Sample Size Re-estimation Research Articles

Adaptive designs for IVPT data with mixed scaled average bioequivalence.

In vitro permeation tests (IVPT) offer accurate and cost-effective development pathways for locally acting drugs, such as topical dermatological products. For assessment of bioequivalence, the FDA draft guidance on generic acyclovir 5% cream introduces a new experimental design, namely the single-dose, multiple-replicate per treatment group design, as IVPT pivotal study design. We examine the statistical properties of its hypothesis testing method-namely the mixed scaled average bioequivalence (MSABE). Meanwhile, some adaptive design features in clinical trials can help researchers make a decision earlier with fewer subjects or boost power, saving resources, while controlling the impact on family-wise error rate. Therefore, we incorporate MSABE in an adaptive design combining the group sequential design and sample size re-estimation. Simulation studies are conducted to study the passing rates of the proposed methods-both within and outside the average bioequivalence limits. We further consider modifications to the adaptive designs applied for IVPT BE trials, such as Bonferroni's adjustment and conditional power function. Finally, a case study with real data demonstrates the advantages of such adaptive methods.

Sample size reestimation and Bayesian predictive probability for single-arm clinical trials with a time-to-event endpoint using Weibull distribution with unknown shape parameter

ABSTRACT This manuscript consists of two topics. Firstly, we explore the utility of internal pilot study (IPS) approach for reestimating sample size at an interim stage when a reliable estimate of the nuisance shape parameter of the Weibull distribution for modeling survival data is unavailable during the planning phase of a study. Although IPS approach can help rescue the study power, it is noted that the adjusted sample size can be as much as twice the initially planned sample size, which may put substantial practical constraints to continue the study. Secondly, we discuss Bayesian predictive probability for conducting interim analyses to obtain preliminary evidence of efficacy or futility of an experimental treatment warranting early termination of a clinical trial. In the context of single-arm clinical trials with time-to-event endpoints following Weibull distribution, we present the calculation of the Bayesian predictive probability when the shape parameter of the Weibull distribution is unknown. Based on the data accumulated at the interim, we propose two approaches which rely on the posterior mode or the entire posterior distribution of the shape parameter. To account for uncertainty in the shape parameter, it is recommended to incorporate its entire posterior distribution in our calculation.

Adaptive Multiple Comparison Sequential Design (AMCSD) for clinical trials

ABSTRACT We propose an adaptive sequential testing procedure for clinical trials that test the efficacy of multiple treatment options, such as dose/regimen, different drugs, sub-populations, endpoints, or a mixture of them in one trial. At any interim analyses, sample size re-estimation can be conducted, and any option can be dropped for lack of efficacy or unsatisfactory safety profile. Inference after the trial, including p-value, conservative point estimate and confidence intervals, are provided.

Denosumab biosimilar (LY01011) in the treatment of bone metastases in patients with solid tumors: A multicenter, randomized, double-blind, active comparator-controlled phase 3 study.

12126 Background: The study was to evaluate the equivalence of LY01011 to the reference product denosumab in terms of reduction of bone metabolism markers. Methods: Eligible patients were randomized at 1:1 ratio to receive 120 mg LY01011 injection or 120 mg denosumab every four weeks subcutaneously. Following the completion of three doses of 120 mg LY01011 or denosumab in a 12-week double-blind treatment period (DBTP), all enrolled patients would continue to receive LY01011 administration until the week 53 of follow-up period. The primary end point was the natural logarithm change at week 13 from baseline in urinary cross-linked N terminal telopeptide of type I collagen, corrected for urine creatinine (uNTx/uCr). The equivalence between LY01011 and denosumab would be concluded if the 90% CI was completely inside the pre-defined equivalence boundary between -0.135 and 0.135. A formal blinded sample-size re-estimation (BSSR) was planned to take place once 60% of the planned participants had completed the Week 13 assessments. After BSSR, the sample size increased from 560 to 850 according to prespecified procedure based on pooled standard deviationfrom the blinded data, which was set as a cap to ensure a feasible sample size for the study and no need to do any multiplicity adjustment. Results: All eligible 850 patients, included in the full analysis set, were randomized into the LY01011 group (n = 424) and denosumab group (n = 426). The samples from the 12-week DBTP were collected for analysis in both groups. The least square mean (LSM) (SE) of the natural logarithm change of uNTX/uCr at week 13 from baseline was -1.740 (0.0420) in the LY01011 group and -1.745 (0.0421) in denosumab group. The LSM difference was 0.005 between LY01011 and denosumab groups with 90% CI of [-0.088, 0.097] (p > 0.05). Skeletal related events occurring in the LY01011 and denosumab groups were 3.5% and 2.8%, respectively. The mean (SD) percentage changes of serum bone-specific alkaline phosphatase (s-BALP) from baseline to week 13 were -31.154% (39.6790) and -33.021% (37.3826) in the LY01011 and denosumab groups, respectively. The median was -36.866% and -37.457% (p > 0.05). The incidence of treatment-emergent adverse events (TEAEs) of any grade was 91.7% and 90.8% in LY01011 and denosumab groups(p > 0.05). Both groups had 38.4% of Grade ≥3 TEAEs, of which 5.4% (LY01011) and 4.5% (denosumab) were judged to be related to the study drug. Immunogenicity analysis showed that no participant (0/399) had a positive ADA test in denosumab group and only one participant (0.2%, 1/401) was tested ADA positive in LY01011 group. Conclusions: The study demonstrated the equivalent efficacy of LY01011 in the reduction of bone metabolism biomarker NTX to reference product, denosumab, which met the primary endpoint. It had a good safety profile with no unexpected adverse reactions in the study. Clinical trial information: NCT04859569 .

Epistemic uncertainty in Bayesian predictive probabilities

ABSTRACT Bayesian predictive probabilities have become a ubiquitous tool for design and monitoring of clinical trials. The typical procedure is to average predictive probabilities over the prior or posterior distributions. In this paper, we highlight the limitations of relying solely on averaging, and propose the reporting of intervals or quantiles for the predictive probabilities. These intervals formalize the intuition that uncertainty decreases with more information. We present four different applications (Phase 1 dose escalation, early stopping for futility, sample size re-estimation, and assurance/probability of success) to demonstrate the practicality and generality of the proposed approach.

Effect of EIT-guided PEEP titration on prognosis of patients with moderate to severe ARDS: study protocol for a multicenter randomized controlled trial

BackgroundAcute respiratory syndrome distress (ARDS) is a clinical common syndrome with high mortality. Electrical impedance tomography (EIT)-guided positive end-expiratory pressure (PEEP) titration can achieve the compromise between lung overdistension and collapse which may minimize ventilator-induced lung injury in these patients. However, the effect of EIT-guided PEEP titration on the clinical outcomes remains unknown. The objective of this trial is to investigate the effects of EIT-guided PEEP titration on the clinical outcomes for moderate or severe ARDS, compared to the low fraction of inspired oxygen (FiO2)-PEEP table.MethodsThis is a prospective, multicenter, single-blind, parallel-group, adaptive designed, randomized controlled trial (RCT) with intention-to-treat analysis. Adult patients with moderate to severe ARDS less than 72 h after diagnosis will be included in this study. Participants in the intervention group will receive PEEP titrated by EIT with a stepwise decrease PEEP trial, whereas participants in the control group will select PEEP based on the low FiO2-PEEP table. Other ventilator parameters will be set according to the ARDSNet strategy. Participants will be followed up until 28 days after enrollment. Three hundred seventy-six participants will be recruited based on a 15% decrease of 28-day mortality in the intervention group, with an interim analysis for sample size re-estimation and futility assessment being undertaken once 188 participants have been recruited. The primary outcome is 28-day mortality. The secondary outcomes include ventilator-free days and shock-free days at day 28, length of ICU and hospital stay, the rate of successful weaning, proportion requiring rescue therapies, compilations, respiratory variables, and Sequential Organ Failure Assessment (SOFA).DiscussionAs a heterogeneous syndrome, ARDS has different responses to treatment and further results in different clinical outcomes. PEEP selection will depend on the properties of patients and can be individually achieved by EIT. This study will be the largest randomized trial to investigate thoroughly the effect of individual PEEP titrated by EIT in moderate to severe ARDS patients to date.Trial registrationClinicalTrial.gov NCT05207202. First published on January 26, 2022.

Early, very high-titre convalescent plasma therapy in clinically vulnerable individuals with mild COVID-19 (COVIC-19): protocol for a randomised, open-label trial

IntroductionCOVID-19 convalescent plasma (CCP) is a possible treatment option for COVID-19. A comprehensive number of clinical trials on CCP efficacy have already been conducted. However, many aspects of CCP treatment...

A Modification of Location Commensurate Power Prior in Clinical Trials

A statistical approach to incorporate historical data efficiently while maintaining statistical integrity is needed when the use of historical data along with current data is justified. Bayesian statistics enables the utilization of historical data in its priors for inference. In clinical trial analysis, if historical data support current data, incorporating priors can help improve statistical power as well as reduce sample sizes. However, if historical and current data are not compatible, incorporating Bayesian priors can result in estimation bias, hence inflated Type I error. A more recent Bayesian approach, location commensurate power prior (LCPP) helps resolve inflated Type I error issue and gain power in certain scenarios. Our simulation results show that in the presence of large conflicts (a conflict is defined as an average of historical data minus current control data), all Bayesian approaches do not perform well especially in the presence of negative large conflicts, all Bayesian approaches have much lower power compared to frequentist. To address this issue, we propose a modification of location commensurate power prior (mLCPP) that leverages frequentist, traditional Bayesian and LCPP. The proposed approach results in better performance across all conflict profiles. We also apply mLCPP to an adaptive clinical trial design and show that if large conflicts present, mLCPP signals sample size re-estimation in a control arm to avoid under-powered studies and if moderate to no conflicts, it signals partial to full use of historical information, thus improving power while allowing an acceptable inflation of Type I error and offering opportunity for sample size reduction.

Guidance on interim analysis methods in clinical trials.

Interim analyses in clinical trials can take on a multitude of forms. They are often used to guide Data and Safety Monitoring Board (DSMB) recommendations to study teams regarding recruitment targets for large, later-phase clinical trials. As collaborative biostatisticians working and teaching in multiple fields of research and across a broad array of trial phases, we note the large heterogeneity and confusion surrounding interim analyses in clinical trials. Thus, in this paper, we aim to provide a general overview and guidance on interim analyses for a nonstatistical audience. We explain each of the following types of interim analyses: efficacy, futility, safety, and sample size re-estimation, and we provide the reader with reasoning, examples, and implications for each. We emphasize that while the types of interim analyses employed may differ depending on the nature of the study, we would always recommend prespecification of the interim analytic plan to the extent possible with risk mitigation and trial integrity remaining a priority. Finally, we posit that interim analyses should be used as tools to help the DSMB make informed decisions in the context of the overarching study. They should generally not be deemed binding, and they should not be reviewed in isolation.

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.

Acipimox in Mitochondrial Myopathy (AIMM): study protocol for a randomised, double-blinded, placebo-controlled, adaptive design trial of the efficacy of acipimox in adult patients with mitochondrial myopathy

BackgroundMitochondrial disease is a heterogenous group of rare, complex neurometabolic disorders. Despite their individual rarity, collectively mitochondrial diseases represent the most common cause of inherited metabolic disorders in the UK; they affect 1 in every 4300 individuals, up to 15,000 adults (and a similar number of children) in the UK. Mitochondrial disease manifests multisystem and isolated organ involvement, commonly affecting those tissues with high energy demands, such as skeletal muscle. Myopathy manifesting as fatigue, muscle weakness and exercise intolerance is common and debilitating in patients with mitochondrial disease. Currently, there are no effective licensed treatments and consequently, there is an urgent clinical need to find an effective drug therapy.AimTo investigate the efficacy of 12-week treatment with acipimox on the adenosine triphosphate (ATP) content of skeletal muscle in patients with mitochondrial disease and myopathy.MethodsAIMM is a single-centre, double blind, placebo-controlled, adaptive designed trial, evaluating the efficacy of 12 weeks’ administration of acipimox on skeletal muscle ATP content in patients with mitochondrial myopathy. Eligible patients will receive the trial investigational medicinal product (IMP), either acipimox or matched placebo. Participants will also be prescribed low dose aspirin as a non-investigational medical product (nIMP) in order to protect the blinding of the treatment assignment.Eighty to 120 participants will be recruited as required, with an interim analysis for sample size re-estimation and futility assessment being undertaken once the primary outcome for 50 participants has been obtained. Randomisation will be on a 1:1 basis, stratified by Fatigue Impact Scale (FIS) (dichotomised as < 40, ≥ 40). Participants will take part in the trial for up to 20 weeks, from screening visits through to follow-up at 16 weeks post randomisation.The primary outcome of change in ATP content in skeletal muscle and secondary outcomes relating to quality of life, perceived fatigue, disease burden, limb function, balance and walking, skeletal muscle analysis and symptom-limited cardiopulmonary fitness (optional) will be assessed between baseline and 12 weeks.DiscussionThe AIMM trial will investigate the effect of acipimox on modulating muscle ATP content and whether it can be repurposed as a new treatment for mitochondrial disease with myopathy.Trial registrationEudraCT2018-002721-29. Registered on 24 December 2018,ISRCTN 12895613. Registered on 03 January 2019, https://www.isrctn.com/search?q=aimm

Acupuncture for acute musculoskeletal pain management in the emergency department and continuity clinic: a protocol for an adaptive pragmatic randomised controlled trial

IntroductionChronic musculoskeletal pain causes a significant burden on health and quality of life and may result from inadequate treatment of acute musculoskeletal pain. The emergency department (ED) represents a novel...

Effect of high-flow nasal therapy on patient-centred outcomes in patients at high risk of postoperative pulmonary complications after cardiac surgery: a statistical analysis plan for NOTACS, a multicentre adaptive randomised controlled trial

BackgroundThe NOTACS trial will assess the efficacy, safety and cost-effectiveness of high-flow nasal therapy (HFNT) compared to standard oxygen therapy (SOT) on the outcomes of patients after cardiac surgery.Methods/designNOTACS is an adaptive, international, multicentre, parallel-group, randomised controlled trial, with a pre-planned interim sample size re-estimation (SSR). A minimum of 850 patients will be randomised 1:1 to receive either HFNT or SOT. The primary outcome is days alive and at home in the first 90 days after the planned surgery (DAH90), with a number of secondary analyses and cost-effectiveness analyses also planned. The interim SSR will take place after a minimum of 300 patients have been followed up for 90 days and will allow for the sample size to increase up to a maximum of 1152 patients.ResultsThis manuscript provides detailed descriptions of the design of the NOTACS trial, and the analyses to be undertaken at the interim and final analyses. The main purpose of the interim analysis is to assess safety and to perform a sample size re-estimation. The main purpose of the final analysis is to examine the safety, efficacy and cost-effectiveness of HFNT compared to SOT on the outcomes of patients after cardiac surgery.DiscussionThis manuscript outlines the key features of the NOTACS statistical analysis plan and was submitted to the journal before the interim analysis in order to preserve scientific integrity under an adaptive design framework. The NOTACS SAP closely follows published guidelines for the content of SAPs in clinical trials.Trial registrationISRCTN14092678. Registered on 13 May 2020.

Effect of HEAT therapy in patiEnts with type 2 Diabetes mellitus (HEATED): protocol for a randomised controlled trial

IntroductionThe burden of type 2 diabetes mellitus (T2DM) is increasing worldwide. Heat therapy has been found effective in improving glycaemic control. However, to date, there is a lack of randomised...

Two-stage re-estimation adaptive design: a simulation study

&#x0D; &#x0D; &#x0D; Background: adaptive clinical trial design has been proposed as a promising new approach to improve the drug discovery process. Among the many options available, adaptive sample size re-estimation is of great interest mainly because of its ability to avoid a large ‘up-front’ commitment of resources. In this simulation study, we investigate the statistical properties of two-stage sample size re-estimation designs in terms of type I error control, study power and sample size, in comparison with the fixed-sample study.&#x0D; Methods: we simulated a balanced two-arm trial aimed at comparing two means of normally distributed data, using the inverse normal method to combine the results of each stage, and considering scenarios jointly defined by the following factors: the sample size re-estimation method, the information fraction, the type of group sequential boundaries and the use of futility stopping. Calculations were performed using the statistical software SAS™ (version 9.2).&#x0D; Results: under the null hypothesis, any type of adaptive design considered maintained the prefixed type I error rate, but futility stopping was required to avoid the unwanted increase in sample size. When deviating from the null hypothesis, the gain in power usually achieved with the adaptive design and its performance in terms of sample size were influenced by the specific design options considered.&#x0D; Conclusions: we show that adaptive designs incorporating futility stopping, a sufficiently high information fraction (50-70%) and the conditional power method for sample size re-estimation have good statistical properties, which include a gain in power when trial results are less favourable than anticipated. &#x0D; &#x0D;

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

ABSTRACT 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.

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

ABSTRACT 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.

Multicenter Randomized Controlled Trial of Surveillance Versus Endoscopic Therapy for Barrett’s Esophagus With Low-grade Dysplasia: The SURVENT Trial: Study Rationale, Methodology, Innovation, and Implications

Multicenter Randomized Controlled Trial of Surveillance Versus Endoscopic Therapy for Barrett’s Esophagus With Low-grade Dysplasia: The SURVENT Trial: Study Rationale, Methodology, Innovation, and Implications

Design and implementation of an adaptive confirmatory trial in Japanese patients with palmoplantar pustulosis

Background/AimsMore and more literature describes how to overcome challenges in the implementation of adaptive designs for trials for drug approval process. Most adaptive trials were conducted in Western Europe or USA for Phase II or Phase II/III settings; however, examples of non-oncology pivotal adaptive trials used for regulatory approval in Japan are rare. This article elaborates on our experience with designing and implementing a Phase III adaptive confirmatory trial permitting unblinded sample size re-estimation and futility analysis after a single interim analysis in Japanese patients with palmoplantar pustulosis (PPP) receiving guselkumab. MethodsWe provide insights into consideration at the design stage of an adaptive study, including design options, development duration, operational risks, and statistical methods. We also share our experience from two aspects of implementation: conducting an adaptive clinical trial in Japan and setting up a Japanese domestic independent data monitoring committee. ResultsFinal analysis results of this study successfully demonstrated the effectiveness of guselkumab for the treatment of PPP. Based on the interim analysis results, it was recommended to continue the study without sample size adjustment. ConclusionWe discuss results versus design assumptions and advantages to the conduct of the study with the adaptive design approach. Significant cost savings were gained and development time was reduced compared to the alternative option of a fixed conservative design. Several limitations of our study are also discussed.

An Optimal Hybrid Approach to Calculate Conditional Power

When used in clinical trial adaptive designs, conditional power (CP) is most commonly calculated assuming the current trend, but this approach has been criticized for substantially deviating from the actual CP due to “double-dipping” the interim data (i.e., using the interim data as part of the final analysis test statistic and as the assumption for the remainder of the trial). By contrast, CP assuming the design assumption preserves the shape of the CP curve by “single-dipping” the interim data (i.e., using the interim data once to construct the final test statistic) but is less robust against misspecification. To improve CP estimation, we propose a “1.w dipping” hybrid approach by using an assumption that optimally combines the current trend assumption with weight w and the design assumption with weight 1-w. The optimization of w accounts for the uncertainty of the true treatment effect size and can be linked with study priorities through customizable penalty functions. Through simulation using sample size re-estimation designs, we demonstrate that the proposed approach maximizes the precision of CP-based interim decision making on average and leads to other desirable operating characteristics. Given its flexibility and superior performance, we recommend this novel approach over the existing methods.