Search

OBJECTIVES: Observational studies report mixed findings regarding the association between vitamin D and JIA incidence or activity, however such studies are susceptible to considerable bias. Since low vitamin D levels are common within the general population and easily corrected, there is potential public health benefit in identifying a causal association between vitamin D insufficiency and JIA incidence. To limit bias due to confounding and reverse causation we examined the causal effect of the major circulating form of vitamin D, 25-(OH)D, on JIA incidence using Mendelian randomization (MR).METHODS: In this two sample MR analysis we used summary level data from the largest and most recent genome wide association study (GWAS) of 25-(OH)D levels (sample size 443,734), alongside summary data from two JIA GWASs (sample sizes 15,872 and 12,501), all from European populations. To test and account for potential bias due to pleiotropy we employed multiple MR methods and sensitivity analyses.RESULTS: We found no evidence of a causal relationship between genetically predicted 25-(OH)D levels and JIA incidence (OR 1.00, 95% CI 0.76-1.33 per standard deviation increase in standardised natural-log transformed 25-(OH)D levels). This estimate was consistent across all methods tested. Additonally there was no evidence that genetically predicted JIA causally influences 25-(OH)D levels (-0.002 standard deviation change in standardised natural-log transformed 25-(OH)D levels per doubling odds in genetically predicted JIA, 95% CI -0.006-0.002).CONCLUSION: Given the lack of a causal relationship between 25-(OH)D levels and JIA, population level vitamin D supplementation is unlikely to reduce JIA incidence.

Objective There is an unmet need for therapies that target the underlying pathophysiology of osteoarthritis (OA). However, defining appropriate measures for clinical trials of such therapies is challenging. Our objective is to propose concept clinical endpoints that directly capture clinical benefit in this setting and evaluate the feasibility of their use. Methods This analysis used the multi-center, longitudinal, observational Osteoarthritis Initiative (OAI) database. OAI participants primarily had knee OA, with follow-up of up to nine years and assessments of joints, surgical interventions, performance outcomes, and patient-reported outcomes (PROs). We examined this dataset to identify existing outcome measures of direct clinical benefit. We evaluated the feasibility of conducting trials using these candidate endpoints by estimating incidence rates and resulting required sample sizes and study durations in time-to-event analyses. Results We identified candidate endpoints based on total knee replacement (TKR) and composite endpoints defined by TKR and conservative thresholds of PROs of pain and function. Using time to TKR as an endpoint, a study with an average follow-up time of three years requires approximately 3,000 to 18,000 subjects depending on effect size. Alternatively, a composite endpoint such as 'time to TKR or severe pain or severely impaired functioning', the required sample sizes ranged from approximately 2,000 to 11,000 for a three-year study. Conclusion The proposed concept endpoints can reliably and feasibly evaluate effectiveness of therapies for this unmet need. In particular, the composite endpoint approach can substantially reduce sample sizes (up to approximately 40%) compared to the use of TKR alone.

Introduction Hyperinflammatory state has a role in the pathogenesis of COVID‐19. Anakinra could reduce inflammation and help to combat the condition. In this study, we aimed to assess the safety and efficacy of anakinra (PerkinRA®) in severe COVID‐19. Method The study was an open‐label, randomized, controlled trial conducted in Imam Hossein Medical Center from May to July 2020. Patients with a confirmed diagnosis of COVID‐19 were included in this study. We administered anakinra 100 mg daily intravenously. All patients received COVID‐19 pharmacotherapy based on the represented national guideline. The need for invasive mechanical ventilation is considered the primary outcome. Results Thirty patients were included in this study, and 15 of them received Anakinra. Nineteen patients were male (63.3%), and 11 were female (36.7%). The mean age of patients was 55.77 ± 15.89 years. In the intervention group, the need for invasive mechanical ventilation was significantly reduced compared to the control group (20.0% vs. 66.7%, p = .010). Also, these patients had a significantly lower length of hospital stay (p = .043). No significant higher rate of infection was recorded. Conclusion Anakinra as an immunomodulatory agent has been associated with the reduced need for mechanical ventilation in patients admitted to intensive care units because of severe COVID‐19. The medication reduced the hospital length of stay. Furthermore, no increased risk of infection was observed. Further randomized placebo‐controlled trials with a larger sample size are needed to confirm these findings., Anakinra administration significantly reduces the need for mechanical ventilation. No increased risk of infection was observed because of anakinra use. Reduced hospitalization duration observed in patients who received anakinra.

Electroencephalographic (EEG) neurofeedback has been utilised to regulate abnormal brain activity associated with chronic pain. In this systematic review, we synthesised the evidence from randomised controlled trials (RCTs) to evaluate the effect of EEG neurofeedback on chronic pain using random effects meta-analyses. Additionally, we performed a narrative review to explore the results of non-randomised studies. The quality of included studies was assessed using Cochrane risk of bias tools, and the GRADE system was used to rate the certainty of evidence. Ten RCTs and 13 non-randomised studies were included. The primary meta-analysis on nine eligible RCTs indicated that although there is low confidence, EEG neurofeedback may have a clinically meaningful effect on pain intensity in short-term. Removing the studies with high risk of bias from the primary meta-analysis resulted in moderate confidence that there remained a clinically meaningful effect on pain intensity. We could not draw any conclusion from the findings of non-randomised studies, as they were mostly non-comparative trials or explorative case series. However, the extracted data indicated that the neurofeedback protocols in both RCTs and non-randomised studies mainly involved the conventional EEG neurofeedback approach, which targeted reinforcing either alpha or sensorimotor rhythms and suppressing theta and/or beta bands on one brain region at a time. A post-hoc analysis of RCTs utilising the conventional approach resulted in a clinically meaningful effect estimate for pain intensity. Although there is promising evidence on the analgesic effect of EEG neurofeedback, further studies with larger sample sizes and higher quality of evidence are required.

Over the past few decades, the rapid democratization of high-throughput sequencing and the growing emphasis on open science practices have resulted in an explosion in the amount of publicly available sequencing data. This opens new opportunities for combining datasets to achieve unprecedented sample sizes, spatial coverage, or temporal replication in population genomic studies. However, a common concern is that non-biological differences between datasets may generate batch effects that can confound real biological patterns. Despite general awareness about the risk of batch effects, few studies have examined empirically how they manifest in real datasets, and it remains unclear what factors cause batch effects and how to best detect and mitigate their impact bioinformatically. In this paper, we compare two batches of low-coverage whole genome sequencing (lcWGS) data generated from the same populations of Atlantic cod (Gadus morhua). First, we show that with a “batch-effect-naive” bioinformatic pipeline, batch effects severely biased our genetic diversity estimates, population structure inference, and selection scan. We then demonstrate that these batch effects resulted from multiple technical differences between our datasets, including the sequencing instrument model/chemistry, read type, read length, DNA degradation level, and sequencing depth, but their impact can be detected and substantially mitigated with simple bioinformatic approaches. We conclude that combining datasets remains a powerful approach as long as batch effects are explicitly accounted for. We focus on lcWGS data in this paper, which may be particularly vulnerable to certain causes of batch effects, but many of our conclusions also apply to other sequencing strategies.

BACKGROUND & AIMS Fungal infections (FIs) have serious implications, yet understated in cirrhosis. Therefore, we reviewed the epidemiology and trends of FIs among cirrhotics. METHODS Four electronic-databases were searched for full-text articles describing prevalence of FIs in cirrhosis. Studies from post-transplant, malignancy and classical-immuno-deficiency patients were excluded. A random-effects meta-analysis was done to pool estimates of FIs (overall, and by type and infection-site), and their variation(I2 ) was explored on moderator-analysis and meta-regression. Risk of bias and asymmetry in estimates was assessed by a checklist and Eggers-regression, respectively.(CRD42019142782) RESULTS: Thirty-four low-risk and four moderate-risk studies (31984 cirrhotics) were included. Pooled-estimates of overall-FIs (17 studies), invasive fungal infections (IFIs; 17 studies), invasive-candidiasis (23 studies), and invasive-aspergillosis (16 studies) in cirrhosis were 10.2%(6.0-16.9), 9.5%(5.4-16.2), 4.0%(2.0-8.0) and 2.8%(1.5-5.3); respectively (I2 >90%;each). Site of FIs in decreasing order of pooled-prevalence was pulmonary, urinary-tract, bloodstream, peritoneal, esophageal, and cerebral. Geographic differences in these estimates were remarkable, with highest burden of overall-FIs from Belgium, USA, and India. Non-albicans-Candida and Aspergillus infections have increased over the last-decade in cirrhosis. Intensive-care-unit (ICU)-admitted and acute-on-chronic liver failure (ACLF) patients had the highest prevalence of IFIs. MELD-score(cases), bias-score, and sample size across studies were the predictors of variance in overall-FI-estimates. Diabetes, steroid and broad-spectrum antibiotic-exposure, and multiple organ failures were the common predispositions reported in patients with FIs. CONCLUSIONS FIs impose a substantial burden in cirrhosis. ACLF and ICU-admission should be considered as a host factor for defining IFIs. Epidemiology of FIs can guide interpretation of biomarkers and antifungal treatment in cirrhosis.

When designing a clinical trial, one key aspect of the design is the sample size calculation. The sample size calculation tends to rely on a target or expected difference. The expected difference can be based on the observed data from previous studies, which results in bias. It has been reported that large treatment effects observed in trials are often not replicated in subsequent trials. If these values are used to design subsequent studies, the sample sizes may be biased which results in an unethical study. Regression to the mean (RTM) is one explanation for this. If only health technologies which meet a particular continuation criterion (such as p

The cure fraction models are intended to analyze lifetime data from populations where some individuals are immune to the event under study, and allow a joint estimation of the distribution related to the cured and susceptible subjects, as opposed to the usual approach ignoring the cure rate. In situations involving small sample sizes with many censored times, the detection of nonfinite coefficients may arise via maximum likelihood. This phenomenon is commonly known as monotone likelihood (ML), occurring in the Cox and logistic regression models when many categorical and unbalanced covariates are present. An existing solution to prevent the issue is based on the Firth correction, originally developed to reduce the estimation bias. The method ensures finite estimates by penalizing the likelihood function. In the context of mixture cure models, the ML issue is rarely discussed in the literature; therefore, this topic can be seen as the first contribution of our paper. The second major contribution, not well addressed elsewhere, is the study of the ML issue in cure mixture modeling under the flexibility of a semiparametric framework to handle the baseline hazard. We derive the modified score function based on the Firth approach and explore finite sample size properties of the estimators via a Monte Carlo scheme. The simulation results indicate that the performance of coefficients related to the binary covariates are strongly affected to the imbalance degree. A real illustration, in the melanoma dataset, is discussed using a relatively novel data set collected in a Brazilian university hospital.

This study aimed to investigate the effectiveness of neoadjuvant chemotherapy in patients with breast cancer in different age groups and evaluate the impact of age group on survival outcome according to different treatment responses. Data were retrospectively collected from the cancer registry database of Kaohsiung Medical University Hospital in Taiwan under an approved protocol. Overall, 96 elder patients (aged >50 years) and 96 younger controls (aged ≤50 years) who received neoadjuvant chemotherapy and breast surgical treatment were examined after 1:1 matching. Logistic regression analysis was used to investigate the effectiveness of treatment response in patients of different age groups. Additionally, the Kaplan-Meier estimator and log-rank test were performed to evaluate the effect of age group and treatment response on disease-free and overall survival (OS). Although no direct significant association was found between age group and treatment response, several significant results were found in treatment response stratification analysis. Among 16 pathological complete response (pCR) patients, elder patients showed significantly greater 5-year disease-free survival (DFS) than younger patients (DFS rate, 85.7% vs. 0%, p = 0.041). However, in 176 non-pCR patients, elder patients showed poor DFS compared to younger patients (DFS rate, 16.6% vs. 32.3%; log-rank test, p = 0.031). With limited sample size and study design, our study results demonstrate that patients aged >50 years who achieved pCR after neoadjuvant chemotherapy could obtain better survival outcome than younger patients. However, the younger patients showed no survival benefits regardless of pCR status.

Patient-centred outcomes are increasingly recognised as crucial measures of healthcare quality. Days alive and at home up to 30 days after surgery (DAH30 ) is a validated and readily obtainable patient-centred outcome measure that integrates much of the peri-operative patient journey. However, the minimal difference in DAH30 that is clinically important to patients is unknown. We designed and administered a 28-item survey to evaluate the minimal clinically important difference in DAH30 among adult patients undergoing inpatient surgery. Patients were approached pre-operatively or within 2 days postoperatively. We did not study patients undergoing day surgery or nursing home residents. Patients ranked their opinions on the importance of discharge home using a Likert scale (from 1, not important at all to 6, extremely important) and the minimum number of extra days at home that would be meaningful using this scale. We recruited 104 patients; the survey was administered pre-operatively to 45 patients and postoperatively to 59 patients. The mean (SD) age was 53.5 (16.5) years, and 51 (49%) patients were male. Patients underwent a broad range of surgery of mainly intermediate (55%) to major (33%) severity. The median minimal clinically important difference for DAH30 was 3 days; this was consistent across a broad range of scenarios, including earlier discharge home, complications delaying hospital discharge and the requirement for admission to a rehabilitation unit. Discharge home earlier than anticipated and discharge home rather than to a rehabilitation facility were both rated as important (median score = 5). Empirical data on the minimal clinically important difference for DAH30 may be useful to determine sample size and to guide the non-inferiority margin for future clinical trials.

The stepped wedge cluster randomized trial (SW-CRT) is an increasingly popular design for evaluating health service delivery or policy interventions. An essential consideration of this design is the need to account for both within-period and between-period correlations in sample size calculations. Especially when embedded in health care delivery systems, many SW-CRTs may have subclusters nested in clusters, within which outcomes are collected longitudinally. However, existing sample size methods that account for between-period correlations have not allowed for multiple levels of clustering. We present computationally efficient sample size procedures that properly differentiate within-period and between-period intracluster correlation coefficients in SW-CRTs in the presence of subclusters. We introduce an extended block exchangeable correlation matrix to characterize the complex dependencies of outcomes within clusters. For Gaussian outcomes, we derive a closed-form sample size expression that depends on the correlation structure only through two eigenvalues of the extended block exchangeable correlation structure. For non-Gaussian outcomes, we present a generic sample size algorithm based on linearization and elucidate simplifications under canonical link functions. For example, we show that the approximate sample size formula under a logistic linear mixed model depends on three eigenvalues of the extended block exchangeable correlation matrix. We provide an extension to accommodate unequal cluster sizes and validate the proposed methods via simulations. Finally, we illustrate our methods in two real SW-CRTs with subclusters.

Hospitals, which play an important role in reducing injuries and casualties, must be prepared for a crowd of people in emergencies. The present study aimed to survey and collect data on the likely behaviours of people under emergency circumstances in hospitals in order to improve emergency response plans in these places. The target population was all individuals present in three hospitals from Tabriz (including patients, companions and treatment staff), and the sample size was 1145. A questionnaire was administered to this population in order to collect the data. The comparison of different groups was performed based on participants' gender and their role. Results showed that, in general, everyone in the surveyed hospitals is more likely to choose a reactive strategy rather than a preventive strategy as well as a cooperative strategy rather than a competitive strategy, and less willing to use lifts during emergencies. Comparing different strategies utilized by different people resulting from ordered logit models revealed that there are significant differences in evacuation strategies between men and women as well as between treatment staff and all respondents. Men were more likely to select a proactive strategy rather than a reactive strategy compared to women. Also, the treatment staff group were more likely to employ a cooperative strategy and less likely to use a competitive strategy compared to other people. In view of the role of hospitals, the results of this study can play a major role in policy-making to predict the behaviour of different individuals in healthcare settings and perform needs assessment to consider the necessary facilities or raining required for different people in various countries.

A random delayed treatment effect is expected in a confirmatory clinical trial for an immunotherapy due to the individual heterogeneity of physiological conditions. For this reason, the delay time will be assumed to follow a continuous distribution that is difficult to estimate accurately based on the early-phase data, which hinders the specification of the most powerful weighted log-rank test. Therefore, we propose a simulation-based maximum duration design with a robustly powerful Maxcombo test for a group sequential trial for the immunotherapy with the random delayed treatment effect. The design obtains the group sequential boundaries by a simulation procedure and determines the required maximum sample size using a one-dimensional search in which another simulation procedure is used to calculate empirical power. The simulation researches proved the accuracy of the group sequential boundaries and their robustness against the misspecified maximum sample sizes for large samples and revealed their moderate sensitivity against the misspecified survival distributions under the null hypothesis of no difference. The studies investigated whether the type I error rate would inflate under the "inferior" null hypothesis and evaluated the robustness against different distributions of the delay time in terms of the empirical power among the Maxcombo tests and component weighted log-rank tests.

Introduction Fetal alcohol spectrum disorders (FASD) are highly prevalent developmental disabilities associated with prenatal alcohol exposure. In addition to varied strengths and unique talents, people with FASD experience significant challenges, including in adaptive functioning. Adaptive functioning refers to skills related to everyday life such as communication, practical skills, and social skills. For the current review we aimed to understand how adaptive functioning in FASD compares to that of nonexposed individuals and those with attention deficit-hyperactivity disorder (ADHD). Additionally, we investigated how this relationship may change based on IQ, executive functioning, and age. Method The current review was registered in the International Prospective Register of Systematic Reviews (PROSPERO). Studies were eligible for inclusion if they measured adaptive functioning and included an FASD group and at least one eligible comparison group. Articles available in May 2021 in PubMed, PsycInfo, Scopus, and ProQuest Dissertations were searched. Publication bias was assessed using Egger's regression and three-level random effects models were computed for all domains of adaptive functioning. Possible moderation of IQ, executive functioning, and age were investigated if heterogeneity analyses were significant. A post-hoc moderation analysis of recruitment method was also completed. Results Thirty studies were included. Individuals with FASD had significantly lower adaptive functioning relative to other groups, with effect sizes ranging from 1.04-1.35 compared to nonexposed groups and .30-.43 compared to ADHD groups. No significant moderating effects were found for IQ, or age; executive functioning significantly moderated communication skills in FASD compared to the nonexposed group. Recruitment method significantly affected this relationship, with larger effect sizes on average found for clinically-identified samples compared to at-risk or population samples. Conclusions Individuals with FASD have impairments in adaptive functioning relative to nonexposed and ADHD groups, regardless of IQ, executive functioning, or age. Limitations include low sample sizes in some comparisons and limited age range.

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.

Estimation of the dose-response curve for efficacy and subsequent selection of an appropriate dose in phase II trials are important processes in drug development. Various methods have been investigated to estimate dose-response curves. Generally, these methods are used with equal allocation of subjects for simplicity; nevertheless, they may not fully optimize performance metrics because of nonoptimal allocation. Optimal allocation methods, which include adaptive allocation methods, have been proposed to overcome the limitations of equal allocation. However, they rely on asymptotics, and thus sometimes cannot efficiently optimize the performance metric with the sample size in an actual clinical trial. The purpose of this study is to construct an adaptive allocation rule that directly optimizes a performance metric, such as power, accuracy of model selection, accuracy of the estimated target dose, or mean absolute error over the estimated dose-response curve. We demonstrate that deep reinforcement learning with an appropriately defined state and reward can be used to construct such an adaptive allocation rule. The simulation study shows that the proposed method can successfully improve the performance metric to be optimized when compared with the equal allocation, D-optimal, and TD-optimal methods. In particular, when the mean absolute error was set to the metric to be optimized, it is possible to construct a rule that is superior for many metrics.

Background & aims A pre-emptive transjugular intrahepatic portosystemic shunt (p-TIPSS) after acute variceal bleeding (AVB) is advocated. However, when compared with the current standard of care (SOC), the survival benefit of p-TIPSS is questionable. We performed a systematic review, meta-analysis and trial sequential analysis of randomised control trials (RCT) to assess the survival benefit of p-TIPSS in patients with cirrhosis and AVB. Methods Comprehensive literature search of 3 bibliographic databases (MEDLINE, EMBASE, Cochrane) was conducted from inception till May2021. All study types evaluating the survival benefit of p-TIPSS in AVB were considered for inclusion. The relative risk (RR) of mortality and re-bleeding at 6-weeks and mortality at 1-year with a random effects model was computed. Trial sequential analysis (TSA) was performed for the primary outcome of 6-weeks mortality. Results A total of 9 studies(4 RCTs and 5 cohort) comprising 2861 patients with AVB were included. The overall pooled risks of mortality at 6-weeks and 1-year were 17.9%(95%CI:16.5-19.3%) and 26.7%(95%CI:25.0-28.3%) respectively. Although p-TIPSS was associated with lower 6-weeks rebleeding risk (RR=0.20;95%CI=0.13-0.29, I2 =0%), data from pooled RCTs showed no significant difference in mortality at 6-weeks (RR=0.33; 95% CI=0.08-1.36, I2 =63%) or at 1-year (RR=0.76;95%CI=0.51-1.14, I2 =30%). Using TSA, required sample size to detect a 20% relative risk reduction in mortality at 6-weeks with p-TIPSS was estimated to be 6317, which is beyond the total number of patients available for analysis. Conclusions This meta-analysis found that the available data from RCTs is insufficient to confer 6-weeks mortality benefit with p-TIPSS compared to SOC, thus adequately powered RCT's are required.

In planning randomized clinical trials (RCTs) for diseases such as Alzheimer's disease (AD), researchers frequently rely on the use of existing data obtained from only two time points to estimate sample size via the subtraction of baseline from follow-up measurements in each subject. However, the inadequacy of this method has not been reported. The aim of this study is to discuss the limitation of sample size estimation based on the subtraction of available data from only two time points for RCTs. Mathematical equations are derived to demonstrate the condition under which the obtained data pairs with variable time intervals could be used to adequately estimate sample size. The MRI-based hippocampal volume measurements from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Monte Carlo simulations (MCS) were used to illustrate the existing bias and variability of estimates. MCS results support the theoretically derived condition under which the subtraction approach may work. MCS also show the systematically under- or over-estimated sample sizes by up to 32.27 % bias. Not used properly, such subtraction approach outputs the same sample size regardless of trial durations partly due to the way measurement errors are handled. Estimating sample size by subtracting two measurements should be treated with caution. Such estimates can be biased, the magnitude of which depends on the planned RCT duration. To estimate sample sizes, we recommend using more than two measurements and more comprehensive approaches such as linear mixed effect models.

OBJECTIVE Many studies have evaluated the effectiveness of topical intraoperative mitomycin (MCC) usage in a wide range of otolaryngologic procedures with variable conclusions on effectiveness. This systematic review aims to provide a qualitative estimation of mitomycin C's treatment effectiveness in maintaining or preventing stenosis after surgical interventions. DESIGN AND SETTING Following the PRISMA guideline, a comprehensive systematic search of MEDLINE, EMBASE and CINAHL databases was performed including hand-searching and cross-reference checking. PARTICIPANTS The search was limited to humans, sample size greater than two and study designs including a comparative arm. MAIN OUTCOME MEASURES Outcome measures varied but included rates of restenosis, number of procedures, and post-surgical patency. RESULTS A total of 571 unique abstracts and 109 full articles were reviewed. Seventy-seven studies were included in the final analysis. The available evidence ranged from case series to randomized control studies. Meta-analysis was deemed inappropriate due to heterogeneity of study design. Thirty-eight studies assessed the effective of MCC in dacryocystorhinostomy, which is reported in a separate meta-analysis. All other studies were categorized into otolaryngologic site and pathology including choanal atresia (n = 5), endoscopic sinus surgery (n = 12), airway procedures (n = 9), esophageal procedures (n = 8) and other (n = 2). CONCLUSIONS The published literature on the effectiveness of MMC was mixed, but suggested topical MMC improved surgical outcomes in many otolaryngologic procedures compared to controls. This was the first review to assemble literature on MMC usage for different surgical procedures. Comprehensive interpretation of the data was limited due to heterogeneity in primary outcome, procedure type and study quality. High-quality prospective and randomized controlled studies are required to further confirm the positive effect of MMC use on surgical outcomes.

Globalized drug development studies, such as multiregional clinical trials (MRCTs), have attracted much attention due to their ability to expedite drug development and shorten the time lag of drug release. While observing the overall effect of a new drug, the region-specific effects to support drug registration in constituent regions can also be evaluated. Several challenges arise in conducting MRCTs, such as the heterogeneity in the variability of the primary endpoint across regions. However, most of the existing statistical methods assume a common variability, which may not be valid in practice due to differences across regions (eg, diversities in ethnicity or disparities in medical culture/practice). We present a statistical method for the design and evaluation of MRCTs to consider the heterogeneous variability across regions. We assessed the overall sample size requirement and addressed the region-specific sample size determination to establish the consistency of treatment effects between the specific region and the entire group. We demonstrate the proposed approach with numerical examples.

As a reaction to the pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), a multitude of clinical trials for the treatment of SARS‐CoV‐2 or the resulting corona disease 2019 (COVID‐19) are globally at various stages from planning to completion. Although some attempts were made to standardize study designs, this was hindered by the ferocity of the pandemic and the need to set up clinical trials quickly. We take the view that a successful treatment of COVID‐19 patients (i) increases the probability of a recovery or improvement within a certain time interval, say 28 days; (ii) aims to expedite favorable events within this time frame; and (iii) does not increase mortality over this time period. On this background, we discuss the choice of endpoint and its analysis. Furthermore, we consider consequences of this choice for other design aspects including sample size and power and provide some guidance on the application of adaptive designs in this particular context.

AIMS The association of renal function and linezolid-induced thrombocytopaenia (LIT) remains controversial. We performed a meta-analysis to determine whether impaired renal function is associated with an increased LIT risk. METHODS We conducted a systematic search of PubMed, EMBASE and the Cochrane Library from inception to February 2021 for eligible studies evaluating the relationship between renal function and LIT. Indicators of renal function included renal impairment (RI), severe RI, haemodialysis status, creatinine clearance rate (Ccr) and estimated glomerular filtration rate (eGFR). Unadjusted and adjusted estimates and 95% confidence intervals (CIs) were calculated separately using a random-effect model. RESULTS A total of 24 studies with 3580 patients were included in the meta-analysis. RI patients had an increased LIT risk compared to non-RI patients in both the unadjusted (OR 3.54; 95% CI 2.27, 5.54; I2 = 77.7%) and adjusted analyses (OR 2.51; 95% CI 1.82, 3.45; I2 = 17.9%). This association persisted in the subset of studies involving only patients receiving a fixed conventional dose (600 mg every 12 h) and other subgroup analyses by ethnicity, sample size and study quality. Moreover, the LIT risk was significantly higher in patients with severe RI and haemodialysis than in patients without severe RI and haemodialysis. The eGFR and Ccr were significantly lower in LIT patients than in non-LIT patients. CONCLUSIONS Impaired renal function is associated with an increased risk of LIT. A reduced linezolid dose may be considered in RI patients at a low risk of treatment failure, ideally guided by therapeutic drug monitoring.

After decades of investigation, the role of near work in myopia remains unresolved, with some studies reporting no relationship and others finding the opposite. This systematic review is intended to summarize classic and recent literature investigating near work and the onset and progression of myopia, potential mechanisms and pertinent clinical recommendations. The impact of electronic device use is considered. PubMed and Medline were used to find peer-reviewed cross-sectional and longitudinal studies related to near work and myopia from 1980 to July 2020 using the PRISMA checklist. Studies were chosen using the Joanna Briggs Institute checklist, with a focus on studies with a sample size greater than 50. Studies were independently evaluated; conclusions were drawn per these evaluations. Numerous cross-sectional studies found increased odds ratio of myopia with increased near work. While early longitudinal studies failed to find this relationship, more recent longitudinal studies have found a relationship between myopia and near work. Rather than daily duration of near work, interest has increased regarding absolute working distance and duration of continuous near viewing. Several reports have found that shorter working distances (30 cm) and continuous near-work activity (30 min) are risk factors for myopia onset and progression. Novel objective continuously measuring rangefinding devices have been developed to better address these questions. The literature is conflicting, likely due to the subjective and variable nature in which near work has been quantified and a paucity of longitudinal studies. We conclude that more precise objective measures of near viewing behaviour are necessary to make definitive conclusions regarding the relationship between myopia and near work. Focus should shift to utilizing objective and continuously measuring instruments to quantify near-work behaviours in children, followed longitudinally, to understand the complex factors related to near work. A better understanding of the roles of absolute working distance, temporal properties, viewing breaks and electronic device use on myopia development and progression will aid in the development of evidence-based clinical recommendations for behavioural modifications to prevent and slow myopia.

PURPOSE To compare myopia progression estimated by the Brien Holden Vision Institute (BHVI) Myopia Calculator with cycloplegic measures in Hong Kong children wearing single-vision distance spectacles over a 1- and 2-year period. METHODS Baseline age, spherical equivalent refraction (SER) and ethnicity of control participants from previous longitudinal myopia studies were input into the BHVI Myopia Calculator to generate an estimate of the SER at 1 and 2 years. Differences between the measured and estimated SER (116 and 100 participants with 1- and 2-year subjective refraction data, respectively, and 111 and 95 participants with 1- and 2-year objective refraction, respectively) were analysed, and the measured SER compared with the 95% confidence interval (CI) of the estimated SER. RESULTS In children aged 7-13 years, 36% progressed within the 95% CI of the Myopia Calculator's estimate, whereas 33% became less myopic than predicted (range 0.31 to 1.92 D less at 2 years) and 31% became more myopic than predicted (range 0.25 to 2.33 D more myopic at 2 years). The average difference between the estimated and measured subjective or objective SER at 1 and 2 years of follow-up was not clinically significant (

Aim To systematically review and comprehensively analyse findings of studies reporting oncology nurses' compassion satisfaction (CS), burnout (BO) and secondary traumatic stress (STS), measured by the professional quality of life (ProQOL) scale, and explore CS and CF related factors. Design Systematic review and meta‐analysis of cross‐sectional and interventional studies. Method Electronic databases were searched using keywords, and the review followed PRISMA guidelines. The prevalence of CS, BO and STS and their instrumental ratings were pooled using random effects meta‐analyses. Meta‐regression studies explored the effects of variables. Results Fifteen studies (sample size 2,509) were reviewed, and nine were in the meta‐analysis. The prevalence of CS, BO, and STS were 22.89%, 62.79% and 66.84%, respectively. No substantial correlation was found for independent variables, possibly due to sample size. There was a weak negative correlation between CS and BO [−0.06(0.90)] and a weak positive correlation between CS and STS [0.20(0.70)].

In many orphan diseases and pediatric indications, the randomized controlled trials may be infeasible because of their size, duration, and cost. Leveraging information on the control through a prior can potentially reduce sample size. However, unless an objective prior is used to impose complete ignorance for the parameter being estimated, it results in biased estimates and inflated type-I error. Hence, it is essential to assess both the confirmatory and supplementary knowledge available during the construction of the prior to avoid "cherry-picking" advantageous information. For this purpose, propensity score methods are employed to minimize selection bias by weighting supplemental control subjects according to their similarity in terms of pretreatment characteristics to the subjects in the current trial. The latter can be operationalized through a proposed measure of overlap in propensity-score distributions. In this paper, we consider single experimental arm in the current trial and the control arm is completely borrowed from the supplemental data. The simulation experiments show that the proposed method reduces prior and data conflict and improves the precision of the of the average treatment effect.

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.

Restricted mean survival time (RMST) is one measure now used to summarize time-to-event type data, but it has been pointed out that the distribution of differences in RMST deviates markedly from a normal distribution for controlled clinical trials with small sample sizes. Therefore, we conducted a numerical simulation of the RMST in which the one-sample survival time follows a Weibull distribution, comparing eight different confidence intervals combining two types of variance with four types of variable transformations, including no transformation. The evaluation items were the coverage probability and the above and below error probabilities for the true value. The variance types were based on Greenwood's formula and its Kaplan-Meier correction. The arcsine square root transformation, logit transformation, and complementary log-log transformation were used as the variable transformations. When the sample size was small and the event rate was low, the confidence interval of the untransformed RMST tended to have a small coverage probability and to be overestimated. Variance by Kaplan-Meier correction improved the coverage. The problems of coverage and overestimation were also improved by variable transformations, and in particular, applying the logit transformation and the complementary log-log transformation both resulted in substantial improvements. Our study suggested that it is preferable to construct the confidence intervals of RMST using the logit transformation for variances based on Greenwood's formula in small sample size trials. The SAS code to replicate the analyses is available at https://github.com/HiroyaHashimoto/SAS-Programs.

Introduction Meta-analytic reviews suggest similar outcomes across trauma-focused psychotherapies for adults with PTSD. However, this conclusion may be premature due to suboptimal statistical-review methodologies. Network meta-analysis allows a detailed rank-ordering of the efficacy of established psychotherapy interventions derived from indirect evidence as well as results from direct head-to-head comparisons. Objective We sought to determine the efficacy and attrition rates of psychotherapy interventions for PTSD by applying network meta-analysis. Methods We searched EMBASE, PsychINFO, PTSDPubs and PubMed for randomised controlled trials that compared psychotherapies either head-to-head or against controls for adults with PTSD. A frequentist network meta-analysis was used to compare direct and indirect effects to determine the efficacy and attrition rates of psychotherapy interventions. Results Of the 5,649 papers identified, 82 trials comprising of 5,838 patients, were included. The network comprised 17 psychotherapies and four control conditions. Network estimates indicated superior efficacy of Meta-Cognitive Therapy and Cognitive Processing Therapy over other psychotherapies (ESs between = 0.21 and 2.32). Written Exposure Therapy and Narrative Exposure Therapy were associated with lower risk of drop out when considered alongside other psychotherapies. Confidence in the network meta-analytic estimates was considered moderate for both outcomes. Conclusions In broad terms, therapeutic commensurability was evident. Nevertheless, with additional studies and larger sample sizes, meta-cognitive and written exposure therapies could indeed differentiate themselves from other approaches as having favourable efficacy and acceptability respectively. These findings may inform clinical decision-making, as well as guide future research for PTSD.

Null hypothesis significance testing is the major statistical procedure in fMRI, but provides only a rather limited picture of the effects in a data set. When sample size and power is low relying only on strict significance testing may lead to a host of false negative findings. In contrast, with very large data sets virtually every voxel might become significant. It is thus desirable to complement significance testing with procedures like inferiority and equivalence tests that allow to formally compare effect sizes within and between data sets and offer novel approaches to obtain insight into fMRI data. The major component of these tests are estimates of standardized effect sizes and their confidence intervals. Here, we show how Hedges' g, the bias corrected version of Cohen's d, and its confidence interval can be obtained from SPM t maps. We then demonstrate how these values can be used to evaluate whether nonsignificant effects are really statistically smaller than significant effects to obtain “regions of undecidability” within a data set, and to test for the replicability and lateralization of effects. This method allows the analysis of fMRI data beyond point estimates enabling researchers to take measurement uncertainty into account when interpreting their findings., Inferiority and equivalence tests allow to compare effect sizes within and between data sets and are based on standardized effect sizes and their confidence intervals. We show how Hedges'g and its confidence interval can be obtained from SPM t maps and demonstrate how these can be used to obtain “regions of undecidability” within a data set, to test for the replicability, and for the lateralization of effects.

In RCTs with an interest in a long-term efficacy endpoint, the follow-up time necessary to observe the endpoint may be substantial. In order to reduce the expected duration of such trials, early-outcome data may be collected to enrich an interim analysis aimed at stopping the trial early for efficacy. We propose to extend such a design with an additional interim analysis using solely early-outcome data in order to expedite the evaluation of treatment's efficacy. We evaluate the potential gain in operating characteristics (power, expected trial duration, and expected sample size) when introducing such an early interim analysis, in function of the properties of the early outcome as a surrogate for the long-term endpoint. In the context of a longitudinal age-related macular degeneration (ARMD) ophthalmology trial, results show potentially substantial gains in both the expected trial duration and the expected sample size. A prerequisite, though, is that the treatment effect on the early outcome has to be strongly correlated with the treatment effect on the long-term endpoint, that is, that the early outcome is a validated surrogate for the long-term endpoint.

Introduction Evidence indicates that assistance dogs placed in the home are effective in supporting individuals with Autism Spectrum Disorder (ASD) by increasing social and community participation and promoting quality of life. This study aimed to examine the outcomes of assistance dog placement on quality of life, independence, and participation of families including individuals with ASD placed with an assistance dog compared to families on the waiting list for an autism assistance dog and to evaluate the feasibility of the design for future studies. Methods A cross-sectional comparative study was conducted. The Adaptive Behaviour Analysis System, Social Responsiveness Scale, Autism Treatment Evaluation Checklist, Canadian Occupational Performance Measure, and Autism Family Experience Questionnaire were used to evaluate adaptive skills, behaviour, social difficulties, daily functioning, and family quality of life. Results Six families who had an autism assistance dog placed with them, and 12 families who were on the waiting list were recruited using purposeful sampling. The pilot data found no significant differences between the two groups. However, trends were observed which suggested that assistance dogs can increase desired social behaviours, decrease ASD severity, and improve family wellbeing. For families with an assistance dog, more positive outcomes were observed for families who were partnered with an assistance dog for longer. Parents (and sometimes individuals with ASD) were able to complete and return the outcome measures via mail to collect the outcome data for the study. Conclusion These study findings add to the developing evidence about the use of assistance dogs with this population. A larger sample size may have allowed for significant associations to be detected. The methods used were feasible to be applied in a larger study. These results may assist health professionals advocate for funding for assistance dog placement to individuals with ASD and their families.

As most ecosystems are being challenged by multiple, co-occurring stressors, an important challenge is to understand and predict how stressors interact to affect biological responses. A popular approach is to design factorial experiments that measure biological responses to pairs of stressors and compare the observed response to a null model expectation. Unfortunately, we believe experiment sample sizes are inadequate to detect most non-null stressor interaction responses, greatly hindering progress. Determination of adequate sample size requires (i) knowledge of the detection ability of the inference method being used, and (ii) a consideration of the smallest biologically meaningful deviation from the null expectation. However, (i) has not been investigated and (ii) is yet to be discussed. Using both real and simulated data we show sample sizes typical of many experiments (

AIM To compare the effects of physical therapy with hippotherapy versus physical therapy alone on the gross motor function of children and adolescents with cerebral palsy (CP). METHOD Electronic searches were conducted in January 2021. We included controlled trials of the gross motor function of children and adolescents with CP, aged 2 to 18 years. We extracted means, standard deviations, and changes from the baseline to the end of the intervention. We used the Cochrane Collaboration's tool modified by Effective Practice and Organization of Care (EPOC) to assess the methodological quality and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) method to verify evidence synthesis. We conducted the meta-analysis using Revman 5.3. RESULTS A total of 315 individuals from six studies were included. Both groups received physical therapy including strength, aerobic, stretch, and mobility exercises, and neurodevelopmental treatment. Studies presented high risk of bias. Both therapies presented similar effects for Gross Motor Function Measure scores, cadence, stride length, and speed during gait. The level of evidence was very low. The change was greater for the physical therapy with hippotherapy group, but inferior to the smallest real difference or the minimal detectable change. INTERPRETATION Physical therapy with hippotherapy presented similar effects to physical therapy alone on the gross motor function of children and adolescents with CP. Future studies should include larger sample sizes and studies with low risk of bias.

The advantages of urgent-start peritoneal dialysis (PD) vis-a-vis urgent-start hemodialysis (HD) are not clear. We performed a systematic review and meta-analysis of studies comparing the two modalities. Databases of PubMed, Embase, Ovoid, and Google Scholar were searched up to November 1, 2020. The primary outcome was mortality, and secondary outcomes were dialysis-related infectious complications and mechanical complications. Risk ratios (RRs) were calculated for all outcomes. Seven studies were included. The pooled analysis revealed a statistically significant reduced risk of all-cause mortality in patients undergoing urgent-start PD as compared to urgent-start HD (RR: 0.61, 95% confidence interval [CI] [0.40, 0.94], I2 = 56.34%). A meta-analysis of dialysis-related infectious complications indicated no statistically significant difference between the two modalities (RR: 0.66, 95% CI [0.29, 1.50], I2 = 69.62%). Our analysis revealed a statistically significant reduced risk of mechanical complications in patients undergoing urgent-start PD (RR: 0.54, 95% CI [0.40, 0.73], I2 = 0%). To conclude, unadjusted data from observational studies are indicative of lower mortality and lower risk of mechanical complications with urgent-start PD versus urgent-start HD. The risk of infectious complications was not different between the two groups. Further studies with a larger sample size using propensity-matched cohorts are needed to strengthen current evidence.

Aims/Introduction The purpose of the present study was to quantify errors in the diagnosis of diabetes for use in the national database, using a sufficient population size. Materials and methods A claims database constructed by the JMDC (Tokyo, Japan), using standardized disease classifications and anonymous record linkage, was used in this validation study. We included patients with health insurance claims data from April 2005 to March 2019 in the JMDC claims database. We excluded patients without a record of specific health checkups in Japan. Sample size calculation was based on a 5% prevalence of diabetes and 0.4% absolute accuracy (i.e., 1,250,000 individuals), to calculate the sensitivity, specificity, positive predictive value and negative predictive value. Results In total, 2,999,152 patients were included in this study, of which 165,515 were classified as having diabetes based on specific health checkups (validation cohort prevalence of 5.5%). The newly devised algorithm had three elements – the diagnosis‐related codes for diabetes without suspected flag, the medication codes for diabetes and then these two codes on the same record – and yielded a sensitivity of 74.6%, positive predictive value of 88.4% and Kappa Index of 0.80 (the highest values). Conclusions In future claims database studies, our validated algorithms will be useful as diagnostic criteria for diabetes.

Epidemiologic studies conducted to quantify the impact of radiation dose d on an outcome typically model the hazard ratio (HR) for association using a linear term, HR ( d ) = 1 + β d , via a linear excess relative risk (ERR) model, based on biological considerations. To study associations of risk of a second cancer with radiation treatment for a first cancer, several nested case-control designs to estimate β have been proposed that use refined doses received by different locations in the organ of interest. Here we first evaluated the small sample bias in maximum likelihood estimates of β for the linear ERR model using location-specific radiation doses in simulations. As we found substantial upward bias for studies of realistic sample sizes (more than 50% relative bias for studies with 75 cases), we also proposed and investigated several approaches to correct this bias. We studied first and second order jackknife bias corrections and we derived a modified set of score functions under retrospective case-control sampling, from which we directly obtained bias-corrected estimates. In simulations based on doses from a study of stomach cancer among testicular cancer survivors and synthetically generated data, neither the first nor second order jackknife bias correction performed well. Estimates based on the modified score equations corrected the bias much better, albeit not completely, and were numerically much more stable.

Burrowing seabirds are important in ecological and conservation terms. Many populations are in flux due to both negative and positive anthropogenic impacts, but their ecology makes measuring changes difficult. Reliably recording key metrics, the proportion of burrows with breeding pairs and the success of breeding attempts requires burrow-level information on occupancy. We investigated the use of camera traps positioned at burrow entrances for determining the number of breeding pairs in a sample to inform population estimates, and for recording breeding success. The performance of two cameras makes we tested differed markedly, with Spypoint Force 10 trail cameras prone to malfunction while Reconyx HC600 Hyperfire cameras performed well. Nevertheless, both makes yielded season-long activity patterns for individual burrows, eliminating uncertainty around successful fledging attempts. Dimensionality reduction of activity metrics derived from camera time series suggests breeding and non-breeding burrows may be identifiable using linear discriminant analyses but sample sizes from our trial were low and group means were only significantly different during certain breeding stages (permutational multivariate analysis of variance: early chick-rearing f = 3.64, P = 0.06; late chick-rearing f = 8.28, P = 0.009). Compared with traditional techniques for determining burrow occupancy (e.g. manual burrow inspection and playback of conspecific calls at burrow entrances), camera traps can reduce uncertainty in estimated breeding success and potentially breeding status of burrows. Significant up-front investment is required in terms of equipment and human resources but for long-term studies, camera traps may deliver advantages, particularly when unanticipated novel observations and the potential for calibrating traditional methods with cameras are factored in.

OBJECTIVE The objective of this review was to elucidate the evidence related to utilizing e-Health as a tool in improving the quality of life of informal caregivers of dependent patients due to cerebrovascular accident (CVA). METHODS This systematic review with meta-analysis includes 13 studies. For these studies, seven databases were searched between 2009 and 2019. A random-effects model was adopted for overall estimation and to explain the heterogeneity. RESULTS A random-effects model was adopted for overall estimation and to explain heterogeneity. The results did not demonstrate statistical significance (p

The standard multiple imputation technique focuses on parameter estimation and assumes that the parameter estimator is asymptotically normally distributed with a Wald-type confidence interval for the parameter of interest. On the other hand, the Miettinen-Nurminen (MN) method for difference in proportions (Miettinen O, Nurminen M. Stat Med. 1985;4:213-226) constructs the confidence interval using an asymptotic score method and hence is not directly amenable to the standard multiple imputation technique. We propose a multiple imputation analysis that is applicable to the MN method for difference in proportions. We use simulation studies to compare the proposed method with that of Li, Mehrotra and Barnard (LMB), which is based on effective sample sizes (Li X, Mehrotra DV, Barnard J. Stat Med. 2006;25:2107-2124). We show that both methods produce confidence intervals with adequate coverage, while the proposed method produces slightly shorter confidence intervals than the LMB method. In addition, the proposed method is evaluable for all datasets, while the LMB method cannot be used when the imputed cell counts are zero across imputations for a treatment group.

Background To demonstrate feasibility and utility of the iPad version of the NIH Toolbox Cognition Battery (NIHTB-CB) in a clinical trial of older adults. Methods Fifty-one adults, aged 55 and older without dementia were tested twice on NIHTB-CB and more traditional paper-and-pencil neuropsychological measures after meal ingestion, with approximately a 4-week interval. We also compared performances at Time 1 and Time 2 for significant change. We also extracted the response times and errors for available NIHTB-CB subtests to determine subtle changes in performance. Results Over the interval, improvement in fluid cognitive measures was noted at Time 2 (t = -3.07, p = 0.004), whereas crystallized measures were unchanged. Tests of fluid cognition negatively correlated with age, particularly for the second visit. Analysis of the average speed per item showed that, for two of the tests, speed increased at Time 2. Traditional neuropsychological tests correlated with many of the NIHTB-CB measures. Response times for all five timed tests decreased at Time 2, although only statistically significant for Picture Sequence and Picture Vocabulary. Conclusions The iPad version of the NIH Toolbox Cognition Battery appears to be an adequate measure to assess cognitive functioning in a clinical trial of older adults. Psychometric analyses suggest stability in measures of crystallized functioning, whereas measures of fluid abilities revealed improvements over the short time frame of the study. Response times and errors for individual tests revealed intriguing relationships that should be further evaluated to determine the utility in clinical sample analysis, as this could aid identification of subtle cognitive change over short periods. Additional studies with larger sample sizes will be helpful to understanding the reliability, sensitivity, and specificity of the NIHTB-CB sub-scores in older adults. In addition, further evaluations with clinical populations, including individuals with cognitive impairment, are warranted.

A common issue for classification in scientific research and industry is the existence of imbalanced classes. When sample sizes of different classes are imbalanced in training data, naively implementing a classification method often leads to unsatisfactory prediction results on test data. Multiple resampling techniques have been proposed to address the class imbalance issues. Yet, there is no general guidance on when to use each technique. In this article, we provide a paradigm-based review of the common resampling techniques for binary classification under imbalanced class sizes. The paradigms we consider include the classical paradigm that minimizes the overall classification error, the cost-sensitive learning paradigm that minimizes a cost-adjusted weighted type I and type II errors, and the Neyman-Pearson paradigm that minimizes the type II error subject to a type I error constraint. Under each paradigm, we investigate the combination of the resampling techniques and a few state-of-the-art classification methods. For each pair of resampling techniques and classification methods, we use simulation studies and a real data set on credit card fraud to study the performance under different evaluation metrics. From these extensive numerical experiments, we demonstrate under each classification paradigm, the complex dynamics among resampling techniques, base classification methods, evaluation metrics, and imbalance ratios. We also summarize a few takeaway messages regarding the choices of resampling techniques and base classification methods, which could be helpful for practitioners.

Background It has been suggested that the results from fragile trials are less likely to translate into benefit in routine clinical practice. Methods We searched the Food and Drug Administration (FDA) archives to identify drug approvals for solid organ malignancies between 2010 and 2019. We calculated the Fragility Index (FI) supporting each approval, using methods to account for time‐to‐event. We compared FI and trial and approval characteristics using Mann‐Whitney U and Kruskal‐Wallis test. Using logistic regression, we examined study characteristics associated with withdrawal of consent or lost to follow‐up (WCLFU) exceeding the calculated FI. Results The median FI among 125 included studies was 23 (range 1–322). The FI was ≤10 in 35 studies (28%), 11–20 in 21 (17%), and >20 in 69 (55%). The median FI/Nexp was 7.7% (range 0.1–51.7%). The median FI was significantly lower among approvals processed through the accelerated vs regular pathway (5.5 vs 25, p = 0.001), but there was no difference in median FI/Nexp. The WCLFU exceeded FI in 42% of studies. Overall survival endpoints were more likely to have a WCLFU exceeding FI (OR 3.16, p = 0.003). WCLFU exceeding FI was also associated with a lesser magnitude of effect (median HR 0.69 vs 0.55, p FI. Conclusion The median FI among all trials was 23, and WCLFU exceeded FI in 42%. Comparative trials in solid tumors supporting approval through the accelerated pathway are more fragile compared to trials approved through the regular pathway, an observation likely explained by a lower sample size in the experimental arm., Trials in solid tumors supporting accelerated approval are more fragile compared to regular approvals. This finding supports the need for post‐marketing trials or real‐world analyses to ensure the benefit observed in clinical trials is robust and reproducible.

Objective This systematic review updates the evidence for association between oral health characteristics and frailty status, identifying gaps in translational dental research and application of frailty assessment into clinical practice. Background Clinicians have little guidance on stage-appropriate dental treatment for medically complex older adults. Oral health characteristics have been associated with frailty status, determined through validated assessment tools representing a concise measure of health. Translation of frailty assessment into dental practice has not been the focus of previous reviews. Methods Utilising the PRISMA framework for systematic reviews, a comprehensive database search identified articles describing the association of interest. Those included were cross-sectional or longitudinal, in English, included participants aged 50 years or older, used validated frailty assessments and measured clinically relevant oral health outcomes. From 835 screened articles, 26 full-text articles were eligible for quality appraisal and synthesis. Results Frailty prevalence ranged from 8.5% to 66.0%. Most studies utilised the Fried frailty criteria. Qualitative synthesis of 17 cross-sectional and nine longitudinal studies demonstrated significant covariate-adjusted association between frailty status and number of teeth, chewing ability, prosthetic characteristics, dental caries, periodontitis, dental utilisation and oral health-related quality of life factors. Variability in findings reflected study sample diversity based on country of study origin, age at recruitment, sample size, frailty assessment type, use of clinical versus self-reported outcome measures and differences in statistical analysis. Conclusion Despite robust evidence of association with oral health characteristics, frailty assessment has yet to be sufficiently applied to translational dental research and clinical practice.