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A new Bayesian–inspired statistic for hypothesis testing is proposed which compares two posterior distributions; the observed posterior and the expected posterior under the null model. The Kullback–Leibler divergence between the two posterior distributions yields a test statistic which can be interpreted as a penalized log–Bayes factor with the penalty term converging to a constant as the sample size increases. Hence, asymptotically, the statistic behaves as a Bayes factor. Viewed as a penalized Bayes factor, this approach solves the long standing issue of using improper priors with the Bayes factor, since only posterior summaries are needed for the new statistic. Further motivation for the new statistic is a minimal move from the Bayes factor which requires no tuning nor splitting of data into training and inference, and can use improper priors. Critical regions for the test can be assessed using frequentist notions of Type I error.

Missing values in data impede the task of inference for population parameters of interest. Multiple Imputation (MI) is a popular method for handling missing data since it accounts for the uncertainty of missing values. Inference in MI involves combining point and variance estimates from each imputed dataset via Rubin’s rules. A sufficient condition for these rules is that the estimator is approximately (multivariate) normally distributed. However, these traditional combining rules get computationally cumbersome for multicomponent parameters of interest, and unreliable at high rates of missingness (due to an unstable variance matrix). New combining rules for univariate F- and Beta-statistics from multiply-imputed data are proposed for decisions about multicomponent parameters. The proposed combining rules have the advantage of being computationally convenient since they only involve univariate F- and Beta-statistics, while providing the same inferential reliability as the traditional multivariate combining rules. Simulation study is conducted to demonstrate that the proposed method has good statistical properties of maintaining low type I and type II error rates at relatively large proportions of missingness. The general applicability of the proposed method is demonstrated within a lead exposure study to assess the association between lead exposure and neurological motor function.

We provide a novel explanation for the empirical failure of the CAPM despite its widespread practical use. In a rational-expectations economy in which information is dispersed, variation in expected returns over time and across investors creates an informational gap between investors and the empiricist. The CAPM holds for investors, but the Securities Market Line appears flat to the empiricist. Variation in expected returns across investors accounts for the larger part of this distortion, which is empirically substantial; it offers a new interpretation of why “Betting Against Beta” works: BAB really bets on true beta. The empiricist retrieves a stronger CAPM on days when public information reduces disagreement among investors.

Motivation Medical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations. Results We validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases. Availability and implementation Our method is implemented in Python and available at https://github.com/mkirchler/transferGWAS/. Supplementary information Supplementary data are available at Bioinformatics online.

Rationale and Objectives A critical performance metric for any quantitative imaging biomarker is its ability to reliably generate similar values on repeat testing. This is known as the repeatability of the biomarker, and it is used to determine the minimum detectable change needed in order to show that a change over time is real change and not just due to measurement error. Test-retest studies are the classic approach for estimating repeatability; however, these studies can be infeasible when the imaging is expensive, time-consuming, invasive, or requires contrast agents. The objective of this study was to develop and test a method for estimating repeatability without a test-retest study. Materials and Methods We present a statistical method for estimating repeatability and testing whether an imaging method meets a specified criterion for repeatability in the absence of a test-retest study. The new method is applicable for the particular situation where a reference standard is available. A Monte Carlo simulation study was conducted to evaluate the performance of the new method. Results The proposed estimator is unbiased, and hypothesis tests with the new estimator have nominal type I error rate and power similar to a test-retest study. We considered the situation where the reference standard provides the true value, as well as when the reference standard itself has various magnitudes of measurement error. An example from CT imaging biomarkers of atherosclerosis illustrates the new method. Conclusion Precision of a QIB can be measured without a test-retest study in the situation where a reference standard is available.

[Abstract] Finding epistatic interactions among loci when expressing a phenotype is a widely employed strategy to understand the genetic architecture of complex traits in GWAS. The abundance of methods dedicated to the same purpose, however, makes it increasingly difficult for scientists to decide which method is more suitable for their studies. This work compares the different epistasis detection methods published during the last decade in terms of runtime, detection power and type I error rate, with a special emphasis on high-order interactions. Results show that in terms of detection power, the only methods that perform well across all experiments are the exhaustive methods, although their computational cost may be prohibitive in large-scale studies. Regarding non-exhaustive methods, not one could consistently find epistasis interactions when marginal effects are absent. If marginal effects are present, there are methods that perform well for high-order interactions, such as BADTrees, FDHE-IW, SingleMI or SNPHarvester. As for false-positive control, only SNPHarvester, FDHE-IW and DCHE show good results. The study concludes that there is no single epistasis detection method to recommend in all scenarios. Authors should prioritize exhaustive methods when sufficient computational resources are available considering the data set size, and resort to non-exhaustive methods when the analysis time is prohibitive. 10.13039/501100010801-Xunta de Galicia (Grant Number: ED431C2016-037, ED431C2017/04 and ED431G2019/01) 10.13039/501100003176-Ministerio de Educacion Cultura y Deporte (Grant Number: FPU16/01333) 10.13039/501100003329-Ministerio de Economia y Competitividad (Grant Number: CGL2016-75482-P, PID2019-104184RB-I00, AEI/FEDER/EU, 10.13039/50110 and TIN2016-75845-P) Xunta de Galicia; ED431C2016-037 Xunta de Galicia; ED431G2019/01 Xunta de Galicia; ED431C 2017/04

Error-related potential (ErrP) are a particular type of Event-Related Potential (ERP) elicited by a person attending a recognizable error. These Electroencephalographic (EEG) signals can be used to train a gaming agent by a Reinforcement Learning (RL) algorithm to learn an optimal policy. The experimental process consists of an observational human critic (OHC) observing a simple game scenario while their brain signals are captured. The game consists of a grid, where a green spot has to reach a desired target in the fewest amount of steps. Results show that there is an effective transfer of information and that the agent successfully learns to solve the game efficiently, from the initial 97 steps on average required to reach the target to the optimal number of 8 steps. Our results are expressed in threefold: (i) the mechanics of a simple grid-based game that can elicit the ErrP signal component, (ii) the verification that the reward function only penalizes wrong steps, which means that type II error (not properly identifying a wrong movement) does not affect significantly the agent learning process; (iii) collaborative rewards from multiple observational human critics can be used to train the algorithm effectively and can compensate low classification accuracies and a limited scope of transfer learning schemes.

Sequential (or adaptive) designs are common in acceptance sampling and pharmaceutical trials. This is because they can achieve the same type 1 and type 2 error rate with fewer subjects on average than fixed sample trials. After the trial is completed and the test result decided, we need full inference on the main parameter Δ . In this paper, we are interested in exact one-sided lower and upper limits. Unlike standard trials, for sequential trials there need not be an explicit test statistic, nor even p -value. This motivates the more general approach of defining an ordering on the sample space and using the construction of Buehler (1957). This is guaranteed to produce exact limits, however, there is no guarantee that the limits will agree with the test. For instance, we might reject Δ ≤ Δ 0 at level α but have a lower 1 − α limit being less then Δ 0 . This paper gives a very simple condition to ensure that this unfortunate feature does not occur. When the condition fails, the ordering is easily modified to ensure compatibility.

This chapter covers the topic of program development and testing. An incremental approach to program development is introduced, in which a working version of the program is always maintained, starting with a trivial program, with additions to the program being made incrementally. Testing and validation are always performed after each increment. Error handling is also discussed, including ways to check that user input is valid, and MATLAB functions for reporting errors are described: the ‘error’, ‘warning’ and ‘try–catch’ statements. Program debugging is revisited and discussed in more detail along with common types of error that prevent programs from running successfully.

Curbstoning, the willful fabrication of survey responses by outside data collectors, threatens the integrity of the inferences drawn from data. Researchers who outsource data collection to survey collection panels, field interviewers, or research assistants should validate whether each collection agent actually collected the data. Our review of the survey auditing literature demonstrates a consistent presence of curbstoning, even at professional levels. This study proposes several general simple survey questions that have statistical distributions known a priori, as a method to detect curbstoning. By exploiting common deficiencies in statistical understanding, survey collectors imputing data to these questions can leverage empirically known distributions to determine deviation from the expected distribution of responses. We examined both authentic and fabricated surveys that included these questions and we compared the observed distributions with the expected distributions. The majority of the proposed methods had Type I error rates near or below the specified alpha level (.05). The methods demonstrated the ability to detect false responses correctly 48%-90% of the time across two samples when surveying at least 50 participants. While the methods varied in effectiveness, combining these methods demonstrated the highest statistical power, with Type I error rates lower than 1%. Additionally, even in situations with smaller sample sizes (e.g., N = 30), combining these methods allows them to be effective in detecting curbstoning. These methods provide a simple and generalizable way for researchers not present during data collection to possess accurate data. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Researchers have increasingly warned about "p-hacking" and the improper use of control variables. This paper considers the risk that a "researcher's degrees of freedom" with respect to the use of control variables has on the probability of Type I and Type II errors. We also examine the extent that control variables can make marginal effect sizes (i.e., nonzero effects but less than should be statistically significant) appear significant (which we refer to as Type III errors) and how much control variable use can increase effect sizes. We report the results of two computer simulations that include up to 10 control variables. We find that the inappropriate use of control variables is not really a risk for Type I errors, given that the chance of there being truly a null effect in a typical multivariate analysis is very low. We also show that the use of control variables does not have a large effect on Type II errors, and that the practice of running analyses both with and without control variables will most often yield the same conclusion in both analyses. That said, we did find that p-hacking substantially increases the probability of inappropriately being able to detect statistical significance and can notably increase effect sizes. The practice of running analyses both with and without control variables can indeed reveal the potential for p-hacking, and discrepant results between bivariate and multivariate analyses suggest that authors need to carefully and clearly explain why the noted differences are theoretically and logically appropriate. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A minimax converse for the identification via channels is derived. By this converse, a general formula for the identification capacity, which coincides with the transmission capacity, is proved without the assumption of the strong converse property. Furthermore, the optimal second-order coding rate of the identification via channels is characterized when the type I error probability is non-vanishing and the type II error probability is vanishing. Our converse is built upon the so-called partial channel resolvability approach; however, the minimax argument enables us to circumvent a flaw reported in the literature., Comment: 18 pages, no figure

A novel method for testing the null hypothesis of no effect of a covariate on the response is proposed in functional linear concurrent regression. An equivalent random effects formulation of the functional regression model is established under which the testing problem reduces to testing for zero variance component for random effects. For this purpose, a one-sided score test approach is used, which is an extension of the classical score test. Theoretical justification is provided as to why the proposed testing procedure has the correct levels (asymptotically) under null using standard assumptions. Using numerical simulations, the testing method is shown to have the desired type I error rate and higher power compared to a bootstrapped F test currently existing in the literature. The model and the testing procedure give good performances even when the data are sparsely observed, and the functional covariate is contaminated with noise. Applications of the proposed testing method are demonstrated on gait data and a study of child mortality.

Combining historical control data with current control data may reduce the necessary study size of a clinical trial. However, this only applies when the historical control data are similar enough to the current control data. Several Bayesian approaches for incorporating historical data in a dynamic way have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP). Here we discuss the generalization of the MPP approach for multiple historical control groups for the linear regression model. This approach is useful when the controls differ more than in a random way, but become again (approximately) exchangeable conditional on covariates. The proposed approach builds on the approach previously developed for binary outcomes by some of the current authors. Two MPP approaches have been developed with multiple controls. The first approach assumes independent powers, while in the second approach the powers have a hierarchical structure. We conducted several simulation studies to investigate the frequentist characteristics of borrowing methods and analyze a real-life data set. When there is between-study variation in the slopes of the model or in the covariate distributions, the MPP approach achieves approximately nominal type I error rates and greater power than the MAP prior, provided that the covariates are included in the model. When the intercepts vary, the MPP yields a slightly inflated type I error rate, whereas the MAP does not. We conclude that our approach is a worthy competitor to the MAP approach for the linear regression case.

Phase II clinical trials in oncology are used to initially evaluate the therapeutic efficacy of a new treatment. In the past, the total response was a frequently used endpoint to access the effectiveness of the treatment. When the total response is modest, clinicians may also be interested in disease control (defined as the total response or stable disease) since it may better predict clinical outcomes. Thus, formally testing both the total response and disease control in a phase II trial has an important clinical implication. In this paper, we propose a new method to construct optimal subset designs for one-stage and two-stage phase II clinical trials with two binary endpoints, i.e. the total response and disease control. A new set of hypotheses under the framework of intersection-union tests is provided in which the treatment is considered promising if both the total response and disease control are good. We show that the power function for each test in a large family of tests is nondecreasing in both the total response rate and disease control rate; identify the parameter configurations at which the maximum Type I error rate and minimum power are achieved and derive level- α tests. We also provide optimal one-stage designs with the minimum sample size and optimal two-stage designs with the least expected total sample size.

This paper proposes an asymptotically valid permutation test for heterogeneous treatment effects in the presence of an estimated nuisance parameter. Not accounting for the estimation error of the nuisance parameter results in statistics that depend on the particulars of the data generating process, and the resulting permutation test fails to control the Type 1 error, even asymptotically. In this paper we consider a permutation test based on a martingale transformation of the empirical process to render an asymptotically pivotal statistic, effectively nullifying the effect associated with the estimation error on the limiting distribution of the statistic. Under weak conditions, we show that the permutation test based on the martingale-transformed statistic results in the asymptotic rejection probability of α in general while retaining the exact control of the test level when testing for the more restrictive sharp null. We also show how our martingale-based permutation test extends to testing whether there exists treatment effect heterogeneity within subgroups defined by observable covariates. Our approach comprises testing the joint null hypothesis that treatment effects are constant within mutually exclusive subgroups while allowing the treatment effects to vary across subgroups. Monte Carlo simulations show that the permutation test presented here performs well in finite samples, and is comparable to those existing in the literature. To gain further understanding of the test to practical problems, we investigate the gift exchange hypothesis in the context of two field experiments from Gneezy and List (2006). Lastly, we provide the companion RATest R package to facilitate and encourage the application of our test in empirical research.

For semi-competing risks data involving a non-terminal event and a terminal event we derive the asymptotic distributions of the event-specific win ratios under proportional hazards (PH) assumptions for the relevant cause-specific hazard functions of the non-terminal and terminal event, respectively. The win ratios converge to the respective hazard ratios under the PH assumptions and therefore are censoring-free, whether or not the censoring distributions in the two treatment arms are the same. With the asymptotic bivariate normal distributions of the win ratios, confidence intervals and testing procedures are obtained. Through extensive simulation studies and data analysis, we identified proper transformations of the win ratios that yield good control of the type one error rate for various testing procedures while maintaining competitive power. The confidence intervals also have good coverage probabilities. Furthermore, a test for the PH assumptions and a test of equal hazard ratios are developed. The new procedures are illustrated in the clinical trial Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function, which evaluated the effects of spironolactone in patients with heart failure and a preserved left ventricular ejection fraction.

Grammar is complexity in learning English it makes many students often commit errors in learning. Making errors in writing is one of the processes for learning English .The aim of this research was to describe the students’ errors and its causes in using auxiliary verbs in the sentences. In this research, the writer limited the discussion on the error made by the students in using auxiliary verbs. The design of this research was quantitative approach in a form of descriptive analysis (percentage) that included observation, collecting the data the researcher used test as the instrument. The writer conducted the research to 31 students in first semester of English Literature in academic 2021/2022. The data were analyzed by using error analysis method. The error types were classified based on linguistic category, especially surface strategy taxonomy. The writer classified the types of error into omission, addition, misformation, and misordering. Among those four types, the most frequency error was misformation that reached 102 errors or 61.44% . the other finding was the sources of error which are divided into three categories. Those are interlingual errors, intralingual errors, and communicative strategies. Communicative strategies errors is the most common source of error, there were 45.78%

Definitive clinical trials are resource intensive, often requiring a large number of participants over several years. One approach to improving the efficiency of clinical trials is to incorporate historical information into the primary trial analysis. This approach has tremendous potential in the areas of pediatric or rare disease trials, where achieving reasonable power is difficult. In this manuscript, we introduce a novel Bayesian group-sequential trial design based on Multisource Exchangeability Models, which allows for dynamic borrowing of historical information at the interim analyses. Our approach achieves synergy between group sequential and adaptive borrowing methodology to attain improved power and reduced sample size. We explore the frequentist operating characteristics of our design through simulation and compare our method to a traditional group-sequential design. Our method achieves earlier stopping of the primary study while increasing power under the alternative hypothesis but has a potential for type I error inflation under some null scenarios. We discuss the issues of decision boundary determination, power and sample size calculations, and the issue of information accrual. We present our method for a continuous and binary outcome, as well as in a linear regression setting.

Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Statistical methods for testing in this area exist, with impacts of explosive type I error in the presence of bias well understood. However, for study design, the more important and understudied problem is the type II error and power. As the biological questions for this type of study explode, and the costs of the technology plummet, what is more important: reads or replicates? How small of an interaction can be detected while keeping the type I error at bay? Here we present a simulation study that demonstrates that the proper model can control type I error below 5% for most scenarios. We find that a minimum of 2400, 480, and 240 allele specific reads divided equally among 12, 5, and 3 replicates is needed to detect a 10%, 20%, and 30%, respectively, deviation from allelic balance in a condition with power >80%. A minimum of 960 and 240 allele specific reads is needed to detect a 20% or 30% difference in AI between conditions with comparable power but these reads need to be divided amongst 8 replicates. Higher numbers of replicates increase power more than adding coverage without affecting type I error. We provide a Python package that enables simulation of AI scenarios and enables individuals to estimate type I error and power in detecting AI and differences in AI between conditions tailored to their own specific study needs.

Clinical trials require participation of numerous patients, enormous research resources and substantial public funding. Time-consuming trials lead to delayed implementation of beneficial interventions and to reduced benefit to patients. This manuscript discusses two methods for the allocation of research resources and reviews a framework for prioritisation and design of clinical trials. The traditional error-driven approach of clinical trial design controls for type I and II errors. However, controlling for those statistical errors has limited relevance to policy makers. Therefore, this error-driven approach can be inefficient, waste research resources and lead to research with limited impact on daily practice. The novel value-driven approach assesses the currently available evidence and focuses on designing clinical trials that directly inform policy and treatment decisions. Estimating the net value of collecting further information, prior to undertaking a trial, informs a decision maker whether a clinical or health policy decision can be made with current information or if collection of extra evidence is justified. Additionally, estimating the net value of new information guides study design, data collection choices, and sample size estimation. The value-driven approach ensures the efficient use of research resources, reduces unnecessary burden to trial participants, and accelerates implementation of beneficial healthcare interventions.

Background The size of the margin strongly influences the required sample size in non-inferiority and equivalence trials. What is sometimes ignored, however, is that for trials with binary outcomes, the scale of the margin – risk difference, risk ratio or odds ratio – also has a large impact on power and thus on sample size requirement. When considering several scales at the design stage of a trial, these sample size consequences should be taken into account. Sometimes, changing the scale may be needed at a later stage of a trial, for example, when the event proportion in the control arm turns out different from expected. Also after completion of a trial, a switch to another scale is sometimes made, for example, when using a regression model in a secondary analysis or when combining study results in a meta-analysis that requires unifying scales. The exact consequences of such switches are currently unknown. Methods and Results This article first outlines sample size consequences for different choices of analysis scale at the design stage of a trial. We add a new result on sample size requirement comparing the risk difference scale with the risk ratio scale. Then, we study two different approaches to changing the analysis scale after the trial has commenced: (1) mapping the original non-inferiority margin using the event proportion in the control arm that was anticipated at the design stage or (2) mapping the original non-inferiority margin using the observed event proportion in the control arm. We use simulations to illustrate consequences on type I and type II error rates. Methods are illustrated on the INES trial, a non-inferiority trial that compared single birth rates in subfertile couples after different fertility treatments. Our results demonstrate large differences in required sample size when choosing between risk difference, risk ratio and odds ratio scales at the design stage of non-inferiority trials. In some cases, the sample size requirement is twice as large on one scale compared with another. Changing the scale after commencing the trial using anticipated proportions mainly impacts type II error rate, whereas switching using observed proportions is not advised due to not maintaining type I error rate. Differences were more pronounced with larger margins. Conclusions Trialists should be aware that the analysis scale can have large impact on type I and type II error rates in non-inferiority trials.

Summary In recent years, a number of methods have been proposed to estimate the times at which a neuron spikes on the basis of calcium imaging data. However, quantifying the uncertainty associated with these estimated spikes remains an open problem. We consider a simple and well-studied model for calcium imaging data, which states that calcium decays exponentially in the absence of a spike, and instantaneously increases when a spike occurs. We wish to test the null hypothesis that the neuron did not spike—i.e., that there was no increase in calcium—at a particular timepoint at which a spike was estimated. In this setting, classical hypothesis tests lead to inflated Type I error, because the spike was estimated on the same data used for testing. To overcome this problem, we propose a selective inference approach. We describe an efficient algorithm to compute finite-sample $p$-values that control selective Type I error, and confidence intervals with correct selective coverage, for spikes estimated using a recent proposal from the literature. We apply our proposal in simulation and on calcium imaging data from the $\texttt{spikefinder}$ challenge.

The small sample sizes inherent in rare and pediatric disease settings offer significant challenges for clinical trial design. In such settings, Bayesian adaptive trial methods can often pay dividends, allowing the sensible incorporation of auxiliary data and other relevant information to bolster that collected by the trial itself. Previous work has also included the use of one-arm trials augmented by the participants’ own natural history data, from which the future course of the disease in the absence of intervention can be predicted. Patient response can then be defined by the degree to which post-intervention observations are inconsistent with the predicted “natural” trajectory. While such trials offer obvious advantages in efficiency and ethical hazard (since they expose no new patients to a placebo, anathema to patients or their parents and caregivers), they can offer no protection against bias arising from the presence of any “placebo effect,” the tendency of patients to improve merely by being in the trial. In this paper, we investigate the impact of both static and transient placebo effects on one-arm responder studies of this type, as well as two-arm versions that incorporate a small concurrent placebo group but still borrow strength from the natural history data. We also propose more traditional Bayesian changepoint models that specify a parametric functional form for the patient’s post-intervention trajectory, which in turn allow quantification of the treatment benefit in terms of the model parameters, rather than semi-parametrically in terms of a response relative to some “null” model. We compare the operating characteristics of our designs in the context of an ongoing investigation of centronuclear myopathies (CNMs), a group of congenital neuromuscular diseases whose most common and severe form is X-linked, affecting approximately 1 in 50,000 newborn boys. Our results indicate our two-arm responder and changepoint methods can offer protection against placebo effects, improving power while protecting the trial’s Type I error rate. However, further research into innovative trial designs as well as ongoing dialog with regulatory authorities remain critically important in rare disease research.

This study aims to examine the effect of audit quality determination factors which consist of restatement, small profit, prior roa meet, going concern opinion and type 1 error going concern opinion on audit quality. This research was conducted on BUMN companies in Indonesia in 2015-2019 and the research sample was determined using the purposive sampling method so that 46 sample companies were obtained. The results show that companies that do restatement do not have a worse audit quality than those that do not restate, companies that are categorized as small profit do not have audit quality that are worse than those that are not categorized as small profit, companies that are categorized as prior-roa meet do not have audit quality that is worse than those who are not categorized as prior roa meet, auditors who provide going concern opinions do not have better audit quality than those who do not provide going concern opinions and auditors who make type 1 errors going concern opinions do not have worse audit quality than those who do not commits a type 1 error of going-concern opinion.

To recognize the students’ difficulties, this research analyzes errors in Students’ writing composition of Passive Voice. This study of error (error analysis) is part of an investigation of the process of English language learning. This research is to find the types of error analysis in students’ writing composition and to find the most dominant types of error analysis in students’ writing composition of passive voice. This research is qualitatively accomplished to look into errors in the second language acquisition. This research focused on the qualitative research by using document analysis to collect empirical data from the students’ writing. This method is applied to get the accurate data collection. The object of the research is students’ writing composition randomly taken from sixteen ESP students of Strata 1 – Arabic Department of STAI Ali bin Abi Thalib Surabaya. The researcher investigates more details by using surface strategy taxonomy to get the result of this error analysis. It is limited on the students’ passive sentences error in writing composition consisting of four error types: omission, addition, misformation, and misordering. Finally, after identifying the entire data and analyzing the research findings, it can be summarized that the types of passive voice errors produced by the students are (1) omission that occurred 24 times or 51,06% of all, (2) addition that occurred 15 times or 31,96% of all, (3) misordering that occurred 5 times or 10,64% of all, and (4) misformation that occurred 3 times or 6,38% of all. According to the research findings, the most dominant type of passive voice error is omission.

Researchers conducting small-scale cluster randomized controlled trials (RCTs) during the pilot testing of an intervention often look for evidence of promise to justify an efficacy trial. We developed a method to test for intervention effects that is adaptive (i.e., responsive to data exploration), requires few assumptions, and is statistically valid (i.e., controls the type I error rate), by adapting masked visual analysis techniques to cluster RCTs. We illustrate the creation of masked graphs and their analysis using data from a pilot study in which 15 high school programs were randomly assigned to either business as usual or an intervention developed to promote psychological and academic well-being in 9th grade students in accelerated coursework. We conclude that in small-scale cluster RCTs there can be benefits of testing for effects without a priori specification of a statistical model or test statistic.

We study statistical inference for stochastic partial differential equations (SPDEs). Though inference linear SPDEs have been studied well (with lot of problems still remain to be investigated) in the last two decades, inference for nonlinear SPDEs is in its infancy. The inference methods use both inference for finite-dimensional diffusions and inference for classical i.i.d. sequences. Solving 2D Navier-Stokes equation is one of the challenging problem of the last century. However, with additive white noise, the equation has a strong solution. We estimate the viscosity coefficient of the 2D stochastic Navier-Stokes (SNS) equation by minimum contrast method. We show $n$ consistency in contrast to $\sqrt n$ consistency in the classical i.i.d. case where $n$ is the number of observations. We consider both continuous and discrete observations in time. We also obtain the Berry-Esseen bounds. Then we estimate and control the Type I and Type II error of a simple hypothesis testing problem of the viscosity coefficient of the SNS equation. We study a class of rejection regions and provide thresholds that guarantee that the statistical errors are smaller than the given upper bound. The tests are of likelihood ratio type. The proofs are based on the large deviation bounds. Finally we give Monte Carlo test procedure for simulated data.

Facial expression processing is a critical component of social cognition yet, whether it is influenced by task demands at the neural level remains controversial. Past ERP studies have found mixed results with classic statistical analyses, known to increase both Type I and Type II errors, which Mass Univariate statistics (MUS) control better. However, MUS open-access toolboxes can use different fundamental statistics, which may lead to inconsistent results. Here, we compared the output of two MUS toolboxes, LIMO and FMUT, on the same data recorded during the processing of angry and happy facial expressions investigated under three tasks in a within-subjects design. Both toolboxes revealed main effects of emotion during the N170 timing and main effects of task during later time points typically associated with the LPP component. Neither toolbox yielded an interaction between the two factors at the group level, nor at the individual level in LIMO, confirming that the neural processing of these two face expressions is largely independent from task demands. Behavioural data revealed main effects of task on reaction time and accuracy, but no influence of expression or an interaction between the two. Expression processing and task demands are discussed in the context of the consistencies and discrepancies between the two toolboxes and existing literature.

Purpose Recently a realistic simulator of patient seizure diaries was developed that can reproduce effects seen in randomized clinical trials (RCTs). RCTs suffer from high costs and statistical inefficiencies. Using realistic simulation and machine learning this study aimed to identify a more statistically efficient outcome metric. Methods Five candidate deep learning architectures with 54 permutations of hyperparameters were compared to the traditional standard, median percent change (MPC). Each were also tested for type 1 error. All models had similar outcomes, with appropriate low levels of type 1 error. Results The simplest model was equivalent to a logistic regression of a histogram of individual percentage changes in seizure rate, requiring 21-22% less patients to discriminate drug from placebo at 90% power. This model was referred to as LPC. Conclusion Future studies to validate LPC may enable faster, cheaper and more efficient clinical trials.

The research is to find out the types of error analysis in students’ sentences and the most dominant types of error analysis in students’ sentences. This research uses qualitative method to look into errors in the second language acquisition and to collect empirical data. This method is applied to get the accurate data collection. The object of the research is students’ practice randomly taken from twelve advanced students of TOEFL Preparation Class – The Mushlih Institute (TMI) Surabaya. In acquiring the result of this error analysis, surface strategy taxonomy is applied to investigate more details. It is limited on the students’ sentences with inverted subjects and verbs error in their practice consisting of four error types. In summary, the types of sentences with inverted subjects and verbs errors produced by the students are (1) omission appearing 49 times or 79,03% of all, (2) addition appearing 9 times or 14,52% of all, (3) misordering appearing 3 times or 4,84% of all, and (4) misformation appearing a time or 1,61% of all. According to the research findings, the most dominant type of sentences with inverted subjects and verbs error is omission. Keywords: Error Analysis, Writing Practice, Sentences.

The paired sample t-test is a type of classical test statistics that is used to test the difference between two means in paired data, but it is not robust against the violation of the normality assumption. In this paper, some alternative robust tests are suggested by combining the Jackknife resampling with each of the Wilcoxon signed-rank test for small sample size and Wilcoxon signed-rank test for large sample size, using normal approximation. The Monte Carlo simulation experiments were employed to study the performance of the test statistics of each of these tests depending on the type one error rates and the power rates of the test statistics. All these tests were applied on different sample sizes generated from three distributions, represented by Bivariate normal distribution, contaminated Bivariate normal distribution, and Bivariate exponential distribution.

Summary A nonparametric framework for changepoint detection, based on scan statistics utilizing graphs that represent similarities among observations, is gaining attention owing to its flexibility and good performance for high-dimensional and non-Euclidean data sequences. However, this graph-based framework faces challenges when there are repeated observations in the sequence, which is often the case for discrete data such as network data. In this article we extend the graph-based framework to solve this problem by averaging or taking the union of all possible optimal graphs resulting from repeated observations. We consider both the single-changepoint alternative and the changed-interval alternative, and derive analytical formulas to control the Type I error for the new methods, making them readily applicable to large datasets. The extended methods are illustrated on an application in detecting changes in a sequence of dynamic networks over time. All proposed methods are implemented in an $\texttt{R}$ package $\texttt{gSeg}$ available on CRAN.

Two-stage single arm designs are widely used in phase II clinical trials with binary endpoints. The trial may be stopped early due to insufficient positive responses in the first stage. There may be some enrolled subjects who have yet to respond by the end of the first stage, and their data are ignored if the first stage results in rejection of the trial. It is possible that the result after the first stage is rejection by a slim margin, while the results of pipeline subjects are quite positive. In this case, combining the data from the two sources may provide a valuable opportunity to rescue a promising treatment that was mistakenly rejected. We propose a novel double-check design to take advantage of the pipeline subjects' data to establish a rescue criterion based on two-stage design. When the rescue criterion is met, the decision to reject the trial at the end of the first stage can be reversed, allowing the trial to continue. A derivation based on a binomial distribution shows that the double-check strategy can strictly preserve the type I error rate. Further examination shows that the strategy can provide a slight increase in overall power and a substantial increase in conditional power when the proportion of positive response at the end of the first stage is at the margin. The extra rescue opportunity's cost is pretty low, only a slight increasing in the expected sample size.

A sequential multiple assignment randomized trial (SMART) facilitates the comparison of multiple adaptive treatment strategies (ATSs) simultaneously. Previous studies have established a framework to test the homogeneity of multiple ATSs by a global Wald test through inverse probability weighting. SMARTs are generally lengthier than classical clinical trials due to the sequential nature of treatment randomization in multiple stages. Thus, it would be beneficial to add interim analyses allowing for an early stop if overwhelming efficacy is observed. We introduce group sequential methods to SMARTs to facilitate interim monitoring based on the multivariate chi-square distribution. Simulation studies demonstrate that the proposed interim monitoring in SMART (IM-SMART) maintains the desired type I error and power with reduced expected sample size compared to the classical SMART. Finally, we illustrate our method by reanalyzing a SMART assessing the effects of cognitive behavioral and physical therapies in patients with knee osteoarthritis and comorbid subsyndromal depressive symptoms.

In medical and social science research, reliability of testing methods measured through inter- and intraobserver agreement is critical in disease diagnosis. Often comparison of agreement across multiple testing methods is sought in situations where testing is carried out on the same experimental units rendering the outcomes to be correlated. In this article, we first developed a Bayesian method for comparing dependent agreement measures under a grouped data setting. Simulation studies showed that the proposed methodology outperforms the competing methods in terms of power, while maintaining a decent type I error rate. We further developed a Bayesian joint model for comparing dependent agreement measures adjusting for subject and rater-level heterogeneity. Simulation studies indicate that our model outperforms a competing method that is used in this context. The developed methodology was implemented on a key measure on a dichotomous rating scale from a study with six raters evaluating three classification methods for chest radiographs for pneumoconiosis developed by the International Labor Office.

In this paper, we consider the Bayesian design of a randomized, double-blind, placebo-controlled superiority clinical trial. To leverage multiple historical datasets to augment the placebo-controlled arm, we develop three conditional borrowing approaches built upon the borrowing-by-parts prior, the hierarchical prior, and the robust mixture prior. The operating characteristics of the conditional borrowing approaches are examined. Extensive simulation studies are carried out to empirically demonstrate the superiority of the conditional borrowing approaches over the unconditional borrowing or no-borrowing approaches in terms of controlling type I error, maintaining good power, having a large “sweet-spot” region, minimizing bias, and reducing the mean-squared error of the posterior estimate of the mean parameter of the placebo-controlled arm. Computational algorithms are also developed for calculating the Bayesian type I error and power as well as the corresponding simulation errors.

Accounting for dependent observations in cluster-randomized trials (CRTs) using nested data is necessary in order to avoid misestimated standard errors resulting in questionable inferential statistics. Cluster-robust standard errors (CRSEs) are often used to address this issue. However, CRSEs are still well-known to underestimate standard errors for group-level variables when the number of clusters is low (e.g., < 50) and with CRTs, a small number of clusters, due to logistical or financial considerations, is the norm rather than the exception. Using a simulation with various conditions, we investigate the use of a small sample correction (i.e., CR2 estimator) proposed by Bell and McCaffrey (2002) together with empirically derived degrees of freedom estimates (dofBM). Findings indicate that even with as few as 10 clusters, the CR2 estimator used with dofBM yields generally unbiased results with acceptable type I error and coverage rates. Results show that coverage and type I error rates can be largely influenced by the choice of dof, not just the standard error adjustments. An applied example is provided together with R syntax to conduct the analysis. To facilitate the use of different CRSEs, a free graphical, menu-driven SPSS add-on to compute the various cluster-robust variance estimates can be downloaded from https://github.com/flh3/CR2/tree/master/SPSS .

Applications of Bayesian designs allow the borrowing of the strength of historical information and become more and more attractive in new drug developments. Nonetheless, according to the FDA guidance issued in 2020, Bayesian designs are classified as complex innovative designs that have rarely been used to provide substantial evidence of effectiveness in new drug applications. Moreover, as the historical data have already been observed and fixed, a question which arises is whether we should treat the Bayesian analysis as a conditional or unconditional analysis. Basically, it is essential to understand the frequentist operating characteristics of a Bayesian design either theoretically or through simulation in order to appropriately assess the right type I error probability and apply it to a clinical trial. In this research, we use a relatively simple setting of a normal distribution for the study endpoint to illustrate and compare the conditional and unconditional Bayesian analysis. Both scenarios of borrowing historical information of treatment effect and historical control data are considered. The thinking is applicable to the other settings or endpoints through the asymptotic normality of the distributions for the estimators of either the within or between treatment effects. Simulations are conducted to evaluate the characteristics of the methods.