Sample Size Re-estimation Research Articles

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

BackgroundSample 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. MethodsThis 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. ConclusionThe 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.

Effect of high-flow nasal therapy on patient-centred outcomes in patients at high risk of postoperative pulmonary complications after cardiac surgery: update to the 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 1280 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 final analysis in order to preserve scientific integrity under an adaptive design framework. A previous version of this SAP was published prior to the interim analysis (Dawson, 2022). The NOTACS SAP closely follows published guidelines for the content of SAPs in clinical trials (Gamble, 2017).Trial registrationISRCTN14092678. (13 May 2020).

Optimizing Sample Size Determinations for Phase 3 Clinical Trials in Type 2 Diabetes.

An informed estimate of subject-level variance is a key determinate for accurate estimation of the required sample size for clinical trials. Evaluating completed adult Type 2 diabetes studies submitted to the FDA for accuracy of the variance estimate at the planning stage provides insights to inform the sample size requirements for future studies. From the U.S. Food and Drug Administration (FDA) database of new drug applications containing 14,106 subjects from 26 phase 3 randomized studies submitted to the FDA in support of drug approvals in adult type 2 diabetes studies reviewed between 2013 and 2017, we obtained estimates of subject-level variance for the primary endpoint-change in glycated hemoglobin (HbA1c) from baseline to 6 months. In addition, we used nine additional studies to examine the impact of clinically meaningful covariates on residual standard deviation and sample size re-estimation. Our analyses show that reduced sample sizes can be used without interfering with the validity of efficacy results for adult type 2 diabetes drug trials. This finding has implications for future research involving the adult type 2 diabetes population, including the potential to reduce recruitment period length and improve the timeliness of results. Furthermore, our findings could be utilized in the design of future endocrinology clinical trials.

Adaptive Clinical Trials in Stroke.

Designing a clinical trial to evaluate the efficacy of an intervention is often complicated by uncertainty over aspects of the study population, potential treatment effects, most relevant outcomes, dropouts, and other factors. However, once participants begin to be enrolled and partial trial data become available, this level of uncertainty is reduced. Adaptive clinical trials are designed to take advantage of the accumulating data during the conduct of a trial to make changes according to prespecified decision rules to increase the likelihood of success or statistical efficiency. Common adaptive rules address early stopping for benefit or futility, sample size reestimation, adding or dropping treatment arms or altering randomization ratios, and changing the eligibility criteria to focus on responder patient subgroups. Adaptive clinical trials are gaining popularity for clinical stroke research. We provide an overview of the methods, practical considerations, challenges and limitations, and potential future role of adaptive clinical trials in advancing knowledge and practice in stroke.

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.

Blinded sample size re-estimation in 2 × 2 crossover trial

Blinded sample size re-estimation is often considered to have realistic estimate of the required sample size, during the mid-course of a trial, without compromising the trial's integrity, to save a trial from being under or over-powered. We consider blinded sample size re-estimation, in the context of 2 × 2 crossover trial, following permuted block randomization. The underlying likelihood function suffers from identifiability problems. Blinded re-estimation procedures are prescribed under this setting and it is shown that the associated risk of unblinding may not always be negligible.

A hybrid approach to sample size re-estimation in cluster randomized trials with continuous outcomes.

This study presents a hybrid (Bayesian-frequentist) approach to sample size re-estimation (SSRE) for cluster randomised trials with continuous outcome data, allowing for uncertainty in the intra-cluster correlation (ICC). In the hybrid framework, pre-trial knowledge about the ICC is captured by placing a Truncated Normal prior on it, which is then updated at an interim analysis using the study data, and used in expected power control. On average, both the hybrid and frequentist approaches mitigate against the implications of misspecifying the ICC at the trial's design stage. In addition, both frameworks lead to SSRE designs with approximate control of the type I error-rate at the desired level. It is clearly demonstrated how the hybrid approach is able to reduce the high variability in the re-estimated sample size observed within the frequentist framework, based on the informativeness of the prior. However, misspecification of a highly informative prior can cause significant power loss. In conclusion, a hybrid approach could offer advantages to cluster randomised trials using SSRE. Specifically, when there is available data or expert opinion to help guide the choice of prior for the ICC, the hybrid approach can reduce the variance of the re-estimated required sample size compared to a frequentist approach. As SSRE is unlikely to be employed when there is substantial amounts of such data available (ie, when a constructed prior is highly informative), the greatest utility of a hybrid approach to SSRE likely lies when there is low-quality evidence available to guide the choice of prior.

Sample Size Reestimation in Stochastic Curtailment Tests With Time-to-Events Outcome in the Case of Nonproportional Hazards Utilizing Two Weibull Distributions With Unknown Shape Parameters.

Stochastic curtailment tests for Phase II two-arm trials with time-to-event end points are traditionally performed using the log-rank test. Recent advances in designing time-to-event trials have utilized the Weibull distribution with a known shape parameter estimated from historical studies. As sample size calculations depend on the value of this shape parameter, these methods either cannot be used or likely underperform/overperform when the natural variation around the point estimate is ignored. We demonstrate that when the magnitude of the Weibull shape parameters changes, unblinded interim information on the shape of the survival curves can be useful to enrich the final analysis for reestimation of the sample size. For such scenarios, we propose two Bayesian solutions to estimate the natural variations of the Weibull shape parameter. We implement these approaches under the framework of the newly proposed relative time method that allows nonproportional hazards and nonproportional time. We also demonstrate the sample size reestimation for the relative time method using three different approaches (internal pilot study approach, conditional power, and predictive power approach) at the interim stage of the trial. We demonstrate our methods using a hypothetical example and provide insights regarding the practical constraints for the proposed methods.

A randomized, double-blind, placebo-controlled phase 3 study to assess efficacy and safety of ropeginterferon alfa-2b in patients with early/lower-risk primary myelofibrosis

Primary myelofibrosis (PMF) is the most aggressive of the myeloproliferative neoplasms and patients require greater attention and likely require earlier therapeutic intervention. Currently approved treatment options are limited in their selective suppression of clonal proliferation resulting from driver- and coexisting gene mutations. Janus kinase inhibitors are approved for symptomatic patients with higher-risk PMF. Additionally, most ongoing clinical studies focus on patients with higher-risk disease and/or high rates of transfusion dependency. Optimal treatment of early/lower-risk PMF remains to be identified and needs randomized clinical trial evaluations. Pegylated interferon alfa is recommended for symptomatic lower-risk PMF patients based on phase 2 non-randomized studies and expert opinion. Ropeginterferon alfa-2b (ropeg) is a new-generation pegylated interferon-based therapy with favorable pharmacokinetics and safety profiles, requiring less frequent injections than prior formulations. This randomized, double-blind, placebo-controlled phase 3 trial will assess its efficacy and safety in patients with “early/lower-risk PMF”, defined as pre-fibrotic PMF or PMF at low or intermediate-1 risk according to Dynamic International Prognostic Scoring System-plus. Co-primary endpoints include clinically relevant complete hematologic response and symptom endpoint. Secondary endpoints include progression- or event-free survival, molecular response in driver or relevant coexisting gene mutations, bone marrow response, and safety. Disease progression and events are defined based on the International Working Group criteria and well-published reports. 150 eligible patients will be randomized in a 2:1 ratio to receive either ropeg or placebo. Blinded sample size re-estimation is designed. Ropeg will be administered subcutaneously with a tolerable, higher starting-dose regimen. The study will provide important data for the treatment of early/lower-risk PMF for which an anti-clonal, disease-modifying agent is highly needed.

Exact test and exact confidence interval for the Cox model.

The Cox proportional hazards model is commonly used to analyze time-to-event data in clinical trials. Standard inference procedures for the Cox model are based on asymptotic approximations and may perform poorly when there are few events in one or both treatment groups, as may be the case when the event of interest is rare or when the experimental treatment is highly efficacious. In this article, we propose an exact test of equivalence and efficacy under a proportional hazard model with treatment effect as the only fixed effect, together with an exact confidence interval that is obtained by inverting the exact test. The proposed test is based on a conditional error method originally proposed for sample size reestimation problems. In the present context, the conditional error method is used to combine information from a sequence of hypergeometric distributions, one at each observed event time. The proposed procedures are evaluated in simulation studies and illustrated using real data from an HIV prevention trial. A companion R package "ExactCox" is available for download on CRAN.

A seamless phase II/III design with dose optimization for oncology drug development.

The US FDA's Project Optimus initiative that emphasizes dose optimization prior to marketing approval represents a pivotal shift in oncology drug development. It has a ripple effect for rethinking what changes may be made to conventional pivotal trial designs to incorporate a dose optimization component. Aligned with this initiative, we propose a novel seamless phase II/III design with dose optimization (SDDO framework). The proposed design starts with dose optimization in a randomized setting, leading to an interim analysis focused on optimal dose selection, trial continuation decisions, and sample size re-estimation (SSR). Based on the decision at interim analysis, patient enrollment continues for both the selected dose arm and control arm, and the significance of treatment effects will be determined at final analysis. The SDDO framework offers increased flexibility and cost-efficiency through sample size adjustment, while stringently controlling the Type I error. This proposed design also facilitates both accelerated approval (AA) and regular approval in a "one-trial" approach. Extensive simulation studies confirm that our design reliably identifies the optimal dosage and makes preferable decisions with a reduced sample size while retaining statistical power.

Adaptive clinical trials in surgery: A scoping review of methodological and reporting quality.

Adaptive surgical trials are scarce, but adopting these methods may help elevate the quality of surgical research when large-scale RCTs are impractical. Randomized-controlled trials (RCTs) are the gold standard for evidence-based healthcare. Despite an increase in the number of RCTs, the number of surgical trials remains unchanged. Adaptive clinical trials can streamline trial design and time to trial reporting. The advantages identified for ACTs may help to improve the quality of future surgical trials. We present a scoping review of the methodological and reporting quality of adaptive surgical trials. We performed a search of Ovid, Web of Science, and Cochrane Collaboration for all adaptive surgical RCTs performed from database inception to October 12, 2023. We included any published trials that had at least one surgical arm. All review and abstraction were performed in duplicate. Risk of bias (RoB) was assessed using the RoB 2.0 instrument and reporting quality was evaluated using CONSORT ACE 2020. All results were analyzed using descriptive methods. Of the 1338 studies identified, six trials met inclusion criteria. Trials were performed in cardiothoracic, oral, orthopedic, and urological surgery. The most common type of adaptive trial was group sequential design with pre-specified interim analyses planned for efficacy, futility, and/or sample size re-estimation. Two trials did use statistical simulations. Our risk of bias evaluation identified a high risk of bias in 50% of included trials. Reporting quality was heterogeneous regarding trial design and outcome assessment and details in relation to randomization and blinding concealment. Surgical trialists should consider implementing adaptive components to help improve patient recruitment and reduce trial duration. Reporting of future adaptive trials must adhere to existing CONSORT ACE 2020 guidelines. Future research is needed to optimize standardization of adaptive methods across medicine and surgery.

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

ABSTRACT 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.

Monitoring Ongoing Clinical Trials Under Fractional Brownian Motion With Drift

The standard Brownian motion (Bm) with a linear drift is a convenient statistical structure for monitoring ongoing clinical trials in practice for more than four decades (Lan and DeMets). Under this model, the most current one-point statistic is sufficient. However, in our experience, the sponsor and the data monitoring committee often would like to make decision or recommendation based on the “trend” observed from the history of data, not just a one-point snapshot. In this article, we introduce and advance the fractional Brownian motion (fBm) with drift model to formally accommodate this need. The possible dependence and/or the nonlinear trend (e.g., piecewise linear drift with change-point) of observations in clinical trials may come from uncontrollable factors such as patient entry processes may have seasonal patterns over time, patient survival time may depend on the practices of clinical centers, physicians or censoring time (Lai et al.). The violations of the standard Bm and the need for the fBm are discussed with illustrative examples. The common methods including conditional power and sample size re-estimation used for monitoring clinical trials are derived and implemented in the Dynamic Data Monitoring (DDM) system for practitioners under the fBm.

Seamless phase 2/3 design for trials with multiple co-primary endpoints using Bayesian predictive power

Seamless phase 2/3 design has become increasingly popular in clinical trials with a single endpoint. Trials that define success based on the achievement of all co-primary endpoints (CPEs) encounter the challenge of inflated type 2 error rates, often leading to an overly large sample size. To tackle this challenge, we introduced a seamless phase 2/3 design strategy that employs Bayesian predictive power (BPP) for futility monitoring and sample size re-estimation at interim analysis. The correlations among multiple CPEs are incorporated using a Dirichlet-multinomial distribution. An alternative approach based on conditional power (CP) was also discussed for comparison. A seamless phase 2/3 vaccine trial employing four binary endpoints under the non-inferior hypothesis serves as an example. Our results spotlight that, in scenarios with relatively small phase 2 sample sizes (e.g., 50 or 100 subjects), the BPP approach either outperforms or matches the CP approach in terms of overall power. Particularly, with n1 = 50 and ρ = 0, BPP showcases an overall power advantage over CP by as much as 8.54%. Furthermore, when the phase 2 stage enrolled more subjects (e.g., 150 or 200), especially with a phase 2 sample size of 200 and ρ = 0, the BPP approach evidences a peak difference of 5.76% in early stop probability over the CP approach, emphasizing its better efficiency in terminating futile trials. It’s noteworthy that both BPP and CP methodologies maintained type 1 error rates under 2.5%. In conclusion, the integration of the Dirichlet-Multinominal model with the BPP approach offers improvement in certain scenarios over the CP approach for seamless phase 2/3 trials with multiple CPEs.

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

BackgroundSample 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. MethodsPubMed 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. ResultsIn 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. ConclusionOur 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.

Interim decision making in seamless trial designs: An application in an adaptive dose-finding study in a rare kidney disease.

Adaptive seamless trial designs, combining the learning and confirming cycles of drug development in a single trial, have gained popularity in recent years. Adaptations may include dose selection, sample size re-estimation and enrichment of the study population. Despite methodological advances and recognition of the potential efficiency gains such designs offer, their implementation, including how to enable efficient decision making on the adaptations in interim analyzes, remains a key challenge in their adoption. This manuscript uses a case study of an adaptive seamless proof-of-concept (Phase 2a)/dose-finding (Phase 2b) to showcase potential adaptive features that can be implemented in trial designs at earlier development stages and the role of simulations in assessing the design operating characteristics and specifying the decision rules for the adaptations. It further outlines the elements needed to support successful interim analysis decision making on the adaptations while safeguarding study integrity, including the role of different stakeholders, interactive simulation-based tools to facilitate decision making and operational aspects requiring preplanning. The benefits of the adaptive Phase 2a/2b design chosen compared to following the traditional two separate studies (2a and 2b) paradigm are discussed. With careful planning and appreciation of their complexity and components needed for their implementation, seamless adaptive designs have the potential to yield significant savings both in terms of time and resources.

A Case Study of 2-Stage Seamless Adaptive Sample Size Re-Estimation Design with Efficacy Interim Analysis When Slope Is the Primary Endpoint

Due to highly unmet medical needs in rare disease areas, there is great desire to speed up the drug development process. A two-stage adaptive design option for a placebo-controlled registration study is being evaluated. Stage 1 consists of participants in an ongoing phase 2 study with 1-year double blinded (DB) treatment and Stage 2 includes newly enrolled participants with 2-year DB treatment period. The primary endpoint is the annualized rate of change (slope) for a continuous longitudinal measurement, which will be evaluated through a random coefficient linear mixed model. An unblinded interim analysis will be performed using Stage 1 data to re-estimate the sample size for Stage 2, followed by another interim analysis for potential early efficacy stopping when all participants completed the 1-year DB treatment. To control the overall Type 1 error rate, rather than using a conservative approach, the actual correlation between the interim and final test statistics will be taken into account to determine the final significance level given the pre-specified significance level for the interim efficacy analysis. Multiplicity adjustments for secondary endpoints including considerations for order switching between interim and final analysis in this specific case study will also be discussed.

A systematic review of randomised controlled trials with adaptive and traditional group sequential designs – applications in cardiovascular clinical trials

BackgroundTrial design plays a key role in clinical trials. Traditional group sequential design has been used in cardiovascular clinical trials over decades as the trials can potentially be stopped early, therefore, it can reduce pre-planned sample size and trial resources. In contrast, trials with adoptive designs provide greater flexibility and are more efficient due to the ability to modify trial design according to the interim analysis results. In this systematic review, we aim to explore characteristics of adaptive and traditional group sequential trials in practice and to gain an understanding how these trial designs are currently being reported in cardiology.MethodsPubMed, Embase and Cochrane Central Register of Controlled Trials database were searched from January 1980 to June 2022. Randomised controlled phase 2/3 trials with either adaptive or traditional group sequential design in patients with cardiovascular disease were included. Descriptive statistics were used to present the collected data.ResultsOf 456 articles found in the initial search, 56 were identified including 43 (76.8%) trials with traditional group sequential design and 13 (23.2%) with adaptive. Most trials were large, multicentre, led by the USA (50%) and Europe (28.6%), and were funded by companies (78.6%). For trials with group sequential design, frequency of interim analyses was determined mainly by the number of events (47%). 67% of the trials stopped early, in which 14 (32.6%) were due to efficacy, and 5 (11.6%) for futility. The commonly used stopping rule to terminate trials was O’Brien- Fleming-type alpha spending function (10 (23.3%)). For trials with adaptive designs, 54% of the trials stopped early, in which 4 (30.8%) were due to futility, and 2 (15.4%) for efficacy. Sample size re-estimation was commonly used (8 (61.5%)). In 69% of the trials, simulation including Bayesian approach was used to define the statistical stopping rules. The adaptive designs have been increasingly used (from 0 to 1999 to 38.6% after 2015 amongst adaptive trials). 25% of the trials reported “adaptive” in abstract or title of the studies.ConclusionsThe application of adaptive trials is increasingly popular in cardiovascular clinical trials. The reporting of adaptive design needs improving.

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

IntroductionSepsis 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,...