. AlaGln supplementation significantly increased CA-125 appearance rate and ex vivo stimulated IL-6 release. AlaGln supplementation also reduced peritoneal protein loss, increased ex vivo stimulated tumor necrosis factor (TNF)-α release, and reduced systemic IL-8 levels. No adverse safety signals were observed. All 4 peritonitis episodes occurred during standard PD fluid treatment. A novel AlaGln-supplemented PD fluid improves biomarkers of peritoneal membrane integrity, immune competence, and systemic inflammation compared to unsupplemented PD fluid with neutral pH and low-glucose degradation. A phase 3 trial is needed to determine the impact of AlaGln supplementation on hard clinical outcomes.

Clearance of Selected Plasma Cytokines with Continuous Veno-Venous Hemodialysis Using Ultraflux EMiC2 versus Ultraflux AV1000S.

BACKGROUND: High cutoff hemofilters might support the restoration of immune homeostasis in systemic inflammation by depleting inflammatory mediators from the circulation. METHODS: Interleukin (IL)-6, IL-8, IL-10, and tumor necrosis factor-alpha depletion was assessed in 30 sepsis patients with acute renal failure using continuous veno-venous hemodialysis with high cutoff versus standard filters (CVVHD-HCO vs. CVVHD-STD) over 48 h. RESULTS: The transfer of IL-6 and IL-8 was significantly higher for CVVHD-HCO, as shown by increased IL-6 and IL-8 effluent concentrations. The mean plasma cytokine concentrations decreased over time for all cytokines without detectable differences for the treatment modalities. No transfer of albumin was observed for either of the filters. C-reactive protein remained stable over time and did not differ between CVVHD-HCO and CVVHD-STD, while procalcitonin decreased significantly over 48 h for both treatment modalities. CONCLUSION: CVVHD-HCO achieved enhanced removal of IL-6 and IL-8 as compared to CVVHD-STD, without differentially reducing plasma cytokine levels.

Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls.

'Multistage testing with adaptive designs' was the title of an article by Peter Bauer that appeared 1989 in the German journal Biometrie und Informatik in Medizin und Biologie. The journal does not exist anymore but the methodology found widespread interest in the scientific community over the past 25 years. The use of such multistage adaptive designs raised many controversial discussions from the beginning on, especially after the publication by Bauer and Köhne 1994 in Biometrics: Broad enthusiasm about potential applications of such designs faced critical positions regarding their statistical efficiency. Despite, or possibly because of, this controversy, the methodology and its areas of applications grew steadily over the years, with significant contributions from statisticians working in academia, industry and agencies around the world. In the meantime, such type of adaptive designs have become the subject of two major regulatory guidance documents in the US and Europe and the field is still evolving. Developments are particularly noteworthy in the most important applications of adaptive designs, including sample size reassessment, treatment selection procedures, and population enrichment designs. In this article, we summarize the developments over the past 25 years from different perspectives. We provide a historical overview of the early days, review the key methodological concepts and summarize regulatory and industry perspectives on such designs. Then, we illustrate the application of adaptive designs with three case studies, including unblinded sample size reassessment, adaptive treatment selection, and adaptive endpoint selection. We also discuss the availability of software for evaluating and performing such designs. We conclude with a critical review of how expectations from the beginning were fulfilled, and - if not - discuss potential reasons why this did not happen.

Adaptive paediatric investigation plans, a small step to improve regulatory decision making in drug development for children?

Different arguments have been put forward why drug developers should commit themselves early for what they are planning to do for children. By EU regulation, paediatric investigation plans should be agreed on in early phases of drug development in adults. Here, extrapolation from adults to children is widely applied to reduce the burden and avoids unnecessary clinical trials in children, but early regulatory decisions on how far extrapolation can be used may be highly uncertain. Under special circumstances, the regulatory process should allow for adaptive paediatric investigation plans explicitly foreseeing a re-evaluation of the early decision based on the information accumulated later from adults or elsewhere. A small step towards adaptivity and learning from experience may improve the quality of regulatory decisions in particular with regard to how much information can be borrowed from adults. © 2016 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd.

Long-term outcome of belatacept therapy in de novo kidney transplant recipients - a case-match analysis.

While belatacept has shown favorable short- and midterm results in kidney transplant recipients, only projections exist regarding its potential impact on long-term outcome. Therefore, we performed a retrospective case-match analysis of the 14 belatacept patients originally enrolled in the phase II multicenter trial at our center. Fifty six cyclosporine (CyA)-treated patients were matched according to age at transplantation, first/retransplant, and donor type. Ten years after kidney transplantation, kidney function remained superior in belatacept-treated patients compared with the CyA control group. Moreover, none of the belatacept-treated patients had donor-specific antibodies ≥10 years post-transplantation compared with 38.5% of tested CyA-treated subject (0/10 vs. 5/13; P = 0.045). Notably, however, patient and graft survival was virtually identical in both groups (71.4% vs. 71.3%; P = 0.976). In the present single-center study population, patients treated with belatacept demonstrated a patient and graft survival at 10 years post-transplant which was comparable to that of similarly selected CNI-treated patients. Larger studies with sufficient statistical power are necessary to definitively determine long-term graft survival with belatacept.

ABO antibody and complement depletion by immunoadsorption combined with membrane filtration--a randomized, controlled, cross-over trial.

BACKGROUND: Potent antibody depletion techniques have paved the way to successful ABO-incompatible transplantation. Considering its efficiency regarding IgG removal, the use of non-antigen-specific semi-selective immunoadsorption (IA) has been advocated. One attractive strategy to overcome the caveat of incomplete IgM depletion and to interfere with complement activation could be the adjunctive use of membrane filtration (MF) to enhance the removal of macromolecules. METHODS: To investigate the depletion efficiency of semi-selective IA plus MF, we conducted a randomized, controlled, cross-over trial including patients on regular IA treatment for indications outside recipient desensitization. According to the results of sample size calculation, 14 subjects were enrolled. Two treatment sequences, a single session of IA plus MF followed by IA alone after ≥7 days (and vice versa), were analysed. RESULTS: IA plus MF markedly enhanced the median per cent reduction of ABO-specific IgM determined by flow cytometry (primary end point; 59 versus 23%, P < 0.001) and haemagglutination (2 versus 1 titre steps, P < 0.001), respectively. Combined treatment also substantially lowered C1q concentrations (86 versus 58% reduction, P < 0.001) and the functionality of classical complement as reflected by impaired in vitro C3 activation capability. IgG was strongly reduced without any additional effect of MF. CONCLUSIONS: We demonstrate that the innovative strategy of combining MF with semi-selective IA may substantially increase IgM elimination and affect classical complement activation. Our findings suggest that this new treatment concept could be an efficient strategy for recipient desensitization in ABO- and HLA-incompatible transplantation.