Modification of sample size in group sequential clinical trials

Preservation of Type I Error for Partially-Unblinded Sample Size Re-Estimation

Sample size re-estimation (SSR) at an interim analysis allows for adjustments based on accrued data. Existing strategies rely on either blinded or unblinded methods to inform such adjustments and, ideally, perform these adjustments in a way that preserves Type I error at the nominal level. Here, we propose an approach that uses partially-unblinded methods for SSR for both binary and continuous endpoints. Although this approach has operational unblinding, its partial use of the unblinded information for SSR does not include the interim effect size, hence the term 'partially-unblinded.' Through proof-of-concept and simulation studies, we demonstrate that these adjustments can be made without compromising the Type I error rate. We also investigate different mathematical expressions for SSR under different variance scenarios: homogeneity, heterogeneity, and a combination of both. Of particular interest is the third form of dual variance, for which we provide additional clarifications for binary outcomes and derive an analogous form for continuous outcomes. We show that the corresponding mathematical expressions for the dual variance method are a compromise between those for variance homogeneity and heterogeneity, resulting in sample size estimates that are bounded between those produced by the other expressions, and extend their applicability to adaptive trial design.

Sample Size Adjustment in Sequential Multiple Assignment Randomized Trials

Clinical trials are often designed based on limited information about effect sizes and precision parameters with risks of underpowered studies. This is more problematic for SMARTs where strategy effects are based on sequences of treatments. Sample size adjustment offers flexibility through re-estimating sample size during the trial to ensure adequate power at the final analysis. While this adaptation is common for standard clinical trials, corresponding methods to perform sample size adjustment have not been adapted to SMARTs. In this paper, we propose a sample size adjustment procedure for SMARTs. Sample sizes are re-calculated at the interim analysis based on the conditional power derived from a bivariate non-central chi-square distribution. We demonstrate through simulation studies that even with an underpowered initial sample size due to miss-specified parameters at the design stage, the proposed method can maintain desirable power at the end of the study, and additional resources are only invested in trials that show promising conditional power at the interim analysis.

Testing One Primary and Two Secondary Endpoints in a Two-Stage Group Sequential Trial With Extensions

We study the problem of testing multiple secondary endpoints conditional on a primary endpoint being significant in a two-stage group sequential procedure, focusing on two secondary endpoints. This extends our previous work with one secondary endpoint. The test for the secondary null hypotheses is a closed procedure. Application of the Bonferroni test for testing the intersection of the secondary hypotheses results in the Holm procedure while application of the Simes test results in the Hochberg procedure. The focus of the present paper is on developing normal theory analogs of the abovementionedp $$ p $$ -value based tests that take into account (i) the gatekeeping effect of the test on the primary endpoint and (ii) correlations between the endpoints. The normal theory boundaries are determined by finding the least favorable configuration of the correlations and so their knowledge is not needed to apply these procedures. Thep $$ p $$ -value based procedures are easy to apply but they are less powerful than their normal theory analogs because they do not take into account the correlations between the endpoints and the gatekeeping effect referred to above. On the other hand, the normal theory procedures are restricted to two secondary endpoints and two stages mainly because of computational difficulties with more than two secondary endpoints and stages. Comparisons between the two types of procedures are given in terms of secondary powers. The sensitivity of the secondary type I error rate and power to unequal information times is studied. Numerical examples and a real case study illustrate the procedures.

Past, present, and future of Phase 3 vaccine trial design: rethinking statistics for the 21st century

Vaccines are crucial for protecting health globally; however, their widespread use relies on rigorous clinical development programmes. This includes Phase 3 randomized controlled trials (RCTs) to confirm their safety, immunogenicity, and efficacy. Traditionally, such trials used fixed designs with predetermined assumptions, lacking the flexibility to change during the trial or stop early due to overwhelming evidence of either efficacy or futility. Modern vaccine trials benefit from innovative approaches like adaptive designs, allowing for planned trial adaptations based on accumulating data. Here, we provide an overview of the evolution of Phase 3 vaccine trial design and statistical analysis methods from traditional to more innovative contemporary methods. This includes adaptive trial designs, which offer ethical advantages and enable early termination if indicated; Bayesian methods, which combine prior knowledge and observed trial data to increase efficiency and enhance result interpretation; modern statistical analysis methods, which enable more accurate and precise inferences; the estimand framework, which ensures the primary question of interest is addressed in a trial; novel approaches using machine learning methods to assess heterogeneity of treatment effects; and statistical advances in safety analysis to evaluate reactogenicity and clinical adverse events. We conclude with insights into the future direction of vaccine trials, aiming to inform clinicians and researchers about conventional and novel RCT design and analysis approaches to facilitate the conduct of efficient, timely trials.

Practical considerations of promising zone design for interim sample size Re-estimation: An application to GRAPHITE for graft vs host disease

BACKGROUND

Sample size calculation and power estimate are an integral part of clinical trials. With accelerated development to address the unmet medical needs, the fast-paced development may lead to uncertainties in initial planning and assumptions of clinical trials. Promising zone design presents sponsors an opportunity to re-estimate the sample size based on the interim data to mitigate risks, reduce uncertainties, and increase probability of trial success.

METHODS

This paper aims to use the GRAPHITE trial (NCT03657160) as a real data application to showcase the practical considerations in implementation of promising zone design for interim sample size re-estimation (SSR), in light of sample size adaptation rules, maximum sample size allowed, multiplicity adjustment, and sponsor access to interim results. GRAPHITE is a phase 3 trial with vedolizumab for prophylaxis of acute graft vs host disease (aGvHD) after allogeneic hematopoietic stem cell transplant (allo-HSCT). The primary efficacy endpoint is lower intestinal aGVHD-free survival by Day +180 after allo-HSCT. A simulation study was conducted to demonstrate the evaluation of operating characteristics by various true underlying treatment effects at the design stage.

CONCLUSION

The application of promising zone design for interim SSR is novel and has successfully helped the sponsor achieve the balance between minimizing the risks and maintaining scientific integrity. This work aims to highlight the necessity of empirical guidance to gain better insights for clinical researchers in practice and is expected to facilitate the understanding and implementation of promising zone design for interim SSR in phase 3 trials.

Bayesian Sample Size Calculation in Small n, Sequential Multiple Assignment Randomized Trials (snSMART)

A recent study design for clinical trials with small sample sizes is the small n, sequential, multiple assignment, randomized trial (snSMART). An snSMART design has been previously proposed to compare the efficacy of two dose levels versus placebo. In such a trial, participants are initially randomized to receive either low dose, high dose or placebo in stage 1. In stage 2, participants are re-randomized to either dose level depending on their initial treatment and a dichotomous response. A Bayesian analytic approach borrowing information from both stages was proposed and shown to improve the efficiency of estimation. In this paper, we propose two sample size determination (SSD) methods for the proposed snSMART comparing two dose levels with placebo. Both methods adopt the average coverage criterion (ACC) approach. In the first approach, the sample size is calculated in one step, taking advantage of the explicit posterior variance of the treatment effect. In the other two step approach, we update the sample size needed for a single-stage parallel design with a proposed adjustment factor (AF). Through simulations, we demonstrate that the required sample sizes calculated using the two SSD approaches both provide the desired power. We also provide an applet to allow for convenient and fast sample size calculation in this snSMART setting.

An Adaptive Three-Arm Comparative Clinical Endpoint Bioequivalence Study Design With Unblinded Sample Size Re-Estimation and Optimized Allocation Ratio

A three-arm comparative clinical endpoint bioequivalence (BE) study is often used to establish bioequivalence (BE) between a locally acting generic drug (T) and reference drug (R), where superiority needs to be established for T and R over Placebo (P) and equivalence needs to be established for T vs. R. Sometimes, when study design parameters are uncertain, a fixed design study may be under- or over-powered and result in study failure or unnecessary cost. In this paper, we propose a two-stage adaptive clinical endpoint BE study with unblinded sample size re-estimation, standard or maximum combination method, optimized allocation ratio, optional re-estimation of the effect size based on likelihood estimation, and optional re-estimation of the R and P treatment means at interim analysis, which have not been done previously. Our proposed method guarantees control of Type 1 error rate analytically. It helps to reduce the average sample size when the original fixed design is overpowered and increases the sample size and power when the original study and group sequential design are under-powered. Our proposed adaptive design can help generic drug sponsors cut cost and improve success rate, making clinical study endpoint BE studies more affordable and more generic drugs accessible to the public.

The analysis and reporting of multiple outcomes in mental health trials: a methodological systematic review

BACKGROUND

The choice of a single primary outcome in randomised trials can be difficult, especially in mental health where interventions may be complex and target several outcomes simultaneously. We carried out a systematic review to assess the quality of the analysis and reporting of multiple outcomes in mental health RCTs, comparing approaches with current CONSORT and other regulatory guidance.

METHODS

The review included all late-stage mental health trials published between 1st January 2019 to 31st December 2020 in 9 leading medical and mental health journals. Pilot and feasibility trials, non-randomised trials, and early phase trials were excluded. The total number of primary, secondary and other outcomes was recorded, as was any strategy used to incorporate multiple primary outcomes in the primary analysis.

RESULTS

There were 147 included mental health trials. Most trials (101/147) followed CONSORT guidance by specifying a single primary outcome with other outcomes defined as secondary and analysed in separate statistical analyses, although a minority (10/147) did not specify any outcomes as primary. Where multiple primary outcomes were specified (33/147), most (26/33) did not correct for multiplicity, contradicting regulatory guidance. The median number of clinical outcomes reported across studies was 8 (IQR 5-11 ).

CONCLUSIONS

Most trials are correctly following CONSORT guidance. However, there was little consideration given to multiplicity or correlation between outcomes even where multiple primary outcomes were stated. Trials should correct for multiplicity when multiple primary outcomes are specified or describe some other strategy to address the multiplicity. Overall, very few mental health trials are taking advantage of multiple outcome strategies in the primary analysis, especially more complex strategies such as multivariate modelling. More work is required to show these exist, aid interpretation, increase efficiency and are easily implemented.

REGISTRATION

Our systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO) on 11th January 2023 (CRD42023382274).

Interatrial Shunt Treatment for Heart Failure: The Randomized RELIEVE-HF Trial

BACKGROUND

An interatrial shunt may provide an autoregulatory mechanism to decrease left atrial pressure and improve heart failure (HF) symptoms and prognosis.

METHODS

Patients with symptomatic HF with any left ventricular ejection fraction (LVEF) were randomized 1:1 to transcatheter shunt implantation versus a placebo procedure, stratified by reduced (≤40%) versus preserved (>40%) LVEF. The primary safety outcome was a composite of device-related or procedure-related major adverse cardiovascular or neurological events at 30 days compared with a prespecified performance goal of 11%. The primary effectiveness outcome was the hierarchical composite ranking of all-cause death, cardiac transplantation or left ventricular assist device implantation, HF hospitalization, outpatient worsening HF events, and change in quality of life from baseline measured by the Kansas City Cardiomyopathy Questionnaire overall summary score through maximum 2-year follow-up, assessed when the last enrolled patient reached 1-year follow-up, expressed as the win ratio. Prespecified hypothesis-generating analyses were performed in patients with reduced and preserved LVEF.

RESULTS

Between October 24, 2018, and October 19, 2022, 508 patients were randomized at 94 sites in 11 countries to interatrial shunt treatment (n=250) or a placebo procedure (n=258). Median (25th and 75th percentiles) age was 73.0 years (66.0, 79.0), and 189 patients (37.2%) were women. Median LVEF was reduced (≤40%) in 206 patients (40.6%) and preserved (>40%) in 302 patients (59.4%). No primary safety events occurred after shunt implantation (upper 97.5% confidence limit, 1.5%; P<0.0001). There was no difference in the 2-year primary effectiveness outcome between the shunt and placebo procedure groups (win ratio, 0.86 [95% CI, 0.61-1.22]; P=0.20). However, patients with reduced LVEF had fewer adverse cardiovascular events with shunt treatment versus placebo (annualized rate 49.0% versus 88.6%; relative risk, 0.55 [95% CI, 0.42-0.73]; P<0.0001), whereas patients with preserved LVEF had more cardiovascular events with shunt treatment (annualized rate 60.2% versus 35.9%; relative risk, 1.68 [95% CI, 1.29-2.19]; P=0.0001; Pinteraction<0.0001). There were no between-group differences in change in Kansas City Cardiomyopathy Questionnaire overall summary score during follow-up in all patients or in those with reduced or preserved LVEF.

CONCLUSIONS

Transcatheter interatrial shunt implantation was safe but did not improve outcomes in patients with HF. However, the results from a prespecified exploratory analysis in stratified randomized groups suggest that shunt implantation is beneficial in patients with reduced LVEF and harmful in patients with preserved LVEF.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT03499236.

Incorporating external real-world data (RWD) in confirmatory adaptive design

Adaptive designs, such as group sequential designs (and the ones with additional adaptive features) or adaptive platform trials, have been quintessential efficient design strategies in trials of unmet medical needs, especially for generating evidence from global regions. Such designs allow interim decision making and making adjustment to study design when necessary, meanwhile maintaining study integrity and operating characteristics. However, driven by the heightened competitive landscape and the desire to bring effective treatment to patients faster, innovation in the already functional designs is still germane to further propel drug development to a more efficient path. One way to achieve this is by leveraging external real-world data (RWD) in the adaptive designs to support interim or final decision making. In this paper, we propose a novel framework of incorporating external RWD in adaptive design to improve interim and/or final analysis decision making. Within this framework, researchers can prespecify the decision process and choose the timing and amount of borrowing while maintaining objectivity and controlling of type I error. Simulation studies in various scenarios are provided to describe power, type I error, and other performance metrics for interim/final decision making. A case study in non-small cell lung cancer is used for illustration on proposed design framework.

Flexible seamless 2-in-1 design with sample size adaptation

The 2-in-1 design is becoming popular in oncology drug development, with the flexibility in using different endpoints at different decision time. Based on the observed interim data, sponsors can choose to seamlessly advance a small phase 2 trial to a full-scale confirmatory phase 3 trial with a pre-determined maximum sample size or remain in a phase 2 trial. While this approach may increase efficiency in drug development, it is rigid and requires a pre-specified fixed sample size. In this paper, we propose a flexible 2-in-1 design with sample size adaptation, while retaining the advantage of allowing an intermediate endpoint for interim decision-making. The proposed design reflects the needs of the recent FDA's Project FrontRunner initiative, which encourages the use of an earlier surrogate endpoint to potentially support accelerated approval with conversion to standard approval with long-term endpoints from the same randomized study. Additionally, we identify the interim decision cut-off to allow a conventional test procedure at the final analysis. Extensive simulation studies showed that the proposed design requires much a smaller sample size and shorter timeline than the simple 2-in-1 design, while achieving similar power. We present a case study in multiple myeloma to demonstrate the benefits of the proposed design.

Use of Seamless Study Designs in Oncology Clinical Development- A Survey Conducted by IDSWG Oncology Sub-team

Seamless study designs have the potential to accelerate clinical development. The use of innovative seamless designs has been increasing in the oncology area; however, while the concept of seamless designs becomes more popular and accepted, many challenges remain in both the design and conduct of these trials. This may be especially true when seamless designs are used in late phase development supporting regulatory decision-making. The Innovative Design Scientific Working Group (IDSWG) Oncology team conducted a survey to understand the current use of seamless study designs for registration purposes in oncology clinical development. The survey was designed to provide insights into the benefits and to identify the roadblocks. A total of 16 questions were included in the survey that was distributed using the ASA Biopharmaceutical Section and IDSWG email listings from August to September 2022. A total of 51 responses were received, with 39 (76%) respondents indicating that their organizations had seamless oncology studies in planning or implementation for registration purposes. Detailed survey results are presented in the manuscript. Overall, while seamless designs offer advantages in terms of timeline reduction and cost saving, they also present challenges related to additional complexity and the need for efficient surrogate clinical endpoints in oncology drug development.

Performance Evaluation of Interim Analysis in Bioequivalence Studies

Under current bioequivalence guidelines in Japan, it is mandatory to establish bioequivalence using a single pivotal study. Clinical trials with limited resources usually have a pre-defined maximum permissible number of participants. In this manuscript, we considered a trial design that would allow for bioequivalence evaluation at an interim analysis in which the total number of participants takes into account the resource constraints. Then, available options at the interim analysis are group sequential designs and adaptive designs, A comparison of the performance of the two methods under same maximum participant number has not been conducted thus far. So we examined which method should be used by conducting a simulation study. Since bioequivalence is expected to be achieved at the interim analysis, a study design using a Pocock-type alpha spending function is preferrable. Simulation results using a Pocock-type alpha spending function showed similar performance between group sequential and adaptive designs. Consequently, due to statistical and operational complexity, it is preferable to choose group sequential designs for bioequivalence study in Japan.

Rationale and Design of PURE: A Randomized Controlled Trial to Evaluate Peritoneal Ultrafiltration with PolyCore™ in Refractory Congestive Heart Failure

INTRODUCTION

Peritoneal ultrafiltration (PUF) has been proposed as an additional therapeutic option for refractory congestive heart failure (RCHF) patients. Despite promising observational studies and/or case report results, limited clinical trial data exist, and so far, PUF solutions remain only indicated for chronic kidney diseases. In this article, we described a multicenter, randomized, controlled, unblinded, adaptive design clinical trial, about to start, investigating the effects of PolyCore™, an innovative PUF solution, in the treatment of RCHF patients.

METHODS

The Peritoneal Ultrafiltration in Cardiorenal Syndrome (PURE) study is a phase II, multicenter, randomized, controlled, unblinded, adaptive design clinical trial that aims to evaluate the safety and efficacy of PUF, using PolyCore™ as the investigational solution, in the treatment of RCHF patients who present with prominent right ventricular failure due to afterload mismatch, functional tricuspid regurgitation and enlarged cava vein consequent to intravascular fluid overload. Approximately 84 patients will be randomized 1:1 either to continue with their prescribed guideline-directed medical therapy or to add the PUF treatment on top of it. The primary objective is to evaluate if PUF treatment has an impact on the composite endpoint of the patient's mortality or worsening of the patient's condition such as hospitalization for cardiovascular causes, increasing the initial daily dose of loop diuretic or worsening of renal function. Statistical analysis for the primary endpoint will be standard survival analysis to estimate the failure rate at month 7 for each group via Kaplan-Meier curves. Sensitivity analysis and various secondary analyses, including a multiple events analysis, will be conducted to evaluate the robustness of the primary endpoint results. Safety will be evaluated for up to 12 months.

CONCLUSION

The PURE study was designed to evaluate the safety and efficacy of peritoneal ultrafiltration with PolyCore™ on top of guideline-directed medical therapy in patients with RCHF, assuming a combined clinical endpoint of mortality or worsening patients' condition. If successful, the treatment should allow for an improvement of the RCHF symptoms, decreasing hospitalization rate of patients.

Two-stage randomized clinical trials with a right-censored endpoint: Comparison of frequentist and Bayesian adaptive designs

Adaptive randomized clinical trials are of major interest when dealing with a time-to-event outcome in a prolonged observation window. No consensus exists either to define stopping boundaries or to combinep $$ p $$ values or test statistics in the terminal analysis in the case of a frequentist design and sample size adaptation. In a one-sided setting, we compared three frequentist approaches using stopping boundaries relying onα $$ \alpha $$ -spending functions and a Bayesian monitoring setting with boundaries based on the posterior distribution of the log-hazard ratio. All designs comprised a single interim analysis with an efficacy stopping rule and the possibility of sample size adaptation at this interim step. Three frequentist approaches were defined based on the terminal analysis: combination of stagewise statistics (Wassmer) or ofp $$ p $$ values (Desseaux), or on patientwise splitting (Jörgens), and we compared the results with those of the Bayesian monitoring approach (Freedman). These different approaches were evaluated in a simulation study and then illustrated on a real dataset from a randomized clinical trial conducted in elderly patients with chronic lymphocytic leukemia. All approaches controlled for the type I error rate, except for the Bayesian monitoring approach, and yielded satisfactory power. It appears that the frequentist approaches are the best in underpowered trials. The power of all the approaches was affected by the violation of the proportional hazards (PH) assumption. For adaptive designs with a survival endpoint and a one-sided alternative hypothesis, the Wassmer and Jörgens approaches after sample size adaptation should be preferred, unless violation of PH is suspected.

A systematic approach to adaptive sequential design for clinical trials: using simulations to select a design with desired operating characteristics

The failure rates of phase 3 trials are high. Incorrect sample size due to uncertainty of effect size could be a critical contributing factor. Adaptive sequential design (ASD), which may include one or more sample size re-estimations (SSR), has been a popular approach for dealing with such uncertainties. The operating characteristics (OCs) of ASD, including the unconditional power and mean sample size, can be substantially affected by many factors, including the planned sample size, the interim analysis schedule and choice of critical boundaries and rules for interim analysis. We propose a systematic, comprehensive strategy which uses iterative simulations to investigate the operating characteristics of adaptive designs and help achieve adequate unconditional power and cost-effective mean sample size if the effect size is in a pre-identified range.

Designing cancer screening trials for reduction in late-stage cancer incidence

Before implementing a biomarker test for early cancer detection into routine clinical care, the test must demonstrate clinical utility, that is, the test results should lead to clinical actions that positively affect patient-relevant outcomes. Unlike therapeutical trials for patients diagnosed with cancer, designing a randomized controlled trial (RCT) to demonstrate the clinical utility of an early detection biomarker with mortality and related endpoints poses unique challenges. The hurdles stem from the prolonged natural progression of the disease and the lack of information regarding the time-varying screening effect on the target asymptomatic population. To facilitate the study design of screening trials, we propose using a generic multistate disease history model and derive model-based effect sizes. The model links key performance metrics of the test, such as sensitivity, to primary endpoints like the incidence of late-stage cancer. It also incorporates the practical implementation of the biomarker-testing program in real-world scenarios. Based on the chronological time scale aligned with RCT, our method allows the assessment of study powers based on key features of the new program, including the test sensitivity, the length of follow-up, and the number and frequency of repeated tests. The calculation tool from the proposed method will enable practitioners to perform realistic and quick evaluations when strategizing screening trials for specific diseases. We use numerical examples based on the National Lung Screening Trial to demonstrate the method.

Sample size adaptation designs and efficiency comparison with group sequential designs

This study is to give a systematic account of sample size adaptation designs (SSADs) and to provide direct proof of the efficiency advantage of general SSADs over group sequential designs (GSDs) from a different perspective. For this purpose, a class of sample size mapping functions to define SSADs is introduced. Under the two-stage adaptive clinical trial setting, theorems are developed to describe the properties of SSADs. Sufficient conditions are derived and used to prove analytically that SSADs based on the weighted combination test can be uniformly more efficient than GSDs in a range of likely values of the true treatment differenceδ $$ \delta $$ . As shown in various scenarios, given a GSD, a fully adaptive SSAD can be obtained that has sufficient statistical power similar to that of the GSD but has a smaller average sample size for allδ $$ \delta $$ in the range. The associated sample size savings can be substantial. A practical design example and suggestions on the steps to find efficient SSADs are also provided.

New results on optimal conditional error functions for adaptive two-stage designs

Unblinded interim analyses in clinical trials with adaptive designs are gaining increasing popularity. Here, the type I error rate is controlled by defining an appropriate conditional error function. Since various approaches to the selection of the conditional error function exist, the question of an optimal choice arises. In this article, we extend existing work on optimal conditional error functions by two results. Firstly, we prove that techniques from variational calculus can be applied to derive existing optimal conditional error functions. Secondly, we answer the question of optimizing the conditional error function of an optimal promising zone design and investigate the efficiency gain.

Adaptive sequential design for phase II single-arm oncology trials: an expansion of Simon's design

Single-arm phase II trials are very common in oncology. A fixed sample trial may lack sufficient power if the true efficacy is less than the assumed one. Adaptive designs have been proposed in the literature. We propose a Simon's design based, adaptive sequential design. Simon's design is the most used fixed sample design for single-arm phase II oncology trials. A prominent feature of Simon's design is that it minimizes the sample size when there is no clinically meaningful efficacy. We identify Simon's trial as a special group sequential design. Established methods for sample size re-estimation (SSR) can be readily applied to Simon's design. Simulations show that simply adding SSR to Simon's design may still not provide desirable power. We propose some expansions to Simon's design. The expanded design with SSR can provide even more power.

Adaptive promising zone design for cancer immunotherapy with heterogeneous delayed treatment effect

Indirect mechanisms of cancer immunotherapies result in delayed treatment effects that vary among patients. Consequently, the use of the log-rank test in trial design and analysis can lead to significant power loss and pose additional challenges for interim decisions in adaptive designs. In this paper, we describe patients' survival using a piecewise proportional hazard model with random lag time and propose an adaptive promising zone design for cancer immunotherapy with heterogeneous delayed effects. We provide solutions for calculating conditional power and adjusting the critical value for the log-rank test with interim data. We divide the sample space into three zones - unfavourable, promising, and favourable -based on re-estimations of the survival parameters, the log-rank test statistic at the interim analysis, and the initial and maximum sample sizes. If the interim results fall into the promising zone, the sample size is increased; otherwise, it remains unchanged. We show through simulations that our proposed approach has greater overall power than the fixed sample design and similar power to the matched group sequential trial. Furthermore, we confirm that critical value adjustment effectively controls the type I error rate inflation. Finally, we provide recommendations on the implementation of our proposed method in cancer immunotherapy trials.

A Bayesian approach based on discounting factor for consistency assessment in multi-regional clinical trial

Multi-regional clinical trial (MRCT) has become an increasing trend for its supporting simultaneous global drug development. After MRCT, consistency assessment needs to be conducted to evaluate regional efficacy. The weighted Z-test approach is a common consistency assessment approach in which the weighting parameter W does not have a good practical significance; the discounting factor approach improved from the weighted Z-test approach by converting the estimation of W in original weighted Z-test approach to the estimation of discounting factor D. However, the discounting factor approach is an approach of frequency statistics, in which D was fixed as a certain value; the variation of D was not considered, which may lead to un-reasonable results. In this paper, we proposed a Bayesian approach based on D to evaluate the treatment effect for the target region in MRCT, in which the variation of D was considered. Specifically, we first took D random instead of fixed as a certain value and specified a beta distribution for it. According to the results of simulation, we further adjusted the Bayesian approach. The application of the proposed approach was illustrated by Markov Chain Monte Carlo simulation.

Propensity score-incorporated adaptive design approaches when incorporating real-world data

The propensity score-integrated composite likelihood (PSCL) method is one method that can be utilized to design and analyze an application when real-world data (RWD) are leveraged to augment a prospectively designed clinical study. In the PSCL, strata are formed based on propensity scores (PS) such that similar subjects in terms of the baseline covariates from both the current study and RWD sources are placed in the same stratum, and then composite likelihood method is applied to down-weight the information from the RWD. While PSCL was originally proposed for a fixed design, it can be extended to be applied under an adaptive design framework with the purpose to either potentially claim an early success or to re-estimate the sample size. In this paper, a general strategy is proposed due to the feature of PSCL. For the possibility of claiming early success, Fisher's combination test is utilized. When the purpose is to re-estimate the sample size, the proposed procedure is based on the test proposed by Cui, Hung, and Wang. The implementation of these two procedures is demonstrated via an example.

Design and monitoring of clinical trials with an interim analysis and a negative binomial endpoint

There are very rich publications devoted to group sequential design, adaptive design and trial monitoring for continuous, binary and time to event endpoints. Many authors also discuss fixed design, blinded sample size re-estimation design and group sequential design for studies with a negative binomial outcome. Nonetheless, literature is sparse in adaptive design for a trial with a negative binomial endpoint. The features of such an endpoint in a flexible trial design setting remains inadequately understood. In this research, we seek to bridge this knowledge gap by offering a thorough examination of utilizing data components from a two-stage adaptive design for unblinded conditional power calculation and corresponding sample size re-estimation. We also provide expression for calculating the probability of meeting the futility criterion to determine the appropriate timing for the interim analysis. To evaluate the performance of the design, we conduct simulations to assess its operation characteristics. Finally, we provide a helpful and illustrative example to demonstrate the practical applications of the methods.

Efficacy and safety of Y-2 sublingual tablet for patients with acute ischaemic stroke: protocol of a phase III randomised double-blind placebo-controlled multicentre trial

BACKGROUND AND PURPOSE

Clinical studies have demonstrated that edaravone dexborneol can improve the functional outcomes in patients with acute ischaemic stroke (AIS). The present clinical trial aimed at testing the efficacy and safety of Y-2 sublingual tablet on 90-day functional outcome in patients with AIS.

METHODS AND DESIGN

This is a randomised, double-blind, placebo-controlled, multicentre, parallel-group trial of Y-2 sublingual tablet on patients with AIS.An estimated 914 patients at age of 18-80 years with AIS within 48 hours after symptom onset from 40 hospitals will be randomly assigned to receive Y-2 sublingual tablet or placebo for 14 days. Patients are at score 6-20 points on National Institutes of Health Stroke Scale (NIHSS) and had a modified Rankin Scale (mRS) ≤1 before this stroke, except mechanical thrombectomy and neuroprotective agents treatment.

STUDY OUTCOMES

The primary outcome is the proportion of patients with mRS ≤1 on day 90 after randomisation. Secondary efficacy outcomes include mRS score on day 90, the proportion of patients with mRS ≤2 on day 90; the change of NIHSS score from baseline to day 14 and the proportion of patients with NIHSS score ≤1 at the days 14, 30 and 90.

DISCUSSION

This trial will provide valuable evidence for the efficacy and safety of Y-2 sublingual table for improving 90 days the functional outcomes in patients with AIS.

TRIAL REGISTRATION NUMBER

NCT04950920.

Extension of a conditional performance score for sample size recalculation rules to the setting of binary endpoints

BACKGROUND

Sample size calculation is a central aspect in planning of clinical trials. The sample size is calculated based on parameter assumptions, like the treatment effect and the endpoint's variance. A fundamental problem of this approach is that the true distribution parameters are not known before the trial. Hence, sample size calculation always contains a certain degree of uncertainty, leading to the risk of underpowering or oversizing a trial. One way to cope with this uncertainty are adaptive designs. Adaptive designs allow to adjust the sample size during an interim analysis. There is a large number of such recalculation rules to choose from. To guide the choice of a suitable adaptive design with sample size recalculation, previous literature suggests a conditional performance score for studies with a normally distributed endpoint. However, binary endpoints are also frequently applied in clinical trials and the application of the conditional performance score to binary endpoints is not yet investigated.

METHODS

We extend the theory of the conditional performance score to binary endpoints by suggesting a related one-dimensional score parametrization. We moreover perform a simulation study to evaluate the operational characteristics and to illustrate application.

RESULTS

We find that the score definition can be extended without modification to the case of binary endpoints. We represent the score results by a single distribution parameter, and therefore derive a single effect measure, which contains the difference in proportions [Formula: see text] between the intervention and the control group, as well as the endpoint proportion [Formula: see text] in the control group.

CONCLUSIONS

This research extends the theory of the conditional performance score to binary endpoints and demonstrates its application in practice.

Application of sample size re-estimation in clinical trials: A systematic review

Background

Sample size re-estimation (SSR) is a method used to recalculate sample size during clinical trial conduct to address a lack of adequate information and can have a significant impact on study size, duration, resources, and cost. Few studies to date have summarized the conditions and circumstances under which SSR is applied. We therefore performed a systematic review of the literature related to SSR to better understand its application in clinical trial settings.

Methods

PubMed was used as the primary search source, supplemented with information from ClinicalTrials.gov where necessary details were lacking from PubMed. A systematic review was performed according to a pre-specified search strategy to identify clinical trials using SSR. Features of SSR, such as study phase and study start year, were summarized.

Results

In total, 253 publications met the pre-specified search criteria and 27 clinical trials were subsequently determined as relevant in SSR usage. Among trials where the study phase was provided, 2 (7.4%) trials were Phase I, 5 (18.5%) trials were Phase II, 11 (40.7%) trials were Phase III, and 2 (7.4%) trials were Phase IV.

Conclusion

Our results showed that SSR is also used in Phase I and II, which involve earlier decision making. We expect that SSR will continue to be used in early-phase trials where sufficient prior information may not be available. Furthermore, no major trends were observed in relation to therapy area or type of SSR, meaning that SSR may become a feasible and widely applied method in the future.

Effects of intravenous sivelestat sodium on prevention of acute respiratory distress syndrome in patients with sepsis: study protocol for a double-blind multicentre randomised controlled trial

INTRODUCTION

Sepsis is one of the most common risk factors for acute respiratory distress syndrome (ARDS). Neutrophil elastase (NE) is believed to be an important mediator of ARDS. When sepsis occurs, a large number of inflammatory factors are activated and released, which makes neutrophils migrate into the lung, eventually leading to the occurrence of ARDS. Sivelestat sodium is an NE inhibitor that can inhibit the inflammatory reaction during systemic inflammatory response syndrome and alleviate lung injury. Therefore, we hypothesise that intravenous sivelestat sodium may prevent the occurrence of ARDS in patients with sepsis.

METHODS AND ANALYSIS

This is a prospective, investigator-initiated, double-blind, adaptive, multicentre, randomised, controlled clinical trial with an adaptive 'sample size re-estimation' design. Patients meeting the inclusion criteria who were transferred into the intensive care unit will be randomly assigned to receive sivelestat sodium or placebo for up to 7 days. The primary outcome is the development of ARDS within 7 days after randomisation. A total of 238 patients will be recruited based on a 15% decrease in the incidence of ARDS in the intervention group in this study. A predefined interim analysis will be performed to ensure that the calculation is reasonable after reaching 50% (120) of the planned sample size.

ETHICS AND DISSEMINATION

The study protocol was approved by the Ethics Committee of ZhongDa Hospital affiliated to Southeast University (identifier: Clinical Ethical Approval No. 2021ZDSYLL153-P03). Results will be submitted for publication in peer-reviewed journals and presented at relevant conferences and meetings.

TRIAL REGISTRATION NUMBER

NCT04973670.

Defibrotide plus best standard of care compared with best standard of care alone for the prevention of sinusoidal obstruction syndrome (HARMONY): a randomised, multicentre, phase 3 trial

BACKGROUND

Sinusoidal obstruction syndrome, also known as veno-occlusive disease, is a potentially life-threatening complication of haematopoietic stem-cell transplantation (HSCT). We aimed to compare defibrotide prophylaxis plus best supportive care versus best supportive care alone for sinusoidal obstruction syndrome prevention after HSCT.

METHODS

This open-label, randomised, multicentre, phase 3 trial was done in 104 centres in 14 countries. Patients who were at least 1 month old, were scheduled to receive allogeneic HSCT (adult [aged >16 years] or paediatric [aged >1 month to ≤16 years] patients) or autologous HSCT (paediatric patients only), and were at high risk or very high risk of developing sinusoidal obstruction syndrome were eligible for inclusion. Patients were randomly assigned (1:1) by an interactive web response system to receive intravenous defibrotide 25 mg/kg per day (four equal doses [6·25 mg/kg per dose]) and best supportive care (determined by individual institutional guidelines; defibrotide prophylaxis group) or best supportive care only (best supportive care group). Randomisation was stratified by sinusoidal obstruction syndrome risk, age, and country. The primary endpoint, sinusoidal obstruction syndrome-free survival at day 30 after HSCT, was assessed by an independent Endpoint Adjudication Committee in the intention-to-treat (ITT) population. Safety was assessed in all patients who received protocol treatment. The trial is registered with ClinicalTrials.gov, NCT02851407.

FINDINGS

Between Jan 11, 2017, and Oct 20, 2020, 372 patients (172 [46%] women and 200 [54%] men; median age 14·0 years [IQR 4·0-41·0] were randomly assigned to the defibrotide prophylaxis group (n=190) or best supportive care group (n=182; ITT population). On the basis of recommendations from the Independent Data Monitoring Committee following completion of the planned interim analysis in the first 280 recruited patients on April 29, 2020, enrolment was prematurely stopped for presumed futility. At the final analysis, sinusoidal obstruction syndrome-free survival by day 30 after HSCT was 67% (95% CI 58-74) in the defibrotide prophylaxis group and 73% (62-80) in the best supportive care group (HR 1·27 [95% CI 0·84-1·93]; p=0·85). Treatment-emergent adverse events were similar between groups during the randomised prophylaxis phase; most treatment-emergent adverse events were related to the transplantation rather than to study drug. The most common grade 3 or 4 treatment-emergent adverse events were stomatitis (grade 3, 52 [29%] of 181 patients in the defibrotide prophylaxis group and 56 [32%] of 174 patients in the best supportive care group; grade 4, two [1%] in the defibrotide prophylaxis group and two [1%] in the best supportive care group) and febrile neutropaenia (grade 3, 51 [28%] in the defibrotide prophylaxis group and 52 [30%] in the best supportive care group; grade 4, no patients in the defibrotide prophylaxis group and three [2%] in the best supportive care group). Serious treatment-emergent adverse events occurred in 74 (41%) of 181 patients in the defibrotide prophylaxis group and 61 (35%) of 174 patients in the best supportive care group. In the rescue phase, when patients in both treatment groups received defibrotide as rescue treatment, fatal treatment-related adverse events occurred in one (4%) of 25 patients in the defibrotide prophylaxis group (intracranial haemorrhage) and one (3%) of 31 patients in the best supportive care group (sinusoidal obstruction syndrome).

INTERPRETATION

Defibrotide did not show a benefit in the prophylaxis of sinusoidal obstruction syndrome. Additional studies of carefully selected patients at high risk of sinusoidal obstruction syndrome after HSCT are warranted.

FUNDING

Jazz Pharmaceuticals.

Using dichotomized survival data to construct a prior distribution for a Bayesian seamless Phase II/III clinical trial

Master protocol designs allow for simultaneous comparison of multiple treatments or disease subgroups. Master protocols can also be designed as seamless studies, in which two or more clinical phases are considered within the same trial. They can be divided into two categories: operationally seamless, in which the two phases are separated into two independent studies, and inferentially seamless, in which the interim analysis is considered an adaptation of the study. Bayesian designs are scarcely studied. Our aim is to propose and compare Bayesian operationally seamless Phase II/III designs using a binary endpoint for the first stage and a time-to-event endpoint for the second stage. At the end of Phase II, arm selection is based on posterior (futility) and predictive (selection) probabilities. The results of the first phase are then incorporated into prior distributions of a time-to-event model. Simulation studies showed that Bayesian operationally seamless designs can approach the inferentially seamless counterpart, allowing for an increasing simulated power with respect to the operationally frequentist design.

Recent innovations in adaptive trial designs: A review of design opportunities in translational research

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.

Sample size re-estimation in Phase 2 dose-finding: Conditional power versus Bayesian predictive power

Unblinded sample size re-estimation (SSR) is often planned in a clinical trial when there is large uncertainty about the true treatment effect. For Proof-of Concept (PoC) in a Phase II dose finding study, contrast test can be adopted to leverage information from all treatment groups. In this article, we propose two-stage SSR designs using frequentist conditional power (CP) and Bayesian predictive power (PP) for both single and multiple contrast tests. The Bayesian SSR can be implemented under a wide range of prior settings to incorporate different prior knowledge. Taking the adaptivity into account, all type I errors of final analysis in this paper are rigorously protected. Simulation studies are carried out to demonstrate the advantages of unblinded SSR in multi-arm trials.

Adaptive clinical trials and master protocols

Methodologies for randomized, double-blind, placebo-controlled clinical trials continue to develop in concert with evolving scientific and translational knowledge. Adaptive trial designs, in which data generated during the study are used to modify subsequent study activity (i.e., sample sizes, entry criteria, or outcomes), can optimize flexibility and expedite the safety and efficacy assessments for interventions of interest. This chapter will summarize general designs, advantages, and pitfalls associated with adaptive clinical trials and compare their features with those of conventional trial designs. It will also review novel ways for which seamless designs and master protocols may improve trial efficiency while offering interpretable data.

Efficacy and safety of Oleogel-S10 (birch triterpenes) for epidermolysis bullosa: results from the phase III randomized double-blind phase of the EASE study

BACKGROUND

Epidermolysis bullosa (EB) is a heterogeneous group of rare, difficult-to-treat, inherited multisystem diseases affecting epithelial integrity. Patients with EB are affected by mechanical fragility of epithelial surfaces including the skin and, as a result, extensive recurrent blistering is a characteristic of the condition. Chronic wounds predispose patients with EB to the development of squamous cell carcinoma, which is a major cause of premature death.

OBJECTIVES

EASE was a double-blind, randomized, vehicle-controlled, phase III study to determine the efficacy and safety of the topical gel Oleogel-S10 (birch triterpenes) in EB. EASE was funded by Amryt Research Limited.

METHODS

Patients with dystrophic EB, junctional EB or Kindler EB and a target partial-thickness wound lasting ≥ 21 days and < 9 months that was 10-50 cm2, were enrolled and randomized via computer-generated allocation tables 1 : 1 to Oleogel-S10 or control gel - both with standard-of-care dressings. Study gel was applied to all wounds at least every 4 days. The primary endpoint was the proportion of patients with first complete closure of target wound within 45 days.

RESULTS

A total of 223 patients were enrolled and treated (109 treated with Oleogel-S10, 114 with control gel). The primary endpoint was met; Oleogel-S10 resulted in 41·3% of patients with first complete target wound closure within 45 days, compared with 28·9% in the control gel arm (relative risk 1·44, 95% confidence interval (CI) 1·01-2·05; P = 0·013). Adverse events (AEs) occurred with similar frequency for Oleogel-S10 (81·7%) compared with control gel (80·7%). AEs were predominantly of mild-to-moderate intensity (4·6% were severe).

CONCLUSIONS

Oleogel-S10 is the first therapy to demonstrate accelerated wound healing in EB. Oleogel-S10 was well -tolerated.

Landscape of Phase II Trials in Alzheimer's Disease

BACKGROUND

Drug development in Alzheimer's disease (AD) over the past two decades has had high rates of failure. Novel trial designs, such as adaptive designs, have the potential to improve the efficiency of drug development in AD.

OBJECTIVE

To evaluate the design characteristics, temporal trends, and differences in design between sponsor types in phase II trials of investigational agents in AD.

METHODS

Phase I/II, II, and II/III trials for AD with drug or other biological interventions registered from December 1996 to December 2021 in ClinicalTrials.gov were included. Descriptive statistics were used to summarize trial characteristics. Linear, logistic, and multinomial regression models assessed temporal trends and differences between sponsor types in design characteristics.

RESULTS

Of N = 474 trials identified, randomized parallel group design was the most common design (72%). Only 12 trials (2.5%) used an adaptive design; adaptive features included early stopping rules, model-based dose-finding, adaptive treatment arm selection, and response adaptive randomization. The use of non-randomized parallel-group and open-label single arm designs increased over time. No temporal trend in the use of adaptive design was identified. Trials sponsored by industry only were more likely to use a randomized parallel-group design and have a larger estimated sample size than trials with other sponsor types.

CONCLUSION

Our systematic review showed that very few phase II trials in AD used an adaptive trial design. Innovation and implementation of novel trial designs in AD trials can accelerate the drug development process.

Neoadjuvant pyrotinib, trastuzumab, and docetaxel for HER2-positive breast cancer (PHEDRA): a double-blind, randomized phase 3 trial

BACKGROUND

Pyrotinib (an irreversible pan-ErbB inhibitor) plus capecitabine has survival benefits and acceptable tolerability in patients with HER2-positive metastatic breast cancer. We further assessed addition of pyrotinib to trastuzumab and docetaxel in the neoadjuvant setting.

METHODS

In this multicenter, double-blind, phase 3 study (PHEDRA), treatment-naive women with HER2-positive early or locally advanced breast cancer were randomly assigned (1:1) to receive four neoadjuvant cycles of oral pyrotinib or placebo (400 mg) once daily, plus intravenous trastuzumab (8 mg/kg loading dose, followed by 6 mg/kg) and docetaxel (100 mg/m²) every 3 weeks. The primary endpoint was the total pathological complete response (tpCR; ypT0/is and ypN0) rate per independent central review.

RESULTS

Between Jul 23, 2018, and Jan 8, 2021, 355 patients were randomly assigned, 178 to the pyrotinib group and 177 to the placebo group. The majority of patients completed four cycles of neoadjuvant treatment as planned (92.7% and 97.7% in the pyrotinib and placebo groups, respectively). The tpCR rate was 41.0% (95% CI 34.0 to 48.4) in the pyrotinib group compared with 22.0% (95% CI 16.6 to 28.7) in the placebo group (difference, 19.0% [95% CI 9.5 to 28.4]; one-sided P < 0.0001). The objective response rate per investigator was 91.6% (95% CI 86.6 to 94.8) in the pyrotinib group and 81.9% (95% CI 75.6 to 86.9) in the placebo group after the neoadjuvant treatment, resulting in an increase of 9.7% (95% CI 2.7 to 16.6). The most common grade 3 or worse adverse events were diarrhea (79 [44.4%] in the pyrotinib group and nine [5.1%] in the placebo group), neutropenia (33 [18.5%] and 36 [20.3%]), and decreased white blood cell count (29 [16.3%] and 24 [13.6%]). No deaths were reported during neoadjuvant treatment.

CONCLUSIONS

The primary endpoint of the study was met. Neoadjuvant pyrotinib, trastuzumab, and docetaxel significantly improved the tpCR rate compared with placebo, trastuzumab, and docetaxel, with manageable toxicity, providing a new option for HER2-positive early or locally advanced breast cancer.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03588091.

Evaluating group-sequential non-inferiority clinical trials following interim stopping: The HIV Prevention Trials Network 083 trial

BACKGROUND/AIMS

The HIV Prevention Trials Network 083 trial was a group-sequential non-inferiority trial designed to compare HIV incidence under a novel experimental regimen for HIV prevention, long-acting injectable cabotegravir, with an active-control regimen of daily oral tenofovir disoproxil fumarate/emtricitabine (brand name Truvada). In March of 2020, just as the trial had completed enrollment, the COVID-19 pandemic threatened to prevent trial participants from attending study visits and obtaining study medication, motivating the study team to update the interim monitoring plan. The Data and Safety Monitoring Board subsequently stopped the trial at the first interim review due to strong early evidence of efficacy.

METHODS

Here we describe some unique aspects of the trial's design, monitoring, analysis, and interpretation. We illustrate the importance of computing point estimates, confidence intervals, and p values based on the sampling distribution induced by sequential monitoring.

RESULTS

Accurate analysis, decision-making and interpretation of trial results rely on pre-specification of a stopping boundary, including the scale on which the stopping rule will be implemented, the specific test statistics to be calculated, and how the boundary will be adjusted if the available information fraction at interim review is different from planned. After appropriate adjustment for the sampling distribution and overrun, the HIV Prevention Trials Network 083 trial provided strong evidence that the experimental regimen was superior to the active control.

CONCLUSIONS

For the HIV Prevention Trials Network 083 trial, the difference between corrected inferential statistics and naive results was quite small-as will often be the case-nevertheless, it is appropriate to report and publish the most accurate and unbiased statistical results.

The Use of IV Vasoactive Intestinal Peptide (Aviptadil) in Patients With Critical COVID-19 Respiratory Failure: Results of a 60-Day Randomized Controlled Trial

OBJECTIVES

Respiratory failure is a lethal complication of COVID-19 that has remained resistant to drug therapy. Vasoactive intestinal peptide (VIP) is shown in nonclinical studies to upregulate surfactant production, inhibit cytokine synthesis, prevent cytopathy, and block replication of the severe acute respiratory syndrome coronavirus 2 virus in pulmonary cells. The study aims to determine whether Aviptadil (synthetic VIP) can improve survival and recovery in patients with COVID-19 respiratory failure compared with placebo and demonstrate biological effects in such patients.

DESIGN

A multicenter, placebo-controlled trial.

SETTING

Ten U.S. hospitals: six tertiary-care hospitals and four community hospitals.

PATIENTS

A total of 196 patients with COVID-19 respiratory failure.

INTERVENTIONS

Participants were randomized 2:1 to receive 3 days of IV Aviptadil or placebo.

MEASUREMENTS AND MAIN RESULTS

The primary end point (alive and free from respiratory failure at day 60) did not reach statistical significance (odds ratio [OR], 1.6; 95% CI, 0.86-3.11) for patients treated with Aviptadil when controlling for baseline ventilation status as prespecified in the protocol. There was, however, a statistically significant two-fold odds of improved survival (OR, 2.0; 95% CI, 1.1-3.9) at 60 days ( p = 0.035). There was significant improvement in respiratory distress ratio and reduced interleukin 6 cytokine release ( p = 0.02) by day 3.Subgroup analysis identified a statistically significant likelihood of achieving primary end point among those treated with high-flow nasal oxygen at baseline ( p = 0.039). Subjects on mechanical ventilation also experienced a 10-fold increased odds of survival with drug versus placebo ( p = 0.031).

CONCLUSIONS

The primary end point did not reach statistical significance, indicating that there was no difference between Aviptadil versus placebo. However, Aviptadil improves the likelihood of survival from respiratory failure at day 60 in critical COVID-19 across all sites of care. Given the absence of drug-related serious adverse events and acceptable safety profile, we believe the benefit versus risk for the use of Aviptadil is favorable for patient treatment.

Using surrogate information to improve confirmatory platform trial with sample size re-estimation

Platform design which allows exploring multiple arms with a common control simultaneously is becoming essential for efficient drug development. However, one of the critical challenges for confirmatory platform trials is immature data for interim decisions, particularly for the treatment arm selection and sample size determination with limited data available. We use a modified conditional power (CP) for both treatment arm selection and sample size determination at interim analysis for the proposed platform trial. The modified CP uses the available data from both primary and surrogate endpoints. We also demonstrated the application in a case study of a lung cancer trial.

Dynamic incorporation of real world evidence within the framework of adaptive design

For the clinical studies in rare diseases or small patient populations, having an adequately powered randomized controlled trial is further complicated by variability. As such, sample size re-estimation can be a useful tool if at an interim look the trial sample size needs to be increased to achieve adequate power to reject the null hypothesis. Meanwhile, borrowing or extrapolating information from real-world data or real-world evidence has gained increasing use in trial design and analysis since 2014. Combining these two strategies, high-quality real-world data, if leveraged properly, has the potential to generate real-world evidence that can assist interim decision-making, lower enrollment burden, and reduce study timeline and costs. With proper borrowing from historical control, some of the challenges in these high unmet medical need studies could be resolved considerably. We examine the incorporation of real-world evidence within the framework of adaptive design strategy in pediatric type II diabetes trials where recruitment has been challenging and the completion is hardly on time. Simulations under various scenarios are conducted to assess the borrowing strategy, i.e., the matching method in combination of sample size re-estimation. Comparisons of performance metrics are presented to showcase the advantages of proposed method.

A simulation-based comparison of estimation methods for adaptive and classical group sequential clinical trials

Statistical methods for controlling the type-I error of hypothesis tests in adaptive group sequential clinical trials are well established and well understood. However, methods for obtaining statistically valid point estimates and confidence intervals for adaptive designs are not as well established or as well understood. At the end of an adaptive trial, one may calculate the repeated confidence interval (RCI), which provides conservative coverage of δ , or the backward image confidence interval (BWCI), which provides exact coverage of δ and is an extension of the stagewise adjusted confidence interval (SWCI, used in classical group sequential designs). The BWCI can also provide a median unbiased estimate (MUE) of δ . There is a need to better understand the coverage and possible biases associated with these methods. We conducted a simulation study exploring parameter estimation following sample size reestimation based on testing methods with strong control of type-I error. Generally, the BWCI provided exact coverage, the naïve CI provided inconsistent coverage, and the RCI provided conservative coverage. Additionally, we note considerable asymmetry in the coverage from above/from below for the RCI, although we did not see any instance where the 95% RCI excluded the true parameter more than 2.5% on either side. At the end of an adaptive group sequential trial, we strongly recommend the use of the BWCI (and associated MUE), with the RCI computed during interim looks; the naïve CI should be avoided. These results and conclusions also hold true for classical group sequential designs.

Strategies to Account for Design Misspecifications in Randomized Controlled Trials

Mortality rates of infants (younger than 1 year of age) and neonates (younger than 1 month of age) remain unacceptably high in some geographic regions.1,2 After the first week of life, infection is a significant cause of neonatal and postneonatal mortality.3,4 Biannual mass azithromycin administration has been shown to reduce all-cause childhood mortality by as much as 25% among infants 1 to 5 months of age in Niger.5.

Design aspects of COVID-19 treatment trials: Improving probability and time of favorable events

As a reaction to the pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a multitude of clinical trials for the treatment of SARS-CoV-2 or the resulting corona disease 2019 (COVID-19) are globally at various stages from planning to completion. Although some attempts were made to standardize study designs, this was hindered by the ferocity of the pandemic and the need to set up clinical trials quickly. We take the view that a successful treatment of COVID-19 patients (i) increases the probability of a recovery or improvement within a certain time interval, say 28 days; (ii) aims to expedite favorable events within this time frame; and (iii) does not increase mortality over this time period. On this background, we discuss the choice of endpoint and its analysis. Furthermore, we consider consequences of this choice for other design aspects including sample size and power and provide some guidance on the application of adaptive designs in this particular context.

Sample size re-estimation for response-adaptive randomized clinical trials

Clinical trials usually take a period of time to recruit volunteers, and they become a steady accumulation of data. Traditionally, the sample size of a trial is determined in advance and data is collected before analysis proceeds. Over the past decades, many strategies have been proposed and rigorous theoretical groundings have been provided to conduct sample size re-estimation. However, the application of these methodologies has not been well extended to take care of trials with adaptive designs. Therefore, we aim to fill the gap by proposing a sample size re-estimation procedure on response-adaptive randomized trial. For ethical and economical concerns, we use multiple stopping criteria with the allowance of early termination. Statistical inference is studied for the hypothesis testing under doubly-adaptive biased coin design. We also prove that the test statistics for each stage are asymptotic independently normally distributed, though dependency exists between the two stages. We find that under our methods, compared to fixed sample size design and other commonly used randomization procedures: (1) power is increased for all scenarios with adjusted sample size; (2) sample size is reduced up to 40% when underestimating the treatment effect; (3) the duration of trials is shortened. These advantages are evidenced by numerical studies and real examples.

Finite-Sample Two-Group Composite Hypothesis Testing via Machine Learning

In the problem of composite hypothesis testing, identifying the potential uniformly most powerful (UMP) unbiased test is of great interest. Beyond typical hypothesis settings with exponential family, it is usually challenging to prove the existence and further construct such UMP unbiased tests with finite sample size. For example in the COVID-19 pandemic with limited previous assumptions on the treatment for investigation and the standard of care, adaptive clinical trials are appealing due to ethical considerations, and the ability to accommodate uncertainty while conducting the trial. Although several methods have been proposed to control Type I error rates, how to find a more powerful hypothesis testing strategy is still an open question. Motivated by this problem, we propose an automatic framework of constructing test statistics and corresponding critical values via machine learning methods to enhance power in a finite sample. In this article, we particularly illustrate the performance using Deep Neural Networks (DNN) and discuss its advantages. Simulations and two case studies of adaptive designs demonstrate that our method is automatic, general and prespecified to construct statistics with satisfactory power in finite-sample. Supplemental materials are available online including R code and an R shiny app.

Optimal adaptive promising zone designs

We develop optimal decision rules for sample size re-estimation in two-stage adaptive group sequential clinical trials. It is usual for the initial sample size specification of such trials to be adequate to detect a realistic treatment effect δ a with good power, but not sufficient to detect the smallest clinically meaningful treatment effect δ min . Moreover it is difficult for the sponsors of such trials to make the up-front commitment needed to adequately power a study to detect δ min . It is easier to justify increasing the sample size if the interim data enter a so-called "promising zone" that ensures with high probability that the trial will succeed. We have considered promising zone designs that optimize unconditional power and promising zone designs that optimize conditional power and have discussed the tension that exists between these two objectives. Where there is reluctance to base the sample size re-estimation rule on the parameter δ min we propose a Bayesian option whereby a prior distribution is assigned to the unknown treatment effect δ , which is then integrated out of the objective function with respect to its posterior distribution at the interim analysis.

Early Detection Initiative: A randomized controlled trial of algorithm-based screening in patients with new onset hyperglycemia and diabetes for early detection of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is the only leading cause of cancer death without an early detection strategy. In retrospective studies, 0.5-1% of subjects >50 years of age who newly develop biochemically-defined diabetes have been diagnosed with PDAC within 3 years of meeting new onset hyperglycemia and diabetes (NOD) criteria. The Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) algorithm further risk stratifies NOD subjects based on age and changes in weight and diabetes parameters. We present the methodology for the Early Detection Initiative (EDI), a randomized controlled trial of algorithm-based screening in patients with NOD for early detection of PDAC. We hypothesize that study interventions (risk stratification with ENDPAC and imaging with Computerized Tomography (CT) scan) in NOD will identify earlier stage PDAC. EDI uses a modified Zelen's design with post-randomization consent. Eligible subjects will be identified through passive surveillance of electronic medical records and eligible study participants randomized 1:1 to the Intervention or Observation arm. The sample size is 12,500 subjects. The ENDPAC score will be calculated only in those randomized to the Intervention arm, with 50% (n = 3125) expected to have a high ENDPAC score. Consenting subjects in the high ENDPAC group will undergo CT imaging for PDAC detection and an estimate of potential harm. The effectiveness and efficacy evaluation will compare proportions of late stage PDAC between Intervention and Observation arm per randomization assignment or per protocol, respectively, with a planned interim analysis. The study is designed to improve the detection of sporadic PDAC when surgical intervention is possible.

The CALCIPHYX study: a randomized, double-blind, placebo-controlled, Phase 3 clinical trial of SNF472 for the treatment of calciphylaxis

Background

Calcific uraemic arteriolopathy (CUA; calciphylaxis) is a rare disease seen predominantly in patients receiving dialysis. Calciphylaxis is characterized by poorly healing or non-healing wounds, and is associated with mortality, substantial morbidity related to infection and typically severe pain. In an open-label Phase 2 clinical trial, SNF472, a selective inhibitor of vascular calcification, was well-tolerated and associated with improvement in wound healing, reduction of wound-related pain and improvement in wound-related quality of life (QoL). Those results informed the design of the CALCIPHYX trial, an ongoing, randomized, placebo-controlled, Phase 3 trial of SNF472 for treatment of calciphylaxis.

Methods

In CALCIPHYX, 66 patients receiving haemodialysis who have an ulcerated calciphylaxis lesion will be randomized 1:1 to double-blind SNF472 (7 mg/kg intravenously) or placebo three times weekly for 12 weeks (Part 1), then receive open-label SNF472 for 12 weeks (Part 2). All patients will receive stable background care, which may include pain medications and sodium thiosulphate, in accordance with the clinical practices of each site. A statistically significant difference between the SNF472 and placebo groups for improvement of either primary endpoint at Week 12 will demonstrate efficacy of SNF472: change in Bates-Jensen Wound Assessment Tool-CUA (a quantitative wound assessment tool for evaluating calciphylaxis lesions) or change in pain visual analogue scale score. Additional endpoints will address wound-related QoL, qualitative changes in wounds, wound size, analgesic use and safety.

Conclusions

This randomized, placebo-controlled Phase 3 clinical trial will examine the efficacy and safety of SNF472 in patients who have ulcerated calciphylaxis lesions. Patient recruitment is ongoing.