Two-stage Adaptive Design Research Articles

Two-Stage Adaptive Enrichment Designs with Survival Outcomes and Adjustment for Misclassification in Predictive Biomarkers

Biomarker enrichment clinical trial designs are versatile tools to assess the treatment effect and increase the efficiency of clinical trials. In this article, we propose a two-stage enrichment clinical trial design with survival outcomes, and consider the situation where the biomarker assay and classification are possibly subject to errors. Specifically, the first stage is a randomized design, stratified by the biomarker appeared status. Depending on the result of the interim analysis and a pre-specified futility criterion, the second stage can be either enriched with only the biomarker appeared positive patients, or remain as the stratified design with both biomarker appeared positive and biomarker appeared negative patients. Compared to continuous and binary outcomes, test statistics for survival outcomes to account for biomarker misclassification are much more complicated and require special care. We develop log-rank statistics for the interim and final analyses, with an adjustment for the sensitivity and specificity of the biomarker assay. Control of Type I error rate is achieved by considering correlations between adjusted log-rank statistics from the same and/or different stages. R code is developed to calculate critical values, global/marginal power, and sample size. Our method is illustrated with examples of a recently successful development of immunotherapy in non-small-cell lung cancer.

Adaptive two-stage seamless sequential design for clinical trials

ABSTRACT We propose an adaptive sequential testing procedure for the selection and testing of multiple treatment options, such as dose/regimen, different drugs, sub-populations, endpoints, or a mixture of them in a seamlessly combined phase II/III trial. The selection is to be made at the end of phase 2 stage. Unlike in many of the published literature, the selection rule is not required to be to “select the best”, and does not need to be pre-specified, which provides flexibility and allows the trial investigators to use any efficacy and safety information/criteria, or surrogate or intermediate endpoint to make the selection. Sample size and power calculations are provided. The calculations have been confirmed to be accurate by simulations. Interim analysis can be performed after the selection, sample size can be modified if the observed efficacy deviates from the assumed. Inference after the trial, including p-value, median unbiased point estimate and confidence intervals, are provided. By applying a dominance theorem, the procedure can be applied to normal, binary, Poisson, negative binomial distributed endpoints and time-to-event endpoints, and a mixture of these distributions (in trials involving endpoint selection).

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.

The use of real-world data for clinical investigation of effectiveness in drug development

ABSTRACT With the growing interest in leveraging real-world data (RWD) to support effectiveness evaluations for new indications, new target populations, and post-market performance, the United States Food and Drug Administration has published several guidance documents on RWD sources and real-world studies (RWS) to assist sponsors in generating credible real-world evidence (RWE). Meanwhile, the randomized controlled trial (RCT) remains the gold standard in drug evaluation. Along this line, we propose a hybrid two-stage adaptive design to evaluate effectiveness based on evidence from both RCT and RWS. At the first stage, a typical non-inferiority test is conducted using RCT data to test for not-ineffectiveness. Once not-ineffectiveness is established, the study proceeds to the second stage to conduct an RWS and test for effectiveness using integrated information from RCT and RWD. The composite likelihood approach is implemented as a down-weighing strategy to account for the impact of high variability in RWS population. An optimal sample size determination procedure for RCT and RWS is introduced, aiming to achieve the minimal expected sample size. Through extensive numerical study, the proposed design demonstrates the ability to control type I error inflation in most cases and consistently maintain statistical power above the desired level. In general, this RCT/RWS hybrid two-stage adaptive design is beneficial for effectiveness evaluations in drug development, especially for oncology and rare diseases.

Point estimation, confidence intervals, and P-valuesforoptimal adaptive two-stage designs withnormal endpoints.

Due to the dependency structure in the sampling process, adaptive trial designs create challenges in point and interval estimation and in the calculation of P-values. Optimal adaptive designs, which are designs where the parameters governing the adaptivity are chosen to maximize some performance criterion, suffer from the same problem. Various analysis methods which are able to handle this dependency structure have already been developed. In this work, we aim to give a comprehensive summary of these methods and show how they can be applied to the class of designs with planned adaptivity, of which optimal adaptive designs are an important member. The defining feature of these kinds of designs is that the adaptive elements are completely prespecified. This allows for explicit descriptions of the calculations involved, which makes it possible to evaluate different methods in a fast and accurate manner. We will explain how to do so, and present an extensive comparison of the performance characteristics of various estimators between an optimal adaptive design and its group-sequential counterpart.

Sample size determination and evaluation for two-stage adaptive designs of single arm clinical trials based on median event time test

Clinical trials play a critical role in drug development which involves a series of phases and requires a significant amount of time and effort. Efficient clinical trial designs are necessary to investigate a new drug. Investigators strongly desire to use the time-to-event endpoint as the primary endpoint for Phase II studies, which evaluates the therapeutic efficacy of the new drug, with the hypothesis that the new drug improves the median survival time. The one-sample log-rank test has been used for single-arm Phase II trials, but it generally requires more samples. Recently, the median event time test was proposed to provide a simple, straightforward decision rule, which compares the observed median survival time for the new drug with the threshold, which is determined through the numerical search. We improve the computation of the method for the two-stage design of single-arm clinical trials based on the median event time test. By utilizing the large sample theory of order statistics, we provide the explicit formulas to calculate the sample size for the first and second stages and propose the testing procedure. The performance of the proposed method is evaluated through simulations and a trial example.

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.

Robust and Adaptive Two-stage Designs in Nonlinear Mixed Effect Models.

To get informative studies for nonlinear mixed effect models (NLMEM), design optimization can be performed based on Fisher Information Matrix (FIM) using the D-criterion. Its computation requires knowledge about models and parameters, which are often prior guesses. Thus, adaptive designs composed of several stages may be used. Robust approach can also be used to account for various candidate models. In the estimation step of a given stage, model selection (MS) or model averaging (MA) can be performed. In this work we propose a new two-stage adaptive design strategy, based on the robust expected FIM and MA over several candidate models. The methodology is applied to a clinical trial simulation in ophthalmology to optimize doses and time measurements. A set of dose-response candidate models is defined, and one-stage designs are compared to two-stage 50/50 designs (i.e., each stage performed with half of the available subjects), using either local optimal design or robust design, and performing analysis with one model, MS or MA. Performing a two-stage design with MS at the interim analysis can correct the choice of a wrong model for designing the first stage. Overall, starting from a robust design (1- or 2-stage) is valuable and leads to reasonable bias and precision. The proposed robust adaptive design strategy is a new tool to design longitudinal studies that could be used in different therapeutic areas.

A confirmatory basket design considering non-inferiority and superiority testing

ABSTRACT For multiple rare diseases as defined by a common biomarker signature, or a disease with multiple disease subtypes of low frequency, it is often possible to provide confirmatory evidence for these disease or subtypes (baskets) as a combined group. A novel drug, as a second generation, may have marginal improvement in efficacy overall but superior efficacy in some baskets. In this situation, it is appealing to test hypotheses of both non-inferiority overall and superiority on certain baskets. The challenge is designing a confirmatory study efficient to address multiple questions in one trial. A two-stage adaptive design is proposed to test the non-inferiority hypothesis at the interim stage, followed by pruning and pooling before testing a superiority hypothesis at the final stage. Such a design enables an efficient and novel registration pathway, including an early claim of non-inferiority followed by a potential label extension with superiority on certain baskets and an improved benefit-risk profile demonstrated by longer term efficacy and safety data. Operating characteristics of this design are examined by simulation studies, and its appealing features make it ready for use in a confirmatory setting, especially in emerging markets, where both the need and the possibility for efficient use of resources may be the greatest.

Abstract OT3-32-01: OPTIMA, a prospective randomized trial to validate the clinical utility and cost-effectiveness of gene expression test-directed chemotherapy decisions in high clinical risk early breast cancer

Abstract Background: Multi-parameter tumor gene expression assays (MPAs) are used to estimate individual patient risk and guide chemotherapy use in hormone-sensitive, HER2-negative early breast cancer. The TAILORx trial supports MPA use in a node-negative population. Evidence for MPA use in postmenopausal node-positive breast cancer has been provided by the RxPONDER trial interim analysis but this relies on the absence of superiority in an analysis where >50% of events were unrelated to breast cancer. There is much uncertainty about MPA use for premenopausal patients. OPTIMA (Optimal Personalised Treatment of early breast cancer usIng Multi-parameter Analysis) (ISRCTN42400492) is a prospective international randomized controlled trial designed to validate MPAs as predictors of chemotherapy sensitivity in a largely node-positive breast cancer population. Methods: OPTIMA is a partially blinded study with an adaptive two-stage design. The trial recruits women and men age 40 or older with resected ER-positive, HER2-negative invasive breast cancer and up to 9 involved axillary lymph nodes. Randomization is to standard management (chemotherapy and endocrine therapy) or to MPA-directed treatment using the Prosigna (PAM50) test. Those with a Prosigna tumor Score (ROR_PT) >60 receive standard management whilst those with a low score (≤60) tumor are treated with endocrine therapy alone. Endocrine therapy for pre-menopausal women includes ovarian suppression for all participants unless they experience a chemotherapy-induced menopause. Adjuvant abemaciclib is permitted. The trial will be analyzed for (1) non-inferiority of recurrence according to randomization and (2) cost-effectiveness. The key secondary outcome is non-inferiority of recurrence for patients with low ROR_PT score tumors. The efficacy analyses will be performed Per Protocol using Invasive Breast Cancer Free Survival (IBCFS) as the primary outcome measure to limit the risk of a false non-inferiority conclusion. Recruitment of 4500 patients over 8 years will permit demonstration of up to 3% non-inferiority of test-directed treatment with at least 83% power, assuming 5-year IBCFS is 87% with standard management. An integrated qualitative recruitment study addresses challenges to consent and recruitment, building on experience from the feasibility study which found that a multidisciplinary approach is important for recruitment success. OPTIMA is strongly supported by a patient group which has helped design all patient documents and which is represented on the TMG. Results: The OPTIMA main trial opened in January 2017 and has continued to recruit throughout the COVID-19 pandemic. Overall recruitment as of 1 July 2022 was 2814 (2593 from UK, 221 from Norway). Patient characteristics are well balanced between the trial arms. Currently 95% of randomized participants are eligible for inclusion in the PP analysis. 66% of the MPA-directed arm participants have been allocated to endocrine therapy only. The test failure rate is < 1%. Conclusion: OPTIMA will provide robust unbiased evidence on test-directed chemotherapy safety for both postmenopausal and premenopausal women with 1-3 involved nodes as well as for patients with 4-9 involved nodes and for patients treated with abemaciclib. Funding: OPTIMA is funded by the UK NIHR HTA Programme (10/34/501) and in Norway by KLINBEFORSK and the Norwegian Cancer Society. Views expressed are those of the authors and not those of the HTA Programme, NIHR, NHS or the Department of Health. Trial Inquiries: OPTIMA@warwick.ac.uk Patient characteristics Citation Format: Robert Stein, Andreas Makris, Iain Macpherson, Luke Hughes-Davies, Andrea Marshall, Georgina Dotchin, David A. Cameron, Belinda E. Kiely, Caroline Wilson, Anne Armstrong, Helena M. Earl, Christopher J. Poole, Janice Tsang, Bjørn Naume, Daniel Rea, Hege Ohnstad, Peter S. Hall, Stuart A. McIntosh, Bethany Shinkins, Christopher McCabe, Adrienne Morgan, John MS Bartlett, Janet A. Dunn. OPTIMA, a prospective randomized trial to validate the clinical utility and cost-effectiveness of gene expression test-directed chemotherapy decisions in high clinical risk early breast cancer. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr OT3-32-01.

Simultaneous population enrichment and endpoint selection in phase 3 randomized controlled trials: An adaptive group sequential design with two binary alternative primary endpoints

Population enrichment designs can play a vital role in detecting treatment benefits, if present, by selecting a study population where the treatment is more likely to show a benefit compared to the unselected population. While population enrichment designs with one primary endpoint have been studied in the current literature, there are limited research on population enrichment with alternative primary endpoints. Many diseases have multiple sequelae and it can be sufficient to show treatment benefits on at least one of these consequences. In this manuscript, we provide the theoretical basis and development of a two-stage adaptive design with two binary alternative primary endpoints that simultaneously evaluates two subgroups for enrichment, selects one or both endpoints for efficacy assessments at the time of the interim analysis using pre-specified decision rules, and maintains an overall type I error rate. Treatment benefits can be declared in one or both endpoints in a selected subgroup or in the overall population at the time of interim or final analyses. We provide stage-wise boundary values for futility and efficacy and rejection probabilities under heterogeneous treatment effects in the subgroups. We further demonstrate the implementation of the proposed design and discuss venues for future development on this topic.

A stratified adaptive two-stage design with co-primary endpoints for phase II clinical oncology trials

BackgroundGiven the inherent challenges of conducting randomized phase III trials in older cancer patients, single-arm phase II trials which assess the feasibility of a treatment that has already been shown to be effective in a younger population may provide a compelling alternative. Such an approach would need to evaluate treatment feasibility based on a composite endpoint that combines multiple clinical dimensions and to stratify older patients as fit or frail to account for the heterogeneity of the study population to recommend an appropriate treatment approach. In this context, stratified adaptive two-stage designs for binary or composite endpoints, initially developed for biomarker studies, allow to include two subgroups whilst maintaining competitive statistical performances. In practice, heterogeneity may indeed affect more than one dimension and incorporating co-primary endpoints, which independently assess each individual clinical dimension, would therefore appear quite pertinent. The current paper presents a novel phase II design for co-primary endpoints which takes into account the heterogeneity of a population. MethodsWe developed a stratified adaptive Bryant & Day design based on the Jones et al. and Parashar et al. algorithm. This two-stage design allows to jointly assess two dimensions (e.g. activity and toxicity) in two different subgroups. The operating characteristics of this new design were evaluated using examples and simulation comparisons with the Bryant & Day design in the context where the study population is stratified according to a pre-defined criterion.ResultsSimulation results demonstrated that the new design minimized the expected and maximum sample sizes as compared to parallel Bryant & Day designs (one in each subgroup), whilst controlling type I error rates and maintaining a competitive statistical power as well as a high probability of detecting heterogeneity.ConclusionsIn a heterogeneous population, this two-stage stratified adaptive phase II design provides a useful alternative to classical one and allows to identify a subgroup of interest without dramatically increasing sample size. As heterogeneity is not limited to older populations, this new design may also be relevant to other study populations such as children or adolescents and young adults or the development of targeted therapies based on a biomarker.

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...

Statistical tests for two-stage adaptive seamless design using short- and long-term binary outcomes.

The adaptive seamless design combining phases II and III into a single trial has been shown growing interest for improving the efficiency of drug development, becoming the most frequent adaptive design type. It typically consists of two stages, the trial objectives being often different in each stage. The primary objectives are to select optimal experimental treatment group(s) in the first stage and compare the efficacy between the selected treatment and control groups in the second stage. In this article, we focus on a two-stage adaptive seamless design, for which treatment selection is based on the short-term binary endpoint and treatment comparison is based on the long-term binary endpoint. We thus propose an exact conditional test as a final analysis, based on the bivariate binomial distribution and given the selected treatment with the most promising short-term endpoint response rate from an interim analysis. Additionally, the mid- approach is incorporated to improve conservativeness for an exact test. Simulation studies were conducted to compare the proposed methods with a method based on the combination test. The proposed exact method controlled for type I error rate at the nominal level, regardless of the number of initial treatments or the correlation between short- and long-term endpoints. In terms of the treatment comparison power, the proposed methods are more powerful than that based on the combination test in the scenarios, with only one treatment being effective.

Optimization of adaptive designs with respect to a performance score.

Adaptive designs are an increasingly popular method for the adaptation of design aspects in clinical trials, such as the sample size. Scoring different adaptive designs helps to make an appropriate choice among the numerous existing adaptive design methods. Several scores have been proposed to evaluate adaptive designs. Moreover, it is possible to determine optimal two-stage adaptive designs with respect to a customized objective score by solving a constrained optimization problem. In this paper, we use the conditional performance score by Herrmann etal.(2020) as the optimization criterion to derive optimal adaptive two-stage designs. We investigate variations of the original performance score, for example, by assigning different weights to the score components and by incorporating prior assumptions on the effect size. We further investigate a setting where the optimization framework is extended by a global power constraint, and additional optimization of the critical value function next to the stage-two sample size is performed. Those evaluations with respect to the sample size curves and the resulting design's performance can contribute to facilitate the score's usage inpractice.

Adaptive enrichment designs with a continuous biomarker.

A popular design for clinical trials assessing targeted therapies is the two-stage adaptive enrichment design with recruitment in stage 2 limited to a biomarker-defined subgroup chosen based on data from stage 1. The data-dependent selection leads to statistical challenges if data from both stages are used to draw inference on treatment effects in the selectedsubgroup. If subgroups considered are nested, as when defined by a continuous biomarker, treatment effect estimates in different subgroups follow the same distribution as estimates in a group-sequential trial. This result is used to obtain tests controlling the familywise type I error rate (FWER) for six simple subgroup selection rules, one of which also controls the FWER for any selection rule. Two approaches are proposed: one based on multivariate normal distributions suitable if the number of possible subgroups, k, is small, and one based on Brownian motion approximations suitable for large k. The methods, applicable in the wide range of settings with asymptotically normal test statistics, are illustrated using survival data from a breast cancertrial.

A two-stage adaptive clinical trial design with data-driven subgroup identification at interim analysis.

In this paper, we consider randomized controlled clinical trials comparing two treatments in efficacy assessment using a time to event outcome. We assume a relatively small number of candidate biomarkers available in the beginning of the trial, which may help define an efficacy subgroup which shows differential treatment effect. The efficacy subgroup is to be defined by one or two biomarkers and cut-offs that are unknown to the investigator and must be learned from the data. We propose a two-stage adaptive design with a pre-planned interim analysis and a final analysis. At the interim, several subgroup-finding algorithms are evaluated to search for a subgroup with enhanced survival for treated versus placebo. Conditional powers computed based on the subgroup and the overall population are used to make decision at the interim to terminate the study for futility, continue the study as planned, or conduct sample size recalculation for the subgroup or the overall population. At the final analysis, combination tests together with closed testing procedures are used to determine efficacy in the subgroup or the overall population. We conducted simulation studies to compare our proposed procedures with several subgroup-identification methods in terms of a novel utility function and several other measures. This research demonstrated the benefit of incorporating data-driven subgroup selection into adaptive clinical trial designs.

Abstract OT1-12-06: Neoadjuvant pyrotinib versus pertuzumab in combination with trastuzumab and nab-Paclitaxel for patients with HER2-positive early or locally advanced breast cancer (Pyramid): A randomized, multicenter, open-label, phase 2 trial

Abstract Background: Pyrotinib is an irreversible pan-ErbB receptor tyrosine kinase inhibitor targeting human epidermal growth factor receptor 1 (HER1), HER2, and HER4. Exploratory studies of neoadjuvant pyrotinib combined with trastuzumab-based standard chemotherapy for HER2-positive (HER2+) early or locally advanced breast cancer (BC) showed promising pathological complete response (pCR) rates of 51.6%-71%, with a manageable toxicity profile. We are to further investigate this combination strategy versus standard therapy in a randomized trial. Trial design: Pyramid is a randomized, multicenter, open-label, two-stage adaptive enrichment phase 2 trial to evaluate the efficacy and safety of pyrotinib plus trastuzumab and nab-Paclitaxel versus pertuzumab plus trastuzumab and nab-Paclitaxel as neoadjuvant treatment in patients with HER2+ early or locally advanced BC (NCT04900311). Eligible patients will be randomized 1:1 to receive four 21-day cycles of pyrotinib (400 mg, po, qd) or pertuzumab (840 mg loading dose, 420 mg maintenance doses, i.v., day 1) with trastuzumab (8 mg/kg loading dose, 6 mg/kg maintenance doses, i.v., day 1) and nab-Paclitaxel (260 mg/m2, day 1 or 125 mg/m2, i.v., day 1/8/15). After completion of neoadjuvant treatment, patients will undergo surgery, followed by standard EC regime for 4 cycles. Subsequent adjuvant therapy will be decided by investigators. Randomization was stratified by hormone receptor (HR) status (ER and/or PR positive vs. negative) and primary tumor size (2<T≤5 cm vs. T>5 cm). There will be two stage of this trial, and population may be enriched to HR-positive based on the results of interim analysis at Stage I. Eligibility criteria: Key inclusion criteria include female, treatment-naïve, histologically confirmed HER2+ invasive BC (cT2-4/cN0-3/cM0), the diameter of primary tumor > 2 cm, known HR status (ER and PR). Specific aims: Primary endpoint is total pathologic complete response (tpCR) rate per central review assessment. Secondary endpoints include invasive disease-free survival, event-free survival, objective response rate, breast-conserving surgery rate, safety. Exploratory endpoint is to investigate the possible predictive biomarkers of efficacy. Statistical methods: A sample size of 220 patients per arm is calculated to provide a power of 80% with a significance level of 0.1 (one-sided) to detect an estimated increase in the tpCR rate from 39% in the pertuzumab group to 49% in the pyrotinib group. Considering a dropout rate of 10%, 490 patients (245 per arm) will be required to be enrolled. A two-stage adaptive enrichment design will be used. The trial will enroll 186 patients at Stage I and an interim analysis will be conducted by an independent data monitoring committee (IDMC) when the pathologic response has been evaluated in at least 132 patients (including at least 66 HR-positive patients) at Stage I. Then, one of five options might be adapted at Stage II depending on the results, including: i. Continue study as planned in whole population; ii. Re-estimate sample size in whole population; iii. Enrich to HR-positive population with pre-defined sample size; iv. Enrich to HR-positive population and re-estimate sample size; v. Early termination. The Clopper-Pearson method will be used to calculate a 95% confidence interval (CI) for the tpCR rate. The CMH chi-square test will be used to compare the tpCR rate between groups and 95% CIs for the difference will be estimated using the Miettinen-Nurminen method. Present accrual and target accrual: Target enrollment will be 490 patients at approximately 20 sites in China. As of June 24, 2021, 4 patients have been enrolled. Contact information: Jin Zhang, MD, Email: zhangjin@tjmuch.com. Citation Format: Jin Zhang, Qiang Liu, Hongchuan Jiang, Jianguo Zhang, Jianghua Ou, Dedian Chen, Fuguo Tian, Yongqing Li, Xiaoming Cheng, Zhong Ouyang. Neoadjuvant pyrotinib versus pertuzumab in combination with trastuzumab and nab-Paclitaxel for patients with HER2-positive early or locally advanced breast cancer (Pyramid): A randomized, multicenter, open-label, phase 2 trial [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr OT1-12-06.

An adaptive design for early clinical development including interim decision for single-arm trial with external controls or randomized trial.

In early clinical development, randomized controlled trials (RCT) or single-arm trials with external controls (SATwEC) are design options, which allow adjustment for confounding: RCT via design, SATwEC via analysis using propensity score methods. SATwEC requires less investment than RCT. However, if the confounder space substantially differs between the experimental and external control group, the SATwEC might lead to inappropriate decisions for further development. We develop an adaptive two-stage design (ATD) for early clinical development that reduces the risk of unreliable decision-making at the end of a SATwEC. In Stage I, subjects are solely assigned to the experimental group. If at the interim the propensity score distributions of internal and external data are comparable based on the preference score, the subjects in stage II will again be solely assigned to the experimental arm; if not, a randomized stage II will be conducted. In a simulation study guided by a motivating example, data is generated using a time-to-event model with observable and unobservable confounders. The confounder space is varied to investigate the impact on false go/stop probabilities as well as a loss function, which reflects the quality of treatment effect estimates and decision-making. The proposed ATD provides a compromise between optimizing quality (as expressed by false go/stop probabilities and the loss function) and investment (defined by sample size and trial duration).

Two-stage adaptive weight vector design method for decomposition based many-objective evolutionary algorithm

Two-stage adaptive weight vector design method for decomposition based many-objective evolutionary algorithm