Stopping Rule Research Articles

An Experimental Analysis of the Prize-Probability Tradeoff in Stopping Problems

Abstract We experimentally examine how individuals commit to a cutoff stopping rule when facing a sequence of independent lotteries. We identify two main behavior patterns: (1) a small share of participants consistently choose stopping rules whose gain bound (i.e., the accumulated gain at which the sequence stops) is larger than the loss bound, and (2) a larger share of participants consistently choose rules whose loss bound is larger than the gain bound. We introduce a procedural choice model that accounts for these patterns and show that the behavior of most of our participants is inconsistent with prominent theories of decision under risk.

Application of the professional maturity scale as a computerized adaptive testing

This study has two main purposes. Firstly, to compare the different item selection methods and stopping rules used in Computerized Adaptive Testing (CAT) applications with simulative data generated based on the item parameters of the Vocational Maturity Scale. Secondly, to test the validity of CAT application scores. For the first purpose, simulative data produced based on Vocational Maturity Scale item parameters were analyzed under different item selection methods (Maximum Fisher Information [MFI],Maximum Likelihood Weighted Information [MLWI] Maximum Posterior Weighted Information [MPWI] Maximum Expected Information [MEI] Minimum Expected Posterior Variance [MEPV] Maximum Expected Posterior Weighted Information [MEPWI]) and stopping rules (Standard Error [SE]<0.30, SE<0.50, SE <0.70, Number of Item [NI]=10, NI=20) by calculating the average number of items, standard error averages, correlation coefficients, bias, and RMSE statistics. For all the conditions of the item selection methods, standard error averages, correlation coefficients, bias, and RMSE statistics showed similar results. When the average number of items is considered, MFI and SE<0.30 were found as most appropriate methods to be used in CAT application. For the second purpose of the study, the paper-pencil form of the Vocational Maturity scale and CAT version were administered to 33 students. A moderate, positive, and statistically significant relationship was found between the CAT application scores and the paper-pencil form scores on the vocational maturity scale. As a result, it can be said that the vocational maturity scale can be applied as a computerized adaptive test and can be used in career guidance processes.

Mindset effects on the regulation of thinking time in problem-solving

Understanding time investment while solving problems is central to metacognitive research. By the Diminishing Criterion Model (DCM), time regulation is guided by two stopping rules: a confidence criterion that drops as time is invested in each problem and the maximum time to be invested. This combination generates curved confidence–time associations. We compared the belief that intelligence is malleable, a growth mindset, to the belief that intelligence is fixed, and to neutral control groups. We hypothesized that a growth mindset leads people to selectively invest time in problems carrying the hope of improvement. This extra time makes the curved DCM pattern curvier. In two experiments, participants primed with growth, fixed, or control mindsets solved analogies (Experiment 1) and compound remote associates (Experiment 2). As expected, in both experiments a growth mindset exhibited a curvier confidence–time pattern, while the fixed mindset and control groups replicated previous confidence–time associations. Most additional time was invested in problems with intermediate difficulty levels, suggesting strategic time allocation. The study offers useful measures for delving into factors that affect thinking time allocation.

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

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

Identifiability Analysis and Noninvasive Online Estimation of the First-Order Neural Activation Dynamics in the Brain With Closed-Loop Transcranial Magnetic Stimulation.

Neurons demonstrate very distinct nonlinear activation dynamics, influenced by the neuron type, morphology, ion channel expression, and various other factors. The measurement of the activation dynamics can identify the neural target of stimulation and detect deviations, e.g., for diagnosis. This paper describes a tool for closed-loop sequential parameter estimation (SPE) of the activation dynamics through transcranial magnetic stimulation (TMS). The proposed SPE method operates in real time, selects ideal stimulus parameters, detects and processes the response, and concurrently estimates the input-output (IO) curve and the first-order approximation of the activated neural target. To develop a method for concurrent SPE of the first-order activation dynamics and IO curve with closed-loop TMS. First, identifiability of an integrated model of the first-order neural activation dynamics and IO curve is assessed, demonstrating that at least two IO curves need to be acquired with different pulse widths. Then, a two-stage SPE method is proposed. It estimates the IO curve by using Fisher information matrix (FIM) optimization in the first stage and subsequently estimates the membrane time constant as well as the coupling gain in the second stage. The procedure continues in a sequential manner until a stopping rule is satisfied. The results of 73 simulation cases confirm the satisfactory estimation of the membrane time constant and coupling gain with average absolute relative errors (AREs) of 6.2% and 5.3%, respectively, with an average of 344 pulses (172 pulses for each IO curve or pulse width). The method estimates the IO curves' lower and upper plateaus, mid-point, and slope with average AREs of 0.2%, 0.7%, 0.9%, and 14.5%, respectively. The conventional time constant estimation method based on the strength-duration (S-D) curve leads to 33.3% ARE, which is 27.0% larger than 6.2% ARE obtained through the proposed real-time FIM-based SPE method in this paper. SPE of the activation dynamics requires acquiring at least two IO curves with different pulse widths, which needs a controllable TMS (cTMS) device with adjustable pulse duration. The proposed SPE method enhances the cTMS functionality, which can contribute novel insights in research and clinical studies.

SALτ: efficiently stopping TAR by improving priors estimates

In high recall retrieval tasks, human experts review a large pool of documents with the goal of satisfying an information need. Documents are prioritized for review through an active learning policy, and the process is usually referred to as Technology-Assisted Review (TAR). TAR tasks also aim to stop the review process once the target recall is achieved to minimize the annotation cost. In this paper, we introduce a new stopping rule called SALτR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ au ^R$$\\end{document} (SLD for Active Learning), a modified version of the Saerens–Latinne–Decaestecker algorithm (SLD) that has been adapted for use in active learning. Experiments show that our algorithm stops the review well ahead of the current state-of-the-art methods, while providing the same guarantees of achieving the target recall.

Superfair Stochastic Games

A two-person zero-sum stochastic game with a nonnegative stage reward function is superfair if the value of the one-shot game at each state is at least as large as the reward function at the given state. The payoff in the game is the limit superior of the expected stage rewards taken over the directed set of all finite stop rules. If the game has countable state and action spaces and if at least one of the players has a finite action space at each state, then the game has a value. The value of the stochastic game is obtained by a transfinite algorithm on the countable ordinals.

The Betel Nut Intervention Trial (BENIT)-A Randomized Clinical Trial for Areca Nut and Betel Quid Cessation: Primary Outcomes.

Areca nut and betel quid (ANBQ) chewing is a widespread carcinogenic habit. The BENIT (ClinicalTrials-NCT02942745) is the first known randomized trial designed for ANBQ chewers. We compared the intensive behavioral treatment intervention condition (IC) with the control condition (CC) in the BENIT and included a 5-stage early stopping rule. We report the primary analysis at stage 3. English-literate adults in Guam and Saipan who self-identified as ANBQ chewers with tobacco were enrolled between August 2016 and August 2020. IC participants (n = 88) received five in-person sessions over 22 days and a brochure containing quitting advice. CC participants (n = 88) received only the brochure. Participants were assessed at baseline and on day 22 of follow-up. Self-reported chewing status at day 22 was determined by a composite of two survey items with disparate wording and response options for cross-verification. Cessation rates were 38.6% (IC) and 9.1% (CC). Proportional hazards regression revealed a p = 0.0058, which met the Stage 3 criteria for significance, and an estimated reduction in ANBQ chewing for IC compared to the CC of 71% (95% CI: 41%-88%). Robust self-reported intervention effects at day 22 suggest that intensive cessation programs such as BENIT should be further developed and implemented on a larger scale.

Can artificial intelligence separate the wheat from the chaff in systematic reviews of health economic articles?

ABSTRACT Objectives Artificial intelligence-powered tools, such as ASReview, could reduce the burden of title and abstract screening. This study aimed to assess the accuracy and efficiency of using ASReview in a health economic context. Methods A sample from a previous systematic literature review containing 4,994 articles was used. Previous manual screening resulted in 134 articles included for full-text screening (FT) and 50 for data extraction (DE). Here, accuracy and efficiency was evaluated by comparing the number of identified relevant articles with ASReview versus manual screening. Pre-defined stopping rules using sampling criteria and heuristic criteria were tested. Robustness of the AI-tool’s performance was determined using 1,000 simulations. Results Considering included stopping rules, median accuracy for FT articles remained below 85%, but reached 100% for DE articles. To identify all relevant articles, a median of 89.9% of FT articles needed to be screened, compared to 7.7% for DE articles. Potential time savings between 49 and 59 hours could be achieved, depending on the stopping rule. Conclusions In our case study, all DE articles were identified after screening 7.7% of the sample, allowing for substantial time savings. ASReview likely has the potential to substantially reduce screening time in systematic reviews of health economic articles.

Selecting a significance level in sequential testing procedures for community detection

While there have been numerous sequential algorithms developed to estimate community structure in networks, there is little available guidance and study of what significance level or stopping parameter to use in these sequential testing procedures. Most algorithms rely on prespecifiying the number of communities or use an arbitrary stopping rule. We provide a principled approach to selecting a nominal significance level for sequential community detection procedures by controlling the tolerance ratio, defined as the ratio of underfitting and overfitting probability of estimating the number of clusters in fitting a network. We introduce an algorithm for specifying this significance level from a user-specified tolerance ratio, and demonstrate its utility with a sequential modularity maximization approach in a stochastic block model framework. We evaluate the performance of the proposed algorithm through extensive simulations and demonstrate its utility in controlling the tolerance ratio in single-cell RNA sequencing clustering by cell type and by clustering a congressional voting network.

Subacromial spacers for adults with symptomatic, irreparable rotator cuff tears: the START:REACTS novel group sequential adaptive RCT

Background A balloon spacer is a relatively simple addition to an arthroscopic debridement procedure for irreparable rotator cuff tears. Objective To evaluate the clinical and cost-effectiveness of a subacromial balloon spacer for individuals undergoing arthroscopic debridement for irreparable rotator cuff tears. Design A multicentre participant-and assessor-blinded randomised controlled trial comparing arthroscopic debridement with the InSpace® (Stryker, Kalamazoo, MI, USA) balloon to arthroscopic debridement alone, using a novel adaptive design. Pretrial simulations informed stopping boundaries for two interim analyses, using outcome data from early and late time points. Setting A total of 24 NHS centres. Participants Adults with a symptomatic, irreparable rotator cuff tear for whom conservative management had been unsuccessful. Interventions Arthroscopic debridement of the subacromial space plus insertion of the InSpace balloon compared with arthroscopic debridement alone. Main outcome measures Oxford Shoulder Score at 12 months. Results A predefined stopping boundary was met at the first interim analysis. Recruitment stopped with 117 participants randomised. We obtained primary outcome data on 114 participants (97%). The mean Oxford Shoulder Score at 12 months was 34.3 in the debridement-only group (59 participants of 61 randomised) and 30.3 in the debridement with balloon group (55 participants of 56 randomised; mean difference: −4.2; 95% confidence interval −8.2 to −0.26; p = 0.037). There was no difference in safety events. In the cost-effectiveness analysis, debridement-only dominated with a probability of <1% that the device is cost-effective. Magnetic resonance imaging substudy To evaluate the function of the balloon, we developed a dynamic magnetic resonance imaging protocol to induce humeral movement by activating the deltoid. The pandemic restricted recruitment, so the sample size was small (n = 17). Statistical methodology study We applied the novel adaptive design approach to data from seven previous randomised controlled trials. The method would have been applicable to five of these trials and would have made substantial savings in time to recruitment, without compromising the main findings of the included trials. Interim analysis interpretation study We asked potential data monitoring committee members to review interim analysis reports presented using Bayesian and frequentist frameworks. They did not always follow the stopping rules and would benefit from additional information to support decision-making. Limitations The InSpace balloon could be beneficial in a different population although we are not aware of it being widely used for other purposes. As a result of the pandemic, we were not able to complete data collection for objective measures. Conclusions In this efficient adaptive trial, clinical and cost-effectiveness favoured the control treatment without the InSpace balloon. Therefore, we do not recommend this device for the treatment of irreparable rotator cuff tears. Future work There is an urgent need for high-quality research into interventions for people with irreparable rotator cuff tears as there is a lack of good evidence for all available treatment options at present. Trial registration This trial is registered as ISRCTN17825590. Funding This project (project reference 16/61/18) was funded by the Efficacy and Mechanism Evaluation (EME) Programme, a Medical Research Council and National Institute for Health and Care Research (NIHR) partnership. The trial is co-sponsored by the University of Warwick and University Hospitals Coventry and Warwickshire NHS Trust. This study will be published in full in Efficacy and Mechanism Evaluation; Vol. 10, No 3. See the NIHR Journals Library website for further project information.

Symmetry and Asymmetry in Moment, Functional Equations, and Optimization Problems

The purpose of this work is to provide applications of real, complex, and functional analysis to moment, interpolation, functional equations, and optimization problems. Firstly, the existence of the unique solution for a two-dimensional full Markov moment problem is characterized on the upper half-plane. The issue of the unknown form of nonnegative polynomials on R×R+ in terms of sums of squares is solved using polynomial approximation by special nonnegative polynomials, which are expressible in terms of sums of squares. The main new element is the proof of Theorem 1, based only on measure theory and on a previous approximation-type result. Secondly, the previous construction of a polynomial solution is completed for an interpolation problem with a finite number of moment conditions, pointing out a method of determining the coefficients of the solution in terms of the given moments. Here, one uses methods of symmetric matrix theory. Thirdly, a functional equation having nontrivial solution (defined implicitly) and a consequence are discussed. Inequalities, the implicit function theorem, and elements of holomorphic functions theory are applied. Fourthly, the constrained optimization of the modulus of some elementary functions of one complex variable is studied. The primary aim of this work is to point out the importance of symmetry in the areas mentioned above.

Kindergarten Children’s Learning of Computational Thinking With the “Sorting Like a Computer” Learning Unit

ABSTRACT Computational thinking (CT) activities are increasingly being integrated into early childhood schools. We focus on studying children’s learning using an “unplugged” (non-computational) learning unit that considers a teacher’s knowledge and classroom space and affords seamless adaptation into the classroom given the objects used in the unit and activities that are reminiscent of classic class activities and games. The gap in research that we address is the focus on unplugged activities, which are less common. A learning unit with card games was developed, focusing on two fundamental sorting algorithms: Linear Sort and Category Sort. Sixteen kindergarten children participated in the study. Their CT skills were assessed using a pretest-intervention-posttest single group research design. An increase in CT skills was observed from an intermediate to a sophisticated level; the number of attempts to completion decreased and the average number of attributes the children could sort with increased. Two children’s interactions with the activities were analyzed in depth and compared. It was found that two concepts related to algorithms had to be learned separately and sequentially: multiple repeating actions and a stopping rule for repeating actions. With advanced challenges that involved multiple attributes and more than one sorting algorithm, children needed significant support.

A class of mean‐field games with optimal stopping and its applications

AbstractThis paper studies the optimal stopping problem under the large‐population framework. In particular, two classes of optimal stopping problems are formulated by taking into account the relative performance criteria. It is remarkable that the relative performance criteria, also understood by the Joneses preference, habit formation utility, or relative wealth concern in economics and finance, play an important role in explaining various decision behaviors such as price bubbles. By introducing such criteria in large‐population setting, a given agent can compare his individual stopping rule with the average behaviors of its cohort. The associated mean‐field games are formulated in order to derive the decentralized stopping rules. The related consistency conditions are characterized via some coupled equation system and the ‐Nash equilibrium properties are also verified. In addition, some inverse mean‐field optimal stopping problem is also introduced and discussed.

Longitudinal Patterns of Engagement and Clinical Outcomes: Results From a Therapist-Supported Digital Mental Health Intervention.

Digital mental health interventions (DMHIs) are an effective treatment modality for common mental disorders like depression and anxiety; however, the role of intervention engagement as a longitudinal "dosing" factor is poorly understood in relation to clinical outcomes. We studied 4978 participants in a 12-week therapist-supported DMHI (June 2020-December 2021), applying a longitudinal agglomerative hierarchical cluster analysis to the number of days per week of intervention engagement. The proportion of people demonstrating remission in depression and anxiety symptoms during the intervention was calculated for each cluster. Multivariable logistic regression models were fit to examine associations between the engagement clusters and symptom remission, adjusting for demographic and clinical characteristics. Based on clinical interpretability and stopping rules, four clusters were derived from the hierarchical cluster analysis (in descending order): a) sustained high engagers (45.0%), b) late disengagers (24.1%), c) early disengagers (22.5%), and d) immediate disengagers (8.4%). Bivariate and multivariate analyses supported a dose-response relationship between engagement and depression symptom remission, whereas the pattern was partially evident for anxiety symptom remission. In multivariable logistic regression models, older age groups, male participants, and Asians had increased odds of achieving depression and anxiety symptom remission, whereas higher odds of anxiety symptom remission were observed among gender-expansive individuals. Segmentation based on the frequency of engagement performs well in discerning timing of intervention disengagement and a dose-response relationship with clinical outcomes. The findings among the demographic subpopulations indicate that therapist-supported DMHIs may be effective in addressing mental health problems among patients who disproportionately experience stigma and structural barriers to care. Machine learning models can enable precision care by delineating how heterogeneous patterns of engagement over time relate to clinical outcomes. This empirical identification may help clinicians personalize and optimize interventions to prevent premature disengagement.

Theory Matters

Abstract: Following an extended perspective of evidence-centered design, this study provides a methodological exemplar of the theory-based construction of process indicators from log data. We investigated decision-making processes in web search as the target construct, assuming that individuals follow a heuristic search (focusing on search results vs. websites as a primary information source) and stopping rule (following a satisficing vs. sampling strategy). Drawing on these assumptions, we describe our reasoning for identifying the empirical evidence needed and selecting an assessment to obtain this evidence to derive process indicators that represent groups differentiated by search and stopping rule combinations. To evaluate our approach, we reanalyzed the processing behavior of 150 university students who were requested in four tasks to select a specific website from a list of five search results. We determined the process indicators per item and conducted multiple cluster analyses to investigate group recovery. For each item, we found three clusters, two of which matched our assumptions. Additionally, we explored the consistency of students’ cluster membership across items and investigated their relationship with students’ skills in evaluating online information. Based on the results, we discuss the tradeoff between construct breadth and process elaboration for deriving meaningful process indicators.

Bayesian stopping

Stopping rules are criteria for determining when data collection can or should be terminated, allowing for inferences to be made. While traditionally discussed in the context of classical statistics, Bayesian statisticians have also begun exploring stopping rules. Kruschke proposed a Bayesian stopping rule utilizing the concept of Highest Density Interval, where data collection can cease once enough probability mass (or density) accumulates in a sufficiently small region of parameter space. This paper presents an alternative to Kruschke’s approach, introducing the novel concept of Relative Importance Interval and considering the distribution of probability mass within parameter space. Using computer simulations, we compare these proposals to each other and to the widely-used Bayes factor-based stopping method. Our results do not indicate a single superior proposal but instead suggest that different stopping rules may be appropriate under different circumstances.

Dual gradient method for ill-posed problems using multiple repeated measurement data

We consider determining -minimizing solutions of linear ill-posed problems , where is a bounded linear operator from a Banach space to a Hilbert space and is a proper strongly convex penalty function. Assuming that multiple repeated independent identically distributed unbiased data of y are available, we consider a dual gradient method to reconstruct the -minimizing solution using the average of these data. By terminating the method by either an a priori stopping rule or a statistical variant of the discrepancy principle, we provide the convergence analysis and derive convergence rates when the sought solution satisfies certain variational source conditions. Various numerical results are reported to test the performance of the method.

Severe hepatitis B flares with hepatic decompensation after withdrawal of nucleos(t)ide analogues: A population-based cohort study.

Finite nucleos(t)ide analogue (NUC) therapy has been proposed as an alternative treatment strategy for chronic hepatitis B (CHB). To quantify the incidence of severe hepatitis flares following NUC cessation in everyday clinical practice. This population-based cohort study enrolled 10,192 patients (male 71.7%, median age 50.9 years, cirrhosis 10.7%) who had received first-line NUCs for at least 1 year before discontinuing treatment. The primary outcome was severe flare with hepatic decompensation. We used competing risk analyses to assess event incidences and associated risk factors. During a median follow-up of 2.2 years, 132 patients developed severe flares with hepatic decompensation, yielding a 4-year cumulative incidence of 1.8% (95% confidence interval [CI], 1.5%-2.2%). Significant risk factors were cirrhosis (adjusted sub-distributional hazard ratio [aSHR], 2.74; 95% CI, 1.82-4.12), manifestations of portal hypertension (aSHR, 2.46; 95% CI, 1.45-4.18), age (aSHR, 1.21 per 10 years; 95% CI, 1.03-1.42) and male sex (aSHR, 1.58; 95% CI, 1.04-2.38). In patients without cirrhosis or portal hypertension (n = 8863), the 4-year cumulative incidence of severe withdrawal flares stood at 1.3% (95% CI, 1.0%-1.7%). For those patients with available data confirming adherence to the standard stopping rules (n = 1274), the incidence was 1.1% (95% CI, 0.6%-2.0%). Severe flares with hepatic decompensation were observed in 1%-2% of patients with CHB after stopping NUC therapy in daily practice. Risk factors included older age, cirrhosis, portal hypertension and male sex. Our findings argue against NUC cessation as part of routine clinical care.

Permutation-based group sequential analyses for cognitive neuroscience

Cognitive neuroscientists have been grappling with two related experimental design problems. First, the complexity of neuroimaging data (e.g. often hundreds of thousands of correlated measurements) and analysis pipelines demands bespoke, non-parametric statistical tests for valid inference, and these tests often lack an agreed-upon method for performing a priori power analyses. Thus, sample size determination for neuroimaging studies is often arbitrary or inferred from other putatively but questionably similar studies, which can result in underpowered designs – undermining the efficacy of neuroimaging research. Second, when meta-analyses estimate the sample sizes required to obtain reasonable statistical power, estimated sample sizes can be prohibitively large given the resource constraints of many labs. We propose the use of sequential analyses to partially address both of these problems. Sequential study designs – in which the data is analyzed at interim points during data collection and data collection can be stopped if the planned test statistic satisfies a stopping rule specified a priori – are common in the clinical trial literature, due to the efficiency gains they afford over fixed-sample designs. However, the corrections used to control false positive rates in existing approaches to sequential testing rely on parametric assumptions that are often violated in neuroimaging settings. We introduce a general permutation scheme that allows sequential designs to be used with arbitrary test statistics. By simulation, we show that this scheme controls the false positive rate across multiple interim analyses. Then, performing power analyses for seven evoked response effects seen in the EEG literature, we show that this sequential analysis approach can substantially outperform fixed-sample approaches (i.e. require fewer subjects, on average, to detect a true effect) when study designs are sufficiently well-powered. To facilitate the adoption of this methodology, we provide a Python package “niseq” with sequential implementations of common tests used for neuroimaging: cluster-based permutation tests, threshold-free cluster enhancement, t-max, F-max, and the network-based statistic with tutorial examples using EEG and fMRI data.