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Abstract Spinal cord injury (SCI) often results in severe motor and sensory deficits, leading to significant disability. Preclinical studies and retrospective studies suggest that a critical window of enhanced neuroplasticity may exist immediately after SCI, during which therapeutic interventions could yield greater functional improvements. The impact of time interval since SCI on efficacy of rehabilitation has not been directly assessed and is the focus of this clinical trial. This study will compare the efficacy of high-intensity gait training, initiated at different time intervals post-injury, on walking performance in individuals with SCI. We hypothesize that early intervention will yield the greatest improvements in walking ability and community ambulation, compared to training initiated at 3 or 6 months after SCI, or standard of care. This randomized, multi-site clinical trial will enroll 108 participants with acute, traumatic SCI. Participants will be randomized to receive 20 h of high-intensity gait training that will be initiated either early (

Abstract When multiple influential covariates need to be balanced during a clinical trial, stratified blocked randomization and covariate-adaptive randomization procedures are frequently used in trials to prevent bias and enhance the validity of data analysis results. The latter approach is increasingly used in practice for a study with multiple covariates and limited sample sizes. Among a group of these approaches, the covariate-adaptive procedures proposed by Pocock and Simon are straightforward to be utilized in practice. We aim to investigate the optimal design parameters for the patient treatment assignment probability of their developed three methods. In addition, we seek to answer the question related to the randomization performance when additional covariates are added to the existing randomization procedure. We conducted extensive simulation studies to address these practically important questions.

Brief episodes of low oxygen breathing (therapeutic acute intermittent hypoxia; tAIH) may serve as an effective plasticity-promoting primer to enhance the effects of transcutaneous spinal stimulation-enhanced walking therapy (WALKtSTIM) in persons with chronic (>1 year) spinal cord injury (SCI). Pre-clinical studies in rodents with SCI show that tAIH and WALKtSTIM therapies harness complementary mechanisms of plasticity to maximize walking recovery. Here, we present a multi-site clinical trial protocol designed to examine the influence of tAIH + WALKtSTIM on walking recovery in persons with chronic SCI. We hypothesize that daily (eight sessions, 2 weeks) tAIH + WALKtSTIM will elicit faster, more persistent improvements in walking recovery than either treatment alone. To test our hypothesis, we are conducting a placebo-controlled clinical trial on 60 SCI participants who randomly receive one of three interventions: tAIH + WALKtSTIM; Placebo + WALKtSTIM; and tAIH + WALKtSHAM. Participants receive daily tAIH (fifteen 90-sec episodes at 10% O2 with 60-sec intervals at 21% O2) or daily placebo (fifteen 90-sec episodes at 21% O2 with 60-sec intervals at 21% O2) before a 45-min session of WALKtSTIM or WALKtSHAM. Our primary outcome measures assess walking speed (10-Meter Walk Test), endurance (6-Minute Walk Test), and balance (Timed Up and Go Test). For safety, we also measure pain levels, spasticity, sleep behavior, cognition, and rates of systemic hypertension and autonomic dysreflexia. Assessments occur before, during, and after sessions, as well as at 1, 4, and 8 weeks post-intervention. Results from this study extend our understanding of the functional benefits of tAIH priming by investigating its capacity to boost the neuromodulatory effects of transcutaneous spinal stimulation on restoring walking after SCI. Given that there is no known cure for SCI and no single treatment is sufficient to overcome walking deficits, there is a critical need for combinatorial treatments that accelerate and anchor walking gains in persons with lifelong SCI. Trial Registration: ClinicalTrials.gov, NCT05563103.

Abstract Background It is unknown if fluid biomarkers reflective of brain pathologies are useful in detecting and following a neurodegenerative process in individuals exposed to repetitive head impacts. This study explores the relationship between blood biomarkers and longitudinal change in cognitive function and regional brain volumes in a cohort of professional fighters. Methods Participants are drawn from a convenience sample of active and retired professional boxers and Mixed Martial Arts fighters and a control group with no prior exposure to head impacts. 3 T MRI brain imaging, plasma samples, and computerized cognitive testing were obtained at baseline and, for a subset, annually. MRI regional volumes were extracted, along with plasma levels of neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), p-tau231, and N-terminal tau (NTA). Statistical analyses were performed to assess the relationship between plasma levels and regional brain volumes and cognitive performance at baseline and longitudinally. Results One hundred forty active boxers (mean age: 31 with standard deviation (SD) of 8), 211 active MMA (mean age of 30 with SD of 5), 69 retired boxers (mean age 49 with SD of 9), and 52 control participants (mean age 36 with SD of 12) were included in the analyses. Baseline GFAP levels were highest in the retired boxers (retired boxers v. active MMA: p = 0.0191), whereas active boxers had higher levels of NfL (active boxers v. MMA: p = 0.047). GFAP showed an increase longitudinally in retired boxers that was associated with decreasing volumes of multiple cortical and subcortical structures (e.g., hippocampus: B = − 1.25, 95% CI, − 1.65 to − 0.85) and increase in lateral ventricle size (B = 1.75, 95% CI, 1.46 to 2.04). Furthermore, performance on cognitive domains including memory, processing speed, psychomotor speed, and reaction time declined over time with increasing GFAP (e.g., processing speed: B = − 0.04, 95% CI, − 0.07 to − 0.02; reaction time: B = 0.52, 95% CI, 0.28 to 0.76). Among active fighters, increasing levels of GFAP were correlated with lower thalamic (B = − 1.42, 95% CI, − 2.34 to -0.49) and corpus callosum volumes, along with worsening scores on psychomotor speed (B = 0.14, 95% CI, 0.01 to 0.27). Conclusion Longitudinal plasma GFAP levels may have a role in identifying individuals exposed to repetitive head impacts who are at risk of showing progressive regional atrophy and cognitive decline.

Abstract Confidence interval for the difference of two proportions has been studied for decades. Many methods were developed to improve the approximation of the limiting distribution of test statistics, such as the profile likelihood method, the score method, and the Wilson method. For the Wilson interval developed by Beal (Biometrics 43:941, 1987), the approximation of the Z test statistic to the standard normal distribution may be further improved by utilizing the continuity correction, in the observation of anti-conservative intervals from the Wilson interval. We theoretically prove that the Wilson interval is nested in the continuity corrected Wilson interval under mild conditions. We compare the continuity corrected Wilson interval with the commonly used methods with regards to coverage probability, interval width, and mean squared error of coverage probability. The proposed interval has good performance in many configurations. An example from a Phase II cancer trial is used to illustrate the application of these methods.

Abstract Cross-validation (CV) is a resampling approach to evaluate machine learning models when sample size is limited. The number of all possible combinations of folds for the training data, known as CV rounds, are often very small in leave-one-out CV. Alternatively, Monte Carlo cross-validation (MCCV) can be performed with a flexible number of simulations when computational resources are feasible for a study with limited sample size. We conduct extensive simulation studies to compare accuracy between MCCV and CV with the same number of simulations for a study with binary outcome (e.g., disease progression or not). Accuracy of MCCV is generally higher than CV although the gain is small. They have similar performance when sample size is large. Meanwhile, MCCV is going to provide reliable performance metrics as the number of simulations increases. Two real examples are used to illustrate the comparison between MCCV and CV.

Abstract Background Nearly one in three unconscious cardiac arrest survivors experience post-anoxic status epilepticus (PASE). Historically, PASE has been deemed untreatable resulting in its exclusion from status epilepticus clinical trials. However, emerging reports of survivors achieving functional independence following early and aggressive treatment of PASE challenged this widespread therapeutic nihilism. In the absence of proven therapies specific to PASE, standard of care treatment leans on general management strategies for status epilepticus. Vigabatrin—an approved therapy for refractory focal-onset seizures in adults—inhibits the enzyme responsible for GABA catabolism, increases brain GABA levels and may act synergistically with anesthetic agents to abort seizures. Our central hypothesis is that early inhibition of GABA breakdown is possible in the post-cardiac arrest period and may be an effective adjunctive treatment in PASE. Methods This is a phase IIa, single-center, open-label, pilot clinical trial with blinded outcome assessment, of a single dose of vigabatrin in 12 consecutive PASE subjects. Subjects will receive a single loading dose of 4500 mg of vigabatrin (or dose adjusted in moderate and severe renal impairment) via enteric tube within 48 h of PASE onset. Vigabatrin levels will be monitored at 0- (baseline), 0.5-, 1-, 2-, 3-, 6-, 12-, 24-, 48-, 72- and 168-h (7 days) post-vigabatrin. Serum biomarkers of neuronal injury will be measured at 0-, 24-, 48-, 72- and 96-h post-vigabatrin. The primary feasibility endpoint is the proportion of enrolled subjects among identified eligible subjects receiving vigabatrin within 48 h of PASE onset. The primary pharmacokinetic endpoint is the measured vigabatrin level at 3 h post-administration. Descriptive statistics with rates and proportions will be obtained regarding feasibility outcomes, along with the noncompartmental method for pharmacokinetic analyses. The area under the vigabatrin concentration-time curve in plasma from zero to the time of the last quantifiable concentration (AUC0-tlqc) will be calculated to estimate dose-linear pharmacokinetics. Perspective Vigabatrin demonstrates high potential for synergism with current standard of care therapies. Demonstration of the feasibility of vigabatrin administration and preliminary safety in PASE will pave the way for future efficacy and safety trials of this pharmacotherapeutic. Trial Registration NCT04772547.

The search for disease modifying therapies in Alzheimers disease (AD) has recently led to promising results but also revealed design issues in clinical trials themselves. Of particular importance is the potential statistical challenges that can arise when dosages change after an interim analysis, which is not uncommon in contemporary AD trials. Following the recent Aducanumab trials, we sought to study the implications of dose changes on the statistical power of an AD trial. We conducted extensive simulations to calculate statistical power when the relationship between treatment effect size and time is linear or non-linear, and the investigated drug has delayed treatment effect or not. Statistical power depends on many design factors including the dose change time, correlation, population homogeneity, and treatment effect time. We recommend that researchers conduct simulation studies at the interim analysis to justify the modified sample size and/or follow-up time modification meanwhile the type I and II error rates are controlled.

Abstract Amyloid- $$\beta$$ β (A $$\beta$$ β ) is the target in many clinical trials for Alzheimer’s disease (AD). Preclinical AD patients are heterogeneous with regards to different backgrounds and diagnosis. Accurately predicting A $$\beta$$ β status of participants by using machine learning (ML) models based on easily accessible data, could improve the effectiveness of AD clinical trials. We will develop optimal ML models for each subpopulation stratified by sex and disease stages using sub scores from screening neurological tests. Data from the AD Neuroimaging Initiative (ADNI) were used to build the ML models, for three groups: individuals with significant memory concern, early mild cognitive impairment (MCI), and late MCI. Data were further separated into 6 groups by disease stage (3 levels) and sex (2 categories). The outcome was defined as the A $$\beta$$ β status confirmed by the PET imaging, and the features include demographic data, newly identified risk factors, screening tests, and the domain scores from screening tests. Monte Carlo simulation studies were used together with k-fold cross-validation technique to compute model performance metric. We also develop a new feature selection method based on the stochastic ordering to avoiding searching all possible combinations of features. Accuracy of the identified optimal model for SMC male was over 90% by using domain scores, and accuracy for LMCI female was above 86%. Domain scores can improve the ML model prediction as compared to the total scores. Accurate ML prediction models can identify the proper population for AD clinical trials.

Traumatic brain injury (TBI) is a common source of functional impairment among athletes, military personnel, and the general population. Professional fighters in both boxing and mixed martial arts (MMA) are at particular risk for repetitive TBI and may provide valuable insight into both the pathophysiology of TBI and its consequences. Currently, effects of fighter weight class on brain volumetrics (regional and total) and functional outcomes are unknown. Fifty-three boxers and 103 MMA fighters participating in the Professional Fighters Brain Health Study (PRBHS) underwent volumetric magnetic resonance imaging (MRI) and neuropsychological testing. Fighters were divided into lightweight (?139.9 lb), middleweight (140.0?178.5 lb), and heavyweight (>178.5 lb). Compared with lightweight fighters, heavyweights displayed greater yearly reductions in regional brain volume (boxers: bilateral thalami; MMA: left thalamus, right putamen) and functional performance (boxers: processing speed, simple and choice reaction; MMA: Trails A and B tests). Lightweights suffered greater reductions in regional brain volume on a per-fight basis (boxers: left thalamus; MMA: right putamen). Heavyweight fighters bore greater yearly burden of regional brain volume and functional decrements, possibly related to differing fight dynamics and force of strikes in this division. Lightweights demonstrated greater volumetric decrements on a per-fight basis. Although more research is needed, greater per-fight decrements in lightweights may be related to practices of weight-cutting, which may increase vulnerability to neurodegeneration post-TBI. Observed decrements associated with weight class may result in progressive impairments in fighter performance, suggesting interventions mitigating the burden of TBI in professional fighters may both improve brain health and increase professional longevity.

Abstract Background Meta-analysis provides a useful statistical tool to effectively estimate treatment effect from multiple studies. When the outcome is binary and it is rare (e.g., safety data in clinical trials), the traditionally used methods may have unsatisfactory performance. Methods We propose using importance sampling to compute confidence intervals for risk difference in meta-analysis with rare events. The proposed intervals are not exact, but they often have the coverage probabilities close to the nominal level. We compare the proposed accurate intervals with the existing intervals from the fixed- or random-effects models and the interval by Tian et al. (2009). Results We conduct extensive simulation studies to compare them with regards to coverage probability and average length, when data are simulated under the homogeneity or heterogeneity assumption of study effects. Conclusions The proposed accurate interval based on the random-effects model for sample space ordering generally has satisfactory performance under the heterogeneity assumption, while the traditionally used interval based on the fixed-effects model works well when the studies are homogeneous.

Abstract Background Survival endpoint is frequently used in early phase clinical trials as the primary endpoint to assess the activity of a new treatment. Existing two-stage optimal designs with survival endpoint either over estimate the sample size or compute power outside the alternative hypothesis space. Methods We propose a new single-arm two-stage optimal design with survival endpoint by using the one-sample log rank test based on exact variance estimates. This proposed design with survival endpoint is analogous to Simon’s two-stage design with binary endpoint, having restricted follow-up. Results We compare the proposed design with the existing two-stage designs, including the two-stage design with survival endpoint based on the nonparametric Nelson-Aalen estimate, and Simon’s two-stage designs with or without interim accrual. The new design always performs better than these competitors with regards to the expected total study length, and requires a smaller expected sample size than Simon’s design with interim accrual. Conclusions The proposed two-stage minimax and optimal designs with survival endpoint are recommended for use in practice to shorten the study length of clinical trials.

Background: There is a current lack of any composite measure for the effective tracking and monitoring of clinical change in individuals exposed to repetitive head impacts (RHI). The aim of this study is to create a composite instrument for the purposes of detecting change over time in cognitive and behavioral function in individuals exposed to RHI.Methods: The data to derive the composite instrument came from the Professional Fighters Brain Health Study (PFBHS), a longitudinal study of active and retired professional fighters [boxers and mixed martial arts (MMA) fighters] and healthy controls. Participants in the PFBHS underwent assessment on an annual basis that included computerized cognitive testing and behavioral questionnaires. Multivariate logistic regression models were employed to compare active fighters (n = 117) with controls (n = 22), and retired fighters (n = 26) with controls to identify the predictors that could be used to differentiate the groups over time. In a second step, linear discriminant analysis was performed to derive the linear discriminant coefficients for the three groups by using the predictors from the two separate logistic regression models.Results: The composite scale is a weighted linear value of 12 standardized scores consisting of both current and yearly change scores in domains including: processing speed, choice reaction time, semantic fluency, letter fluency, and Barrett Impulsiveness Scale. Because the weighting of values differed between active and retired fighters, two versions emerged. The mean and standard deviation ratio (MSDR) showed that the new index had better sensitivity compared to the individual measures, with the ratio of MSDR of the new index to that of the existing measures of at least 1.84.Conclusion: With the increasing need for tools to follow individuals exposed to RHI and the potential of clinical trials on the horizon for CTE, the RHICI is poised to serve as an initial approach to a composite clinical measure.

Restricted mean survival time is an alternative measure of treatment effect to hazard ratio in clinical trials with time-to-event outcome. The current methods have been focused on one-stage designs. In this article, we propose optimal two-stage designs for a single-arm study with the smallest expected sample size. We compare the performance of the new optimal two-stage designs with the existing one-stage design with regards to the expected sample size and the expected total study length. The simulation results indicate that the new two-stage designs can save the expected sample size substantially as compared to the one-stage design. We use a non-small cell lung cancer trial to illustrate the application of the proposed designs. The proposed optimal two-stage designs are recommended for use when time for patient accrual is longer than the pre-specified follow-up time.

Objective: Incidence of concussions and report of symptoms are greater among women across sports. While structural brain changes and cognitive declines are associated with repetitive head impact (RHI), the role of sex is not well-understood. This study aimed to determine if there is a moderating effect of sex on the relationship the number of professional fights has with cognitive functioning and regional brain volumes in a cohort of boxers, mixed martial artists, and martial artists.Methods: A total of 55 women were matched with 55 men based on age, years of education, ethnicity, and fighting style. Cognition was assessed via the CNS Vital Signs computerized cognitive battery and supplemental measures. Structural brain scans, demographic data, and number of professional fights (NoPF) were also considered. The matched pairs were compared via analysis of covariance, accounting for total brain volume. Within-subject moderation models were utilized to assess the moderating effect of sex on the relationship between NoPF and brain volumes and cognitive performance.Results: Men were observed to have poorer performance on measures of psychomotor speed when compared to women. On a series of analyses assessing the role of sex as a moderator of the relationship between NoPF and regional brain volumes/cognitive performance, a significant moderation effect was observed across multiple measures of cognitive functioning, such that men had poorer performance. Differences in numerous regional brain volumes were also observed, such that the relationship between NoPF and brain volumes was steeper among men.Conclusion: Sex was observed to be an important moderator in the relationship between NoPF, aspects of cognitive functioning, and volumes of numerous brain regions, suggesting that sex differences in neuroanatomic and cognitive response to RHI deserve further attention.

Abstract Gaussian network model (GNM), regarded as the simplest and most representative coarse-grained model, has been widely adopted to analyze and reveal protein dynamics and functions. Designing a variation of the classical GNM, by defining a new Kirchhoff matrix, is the way to improve the residue flexibility modeling. We combined information arising from local relative solvent accessibility (RSA) between two residues into the Kirchhoff matrix of the parameter-free GNM. The undetermined parameters in the new Kirchhoff matrix were estimated by using particle swarm optimization. The usage of RSA was motivated by the fact that our previous work using RSA based linear regression model resulted out higher prediction quality of the residue flexibility when compared with the classical GNM and the parameter free GNM. Computational experiments, conducted based on one training dataset, two independent datasets and one additional small set derived by molecular dynamics simulations, demonstrated that the average correlation coefficients of the proposed RSA based parameter-free GNM, called RpfGNM, were significantly increased when compared with the parameter-free GNM. Our empirical results indicated that a variation of the classical GNMs by combining other protein structural properties is an attractive way to improve the quality of flexibility modeling.

Depression is a serious but treatable health issue that affects college students at an alarming rate. Improved cardiorespiratory fitness (CRF) decreases depression risk and severity but this relationship has not been fully evaluated in the college student population. Non-exercise estimated CRF (eCRF) could be used to identify students at risk for or suffering from depression. This study investigated the associations of depression and eCRF in college students. Participants (N = 437) completed a survey which included demographic and student-status questions, eCRF variables, and a validated depression instrument. Descriptive, chi-square, t-test, regression, and odds ratio analyses were employed. Depression was associated with low-fitness (X2 = 4.660, P = 0.031) and eCRF below age-predicted CRF (t = 3.28, P < 0.001). Predictors of increased depression included low-fitness, sexual orientation, current depression treatment, and GPA (R2 = 0.145–0.159; Adj R2 = 0.135–0.149). Odd ratio analyses determined that low-fitness increased the risk of reporting depression (β = 2.39, P = 0.017, 95% CI = 1.17–4.872) which remained significant when adjusted (β = 2.478, P = 0.017, 95% CI = 1.175–5.229). Adjusted odds ratio analyses also indicated increased risk of reporting depression for those in a sexual minority (β = 2.582, P = 0.001, 95% CI = 1.44,4.629) and undergoing current depression treatment (β = 2.393, P < 0.001, 95% CI = 2.393–13.043). High levels of fitness did not reduce the odds of reporting depression compared to age predicted CRF. A simple eCRF algorithm can be used to identify college student depression.

Abstract Background Recently, several adaptive one-arm two-stage designs have been developed by fully using the information from previous stages to reduce the expected sample size in clinical trials with binary endpoints as primary outcome. It is important to compute exact confidence limits for these studies. Methods In this article, we propose three new one-sided limits by ordering the sample space based on p-value, average response rate at each stage, and asymptotic lower limit, as compared to another three existing sample size ordering approaches based on average response rate. Among the three proposed approaches, the one based on the average response rate at each stage is not exact, and the remaining two approaches are exact with the coverage probability guaranteed. Results We compare these exact intervals by using the two commonly used criteria: simple average length and expected length. The existing three approaches based on average response rate have similar performance, and they have shorter expected lengths than the two proposed exact approaches although the gain is small, while this trend is reversed under the simple average criterion. Conclusions We would recommend the two exact proposed approaches based on p-value and asymptotic lower limit under the simple average length criterion, and the approach based on average response rate under the expected length criterion.

Abstract Background Simon’s two-stage design is the most widely implemented among multi-stage designs in phase II clinical trials to assess the activity of a new treatment in a single-arm study. In this two-stage design, the sample size from the second stage is fixed regardless of the number of responses observed in the first stage. Methods We develop a new minimax adaptive design for phase II clinical trials, by using the branch-and-bound intelligent algorithm based on conditional error functions. Results We compare the performance of the proposed design and competitors, including Simon’s minimax design, and a modified Simon’s design that allows early stopping for futility or efficacy. The maximum sample size of the proposed minimax adaptive design is guaranteed to be less than or equal to those from other existing designs. When the proposed design has the same maximum sample size as others, it always has the smallest expected sample size. In addition to the minimax adaptive design, we also introduce admissible adaptive designs determined from a Bayesian perspective. Conclusions The proposed adaptive minimax design can save sample sizes for a clinical trial. The minimum required sample size is critical to reduce the cost of a project.

This research is motivated by one of our survey studies to assess the potential influence of introducing zebra mussels to the Lake Mead National Recreation Area, Nevada. One research question in this study is to investigate the association between the boating activity type and the awareness of zebra mussels. A chi-squared test is often used for testing independence between two factors with nominal levels. When the null hypothesis of independence between two factors is rejected, we are often left wondering where does the significance come from. Cell residuals, including standardized residuals and adjusted residuals, are traditionally used in testing for cell significance, which is often known as a post hoc test after a statistically significant chi-squared test. In practice, the limiting distributions of these residuals are utilized for statistical inference. However, they may lead to different conclusions based on the calculated p-values, and their p-values could be over- o6r under-estimated due to the unsatisfactory performance of asymptotic approaches with regards to type I error control. In this article, we propose new exact p-values by using Fisher's approach based on three commonly used test statistics to order the sample space. We theoretically prove that the proposed new exact p-values based on these test statistics are the same. Based on our extensive simulation studies, we show that the existing asymptotic approach based on adjusted residual is often more likely to reject the null hypothesis as compared to the exact approach due to the inflated family-wise error rates as observed. We would recommend the proposed exact p-value for use in practice as a valuable post hoc analysis technique for chi-squared analysis.

In ophthalmologic studies, measurements obtained from both eyes of an individual are often highly correlated. Ignoring the correlation could lead to incorrect inferences. An asymptotic method was proposed by Tang and others (2008) for testing equality of proportions between two groups under Rosner's model. In this article, we investigate three testing procedures for general g ≥ 2 groups. Our simulation results show the score testing procedure usually produces satisfactory type I error control and has reasonable power. The three test procedures get closer when sample size becomes larger. Examples from ophthalmologic studies are used to illustrate our proposed methods.

The Youden Index is a summary measurement of the receiver operating characteristic (ROC) curve for the accuracy of a diagnostic test with ordinal or continuous endpoints. The bootstrap confidence interval based on the adjusted proportion estimate was shown to have satisfactory performance among the existing confidence intervals, including the parametric interval via the delta method. In this article, we propose two confidence intervals using the square-and-add limits based on the Wilson score method. We compare the two proposed intervals with the existing interval with extensive simulation studies. The new interval based on the empirical proportion estimate generally has better performance than that based on the adjusted proportion estimate. A real example from a clinical trial of prostate cancer is illustrated for the application of the new intervals.

We propose a new nonparametric test for ordered alternative problem based on the rank difference between two observations from different groups. These groups are assumed to be independent from each other. The exact mean and variance of the test statistic under the null distribution are derived, and its asymptotic distribution is proven to be normal. Furthermore, an extensive power comparison between the new test and other commonly used tests shows that the new test is generally more powerful than others under various conditions, including the same type of distribution, and mixed distributions. A real example from an anti-hypertensive drug trial is provided to illustrate the application of the tests. The new test is therefore recommended for use in practice due to easy calculation and substantial power gain.

Asymptotic and exact conditional approaches have often been used for testing agreement between two raters with binary outcomes. The exact conditional approach is guaranteed to respect the test size as compared to the traditionally used asymptotic approach based on the standardized Cohen's kappa coefficient. An alternative to the conditional approach is an unconditional strategy which relaxes the restriction of fixed marginal totals as in the conditional approach. Three exact unconditional hypothesis testing procedures are considered in this article: an approach based on maximization, an approach based on the conditional p-value and maximization, and an approach based on estimation and maximization. We compared these testing procedures based on the commonly used Cohen's kappa with regards to test size and power. We recommend the following two exact approaches for use in practice due to power advantages: the approach based on conditional p-value and maximization and the approach based on estimation and maximization.