Stopping Rule Research Articles

Using a Stopping Rule to Optimize Cost-Quality Tradeoffs in a Large, Mixed-Mode Survey: A Simulation Study

Rising nonresponse and the increasing costs of conducting surveys are creating pressure on survey organizations to efficiently allocate resources. The challenge is to produce the highest quality data possible within a fixed budget. We use a stopping rule designed to minimize a function of cost and errors. The rule is based on the product of predicted costs and the predicted mean squared error of a survey estimate. We simulate the impact of implementing the stopping rule on the 2019 US Survey of Doctorate Recipients (SDR), which is a longitudinal survey conducted every two years in the US using web, mail, and CATI. We vary the types of models used to generate the input predictions (parametric regression vs. nonparametric tree models) and the timing of the implementation of the rule. We found that the modeling approach made less difference, while the timing of the implementation of the stopping rule made a large difference in outcomes. The rule is multivariate and optimizes outcomes for two variables. It performed better for one variable, leading to reduced costs and only small increases in errors. The other variable had larger error increases.

NIMG-82. DEVELOPMENT AND VALIDATION OF AN MRI-BASED DEEP LEARNING MODEL TO DIFFERENTIATEIDH-WILDTYPE GLIOBLASTOMA AND TUMEFACTIVE MULTIPLE SCLEROSIS

Abstract Clinical management of IDH wildtype glioblastoma (GBM) and tumefactive multiple sclerosis (tMS) is drastically different. GBM requires maximal safe resection followed by chemoradiation, while tMS outcome is worsened by surgery and radiotherapy. Noninvasive methods are needed to help with accurate diagnosis of tumor and non-tumor etiologies. To develop an MRI-based classification model, tMS subjects diagnosed prior to January 1, 2020, were matched to tMS by age at diagnosis, sex, index MRI date, and 2D/3D acquisition. Inclusion criteria included one cm minimal lesion size and pre-operative post-contrast T1 and T2 images available for analysis. A 3D-DenseNet121 was used to develop a classification model using prespecified parameters: 650 epochs, batch size 16, learning rate 10-3, cross-entropy loss, and AdamW optimizer. The stopping rule was defined as three sequential differences in epoch cross-entropy loss <0.02. Models were developed using both T1gd and T2, as well as from only T1gd and only T2. Training included 220 subjects (110 GBM, 110 tMS). A 2-stage validation design was used, which included both retrospective and prospective cohorts. Stage 1 consisted of 272 retrospective GBM (diagnosed prior to January 1, 2020). Stage 2 consisted of 69 and 34 prospective (diagnosed after January 1, 2020) GBM and tMS, respectively. External validation on the 272 retrospective GBM demonstrated accuracy of 91%, 84%, and 78% for T1gd+T2, T1gd only, and T2 only, respectively. External validation on the 69 prospective GBM demonstrated an accuracy of 87%, 64%, and 67% for T1gd+T2, T1gd only, and T2 only, respectively. The 34 prospective tMS demonstrated accuracy of 76%, 76%, and 82% for T1gd+T2, T1gd only, and T2 only, respectively. This shows the feasibility of deep learning to aid in differential diagnosis of brain lesions. Future work entails the integration of germline variants into the classification model, including variants associated with the risk of glioma or MS.

CTNI-16. MONOGERM, A NOVEL PROOF-OF-PRINCIPLE BAYESIAN PHASE II TRIAL DESIGN OF CARBOPLATIN OR VINBLASTINE MONOTHERAPY INDUCTION PRIOR TO RADIOTHERAPY FOR INTRACRANIAL GERMINOMA

Abstract BACKGROUND Current European standard-of-care for localised intracranial germinoma is multi-agent chemotherapy (carboPEI: carboplatin/etoposide/ifosfamide) followed by definitive radiotherapy, with excellent survival. MonoGerm is a de-escalation, non-inferiority trial aiming to reduce toxicity. Twelve-week carboplatin (PMID:8039122) AUC10 or vinblastine (PMIDs:32642701/34520101) induction will be evaluated to test if as effective as carboPEI from SIOP-CNS-GCT-II. A novel trial design was required to answer this question pragmatically/safely. METHODS Clinical trials in rare diseases recruit slowly, allowing continuous monitoring of efficacy outcomes. Efficacy-transition-pathways (ETP) are innovative visual tools to aid determination of trial design parameters, and an extension of the dose-transition-pathways concept introduced for dose-finding trials (PMID:28733440). RESULTS MonoGerm includes two monotherapies, with each single arm recruiting six cohorts of three patients, with interim assessment after each recruited cohort and final analysis at 18 patients (total n=36). Insufficient tumour volume response (<30%) at 6-week safety MRI results in 12-weeks carboPEI. Primary outcome is radiological complete response (CR) by 12-weeks of induction monotherapy. A beta-binomial conjugate analysis will generate posterior probability distributions, combining observed trial data as realisations from a binomial distribution with a minimally informative Beta (1,1) prior. Decision criteria to allow early stopping at interim analyses and go/no-go decisions at final analysis are based on probabilities from these posterior distributions. ETP visually maps out parameters used to assert decisions after each interim assessment as a pyramid decision tree. For each recruited cohort and every CR outcome, estimates of the true CR rate and probabilities with associated decisions are mapped out. ETP allows clear communication between statisticians, clinicians, and patient-public-involvement (PPI) teams, facilitating informed decisions in an efficient/realistic trial design. CONCLUSION MonoGerm, a novel Bayesian de-escalation trial, funded by Little Princess Trust (https://www.littleprincesses.org.uk/), uses ETP and continuous monitoring with built-in stopping rules to ensure patient safety in this treatment de-escalation trial.

Bayesian Safety and Futility Monitoring in Phase II Trials Using One Utility-Based Rule.

For phase II clinical trials that determine the acceptability of an experimental treatment based on ordinal toxicity and ordinal response, most monitoring methods require each ordinal outcome to be dichotomized using a selected cut-point. This allows two early stopping rules to be constructed that compare marginal probabilities of toxicity and response to respective upper and lower limits. Important problems with this approach are loss of information due to dichotomization, dependence of treatment acceptability decisions on precisely how each ordinal variable is dichotomized, and ignoring association between the two outcomes. To address these problems, we propose a new Bayesian method, which we call U-Bayes, that exploits elicited numerical utilities of the joint ordinal outcomes to construct one early stopping rule that compares the mean utility to a lower limit. U-Bayes avoids the problems noted above by using the entire joint distribution of the ordinal outcomes, and not dichotomizing the outcomes. A step-by-step algorithm is provided for constructing a U-Bayes rule based on elicited utilities and elicited limits on marginal outcome probabilities. A simulation study shows that U-Bayes greatly improves the probability of determining treatment acceptability compared to conventional designs that use two monitoring rules based on marginal probabilities.

Recommendations for monitoring adherence and fidelity in pragmatic trials based on experience in the Pain Management Collaboratory.

Most pragmatic trials follow the PRagmatic Explanatory Continuum Indicator Summary (PRECIS-2) criteria. The criteria specify unobtrusive measurement of participants' protocol adherence and practitioners' intervention fidelity but suggest no special monitoring strategies to assure trial integrity. We present experience with adherence/fidelity monitoring in the Pain Management Collaboratory (PMC) and provide recommendations for their monitoring in pragmatic trials to preserve inferences of treatment comparisons. In November 2021, we surveyed 10 of 11 originally funded PMC pragmatic trials to determine the extent to which adherence and fidelity data were being monitored. Of the 10 PMC trials, 8 track adherence/fidelity. The electronic health record is the most frequent source for monitoring adherence (7/10) and fidelity (5/10). Most adherence data are used to monitor participant engagement with the trial intervention (4/10) and are reviewed by study teams (8/10) and often with a data and safety monitoring board (DSMB) (5/10). Half of the trials (5/10) reported using fidelity data for feedback/training; such data are not shared with a DSMB (0/10). Only 2 of 10 trials reported having prespecified guidance or rules around adherence/fidelity (eg, stopping rules or thresholds for corrective action, such as retraining). As a best practice for pragmatic trials, we recommend early and regular adherence/fidelity monitoring to determine whether intervention delivery is as intended. We propose a 2-stage process with thresholds for intervening and triggers for conducting a formal futility analysis if adherence and fidelity are not maintained. The level of monitoring should be unobtrusive for both participants and those delivering the intervention; resulting data should be reviewed by an independent DSMB.

사전 정보를 활용한 컴퓨터 적응 검사의 측정 정확성 및 효율성 향상 방안

Objectives This study investigates the accuracy and efficiency of Computerized Adaptive Testing (CAT) by utilizing prior information to predict students' basic academic levels. Methods To achieve this, a simulation study was conducted with varied conditions, including initial item, partic-ipant groups, and test stopping rule. Results Results indicate that selecting initial items based on predictors of basic academic levels significantly im-proved the accuracy and efficiency of CAT measurements, with the most substantial improvements observed in low-achieving groups. Conclusions The findings suggest the need for further exploration of the applicability of basic academic level pre-dictors across different educational contexts to identify strategies for improving the precision and efficiency of CAT systems.

An evaluation of the performance of stopping rules in AI-aided screening for psychological meta-analytical research.

Several AI-aided screening tools have emerged to tackle the ever-expanding body of literature. These tools employ active learning, where algorithms sort abstracts based on human feedback. However, researchers using these tools face a crucial dilemma: When should they stop screening without knowing the proportion of relevant studies? Although numerous stopping rules have been proposed to guide users in this decision, they have yet to undergo comprehensive evaluation. In this study, we evaluated the performance of three stopping rules: the knee method, a data-driven heuristic, and a prevalence estimation technique. We measured performance via sensitivity, specificity, and screening cost and explored the influence of the prevalence of relevant studies and the choice of the learning algorithm. We curated a dataset of abstract collections from meta-analyses across five psychological research domains. Our findings revealed performance differences between stopping rules regarding all performance measures and variations in the performance of stopping rules across different prevalence ratios. Moreover, despite the relatively minor impact of the learning algorithm, we found that specific combinations of stopping rules and learning algorithms were most effective for certain prevalence ratios of relevant abstracts. Based on these results, we derived practical recommendations for users of AI-aided screening tools. Furthermore, we discuss possible implications and offer suggestions for future research.

MCMC stopping rules in latent variable modelling.

Bayesian analysis relies heavily on the Markov chain Monte Carlo (MCMC) algorithm to obtain random samples from posterior distributions. In this study, we compare the performance of MCMC stopping rules and provide a guideline for determining the termination point of the MCMC algorithm in latent variable models. In simulation studies, we examine the performance of four different MCMC stopping rules: potential scale reduction factor (PSRF), fixed-width stopping rule, Geweke's diagnostic, and effective sample size. Specifically, we evaluate these stopping rules in the context of the DINA model and the bifactor item response theory model, two commonly used latent variable models in educational and psychological measurement. Our simulation study findings suggest that single-chain approaches outperform multiple-chain approaches in terms of item parameter accuracy. However, when it comes to person parameter estimates, the effect of stopping rules diminishes. We caution against relying solely on the univariate PSRF, which is the most popular method, as it may terminate the algorithm prematurely and produce biased item parameter estimates if the cut-off value is not chosen carefully. Our research offers guidance to practitioners on choosing suitable stopping rules to improve the precision of the MCMC algorithm in models involving latent variables.

Carrier rates of group A streptococci in Australian wet tropics and their impact on the clinical usefulness of throat swabs.

Rapid point-of-care tests (POCT) are likely to assist with the detection of group A streptococci (GAS), but their usefulness is determined by the presence of carriers of GAS. This is insufficiently explored in the wet tropics. This study included 77 patients attendingprimary care in the wet tropics complaining of a sore throat, and 49healthy controls. Carrier rates of GAS and the positive and negative etiological predictive values (P-EPV and N-EPV, respectively) of a POCT were calculated. The carrier rates were 8.3% among healthy children and 2.7% for adults. TheP-EPV for children was 71% (95%confidence interval [CI]: 0.0-100%) and for adults it was 85% (95% CI: 0.0-100%). The corresponding N-EPV was99% (95% CI: 95-100%) for children and99% (95% CI: 98-100%) for adults. N-EPV, ruling out GAS, was sufficiently high with narrow CIs to allow for defining a stopping rule to avoid unnecessary antibiotic prescribing.

Carrier rates of group A streptococci in Australian wet tropics and their impact on the clinical usefulness of throat swabs.

Rapid point-of-care tests (POCT) are likely to assist with the detection of group A streptococci (GAS), but their usefulness is determined by the presence of carriers of GAS. This is insufficiently explored in the wet tropics. This study included 77 patients attendingprimary care in the wet tropics complaining of a sore throat, and 49healthy controls. Carrier rates of GAS and the positive and negative etiological predictive values (P-EPV and N-EPV, respectively) of a POCT were calculated. The carrier rates were 8.3% among healthy children and 2.7% for adults. TheP-EPV for children was 71% (95%confidence interval [CI]: 0.0-100%) and for adults it was 85% (95% CI: 0.0-100%). The corresponding N-EPV was99% (95% CI: 95-100%) for children and99% (95% CI: 98-100%) for adults. N-EPV, ruling out GAS, was sufficiently high with narrow CIs to allow for defining a stopping rule to avoid unnecessary antibiotic prescribing.

Carfilzomib, lenalidomide, dexamethasone (KRD) in BTKi relapsed or refractory mantle cell lymphoma: A phase II study fromFondazione Italiana Linfomi.

Mantle cell lymphoma (MCL) is a rare lymphoproliferative neoplasm considered incurable, with a median survival of 3-5 years. In recent years, Bruton's tyrosine kinase inhibitors (BTKi) have been introduced, demonstrating high therapeutic activity. However, the prognosis for MCL patients failing ibrutinib therapy is particularly poor, with a survival expectation of a few months. In this phase II trial, we assessed the efficacy and safety of the carfilzomib-lenalidomide-dexamethasone (KRD) combination in MCL patients who were relapsed/refractory (R/R) or intolerant to BTKi and in need of treatment. The primary objective of the study was to evaluate the antitumor efficacy of the KRD combination in terms of 12-month overall survival (12-month OS). From September 2019 to December 2020, 16 patients were enrolled from 11 Italian centers. After a median follow-up of 2.37 months (95% CI 0.92-6.47), the 12-month OS was 13%. The rate of grade 3-4 adverse events (AEs) was 35%, and the overall response rate (ORR) was 19%. These results led to the premature termination of enrollment, as defined in the protocol stopping rules. The efficacy of the KRD combination in advanced-stage MCL patients who are R/R to BTKi is unsatisfactory and too toxic.

Multivariate moment least-squares variance estimators for reversible Markov chains

Markov chain Monte Carlo (MCMC) is a commonly used method for approximating expectations with respect to probability distributions. Uncertainty assessment for MCMC estimators is essential in practical applications. Moreover, for multivariate functions of a Markov chain, it is important to estimate not only the auto-correlation for each component but also to estimate cross-correlations, in order to better assess sample quality, improve estimates of effective sample size, and use more effective stopping rules. Berg and Song (2023) introduced the moment least squares (momentLS) estimator, a shape-constrained estimator for the autocovariance sequence from a reversible Markov chain, for univariate functions of the Markov chain. Based on this sequence estimator, they proposed an estimator of the asymptotic variance of the sample mean from MCMC samples. In this study, we propose novel autocovariance sequence and asymptotic variance estimators for Markov chain functions with multiple components, based on the univariate momentLS estimators from Berg and Song (2023). We demonstrate strong consistency of the proposed auto(cross)-covariance sequence and asymptotic variance matrix estimators. We conduct empirical comparisons of our method with other state-of-the-art approaches on simulated and real-data examples, using popular samplers including the random-walk Metropolis sampler and the No-U-Turn sampler from STAN. Supplemental materials for this article are available online.

Multi-arm multi-stage survival trial design with arm-specific stopping rule

ABSTRACT Traditional two-arm randomized trial designs have played a pivotal role in establishing the efficacy of medical interventions. However, their efficiency is often compromised when confronted with multiple experimental treatments or limited resources. In response to these challenges, the multi-arm multi-stage designs have emerged, enabling the simultaneous evaluation of multiple treatments within a single trial. In such an approach, if an arm meets efficacy success criteria at an interim stage, the whole trial stops and the arm is selected for further study. However when multiple treatment arms are active, stopping the trial at the moment one arm achieves success diminishes the probability of selecting the best arm. To address this issue, we have developed a group sequential multi-arm multi-stage survival trial design with an arm-specific stopping rule. The proposed method controls the familywise type I error in a strong sense and selects the best promising treatment arm with a high probability.

Hemodynamic management of acute kidney injury.

To discuss the role of hemodynamic management in critically ill patients with acute kidney injury. Acute kidney injury (AKI) may be associated with persistent alterations in renal perfusion, even when cardiac output and blood pressure are preserved. The effects of interventions aiming at increasing renal perfusion are best evaluated by renal Doppler or contrast enhance ultrasound. However, limited data have been acquired with these techniques and the essential of the literature is based on surrogates of renal function such as incidence of use of renal replacement therapy. Fluids may increase renal perfusion but their effects are quite unpredictable and can be dissociated from their impact on cardiac output and arterial pressure. Inotropes can also be used in selected conditions. At the de-escalation phase, fluid withdrawal should be considered. Safe fluid withdrawal may be achieved when applied in selected patients with preserved tissue perfusion presenting signs of fluid intolerance. When applied, stopping rules should be set. Dobutamine, milrinone and levosimendan increase renal perfusion in AKI associated with cardiac failure or after cardiac surgery. However, the impact of these agents in sepsis is not well defined. Regarding vasopressors, norepinephrine is the first-line vasopressor agent, but vasopressin derivative may limit the requirement of renal replacement therapy. Angiotensin has promising effects in a limited size post-Hoc analysis of a RCT, but these data need to be confirmed. While correction of severe hypotension is associated with improved renal perfusion and function, the optimal mean arterial pressure (MAP) target level remains undefined, Systematic increase in MAP results in variable changes in renal perfusion. It sounds reasonable to individualize MAP target, paying attention to central venous and intraabdominal pressures, as well as to the response to an increase in MAP. Recent studies have refined the impact of the various hemodynamic interventions on renal perfusion and function in critically ill patients with AKI. Though several of these interventions improve renal perfusion, their impact on renal function is more variable.

Considerations for the design and conduct of pediatric obesity pharmacotherapy clinical trials: Proceedings of expert roundtable meetings.

Anti-obesity medications (AOMs) have emerged as one element of comprehensive obesity clinical care intended to improve long-term health outcomes for children and adolescents. The number of pediatric AOM clinical trials has burgeoned in recent years as new pharmacotherapeutics have been developed. Factors related to growth and development in children and adolescents can present unique challenges in terms of designing and conducting clinical trials investigating the safety and efficacy of AOMs. These barriers can delay the AOM development and evaluation process, increase the cost of performing trials, create challenges in the interpretation of results, influence the generalizability of the findings and present ethical dilemmas. In an effort to address these issues and provide guidance to streamline the process of designing and conducting pediatric AOM clinical trials, relevant key stakeholders convened a series of roundtable meetings to discuss, debate and achieve harmonization on design features. Stakeholder participants included a multidisciplinary group of international pediatric obesity experts, patient (parent) representatives and representatives from academic medicine, key regulatory agencies and industry. Topics of discussion included primary efficacy end-points, secondary end-points, eligibility criteria, trial run-in and follow-up phases, use of active comparators and guidelines for down-titration and/or stopping rules for excessive weight reduction. Consensus recommendations were agreed upon. Regarding end-points, emphasis was placed on moving away from BMI z-score as a primary outcome, incorporating multiple alternative BMI-related outcomes and measuring adiposity/body fat as a prominent secondary end-point. Trial eligibility criteria were carefully considered to maximize generalizability while maintaining safety. The limited value of trial run-in phases was discussed. It was also underscored that designing trials with extended follow-up periods after AOM withdrawal should be avoided owing to ethical issues (including possible psychological harm) related to weight regain without providing the opportunity to access other treatments. The panel emphasized the value of the randomized, placebo-controlled trial but recommended the thoughtful consideration of the use of active comparators in addition to, or instead of, placebo to achieve clinical equipoise when appropriate. Finally, the panel recommended that clinical trial protocols should include clear guidance regarding AOM down-titration to avoid excessive weight reduction when applicable.

Stopping Rule Sampling to Monitor and Protect Endangered Species

Stopping Rule Sampling to Monitor and Protect Endangered Species

A hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism

Both classical forecasting methods and machine learning approaches are used to solve forecasting problems. Deep artificial neural networks, one of the machine learning methods, are widely used today and give very good results. Recurrent neural networks, a type of deep neural network, are very important in forecasting problems. Simple recurrent artificial neural networks, which are the simplest deep recurrent neural networks, are often preferred in solving forecasting problems due to the small number of parameters they use. Simple exponential smoothing, one of the classical forecasting methods, attracts attention with its performance in solving forecasting problems. The motivation of the study is to create a new forecasting method by combining a classical and simple forecasting method with a deep recurrent artificial neural network in an architecture. In this, a new hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism is proposed. The architecture of the proposed method is created as a combination of simple recurrent artificial neural networks and simple exponential smoothing methods. In the training of the proposed method, two training algorithms based on sine cosine optimization and particle swarm optimization algorithms are proposed. In these training algorithms, two different solution strategies such as restarting, and early stopping rule are used to avoid overfitting and local optimum problems. The performance of the proposed method is analysed using stock market datasets and compared with both different deep and shallow artificial neural networks and classical forecasting methods. As a result of the analyses, it is concluded that the proposed method is successful in one step ahead of forecasting performance.

Practical and analytical considerations when performing interim analyses in diagnostic test accuracy studies

Interim analysis is a common methodology in randomised clinical trials but has received less attention in studies of diagnostic test accuracy. In such studies, early termination for futility may be beneficial if early evidence indicates that a diagnostic test is unlikely to achieve a clinically useful level of diagnostic performance, as measured by the sensitivity and specificity. In this paper, we describe relevant practical and analytical considerations when planning and performing interim analysis in diagnostic accuracy studies, focusing on stopping rules for futility. We present an adaptation of the exact group sequential method for diagnostic testing, with R code provided for implementing this method in practice. The method is illustrated using two simulated data sets and data from a published diagnostic accuracy study for point-of-care testing for SARS-CoV-2. The considerations described in this paper can be used to guide decisions as to when an interim analysis in a diagnostic accuracy study is suitable and highlight areas for further methodological development.

Computerized Adaptive Testing for the Berg Balance Scale Improves Measurement Efficiency Without Compromising Precision in People With Stroke.

Objectives of this study were to confirm the Berg Balance Scale's (BBS) measurement properties and unidimensionality with an item response theory analysis in persons with subacute and chronic stroke and examine the precision and efficiency of computerized adaptive testing (CAT). Data were obtained from 519 ambulatory persons with subacute and chronic stroke in 2 retrospective databases. A principal component analysis (PCA) of residuals was used to evaluate unidimensionality. BBS fit to a rating scale model versus a partial credit model was examined and item parameters were generated for CAT calibration. Person measures from all 14 items were defined as actual balance ability. BBS CAT simulations were used to examine changes in measurement precision with increasing number of items administered and a precision-based stopping rule (0.5 logit standard error (SE) threshold). A PCA of residuals supports the BBS's unidimensionality and Rasch analysis supports using the rating scale model for measurement. Maximum precision for BBS CAT was SE = 0.40 logits when administering all items. BBS CAT estimated balance ability was highly correlated with actual ability when 4 or more items were administered (r > 0.9). Precision was within 0.5 logits when 5 or more items were administered (SE < 0.48 logits). BBS CAT estimated balance ability was highly correlated with actual ability (r = 0.952) using a precision-based stopping rule. The average number of items administered with the precision-based stopping rule was 5.43. The BBS is sufficiently unidimensional and the rating scale model can be used for measurement. BBS CAT is efficient and replicates the full instrument's reliability when measuring balance ability in ambulatory persons with subacute and chronic stroke. Future work should aim to enhance the interpretability of measures to facilitate clinical decision making. BBS CAT provides an efficient way of measuring balance ability for individuals in stroke rehabilitation giving clinicians more time with patients.

A stochastic iterative peer-to-peer energy market clearing in smart energy communities considering participation priorities of prosumers

The penetration of distributed energy resources (DERs) has changed the role of a consumer to a prosumer, i.e., producer and consumer. This new role provides the opportunity for peer-to-peer(P2P) energy trading. In this paper, a three-stage iterative framework is proposed to clear the price and quantity of trading in P2P markets while addressing price and DER uncertainties by the Monte Carlo simulation (MCS) method. Initially, bids and offers of customers are determined by implementing an advanced satisfaction-based home energy management system (HEMS) at each home. Subsequently, the market operator prioritizes bids and offers according to the amount of customers’ participation in the market. Finally, the P2P market is cleared by application of the alternating direction method of multipliers (ADMM), and the market clearing prices (MCPs) are determined. MCPs are used as a parameter to repeat the three stages, and the procedure is redone until the stopping rule is met. The proposed method's effectiveness has been investigated in communities with 8, 50, and 100 prosumers. Results indicate a 69.51 % cost reduction in a smart energy community with 50 homes through P2P energy market participation. The proposed market clearing method is compared with the common mid-market rate (MMR) and Stackelberg game methods and demonstrates over 25 % reduction in community costs.