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Journal articles on the topic 'Bayesian Sample size, Prior Elicitation, Clinical trial'
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Azzolina, Danila, Paola Berchialla, Silvia Bressan, Liviana Da Dalt, Dario Gregori, and Ileana Baldi. "A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method." International Journal of Environmental Research and Public Health 19, no. 21 (October 31, 2022): 14245. http://dx.doi.org/10.3390/ijerph192114245.
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Sample size estimation is a fundamental element of a clinical trial, and a binomial experiment is the most common situation faced in clinical trial design. A Bayesian method to determine sample size is an alternative solution to a frequentist design, especially for studies conducted on small sample sizes. The Bayesian approach uses the available knowledge, which is translated into a prior distribution, instead of a point estimate, to perform the final inference. This procedure takes the uncertainty in data prediction entirely into account. When objective data, historical information, and literature data are not available, it may be indispensable to use expert opinion to derive the prior distribution by performing an elicitation process. Expert elicitation is the process of translating expert opinion into a prior probability distribution. We investigated the estimation of a binomial sample size providing a generalized version of the average length, coverage criteria, and worst outcome criterion. The original method was proposed by Joseph and is defined in a parametric framework based on a Beta-Binomial model. We propose a more flexible approach for binary data sample size estimation in this theoretical setting by considering parametric approaches (Beta priors) and semiparametric priors based on B-splines.
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Desai, Yasin, Thomas Jaki, Michael W. Beresford, Thomas Burnett, Despina Eleftheriou, Heidi Jacobe, Valentina Leone, et al. "Prior elicitation of the efficacy and tolerability of Methotrexate and Mycophenolate Mofetil in Juvenile Localised Scleroderma." AMRC Open Research 3 (September 9, 2021): 20. http://dx.doi.org/10.12688/amrcopenres.13008.1.
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Background Evidence is lacking for safe and effective treatments for juvenile localised scleroderma (JLS). Methotrexate (MTX) is commonly used first line and mycophenolate mofetil (MMF) second line, despite a limited evidence base. A head to head trial of these two medications would provide data on relative efficacy and tolerability. However, a frequentist approach is difficult to deliver in JLS, because of the numbers needed to sufficiently power a trial. A Bayesian approach could be considered. Methods An international consensus meeting was convened including an elicitation exercise where opinion was sought on the relative efficacy and tolerability of MTX compared to MMF to produce prior distributions for a future Bayesian trial. Secondary aims were to achieve consensus agreement on critical aspects of a future trial. Results An international group of 12 clinical experts participated. Opinion suggested superior efficacy and tolerability of MMF compared to MTX; where most likely value of efficacy of MMF was 0.70 (95% confidence interval (CI) 0.34-0.90) and of MTX was 0.68 (95% CI 0.41-0.8). The most likely value of tolerability of MMF was 0.77 (95% CI 0.3-0.94) and of MTX was 0.62 (95% CI 0.32-0.84). The wider CI for MMF highlights that experts were less sure about relative efficacy and tolerability of MMF compared to MTX. Despite using a Bayesian approach, power calculations still produced a total sample size of 240 participants, reflecting the uncertainty amongst experts about the performance of MMF. Conclusions Key factors have been defined regarding the design of a future Bayesian approach clinical trial including elicitation of prior opinion of the efficacy and tolerability of MTX and MMF in JLS. Combining further efficacy data on MTX and MMF with prior opinion could potentially reduce the pre-trial uncertainty so that, when combined with smaller trial sample sizes a compelling evidence base is available.
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Manski, Charles F., and Aleksey Tetenov. "Sufficient trial size to inform clinical practice." Proceedings of the National Academy of Sciences 113, no. 38 (September 6, 2016): 10518–23. http://dx.doi.org/10.1073/pnas.1612174113.
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Medical research has evolved conventions for choosing sample size in randomized clinical trials that rest on the theory of hypothesis testing. Bayesian statisticians have argued that trials should be designed to maximize subjective expected utility in settings of clinical interest. This perspective is compelling given a credible prior distribution on treatment response, but there is rarely consensus on what the subjective prior beliefs should be. We use Wald’s frequentist statistical decision theory to study design of trials under ambiguity. We show that ε-optimal rules exist when trials have large enough sample size. An ε-optimal rule has expected welfare within ε of the welfare of the best treatment in every state of nature. Equivalently, it has maximum regret no larger than ε. We consider trials that draw predetermined numbers of subjects at random within groups stratified by covariates and treatments. We report exact results for the special case of two treatments and binary outcomes. We give simple sufficient conditions on sample sizes that ensure existence of ε-optimal treatment rules when there are multiple treatments and outcomes are bounded. These conditions are obtained by application of Hoeffding large deviations inequalities to evaluate the performance of empirical success rules.
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Teramukai, Satoshi, Takashi Daimon, and Sarah Zohar. "A new design for phase II single-arm clinical trials: Bayesian predictive sample size selection design." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 6576. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.6576.
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6576 Background: The aim of phase II trials is to determine if a new treatment is promising for further testing in confirmatory clinical trials. Most phase II clinical trials are designed as single-arm trials using a binary outcome with or without interim monitoring for early stopping. In this context, we propose a Bayesian adaptive design denoted as PSSD, predictive sample size selection design (Statistics in Medicine 2012;31:4243-4254). Methods: The design allows for sample size selection followed by any planned interim analyses for early stopping of a trial, together with sample size determination before starting the trial. In the PSSD, we determined the sample size using the predictive probability criterion with two kinds of prior distributions, that is, an ‘analysis prior’ used to compute posterior probabilities and a ‘design prior’ used to obtain prior predictive distributions. In the sample size determination, we provide two sample sizes, that is, N and Nmax, using two types of design priors. At each interim analysis, we calculate the predictive probability of achieving a successful result at the end of the trial using analysis prior in order to stop the trial in case of low or high efficacy, and we select an optimal sample size, that is, either N or Nmax as needed, on the basis of the predictive probabilities. Results: We investigated the operating characteristics through simulation studies, and the PSSD retrospectively applies to a lung cancer clinical trial. As the number of interim looks increases, the probability of type I errors slightly decreases, and that of type II errors increases. The type I error probabilities of the probabilities of the proposed PSSD are almost similar to those of the non-adaptive design. The type II error probabilities in the PSSD are between those of the two fixed sample size (N or Nmax) designs. Conclusions: From a practical standpoint, the proposed design could be useful in phase II single-arm clinical trials with a binary endpoint. In the near future, this approach will be implemented in actual clinical trials to assess its usefulness and to extend it to more complicated clinical trials.
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Ciarleglio, Maria M., and Christopher D. Arendt. "Sample size re-estimation in a superiority clinical trial using a hybrid classical and Bayesian procedure." Statistical Methods in Medical Research 28, no. 6 (June 5, 2018): 1852–78. http://dx.doi.org/10.1177/0962280218776991.
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When designing studies involving a continuous endpoint, the hypothesized difference in means ([Formula: see text]) and the assumed variability of the endpoint ([Formula: see text]) play an important role in sample size and power calculations. Traditional methods of sample size re-estimation often update one or both of these parameters using statistics observed from an internal pilot study. However, the uncertainty in these estimates is rarely addressed. We propose a hybrid classical and Bayesian method to formally integrate prior beliefs about the study parameters and the results observed from an internal pilot study into the sample size re-estimation of a two-stage study design. The proposed method is based on a measure of power called conditional expected power (CEP), which averages the traditional power curve using the prior distributions of θ and [Formula: see text] as the averaging weight, conditional on the presence of a positive treatment effect. The proposed sample size re-estimation procedure finds the second stage per-group sample size necessary to achieve the desired level of conditional expected interim power, an updated CEP calculation that conditions on the observed first-stage results. The CEP re-estimation method retains the assumption that the parameters are not known with certainty at an interim point in the trial. Notional scenarios are evaluated to compare the behavior of the proposed method of sample size re-estimation to three traditional methods.
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Thall, Peter F., Richard C. Herrick, Hoang Q. Nguyen, John J. Venier, and J. Clift Norris. "Effective sample size for computing prior hyperparameters in Bayesian phase I–II dose-finding." Clinical Trials 11, no. 6 (September 1, 2014): 657–66. http://dx.doi.org/10.1177/1740774514547397.
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Background: The efficacy–toxicity trade-off based design is a practical Bayesian phase I–II dose-finding methodology. Because the design’s performance is very sensitive to prior hyperparameters and the shape of the target trade-off contour, specifying these two design elements properly is essential. Purpose: The goals are to provide a method that uses elicited mean outcome probabilities to derive a prior that is neither overly informative nor overly disperse, and practical guidelines for specifying the target trade-off contour. Methods: A general algorithm is presented that determines prior hyperparameters using least squares penalized by effective sample size. Guidelines for specifying the trade-off contour are provided. These methods are illustrated by a clinical trial in advanced prostate cancer. A new version of the efficacy–toxicity program is provided for implementation. Results: Together, the algorithm and guidelines provide substantive improvements in the design’s operating characteristics. Limitations: The method requires a substantial number of elicited values and design parameters, and computer simulations are required to obtain an acceptable design. Conclusion: The two key improvements greatly enhance the efficacy–toxicity design’s practical usefulness and are straightforward to implement using the updated computer program. The algorithm for determining prior hyperparameters to ensure a specified level of informativeness is general, and may be applied to models other than that underlying the efficacy–toxicity method.
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Ollier, Adrien, Satoshi Morita, Moreno Ursino, and Sarah Zohar. "An adaptive power prior for sequential clinical trials – Application to bridging studies." Statistical Methods in Medical Research 29, no. 8 (November 15, 2019): 2282–94. http://dx.doi.org/10.1177/0962280219886609.
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During drug evaluation trials, information from clinical trials previously conducted on another population, indications or schedules may be available. In these cases, it might be desirable to share information by efficiently using the available resources. In this work, we developed an adaptive power prior with a commensurability parameter for using historical or external information. It allows, at each stage, full borrowing when the data are not in conflict, no borrowing when the data are in conflict or “tuned” borrowing when the data are in between. We propose to apply our adaptive power prior method to bridging studies between Caucasians and Asians, and we focus on the sequential adaptive allocation design, although other design settings can be used. We weight the prior information in two steps: the effective sample size approach is used to set the maximum desirable amount of information to be shared from historical data at each step of the trial; then, in a sort of Empirical Bayes approach, a commensurability parameter is chosen using a measure of distribution distance. This approach avoids elicitation and computational issues regarding the usual Empirical Bayes approach. We propose several versions of our method, and we conducted an extensive simulation study evaluating the robustness and sensitivity to prior choices.
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Moatti, M., S. Zohar, W. F. Rosenberger, and S. Chevret. "A Bayesian Hybrid Adaptive Randomisation Design for Clinical Trials with Survival Outcomes." Methods of Information in Medicine 55, no. 01 (2016): 4–13. http://dx.doi.org/10.3414/me14-01-0132.
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SummaryBackground: Response-adaptive randomisation designs have been proposed to im -prove the efficiency of phase III randomised clinical trials and improve the outcomes of the clinical trial population. In the setting of failure time outcomes, Zhang and Rosen -berger (2007) developed a response-adaptive randomisation approach that targets an optimal allocation, based on a fixed sample size. Objectives: The aim of this research is to propose a response-adaptive randomisation procedure for survival trials with an interim monitoring plan, based on the following optimal criterion: for fixed variance of the esti -mated log hazard ratio, what allocation minimizes the expected hazard of failure? We demonstrate the utility of the design by re -designing a clinical trial on multiple myeloma. Methods: To handle continuous monitoring of data, we propose a Bayesian response-adap -tive randomisation procedure, where the log hazard ratio is the effect measure of interest. Combining the prior with the normal likelihood, the mean posterior estimate of the log hazard ratio allows derivation of the optimal target allocation. We perform a simu lationstudy to assess and compare the perform -ance of this proposed Bayesian hybrid adaptive design to those of fixed, sequential or adaptive – either frequentist or fully Bayesian – designs. Non informative normal priors of the log hazard ratio were used, as well as mixture of enthusiastic and skeptical priors. Stopping rules based on the posterior dis -tribution of the log hazard ratio were com -puted. The method is then illus trated by redesigning a phase III randomised clinical trial of chemotherapy in patients with multiple myeloma, with mixture of normal priors elicited from experts. Results: As expected, there was a reduction in the proportion of observed deaths in the adaptive vs. non-adaptive designs; this reduction was maximized using a Bayes mix -ture prior, with no clear-cut improvement by using a fully Bayesian procedure. The use of stopping rules allows a slight decrease in the observed proportion of deaths under the alternate hypothesis compared with the adaptive designs with no stopping rules. Conclusions: Such Bayesian hybrid adaptive survival trials may be promising alternatives to traditional designs, reducing the duration of survival trials, as well as optimizing the ethical concerns for patients enrolled in the trial.
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Ursino, Moreno, and Nigel Stallard. "Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges." International Journal of Environmental Research and Public Health 18, no. 3 (January 24, 2021): 1022. http://dx.doi.org/10.3390/ijerph18031022.
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The aim of this narrative review is to introduce the reader to Bayesian methods that, in our opinion, appear to be the most important in the context of rare diseases. A disease is defined as rare depending on the prevalence of the affected patients in the considered population, for example, about 1 in 1500 people in U.S.; about 1 in 2500 people in Japan; and fewer than 1 in 2000 people in Europe. There are between 6000 and 8000 rare diseases and the main issue in drug development is linked to the challenge of achieving robust evidence from clinical trials in small populations. A better use of all available information can help the development process and Bayesian statistics can provide a solid framework at the design stage, during the conduct of the trial, and at the analysis stage. The focus of this manuscript is to provide a review of Bayesian methods for sample size computation or reassessment during phase II or phase III trial, for response adaptive randomization and of for meta-analysis in rare disease. Challenges regarding prior distribution choice, computational burden and dissemination are also discussed.
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Muehlemann, Natalia, Rajat Mukherjee, Ali T. Taher, Thordis Gudmundsdottir, Isabelle Morin, and Frank Richard. "Innovative Adaptive Study Design in Transfusion-Dependent Beta-Thalassemia: Bayesian Design with Concurrent Randomization and Borrowing from Historical Data." Blood 138, Supplement 1 (November 5, 2021): 4160. http://dx.doi.org/10.1182/blood-2021-146512.
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Abstract Background Clinical development of new therapies in transfusion-dependent beta-thalassemia has several challenges. Patient enrollment in rare diseases requires multi-center multi-country studies, and the lack of reliable surrogate endpoint for dose selection requires powering for clinical endpoints usually used in Phase 3 trials. An acceptable endpoint from a regulatory perspective which is based on responders analysis, such as proportion of patients experiencing ≥50% reduction in Red Blood Cell (RBC) transfusion burden and a reduction of ≥2 units, requires 12 weeks screening period to establish baseline transfusion burden for reliable comparison. Importantly, higher randomization ratio of treatment:placebo can improve patients' motivation to enroll into a trial, but it is less statistically efficient and requires higher sample size. We designed a Phase-2b, double-blind, randomized, placebo controlled, multi-center study with Vamifeport (NCT04938635) to assess the efficacy and safety of multiple doses of a new therapy in adults with transfusion-dependent beta-thalassemia. The proposed design follows the Bayesian framework with borrowing from published historical control data. The historical control data is used to construct an informative prior for the control arm to reduce the burden of patients randomized to a control arm and improve the trial's efficiency in performing dose selection. Study Design and Methods Adults (18 to 65 y.o.) with documented diagnosis of β-thalassemia or hemoglobin E / β-thalassemia will be randomized to three doses of the investigational drug or placebo plus best supportive care. RBC transfusion dependence is defined as at least 6 RBC Units in the 24 weeks prior to randomization and no transfusion-free period for ≥35 days during that period. The primary endpoint is the proportion of patients experiencing ≥33% reduction of RBC units from baseline and a reduction of ≥2 units assessed from week 13 to week 24. The key secondary endpoints include proportion of patients experiencing ≥33% reduction from week 37 to week 48; proportion of patients experiencing ≥50% reduction over any consecutive 12-week interval from week 1 to week 48 and the mean change from baseline in RBC transfusions (units) from week 13 to week 24. The primary and key-secondary analysis will be conducted in a hierarchical fashion to account for multiplicity. We proposed a Bayesian design with the use of noninformative, or weakly informative, priors for the active dose arms while using a robustified informative prior for the control arm. Historical control data will be "borrowed" in an informative prior for the control arm rate from the Phase 3 trial - BELIEVE. The robustification is required in order to control the level of borrowing depending on the level of prior-data conflict. Prior-data conflict can arise from multiple sources like population heterogeneity between the historical and current study. Therefore, the selection of historical data (BELIEVE trial) addresses similarity in inclusion / exclusion criteria, standard of care etc. The robustification of the informative prior does not take into account prior-data conflict in terms of population or study characteristics but directly focuses on the informative prior of the parameter of interest and the corresponding likelihood of the current data. For example, in the BELIEVE study, out of 112 patients randomized to the control arm, 5 patients (4.5%) had a ≥33% reduction in transfusion burden over 24 weeks. A prior-data conflict may arise if the Phase-2b trial of interest here, suggests that the proportion is substantially different that 4.5% and this can inflate the frequentist Type-I or Type-II error rates examined via simulations. We evaluated Type-I error rates of the proposed design with 5000 Monte-Carlo runs for each scenario of the response rates. Using informative prior with no prior-data conflict the type-I error with no robustification is ≈ 2.4%. As the prior-data conflict increases, without robustification, the type-I error cannot be controlled. However, with a robustification weight of 0.5 the type-I errors can be controlled in line with regulatory requirements. Discussion A proposed Bayesian design with robustified informative prior for the control arm helps reduce patients' burden of randomization to control arm and reduce overall sample size for a rare disease trial when recruitment and trial duration are challenging. Disclosures Muehlemann: Vifor Pharma AG: Consultancy. Mukherjee: Vifor Pharma AG: Consultancy. Taher: Bristol Myers Squibb: Consultancy, Research Funding; Vifor Pharma: Consultancy, Research Funding; Agios Pharmaceuticals: Consultancy; Ionis Pharmaceuticals: Consultancy, Research Funding; Novartis: Consultancy, Research Funding. Gudmundsdottir: Vifor Pharma AG: Current Employment. Morin: Vifor Pharma AG: Current Employment. Richard: Vifor Pharma AG: Current Employment.
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Yagisawa, Masataka, Yoshiaki Nakamura, Takayuki Yoshino, Yoshito Komatsu, Shigenori Kadowaki, Kei Muro, Yu Sunakawa, et al. "A basket trial of trastuzumab deruxtecan, a HER2-targeted antibody-drug conjugate, for HER2-amplified solid tumors identified by circulating tumor DNA analysis (HERALD trial)." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): TPS3650. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.tps3650.
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TPS3650 Background: Trastuzumab deruxtecan, a new HER2-targeting antibody-drug conjugate, has been approved for unresectable or metastatic HER2-positive breast cancer by the Food and Drug Administration. In a phase I/II trial, trastuzumab deruxtecan showed a manageable safety profile and antitumor activity in HER2-positive various cancer types. In addition, a tissue-based HER2 test occasionally cannot identify accurate HER2 status due to spatial and temporal intratumoral heterogeneity, leading to potentially missing an opportunity for responders to receive benefit from anti-HER2-targeted therapy. Circulating tumor DNA (ctDNA) analysis can detect comprehensive somatic genome alterations by assessment of spatial and temporal intratumoral heterogeneity with minimal invasiveness. Methods: We designed an investigator-initiated multicenter phase II basket trial to evaluate efficacy and safety of trastuzumab deruxtecan in advanced solid tumor malignancies with HER2 amplification identified by Guardant360, a 74-gene sequencing ctDNA panel, as a part of the Nationwide Cancer Genome Screening Project (GOZILA study, UMIN000029315). The key eligibility criteria are as follows: 1) Histopathologically confirmed advanced solid tumor malignancy; 2) Identified HER2 amplification by Guardant360; 3) Failed prior standard therapy. The participants will receive intravenously 5.4 mg/kg of trastuzumab deruxtecan every 3 weeks. The primary endpoint is objective response rate (ORR). The planned sample size is 55-65. A Bayesian model considering the potential heterogeneity across cancer types will be applied to detect ORR of 5% versus 25% to a certain level while maintaining the false-positive error rate in each cancer type at 10%. Furthermore, tumor tissue, ctDNA and circulating tumor cells are serially collected and analyzed to investigate the predictive biomarkers and resistance mechanisms. The trial was activated in late 2019. At the time of the abstract submission, 2 patients have been enrolled. This trial is granted by AMED under Grant Number JP18lk0201084. Clinical trial information: JapicCTI-194707 .
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Silk, Ann W., Robert Berman, Vlad Coric, Laura Ruggiero, Allen B. Reitz, Weichung Shih, Michael P. Kane, et al. "A phase I study to evaluate the safety of trigriluzole (BHV-4157) in combination with PD-1 blocking antibodies." Journal of Clinical Oncology 36, no. 5_suppl (February 10, 2018): TPS80. http://dx.doi.org/10.1200/jco.2018.36.5_suppl.tps80.
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TPS80 Background: The Metabotropic Glutamate Receptor 1 (GRM1) is expressed in 60-100% of human melanomas, breast cancers, and other solid tumors. Riluzole, an FDA-approved drug for ALS, inhibits GRM1 signal transduction. A phase 0 trial in melanoma patients demonstrated that riluzole suppressed signaling through the MAPK and PI3K/AKT pathways but no objective responses were seen in a phase 2 study.The clinical use of riluzole outside of ALS has been limited by: (i) poor oral bioavailability, (ii) extensive first-pass hepatic metabolism, (iii) high variability in PK parameters, (iv) food-related decrease in bioavailability, and (v) dose-related hepatotoxicity. Trigriluzole (BHV-4157) is a third generation prodrug of riluzole with improved PK/PD properties. In our MASS20 in vivo murine model of melanoma, GRM1 signal transduction appears to suppress tumor immunity through up-regulation of M-CSF and CCL2 expression with a subsequent increase in the percentage of M2 macrophages in the tumor microenvironment. In an immunocompetent mouse model, treatment with trigriluzole and anti-PD-1 antibody was more effective than either drug alone. Methods: Subjects with advanced or refractory solid cancers or lymphoma will be treated with increasing doses of trigriluzole in this phase 1b study. The dosing cohorts of trigriluzole will be using a semi-Bayesian modified toxicity probability interval dose escalation procedure. Trigriluzole monotherapy will be given for a 14-day lead in period and then patients will receive trigriluzole in combination with nivolumab 240mg IV every 2 weeks. After the maximum tolerated dose (MTD) of trigriluzole is identified, it will be checked in a cohort of 6 patients in combination with pembrolizumab. Total sample size will be 12 – 27 subjects. PD-1-directed therapy prior to entry is allowed. Blood samples and optional biopsies will be collected for correlative analysis. PBMCs and fresh tumor tissue will be grafted into NSG mice to create a patient-derived autologous double-humanized murine model for each participant. Clinical trial information: NCT03229278.
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Vendetti, Nicholas J., Katy Benjamin, Olga Moshkovich, Kelly Lipman, Krupa M. Sivamurthy, and Andreas M. Pleil. "A New Measure Assessing Recovery from a Sickle Cell Crisis." Blood 128, no. 22 (December 2, 2016): 2392. http://dx.doi.org/10.1182/blood.v128.22.2392.2392.
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Abstract Rationale : Vaso-occlusive crisis (VOC) is a recurring complication of sickle cell disease (SCD) and a common reason for emergency department visits and hospitalizations among SCD patients. Few qualitative studies on the symptoms and impacts of SCD have been published. Specifically, while the duration of hospitalization for VOC is well-documented, little is known about the patient experience of recovery from a VOC. The purpose of this research was to understand the experience of recovery from VOC from the patient's perspective and to develop a brief measure to assess recovery to be administered across age groups in future clinical trial programs. Methods : A concept elicitation (CE) study consisted of face-to-face, one-on-one interviews with SCD patients, and caregivers of patients hospitalized for a VOC within 90 days prior to their interview. The semi-structured interview guide focused on the symptoms and impacts of VOC and the activities important to recovery following a VOC. A thematic analysis in MAXQDA qualitative software was conducted to identify key concepts. Results informed the development of a draft measure which was refined in two cognitive debriefing (CD) studies, one using interviewer administration format, and one using an electronic self-administration format. Interviews focused on the meaning and interpretation of the questionnaire instructions, items and responses, and feasibility of administration. Results: The CE sample included 16 participants; 5 adults and adolescents, 6 children ages 8-11, and 5 caregivers of younger children. Patients/caregivers reported that pain is not completely eliminated at time of actual discharge but gradually resolves over the week following hospitalization. In addition to pain, fatigue and the effects of pain medication also affect functional status in the immediate post-hospitalization period. The types of activities impaired during recovery were physical activities, social activities, daily activities such as work or school, and self-care activities. This conceptual framework resulted in a draft "Return to Normal Activity Questionnaire" (RNAQ), with equivalent versions for each age group, including caregiver report. Although the precise item wording and activity examples differed slightly by age group, all three versions shared the same four domains and the same response scale; an 11-point NRS with anchors 0 = "Cannot do at all" to 10 = "Can do completely as usual." The first CD study included 9 patients (3 per age group) with the interviewer administering the RNAQ verbally. Based on these interviews, self-report was determined infeasible for children 8-11. Thus, caregiver report was recommended for all children under 12. The self-administered version of the RNAQ was debriefed in a sample of adult and adolescent patients (N=17). Based on participant feedback, the NRS scale was reversed so that 0 indicates best functioning, to be consistent with a familiar pain rating question frequently used in clinical practice to evaluate VOC severity. Most patients reported that the typical time to complete recovery ranged from 2 days to one week; thus daily administration for 7 days following VOC hospitalization was recommended. Conclusions: The RNAQ was developed according to FDA PRO Guidance and appears to be appropriate for assessing recovery in SCD patients following a VOC hospitalization. Due to the small sample size in the first CD study, additional debriefing on the caregiver-reported version is recommended. In addition, psychometric validation is required to obtain full evidence of validity and to understand how to interpret RNAQ scores. Disclosures Vendetti: Pfizer, Inc.: Employment. Sivamurthy:Pfizer: Employment. Pleil:Pfizer Inc: Employment.
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Paul, Barry, James Symanowski, Paul Osipoff, Sarah Norek, Ami P. Ndiaye, Jordan Robinson, Shebli Atrash, Manisha Bhutani, Peter M. Voorhees, and Saad Z. Usmani. "A Phase 2 Trial of Daratumumab and Pembrolizumab in Refractory Multiple Myeloma." Blood 136, Supplement 1 (November 5, 2020): 2. http://dx.doi.org/10.1182/blood-2020-141623.
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Background: Despite significant advances in the treatment of multiple myeloma (MM), it remains an incurable malignancy and novel treatments are still desperately needed. Daratumumab, a human IgG1 anti-CD38 monoclonal antibody, is an essential component of several regimens approved for the treatment of both newly diagnosed (NDMM) and relapsed/refectory myeloma (RRMM), however nearly all patients exposed to this agent eventually acquire resistance. Daratumumab (Dara) has known immunomodulatory effects including reducing CD38-expressing immunosuppressive regulatory B- and T-cells, and CD4+ T-helper cells and CD8+ cytotoxic T-cells, leading to increased CD8+:CD4+ and CD8+:Treg ratios. Additionally, CD38 has been shown to be upregulated in solid tumors which acquire resistance to PD-1/PD-L1 blockade. Pembrolizumab (Pembro) a humanized IgG4 monoclonal PD-1 antibody has previously shown limited clinical activity in combination with the immunomodulatory agents lenalidomide or pomalidomide in early phase trials. Although phase 3 trials did not confirm the benefit of Pembro in combination with immunomodulatory drugs, alternative combinations are being explored in clinical trials. Given their overlapping mechanisms, we hypothesize that the combination of Dara and Pembo will lead to increased anti-myeloma activity while maintaining an acceptable safety profile. Methods: We are conducting a phase 2 single arm trial of the combination of Dara and Pembro in RRMM patients previously treated with 3 or more lines of therapy, including an immunomodulatory agent, proteasome inhibitor, and daratumumab (ClinicalTrials.gov NCT04361851). Subjects will receive Dara-Pembro induction for 6 cycles (Q21 days) followed by Dara-Pembro maintenance until relapse or progression. Dara will be dosed at 16 mg/kg administered intravenously at days 1, 8 and 15 for the first 2 induction cycles and 16 mg/kg at days 1 and 15 of each cycle thereafter. Pembro will be dosed at 200 mg administered intravenously on day 1 of each cycle. Response/progression parameters will be assessed using IMWG criteria. Toxicity will be assessed during treatment via NCI CTCAE v. 4.03. Bone marrow aspirates and serial peripheral blood samples will be collected for correlative studies. The primary endpoint is 8-month progression-free survival (PFS) with response compared to the historical control of single agent daratumumab. Classically, single agent Dara has resulted in a median PFS of 4 months in a similar (although Dara naïve) population which corresponds to an 8-month PFS of 25%. For this population of subjects treated with Dara + Pembro, the aim is to improve the 8-month PFS rate to 50%. An optimum Simon 2-stage design will be used to test the hypothesis that the 8-month PFS rate is less than or equal to 25%. Sixteen subjects will be enrolled in the first stage, and if at least 5 of the 16 patients are alive and progression free at 8 months, an additional 17 subjects will be enrolled (total of 33 subjects). If at least 13 of 33 subjects are alive and progression free at 8 months, the null hypothesis will be rejected. Assuming a one-sided α = 0.05 significance level, this sample size will provide 90% power to reject the null hypothesis, assuming the true 8-month PFS rate is 50%. Key secondary endpoints include overall response rate, clinical benefit rate (minimal response or better), CR (complete response) rate, sCR (stringent complete response) rate, time to response (TTR), time to best response (TTBR), duration of response (DOR), progression-free survival (PFS), overall survival (OS) and safety. Safety objectives include dose limiting toxicities and immune-related adverse events (monitored with stopping rules), overall treatment-related adverse events, and a Bayesian-based safety stopping rule for Grade 5 AEs. We also have several translational correlates aimed at identifying molecular subtypes, variants, and neoantigen mutations which may serve as prognostic and predicative immune biomarkers of response to the combination. We also plan to characterize the mechanism(s) of immune exhaustion and T cell dysfunction in RRMM patients. These translational endpoints are aimed to determine patients who would be at highest likelihood to derive benefit from this combination in future studies. Figure 1 Disclosures Paul: Regeneron: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Speakers Bureau; Bristol-Myers Squibb: Other: Stock Ownership (prior employee). Atrash:Takeda, Amgen, Karyopharm, BMS, Sanofi, Cellactar, Janssen and Celgene: Honoraria; Amgen, GSK, Karyopharm.: Research Funding; BMS, Jansen oncology, Sanofi: Speakers Bureau. Bhutani:Sanofi Genzyme: Consultancy; Janssen: Other: Clinical Trial Funding to Institute; Takeda: Other: Clinical trial funding to institute, Speakers Bureau; Amgen: Speakers Bureau; MedImmune: Other: Clinical Trial Funding to Institute; Prothena: Other: Clinical Trial Funding to Institute; BMS: Other: Clinical trial funding to institute, Speakers Bureau. Voorhees:TeneoBio: Other: Personal fees; Oncopeptides: Other: Personal fees; Levine Cancer Institute, Atrium Health: Current Employment; Adaptive Biotechnologies: Other: Personal fees; Bristol-Myers Squibb: Other: Personal fees; Celgene: Other: Personal fees; Novartis: Other: Personal fees; Janssen: Other: Personal fees. Usmani:GSK: Consultancy, Research Funding; Celgene: Other; Sanofi: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy; BMS, Celgene: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Amgen: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Array Biopharma: Research Funding; Pharmacyclics: Research Funding; Incyte: Research Funding; Merck: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; SkylineDX: Consultancy, Research Funding; Janssen: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Takeda: Consultancy, Honoraria, Other: Speaking Fees, Research Funding.
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Bhutani, Manisha, Monika House, Jiaxian He, Shebli Atrash, David M. Foureau, Barry Paul, Reed Friend, et al. "Response-Adaptive Phase II Study of Daratumumab Combined with Carfilzomib, Lenalidomide and Dexamethasone in Newly Diagnosed Multiple Myeloma." Blood 136, Supplement 1 (November 5, 2020): 38–39. http://dx.doi.org/10.1182/blood-2020-138485.
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Background Studies combining Daratumumab with a proteasome inhibitor and/ or an immunomodulatory drug have shown to increase the duration and depth of response in patients with newly diagnosed multiple myeloma (NDMM). While assessment of minimal residual status (MRD) after different stages of treatment is increasingly being evaluated in clinical trials as sensitive measure of depth of response and improved survival, published data on its utility as a tool to select the optimal post-induction therapy are not yet available. Study Design This ongoing single arm, two-stage, Phase II trial is designed with the primary objective to evaluate the efficacy in terms of rate of ≥ CR after 8 cycles of Daratumumab, Carfilzomib, Lenalidomide and Dexamethasone (Dara + KRd) induction therapy in patients with NDMM. Induction treatment cycles consist of daratumumab per standard dosing, carfilzomib 56 mg/m2 IV days 1,8,15 (per latest amendment), lenalidomide 25 mg PO days 1-21 and dexamethasone 40 mg PO/IV days 1,8,15,22 repeated every 28 days. After induction, all subjects will undergo disease evaluation. Those who experience ≥very good partial response (VGPR) will undergo an assessment of MRD and will be classified as MRD(+) or MRD(-) as determined by next generation sequencing at 10-5 sensitivity (clonoSEQ®, Adaptive Biotechnologies), with further therapy guided by MRD-based algorithm (Figure). Those with <VGPR will be considered to have MRD(+) disease and will follow MRD(+) algorithm. This trial will allow us to gather preliminary data on use of MRD status to direct post-induction therapy. Based on MRD status post-induction, patients will be divided into 3 separate groups: Group A: MRD(-) will be offered lenalidomide maintenance or no further treatment at the discretion of the investigator; Group B: MRD(+) eligible for transplant, will undergo autologous stem cell transplant (ASCT). Post ASCT, those who remain MRD(+) will receive up to 12 cycles of KRd; Group C: MRD(+) group, not eligible for transplant will receive up to 12 additional cycles of KRd. Study Population and Endpoints Eligible patients ≥ 18 years have NDMM requiring treatment, ECOG performance status 0-2, LVEF ≥45%, and creatinine clearance ≥ 30 mL/min. One prior cycle of systemic therapy is permitted to accommodate patients who needed emergent treatment at the time of diagnosis. Major exclusion criteria include non-secretory MM, active involvement of the central nervous system by MM, POEMS syndrome and severe COPD. Primary endpoint is CR or better after 8 cycles of Dara + KRd induction therapy. Secondary endpoints include PFS, OS, time to disease progression, overall response rate, duration of response, time to next treatment, and post-induction rate of MRD(-) response. Statistical Considerations A minimax 2-stage design will be used to test the hypothesis that the CR or better rate is ≤ 50%. Twenty-three subjects will be enrolled in the first stage, and if at least 12 of the 23 subjects have a CR or better after induction therapy, an additional 16 subjects will be enrolled (a total of 39 patients). If at least 24 of 39 subjects have a CR or better, the null hypothesis will be rejected. Based on a one-sided alpha = 0.10 significance level, this sample size will provide 90% power to reject the null hypothesis, assuming the true CR or better rate is 70%. Bayesian based stopping rules (Grade 3+ cardiovascular/pulmonary-related toxicities or any Grade 5 events) were developed that will be utilized for safety monitoring during induction phase of the study. Correlatives Beyond the direct anti-MM plasma cell activity, the Dara + KRd combination has a potent effect on immune effector cells and overall inflammation. Correlative aims include assessing blood and bone marrow immune biomarkers at baseline and during treatment for association with patient outcome. Mononuclear cells, isolated from peripheral blood samples and bone marrow aspirate will be obtained for NK, NKT, B and T cell immunotyping (including expression of activating/ inhibitory molecules and maturation status), T cell (αβ and γδ) clonotyping and chemokine-profiling. Additionally, MRD testing will be done by next generation flow cytometry (MRD-NGF) at 10-6 sensitivity. The study is actively recruiting at Levine Cancer Institute, Charlotte. At the time of submission, 8 subjects have enrolled and are in induction phase of treatment. Clinical trial information: ClinicalTrials.gov Identifier NCT04113018 Disclosures Bhutani: Janssen: Research Funding; BMS: Research Funding; MedImmune: Research Funding; Sanofi Genzyme: Consultancy. Atrash:Levine Cancer Institute, Atrium Health: Current Employment; Amgen, GSK, Karyopharm.: Research Funding; BMS, Jansen oncology, Sanofi: Speakers Bureau; Takeda, Amgen, Karyopharm, BMS, Sanofi, Cellactar, Janssen and Celgene: Honoraria. Paul:Bristol-Myers Squibb: Other: Stock Ownership (prior employee); Regeneron: Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Speakers Bureau. Friend:Takeda: Speakers Bureau. Symanowski:Immatics: Consultancy; Casgen: Consultancy; Eli Lilly: Consultancy; Novartis: Consultancy. Voorhees:Oncopeptides: Honoraria, Other: Other relationship; Adaptive Biotechnologies: Honoraria, Other: Other relationship; TeneBio: Honoraria, Other: Other relationship; Janssen: Honoraria, Other: Other relationship; Novartis: Honoraria, Other: Other relationship; GSK: Honoraria, Other: Other relationship; BMS/Celgene: Honoraria, Other: Other relationship. Usmani:Celgene: Other; Array Biopharma: Research Funding; Pharmacyclics: Research Funding; Incyte: Research Funding; GSK: Consultancy, Research Funding; Amgen: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; BMS, Celgene: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Abbvie: Consultancy; Sanofi: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; Janssen: Consultancy, Honoraria, Other: Speaking Fees, Research Funding; SkylineDX: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; Merck: Consultancy, Research Funding.
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Lan, Jingxian, Amy C. Plint, Stuart R. Dalziel, Terry P. Klassen, Martin Offringa, and Anna Heath. "Remote, real-time expert elicitation to determine the prior probability distribution for Bayesian sample size determination in international randomised controlled trials: Bronchiolitis in Infants Placebo Versus Epinephrine and Dexamethasone (BIPED) study." Trials 23, no. 1 (April 11, 2022). http://dx.doi.org/10.1186/s13063-022-06240-w.
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Abstract Background Bayesian methods are increasing in popularity in clinical research. The design of Bayesian clinical trials requires a prior distribution, which can be elicited from experts. In diseases with international differences in management, the elicitation exercise should recruit internationally, making a face-to-face elicitation session expensive and more logistically challenging. Thus, we used a remote, real-time elicitation exercise to construct prior distributions. These elicited distributions were then used to determine the sample size of the Bronchiolitis in Infants with Placebo Versus Epinephrine and Dexamethasone (BIPED) study, an international randomised controlled trial in the Pediatric Emergency Research Network (PERN). The BIPED study aims to determine whether the combination of epinephrine and dexamethasone, compared to placebo, is effective in reducing hospital admission for infants presenting with bronchiolitis to the emergency department. Methods We developed a Web-based tool to support the elicitation of the probability of hospitalisation for infants with bronchiolitis. Experts participated in online workshops to specify their individual prior distributions, which were aggregated using the equal-weighted linear pooling method. Experts were then invited to provide their comments on the aggregated distribution. The average length criterion determined the BIPED sample size. Results Fifteen paediatric emergency medicine clinicians from Canada, the USA, Australia and New Zealand participated in three workshops to provide their elicited prior distributions. The mean elicited probability of admission for infants with bronchiolitis was slightly lower for those receiving epinephrine and dexamethasone compared to supportive care in the aggregate distribution. There were substantial differences in the individual beliefs but limited differences between North America and Australasia. From this aggregate distribution, a sample size of 410 patients per arm results in an average 95% credible interval length of less than 9% and a relative predictive power of 90%. Conclusion Remote, real-time expert elicitation is a feasible, useful and practical tool to determine a prior distribution for international randomised controlled trials. Bayesian methods can then determine the trial sample size using these elicited prior distributions. The ease and low cost of remote expert elicitation mean that this approach is suitable for future international randomised controlled trials. Trial registration ClinicalTrials.govNCT03567473
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Calderazzo, Silvia, Manuel Wiesenfarth, and Annette Kopp-Schneider. "A decision-theoretic approach to Bayesian clinical trial design and evaluation of robustness to prior-data conflict." Biostatistics, July 31, 2020. http://dx.doi.org/10.1093/biostatistics/kxaa027.
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Summary Bayesian clinical trials allow taking advantage of relevant external information through the elicitation of prior distributions, which influence Bayesian posterior parameter estimates and test decisions. However, incorporation of historical information can have harmful consequences on the trial’s frequentist (conditional) operating characteristics in case of inconsistency between prior information and the newly collected data. A compromise between meaningful incorporation of historical information and strict control of frequentist error rates is therefore often sought. Our aim is thus to review and investigate the rationale and consequences of different approaches to relaxing strict frequentist control of error rates from a Bayesian decision-theoretic viewpoint. In particular, we define an integrated risk which incorporates losses arising from testing, estimation, and sampling. A weighted combination of the integrated risk addends arising from testing and estimation allows moving smoothly between these two targets. Furthermore, we explore different possible elicitations of the test error costs, leading to test decisions based either on posterior probabilities, or solely on Bayes factors. Sensitivity analyses are performed following the convention which makes a distinction between the prior of the data-generating process, and the analysis prior adopted to fit the data. Simulation in the case of normal and binomial outcomes and an application to a one-arm proof-of-concept trial, exemplify how such analysis can be conducted to explore sensitivity of the integrated risk, the operating characteristics, and the optimal sample size, to prior-data conflict. Robust analysis prior specifications, which gradually discount potentially conflicting prior information, are also included for comparison. Guidance with respect to cost elicitation, particularly in the context of a Phase II proof-of-concept trial, is provided.
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Turner, Rebecca M., Anna Turkova, Cecilia L. Moore, Alasdair Bamford, Moherndran Archary, Linda N. Barlow-Mosha, Mark F. Cotton, et al. "Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial." BMC Medical Research Methodology 22, no. 1 (February 20, 2022). http://dx.doi.org/10.1186/s12874-022-01539-3.
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Abstract Background Clinical trial investigators may need to evaluate treatment effects in a specific subgroup (or subgroups) of participants in addition to reporting results of the entire study population. Such subgroups lack power to detect a treatment effect, but there may be strong justification for borrowing information from a larger patient group within the same trial, while allowing for differences between populations. Our aim was to develop methods for eliciting expert opinions about differences in treatment effect between patient populations, and to incorporate these opinions into a Bayesian analysis. Methods We used an interaction parameter to model the relationship between underlying treatment effects in two subgroups. Elicitation was used to obtain clinical opinions on the likely values of the interaction parameter, since this parameter is poorly informed by the data. Feedback was provided to experts to communicate how uncertainty about the interaction parameter corresponds with relative weights allocated to subgroups in the Bayesian analysis. The impact on the planned analysis was then determined. Results The methods were applied to an ongoing non-inferiority trial designed to compare antiretroviral therapy regimens in 707 children living with HIV and weighing ≥ 14 kg, with an additional group of 85 younger children weighing < 14 kg in whom the treatment effect will be estimated separately. Expert clinical opinion was elicited and demonstrated that substantial borrowing is supported. Clinical experts chose on average to allocate a relative weight of 78% (reduced from 90% based on sample size) to data from children weighing ≥ 14 kg in a Bayesian analysis of the children weighing < 14 kg. The total effective sample size in the Bayesian analysis was 386 children, providing 84% predictive power to exclude a difference of more than 10% between arms, whereas the 85 younger children weighing < 14 kg provided only 20% power in a standalone frequentist analysis. Conclusions Borrowing information from a larger subgroup or subgroups can facilitate estimation of treatment effects in small subgroups within a clinical trial, leading to improved power and precision. Informative prior distributions for interaction parameters are required to inform the degree of borrowing and can be informed by expert opinion. We demonstrated accessible methods for obtaining opinions.
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Wang, Yu, James Travis, and Byron Gajewski. "Bayesian adaptive design for pediatric clinical trials incorporating a community of prior beliefs." BMC Medical Research Methodology 22, no. 1 (April 21, 2022). http://dx.doi.org/10.1186/s12874-022-01569-x.
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Abstract Background Pediatric population presents several barriers for clinical trial design and analysis, including ethical constraints on the sample size and slow accrual rate. Bayesian adaptive design methods could be considered to address these challenges in pediatric clinical trials. Methods We developed an innovative Bayesian adaptive design method and demonstrated the approach as a re-design of a published phase III pediatric trial. The innovative design used early success criteria based on skeptical prior and early futility criteria based on enthusiastic prior extrapolated from a historical adult trial, and the early and late stopping boundaries were calibrated to ensure a one-sided type I error of 2.5%. We also constructed several alternative designs which incorporated only one type of prior belief and the same stopping boundaries. To identify a preferred design, we compared operating characteristics including power, expected trial size and trial duration for all the candidate adaptive designs via simulation when performing an increasing number of equally spaced interim analyses. Results When performing an increasing number of equally spaced interim analyses, the innovative Bayesian adaptive trial design incorporating both skeptical and enthusiastic priors at both interim and final analyses outperforms alternative designs which only consider one type of prior belief, because it allows more reduction in sample size and trial duration while still offering good trial design properties including controlled type I error rate and sufficient power. Conclusions Designing a Bayesian adaptive pediatric trial with both skeptical and enthusiastic priors can be an efficient and robust approach for early trial stopping, thus potentially saving time and money for trial conduction.
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Lin, Xiaolei, Jiaying Lyu, Shijie Yuan, Dehua Bi, Sue-Jane Wang, and Yuan Ji. "Bayesian Sample Size Planning Tool for Phase I Dose-Finding Trials." JCO Precision Oncology, no. 6 (August 2022). http://dx.doi.org/10.1200/po.22.00046.
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PURPOSE Through Bayesian inference, we propose a method called BayeSize as a reference tool for investigators to assess the sample size and its associated scientific property for phase I clinical trials. METHODS BayeSize applies the concept of effect size in dose finding, assuming that the maximum tolerated dose can be identified on the basis of an interval surrounding its true value because of statistical uncertainty. Leveraging a decision framework that involves composite hypotheses, BayeSize uses two types of priors, the fitting prior (for model fitting) and sampling prior (for data generation), to conduct sample size calculation under the constraints of statistical power and type I error. RESULTS Simulation results showed that BayeSize can provide reliable sample size estimation under the constraints of type I/II error rates. CONCLUSION BayeSize could facilitate phase I trial planning by providing appropriate sample size estimation. Look-up tables and R Shiny app are provided for practical applications.
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Zabor, Emily C., Michael J. Kane, Satrajit Roychoudhury, Lei Nie, and Brian P. Hobbs. "Bayesian basket trial design with false-discovery rate control." Clinical Trials, February 7, 2022, 174077452110736. http://dx.doi.org/10.1177/17407745211073624.
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Background: Recent advances in developing “tumor agnostic” oncology therapies have identified molecular targets that define patient subpopulations in a manner that supersedes conventional criteria for cancer classification. These successes have produced effective targeted therapies that are administered to patients regardless of their tumor histology. Trials have evolved as well with master protocol designs. By blending translational and clinical science, basket trials in particular are well-suited to investigate and develop targeted therapies among multiple cancer histologies. However, basket trials intrinsically involve more complex design decisions, including issues of multiple testing across baskets, and guidance for investigators is needed. Methods: The sensitivity of the multisource exchangeability model to prior specification under differing degrees of response heterogeneity is explored through simulation. Then, a multisource exchangeability model design that incorporates control of the false-discovery rate is presented and a simulation study compares the operating characteristics to a design where the family-wise error rate is controlled and to the frequentist approach of treating the baskets as independent. Simulations are based on the original design of a real-world clinical trial, the SUMMIT trial, which investigated Neratinib treatment for a variety of solid tumors. The methods studied here are specific to single-arm phase II trials with binary outcomes. Results: Values of prior probability of exchangeability in the multisource exchangeability model between 0.1 and 0.3 provide the best trade-offs between gain in precision and bias, especially when per-basket sample size is below 30. Application of these calibration results to a re-analysis of the SUMMIT trial showed that the breast basket exceeded the null response rate with posterior probability of 0.999 while having low posterior probability of exchangeability with all other baskets. Simulations based on the design of the SUMMIT trial revealed that there is meaningful improvement in power even in baskets with small sample size when the false-discovery rate is controlled as opposed to the family-wise error rate. For example, when only the breast basket was active, with a sample size of 25, the power was 0.76 when the false-discovery rate was controlled at 0.05 but only 0.56 when the family-wise error rate was controlled at 0.05, indicating that impractical sample sizes for the phase II setting would be needed to achieve acceptable power while controlling the family-wise error rate in this setting of a trial with 10 baskets. Conclusion: Selection of the prior exchangeability probability based on calibration and incorporation of false-discovery rate control result in multisource exchangeability model designs with high power to detect promising treatments in the context of phase II basket trials.
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Azmee, Nor Afzalina, Zulkifley Mohamed, and Azhar Ahmad. "Determination of the Required Sample Size with Assurance for Three-Arm Non-Inferiority Trials." Jurnal Teknologi 63, no. 2 (June 15, 2013). http://dx.doi.org/10.11113/jt.v63.1919.
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The concept of assurance in the two-arm non-inferiority trials has been explored, expressing the non-inferiority margin as a clinically meaningful treatment difference. This short paper focuses on developing an assurance formula in the three-arm non-inferiority trial, based on the ratio of means. The discussion starts with the simple case of known variances and then extends to the case of unknown but equal variances. To avoid complicated integration, assurance for the latter case was studied using Bayesian Clinical Trial Simulation (BCTS). The findings indicate that assurance allows the experimenter to formally take into account the uncertainty surrounding the parameter estimates by using the prior distributions. Furthermore, BCTS can be easily implemented to find the required sample size without having to resort to complex integration.
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Clayton, Gemma L., Daisy Elliott, Julian P. T. Higgins, and Hayley E. Jones. "Use of external evidence for design and Bayesian analysis of clinical trials: a qualitative study of trialists’ views." Trials 22, no. 1 (November 8, 2021). http://dx.doi.org/10.1186/s13063-021-05759-8.
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Abstract Background Evidence from previous studies is often used relatively informally in the design of clinical trials: for example, a systematic review to indicate whether a gap in the current evidence base justifies a new trial. External evidence can be used more formally in both trial design and analysis, by explicitly incorporating a synthesis of it in a Bayesian framework. However, it is unclear how common this is in practice or the extent to which it is considered controversial. In this qualitative study, we explored attitudes towards, and experiences of, trialists in incorporating synthesised external evidence through the Bayesian design or analysis of a trial. Methods Semi-structured interviews were conducted with 16 trialists: 13 statisticians and three clinicians. Participants were recruited across several universities and trials units in the United Kingdom using snowball and purposeful sampling. Data were analysed using thematic analysis and techniques of constant comparison. Results Trialists used existing evidence in many ways in trial design, for example, to justify a gap in the evidence base and inform parameters in sample size calculations. However, no one in our sample reported using such evidence in a Bayesian framework. Participants tended to equate Bayesian analysis with the incorporation of prior information on the intervention effect and were less aware of the potential to incorporate data on other parameters. When introduced to the concepts, many trialists felt they could be making more use of existing data to inform the design and analysis of a trial in particular scenarios. For example, some felt existing data could be used more formally to inform background adverse event rates, rather than relying on clinical opinion as to whether there are potential safety concerns. However, several barriers to implementing these methods in practice were identified, including concerns about the relevance of external data, acceptability of Bayesian methods, lack of confidence in Bayesian methods and software, and practical issues, such as difficulties accessing relevant data. Conclusions Despite trialists recognising that more formal use of external evidence could be advantageous over current approaches in some areas and useful as sensitivity analyses, there are still barriers to such use in practice.
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Ghodratitoostani, Iman, Oilson A. Gonzatto, Zahra Vaziri, Alexandre C. B. Delbem, Bahador Makkiabadi, Abhishek Datta, Chris Thomas, et al. "Dose-Response Transcranial Electrical Stimulation Study Design: A Well-Controlled Adaptive Seamless Bayesian Method to Illuminate Negative Valence Role in Tinnitus Perception." Frontiers in Human Neuroscience 16 (May 12, 2022). http://dx.doi.org/10.3389/fnhum.2022.811550.
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The use of transcranial Electrical Stimulation (tES) in the modulation of cognitive brain functions to improve neuropsychiatric conditions has extensively increased over the decades. tES techniques have also raised new challenges associated with study design, stimulation protocol, functional specificity, and dose-response relationship. In this paper, we addressed challenges through the emerging methodology to investigate the dose-response relationship of High Definition-transcranial Direct Current Stimulation (HD tDCS), identifying the role of negative valence in tinnitus perception. In light of the neurofunctional testable framework and tES application, hypotheses were formulated to measure clinical and surrogate endpoints. We posited that conscious pairing adequately pleasant stimuli with tinnitus perception results in correction of the loudness misperception and would be reinforced by concurrent active HD-tDCS on the left Dorsolateral Prefrontal Cortex (dlPFC). The dose-response relationship between HD-tDCS specificity and the loudness perception is also modeled. We conducted a double-blind, randomized crossover pilot study with six recruited tinnitus patients. Accrued data was utilized to design a well-controlled adaptive seamless Bayesian dose-response study. The sample size (n = 47, for 90% power and 95% confidence) and optimum interims were anticipated for adaptive decision-making about efficacy, safety, and single session dose parameters. Furthermore, preliminary pilot study results were sufficient to show a significant difference (90% power, 99% confidence) within the longitudinally detected self-report tinnitus loudness between before and under positive emotion induction. This study demonstrated a research methodology used to improve emotion regulation in tinnitus patients. In the projected method, positive emotion induction is essential for promoting functional targeting under HD-tDCS anatomical specificity to indicate the efficacy and facilitate the dose-finding process. The continuous updating of prior knowledge about efficacy and dose during the exploratory stage adapts the anticipated dose-response model. Consequently, the effective dose range to make superiority neuromodulation in correcting loudness misperception of tinnitus will be redefined. Highly effective dose adapts the study to a standard randomized trial and transforms it into the confirmatory stage in which active HD-tDCS protocol is compared with a sham trial (placebo-like). Establishing the HD-tDCS intervention protocols relying on this novel method provides reliable evidence for regulatory agencies to approve or reject the efficacy and safety. Furthermore, this paper supports a technical report for designing multimodality data-driven complementary investigations in emotion regulation, including EEG-driven neuro markers, Stroop-driven attention biases, and neuroimaging-driven brain network dynamics.