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Summary Introduction Prehabilitation is defined as preoperative conditioning of patients in order to improve post-operative outcomes. Some studies showed an increase in functional recovery following colorectal surgery, but its effect in hepato-pancreato-biliary (HPB) surgery is unclear. The aim of this study was to realize a systematic literature review and meta-analysis on the current available evidence on prehabilitation in HPB surgery. Materials and methods A systematic review and a metanalysis were carried out on prehabilitation (physical, nutritional and psychological interventions) in HPB surgery (2009-2019). Assessed outcomes were postoperative complications, length of stay (LOS), 30-day readmission, and mortality. Main results Four studies among the 191 screened were included in this systematic review (3 randomized controlled trials, 1 case-control propensity score study), involving 419 patients (prehabilitation group, n = 139; control group, n = 280). After pooling, no difference was observed on LOS ((−4.37 days [95% CI: −8.86; 0.13]) or postoperative complications (RR 0.83 [95%CI: 0.62; 1.10]), reported by all the included studies. Two trials reported on readmission rate, but given the high heterogeneity, a meta-analysis was not realized. No deaths were reported among the included studies. Conclusion No effect of prehabilitation programs in HPB surgery was observed on LOS or postoperative complications rate. Future trials with standardized outcomes of measure, and adequately powered samples calculations are thus required. PROSPERO registration CRD42020165218.

Background:Randomized controlled trials play a central role in evidence-based practice, but recruitment of participants, and retention of them once in the trial, is challenging. Moreover, there is a dearth of evidence that research teams can use to inform the development of their recruitment and retention strategies. As with other healthcare initiatives, the fairest test of the effectiveness of a recruitment strategy is a trial comparing alternatives, which for recruitment would mean embedding a recruitment trial within an ongoing host trial. Systematic reviews indicate that such studies are rare. Embedded trials are largely delivered in anad hocway, with interventions almost always developed in isolation and tested in the context of a single host trial, limiting their ability to contribute to a body of evidence with regard to a single recruitment intervention and to researchers working in different contexts. Methods/Design:The Systematic Techniques for Assisting Recruitment to Trials (START) program is funded by the United Kingdom Medical Research Council (MRC) Methodology Research Programme to support the routine adoption of embedded trials to test standardized recruitment interventions across ongoing host trials. To achieve this aim, the program involves three interrelated work packages: (1) methodology - to develop guidelines for the design, analysis and reporting of embedded recruitment studies; (2) interventions - to develop effective and useful recruitment interventions; and (3) implementation - to recruit host trials and test interventions through embedded studies. Discussion:Successful completion of the START program will provide a model for a platform for the wider trials community to use to evaluate recruitment interventions or, potentially, other types of intervention linked to trial conduct. It will also increase the evidence base for two types of recruitment intervention. Trial registration:The START protocol covers the methodology for embedded trials. Each embedded trial is registered separately or as a substudy of the host trial.

We build a double-layer, multiple temporal-resolution classification model for decoding single-trial spatiotemporal patterns of spikes. The model takes spiking activities as input signals and binary behavioral or cognitive variables as output signals and represents the input-output mapping with a double-layer ensemble classifier. In the first layer, to solve the underdetermined problem caused by the small sample size and the very high dimensionality of input signals, B-spline functional expansion and L1-regularized logistic classifiers are used to reduce dimensionality and yield sparse model estimations. A wide range of temporal resolutions of neural features is included by using a large number of classifiers with different numbers of B-spline knots. Each classifier serves as a base learner to classify spatiotemporal patterns into the probability of the output label with a single temporal resolution. A bootstrap aggregating strategy is used to reduce the estimation variances of these classifiers. In the second layer, another L1-regularized logistic classifier takes outputs of first-layer classifiers as inputs to generate the final output predictions. This classifier serves as a meta-learner that fuses multiple temporal resolutions to classify spatiotemporal patterns of spikes into binary output labels. We test this decoding model with both synthetic and experimental data recorded from rats and human subjects performing memory-dependent behavioral tasks. Results show that this method can effectively avoid overfitting and yield accurate prediction of output labels with small sample size. The double-layer, multi-resolution classifier consistently outperforms the best single-layer, single-resolution classifier by extracting and utilizing multi-resolution spatiotemporal features of spike patterns in the classification.

Of 25% of randomised controlled trials (RCTs) on interventions for inflammatory bowel disease (IBD) have no power calculation. To systematically review RCTs reporting interventions for the management of IBD and to produce data for minimum sample sizes that would achieve appropriate power using the actual clinical data. We included RCTs retrieved from Cochrane IBD specialised Trial register and CENTRAL investigating any form of therapy for either induction or maintenance of remission against control, placebo, or no intervention of IBD in patients of any age. The relevant data was extracted, and the studies were grouped according to the intervention used. We recalculated sample size and the achieved difference, as well as minimum sample sizes needed in the future. A total of 105 trials were included. There was a large discrepancy between the estimated figure for the minimal clinically important difference used for the calculations (15% group differences observed 30% used for calculation) explaining substantial actual sample size deficits. The minimum sample sizes indicated for future trials based on the 25 years of trial data were calculated and grouped by the intervention. A third of intervention studies in IBD within the last 25 years are underpowered, with large variations in the calculation of sample sizes. The authors present a sample size estimate resource constructed on the published evidence base for future researchers and key stakeholders within the IBD trial field. [Abstract copyright: ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.]

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.

Background/Aims When participants in individually randomized group treatment trials are treated by multiple clinicians or in multiple group treatment sessions throughout the trial, this induces partially nested clusters which can affect the power of a trial. We investigate this issue in the Whole Health Options and Pain Education trial, a three-arm pragmatic, individually randomized clinical trial. We evaluate whether partial clusters due to multiple visits delivered by different clinicians in the Whole Health Team arm and dynamic participant groups due to changing group leaders and/or participants across treatment sessions during treatment delivery in the Primary Care Group Education arm may impact the power of the trial. We also present a Bayesian approach to estimate the intraclass correlation coefficients. Methods We present statistical models for each treatment arm of Whole Health Options and Pain Education trial in which power is estimated under different intraclass correlation coefficients and mapping matrices between participants and clinicians or treatment sessions. Power calculations are based on pairwise comparisons. In practice, sample size calculations depend on estimates of the intraclass correlation coefficients at the treatment sessions and clinician levels. To accommodate such complexities, we present a Bayesian framework for the estimation of intraclass correlation coefficients under different participant-to-session and participant-to-clinician mapping scenarios. We simulated continuous outcome data based on various clinical scenarios in Whole Health Options and Pain Education trial using a range of intraclass correlation coefficients and mapping matrices and used Gibbs samplers with conjugate priors to obtain posteriors of the intraclass correlation coefficients under those different scenarios. Posterior means and medians and their biases are calculated for the intraclass correlation coefficients to evaluate the operating characteristics of the Bayesian intraclass correlation coefficient estimators. Results Power for Whole Health Team versus Primary Care Group Education is sensitive to the intraclass correlation coefficient in the Whole Health Team arm. In these two arms, an increased number of clinicians, more evenly distributed workload of clinicians, or more homogeneous treatment group sizes leads to increased power. Our simulation study for the intraclass correlation coefficient estimation indicates that the posterior mean intraclass correlation coefficient estimator has less bias when the true intraclass correlation coefficients are large (i.e. 0.10), but when the intraclass correlation coefficient is small (i.e. 0.01), the posterior median intraclass correlation coefficient estimator is less biased. Conclusion Knowledge of intraclass correlation coefficients and the structure of clustering are critical to the design of individually randomized group treatment trials with partially nested clusters. We demonstrate that the intraclass correlation coefficient of the Whole Health Team arm can affect power in the Whole Health Options and Pain Education trial. A Bayesian approach provides a flexible procedure for estimating the intraclass correlation coefficients under complex scenarios. More work is needed to educate the research community about the individually randomized group treatment design and encourage publication of intraclass correlation coefficients to help inform future trial designs.

Background: Fragility analysis is increasingly utilized to evaluate the robustness of results within the orthopaedic literature and has frequently revealed instability of reported outcomes. Purpose/Hypothesis: The purpose of this investigation was to utilize a fragility analysis to evaluate the stability of reported results in the patellofemoral instability (PFI) literature. We hypothesized the demonstration of significant fragility in patellofemoral research to be similar to that identified throughout other areas of the orthopaedic literature. Study Design: Systematic review; Level of evidence, 4. Methods: The PubMed database was queried from January 1, 2000, to October 10, 2020 for comparative trials in 10 prominent orthopaedic journals that reported dichotomous outcomes related to the management of PFI. The fragility index (FI) and the fragility quotient (FQ) were calculated for each individual outcome event, and the overall FI and FQ were determined for all included studies. Results: A total of 22 comparative studies comprising 11 randomized controlled trials and 11 nonrandomized trials were included for the analysis. A total of 75 outcome events underwent a fragility analysis and revealed a median FI and FQ of 3 (interquartile range [IQR], 1-5) and 0.043 (IQR, 0.018-0.081), respectively. Also 27% of included studies reported loss to follow-up greater than the overall FI, therefore suggesting the maintenance of the follow-up may have resulted in the reversal of significance. Conclusion: The result of the comprehensive fragility analysis demonstrated a lack of robustness in PFI research with the alteration of only a few outcome events required to reverse statistical significance. We therefore recommend the triple reporting of the P value, the FI, and the FQ to aid in the interpretation of the statistical integrity of future comparative trials in the PFI literature.

The correct classification of cancer subtypes is of great significance for the in-depth study of cancer pathogenesis and the realization of accurate treatment for cancer patients. In recent years, the classification of cancer subtypes using deep neural networks and gene expression data has become a hot topic. However, most classifiers may face the challenges of overfitting and low classification accuracy when dealing with small sample size and high-dimensional biological data. In this paper, the Cascade Flexible Neural Forest (CFNForest) Model was proposed to accomplish cancer subtype classification. CFNForest extended the traditional flexible neural tree structure to FNT Group Forest exploiting a bagging ensemble strategy and could automatically generate the model’s structure and parameters. In order to deepen the FNT Group Forest without introducing new hyperparameters, the multilayer cascade framework was exploited to design the FNT Group Forest model, which transformed features between levels and improved the performance of the model. The proposed CFNForest model also improved the operational efficiency and the robustness of the model by sample selection mechanism between layers and setting different weights for the output of each layer. To accomplish cancer subtype classification, FNT Group Forest with different feature sets was used to enrich the structural diversity of the model, which make it more suitable for processing small sample size datasets. The experiments on RNA-seq gene expression data showed that CFNForest effectively improves the accuracy of cancer subtype classification. The classification results have good robustness.

Purpose we attempted to summarize the clinical characteristics of recurrent intussusception through a large sample size retrospective study. Methods 5778 patients who were diagnosed with intussusception and treated in our hospital between January 2014 and December 2018 were reviewed. Results the overall recurrence rate was 20.0%, with the recurrence frequency ranged from 1 to 17 episodes and the recurrence interval ranged from 0 to 5 years. 80.0% (926/1158) patients had only 1 to 2 episodes of recurrence. Most of the patients (88.3%) relapsed within 1 year after successful reduction, among which 616 patients (53.2%) relapsed within 3 days. The recurrence rate of intussusception was the lowest when the age was less than 1 year, increased and kept high level in patients aged from 1 to 5 years old, then decreased after 5 years. The pathologic lead point (PLP) proportion of recurrence group was higher than that of non-recurrence group (2.2% VS 1.2%, P = 0.005). Conclusions the overall recurrence rate was 20.0%. Most of the patients had only 1 to 2 episodes of recurrence and relapsed within 1 year after successful reduction. Age and PLP were risk factors for intussusception recurrence.

Background Numerous statistical methods can be used to calculate the confidence interval (CI) of risk differences. There is consensus in previous literature that the Wald method should be discouraged. We compared five statistical methods for estimating the CI of risk difference in terms of CI width and study conclusion in antibiotic non-inferiority trials. Methods In a secondary analysis of a systematic review, we included non-inferiority trials that compared different antibiotic regimens, reported risk differences for the primary outcome, and described the number of successes and/or failures as well as patients in each arm. For each study, we re-calculated the risk difference CI using the Wald, Agresti-Caffo, Newcombe, Miettinen-Nurminen, and skewness-corrected asymptotic score (SCAS) methods. The CIs by different statistical methods were compared in terms of CI width and conclusion on non-inferiority. A wider CI was considered to be more conservative. Results The analysis included 224 comparisons from 213 studies. The statistical method used to calculate CI was not reported in 134 (59.8%) cases. The median (interquartile range IQR) for CI width by Wald, Agresti-Caffo, Newcombe, Miettinen-Nurminen, and SCAS methods was 13.0% (10.8%, 17.4%), 13.3% (10.9%, 18.5%), 13.6% (11.1%, 18.9%), 13.6% (11.1% and 19.0%), and 13.4% (11.1%, 18.9%), respectively. In 216 comparisons that reported a non-inferiority margin, the conclusion on non-inferiority was the same across the five statistical methods in 211 (97.7%) cases. The differences in CI width were more in trials with a sample size of 100 or less in each group and treatment success rate above 90%. Of the 18 trials in this subgroup with a specified non-inferiority margin, non-inferiority was shown in 17 (94.4%), 16 (88.9%), 14 (77.8%), 14 (77.8%), and 15 (83.3%) cases based on CI by Wald, Agresti-Caffo, Newcombe, Miettinen-Nurminen, and SCAS methods, respectively. Conclusions The statistical method used to calculate CI was not reported in the majority of antibiotic non-inferiority trials. Different statistical methods for CI resulted in different conclusions on non-inferiority in 2.3% cases. The differences in CI widths were highest in trials with a sample size of 100 or less in each group and a treatment success rate above 90%. Trial registration PROSPERO CRD42020165040. April 28, 2020.

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.

A noninferiority trial is designed to show that the new treatment is not unacceptably worse than the standard treatment by more than a predefined noninferiority margin. These trials are typically performed when standard placebo-controlled trials are considered to be unethical or impractical and the new treatment offers advantages over the existing standard treatment in terms of safety, convenience, or cost. Given that noninferiority trials are being performed with increasing frequency in cardiovascular applications, it is important to understand their complex trial design and analysis. This narrative review aims to provide readers with a detailed perspective on the goals, characteristics, design, and analysis of noninferiority trials. Trials designed to show noninferiority require an appropriate reference population, a proven standard treatment and dose, an appropriate margin of noninferiority that is statistically justifiable (based on historical placebo-controlled trials evaluating standard treatment effect) and clinically reasonable (choosing the fraction of the effect of the standard drug that should be “preserved” by the new drug), a high level of adherence to treatment, and adequate statistical power to reliably conclude that a treatment is truly noninferior and therefore effective. The merits and pitfalls of noninferiority trials, with representative contemporary cardiovascular clinical trials in interventional cardiology, cardiac surgery, and atrial fibrillation management as exemplars, are described. The key issues that challenge the design, conduct, analysis, interpretation, and implementation of these trials are discussed, and a variety of ways to identify and mitigate key errors are recommended to allow for optimal evaluation of noninferiority trials conducted in cardiovascular medicine.

Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.

Several different reporting biases cited in scientific literature have raised concerns about the overestimation of effects and the subsequent potential impact on the practice of evidence-based medicine and human health. Up to 7-8% of the population experiences neuropathic pain, and established treatment guidelines are based predominately on published, clinical trial results. Therefore, we examined published randomized controlled trials (RCTs) of first-line drugs for neuropathic pain and assessed the relative proportions with statistically significant (i.e., positive) and non-significant (i.e., negative) results and their rates of citation. We determined the relationships between reported study outcome and the frequency of their citations with journal impact factor, sample size, time to publication after study completion, and study quality metrics. We also examined the association of study outcome with maximum study drug dosage and conflict of interest. We found that of 107 published RCTs, 68.2% reported a statistically significant outcome regarding drug efficacy for chronic peripheral and central neuropathic pain. Positive studies were cited nearly twice as often as negative studies in the literature (P=0.01), despite similar study sample size, quality metrics, and publication in journals with similar impact factors. The time to publication, journal impact factor, and conflict of interest did not differ statistically between positive and negative studies. Our observations that negative and positive RCTs were published in journals with similar impact at comparable time-lags after study completion are encouraging. However, the citation bias for positive studies could affect the validity and generalization of conclusions in literature and potentially influence clinical practice.

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.

We recently developed a simple and general sequential sampling method for testing null hypotheses, the independent segments procedure (ISP; Miller & Ulrich, 2021). In this reply, we discuss the comments of Erdfelder and Schnuerch (2021) and Lakens (2021), who consider alternative methods such as the sequential probability ratio test (SPRT) and the group sequential design (GSD), respectively. We evaluate the pros and cons of these alternatives and conclude that the ISP does have several advantages over these other methods, especially for psychological research. All of these sequential methods can save research resources because smaller sample sizes are required compared to standard nonsequential methods, so it seems appropriate for researchers to choose from a variety of sequential methods based on the practical requirements of their research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Background The P value is a statistical tool used to assess the statistical significance of clinical trial outcomes in orthopedic surgery. However, the P value does not evaluate research quality or clinical significance. The Fragility Index (FI) is an alternative statistical method that can be used to assess the quality and significance of clinical research and is defined as the number of patients in a study intervention group necessary to convert an outcome from statistically significant to statistically insignificant or vice versa. The primary purpose of this study was to evaluate the statistical robustness of clinical trials regarding shoulder arthroplasty using the FI. The secondary goal was to identify trial characteristics associated greater statistical fragility. Methods A systematic review of randomized clinical trials in shoulder arthroplasty was performed. The FI was calculated for all dichotomous, categorical study outcomes discussed in the identified studies. Descriptive statistics and the Pearson correlation coefficient were used to evaluate all studies and characterize associations between study variables. Results A total of 13 randomized controlled trials were identified and evaluated; these trials had a median sample size of 47 patients (mean, 54 patients; range, 26-102 patients) and a median of 7 patients (mean, 5.8 patients; range, 0-14 patients) lost to follow-up. The median FI was 6 (mean, 5; range, 1-11), a higher FI than what has been observed in other orthopedic subspecialties. However, the majority of outcomes (74.4%) had an FI that was less than the number of patients lost to follow-up, and most outcomes (89.7%) were statistically insignificant. Conclusion Randomized controlled trials in shoulder arthroplasty have comparable statistical robustness to the literature in other orthopedic surgical subspecialties. We believe that the inclusion of the FI in future comparative studies in the shoulder arthroplasty literature will allow surgeons to better assess the statistical robustness of future research.

BackgroundNon-proportional hazards are common with time-to-event data but the majority of randomised clinical trials (RCTs) are designed and analysed using approaches which assume the treatment effect follows proportional hazards (PH). Recent advances in oncology treatments have identified two forms of non-PH of particular importance - a time lag until treatment becomes effective, and an early effect of treatment that ceases after a period of time. In sample size calculations for treatment effects on time-to-event outcomes where information is based on the number of events rather than the number of participants, there is crucial importance in correct specification of the baseline hazard rate amongst other considerations. Under PH, the shape of the baseline hazard has no effect on the resultant power and magnitude of treatment effects using standard analytical approaches. However, in a non-PH context the appropriateness of analytical approaches can depend on the shape of the underlying hazard.MethodsA simulation study was undertaken to assess the impact of clinically plausible non-constant baseline hazard rates on the power, magnitude and coverage of commonly utilized regression-based measures of treatment effect and tests of survival curve difference for these two forms of non-PH used in RCTs with time-to-event outcomes.ResultsIn the presence of even mild departures from PH, the power, average treatment effect size and coverage were adversely affected. Depending on the nature of the non-proportionality, non-constant event rates could further exacerbate or somewhat ameliorate the losses in power, treatment effect magnitude and coverage observed. No single summary measure of treatment effect was able to adequately describe the full extent of a potentially time-limited treatment benefit whilst maintaining power at nominal levels.ConclusionsOur results show the increased importance of considering plausible potentially non-constant event rates when non-proportionality of treatment effects could be anticipated. In planning clinical trials with the potential for non-PH, even modest departures from an assumed constant baseline hazard could appreciably impact the power to detect treatment effects depending on the nature of the non-PH. Comprehensive analysis plans may be required to accommodate the description of time-dependent treatment effects.

We investigated 2019 and 2020 U.S. NewsWorld Report methodologies of assessing pediatric urology surgical revision rates for distal hypospadias, pyeloplasty, and ureteral reimplantation to evaluate statistical power and misclassification risks.Median annual volumes of distal hypospadias, pyeloplasty, and ureteral reimplantation procedures by hospital from 2016 to 2018 were calculated using the Pediatric Health Information System® database. U.S. NewsWorld Report 2019 and 2020 methodologies were assessed to calculate power required to detect differences between hospitals and risk of hospital misclassifications.Median (IQR) annual hospital procedure volume was 72 (43-97) for distal hypospadias procedures, 19 (9-34) for pyeloplasties, and 35 (19-50) for ureteral reimplantations. Based on 2019 methodology, in order to achieve 80% power 764 cases/hospital are required to distinguish between a 1% vs 3% surgical revision rate, 1,500 cases/hospital are required to distinguish between a 3% vs 5% revision rate, and 282 cases/hospital are required to distinguish between a 1% vs 5% revision rate. Based on 2020 methodology, 98.0% of hospitals do not have adequate ureteral reimplantation volume to achieve full points even when reporting no revisions; similarly, 66.0% do not have adequate pyeloplasty volume, and 10.9% do not have adequate distal hypospadias volume. Risks of misclassification exceed 50% in several instances among hospitals reporting distal hypospadias and pyeloplasty revisions using both 2019 and 2020 methodology.Based on median-volume hospitals, current U.S. NewsWorld Report methods for classifying revision rates for distal hypospadias, pyeloplasty, and ureteral reimplantation have insufficient power and are at high risk for misclassification.

Purpose of review Randomized clinical trials (RCTs) have come to be accepted as the gold standard for assessing the efficacy and effectiveness of therapeutics and interventions in medicine. In this paper, we aim to describe some evolving concepts associated with the design and conduct of RCTs and outline new approaches aiming to increase efficiency and reduce costs. Recent findings A well-powered and performed RCT is usually a study involving several different centers from different geographical areas that enrolls a large number of patients in diverse clinical settings. Altogether, these features increase the generalizability of the study and make the rapid implementation of the findings more likely. However, this does not come without cost. Among several possible alternatives to conventional RCTs, the most important ones are related to the unit of randomization (individual vs. cluster), study design (conventional vs. adaptive), randomization scheme (fixed vs. response-adaptive), data collection (conventional case report forms vs. registry-embedded) and statistical approach (frequentist vs. Bayesian). Summary While conventional RCTs remain the gold standard for generating evidence, new trial designs may be considered to reduce sample size and costs while improving trial efficiency and power. However, they raise new challenges for testing feasibility, conduct, ethical oversight and statistical analysis.

Existing methods concerning the assessment of long-term survival outcomes in one-armed trials are commonly restricted to one primary endpoint. Corresponding adaptive designs suffer from limitations regarding the use of information from other endpoints in interim design changes. Here we provide adaptive group sequential one-sample tests for testing hypotheses on the multivariate survival distribution derived from multi-state models, while making provision for data-dependent design modifications based on all involved time-to-event endpoints. We explicitly elaborate application of the methodology to one-sample tests for the joint distribution of (i) progression-free survival (PFS) and overall survival (OS) in the context of an illness-death model, and (ii) time to toxicity and time to progression while accounting for death as a competing event. Large sample distributions are derived using a counting process approach. Small sample properties are studied by simulation. An already established multi-state model for non-small cell lung cancer is used to illustrate the adaptive procedure.

The assurance of a future clinical trial is a key quantitative tool for decision-making in drug development. It is derived from prior knowledge (Bayesian approach) about the clinical endpoint of interest, typically from previous clinical trials. In this paper, we examine assurance in the specific context of vaccine development, where early development (Phase 2) is often based on immunological endpoints (e.g., antibody levels), while the confirmatory trial (Phase 3) is based on the clinical endpoint (very large sample sizes and long follow-up). Our proposal is to use the Phase 2 vaccine efficacy predicted by the immunological endpoint (using a model estimated from epidemiological studies) as prior information for the calculation of the assurance.

Background: Cluster-randomized trials allow for the evaluation of a community-level or group-/cluster-level intervention. For studies that require a cluster-randomized trial design to evaluate cluster-level interventions aimed at controlling vector-borne diseases, it may be difficult to assess a large number of clusters while performing the additional work needed to monitor participants, vectors, and environmental factors associated with the disease. One such example of a cluster-randomized trial with few clusters was the “efficacy and risk of harms of repeated ivermectin mass drug administrations for control of malaria” trial. Although previous work has provided recommendations for analyzing trials like repeated ivermectin mass drug administrations for control of malaria, additional evaluation of the multiple approaches for analysis is needed for study designs with count outcomes. Methods: Using a simulation study, we applied three analysis frameworks to three cluster-randomized trial designs (single-year, 2-year parallel, and 2-year crossover) in the context of a 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria. Mixed-effects models, generalized estimating equations, and cluster-level analyses were evaluated. Additional 2-year parallel designs with different numbers of clusters and different cluster correlations were also explored. Results: Mixed-effects models with a small sample correction and unweighted cluster-level summaries yielded both high power and control of the Type I error rate. Generalized estimating equation approaches that utilized small sample corrections controlled the Type I error rate but did not confer greater power when compared to a mixed model approach with small sample correction. The crossover design generally yielded higher power relative to the parallel equivalent. Differences in power between analysis methods became less pronounced as the number of clusters increased. The strength of within-cluster correlation impacted the relative differences in power. Conclusion: Regardless of study design, cluster-level analyses as well as individual-level analyses like mixed-effects models or generalized estimating equations with small sample size corrections can both provide reliable results in small cluster settings. For 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria, we recommend a mixed-effects model with a pseudo-likelihood approximation method and Kenward–Roger correction. Similarly designed studies with small sample sizes and count outcomes should consider adjustments for small sample sizes when using a mixed-effects model or generalized estimating equation for analysis. Although the 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria is already underway as a parallel trial, applying the simulation parameters to a crossover design yielded improved power, suggesting that crossover designs may be valuable in settings where the number of available clusters is limited. Finally, the sensitivity of the analysis approach to the strength of within-cluster correlation should be carefully considered when selecting the primary analysis for a cluster-randomized trial.

Background This study was performed to investigate clinical features of patients with severe SARS-CoV-2 pneumonia and identify risk factors for converting to severe cases in those who had mild to moderate diseases at the start of the pandemic in China. Methods In this retrospective, multicenter cohort study, patients with mild to moderate SARS-CoV-2 pneumonia were included. Demographic data, symptoms, laboratory values, and clinical outcomes were collected. Data were compared between non-severe and severe patients. Results 58 patients were included in the final analysis. Compared with non-severe cases, severe patients with SARS-CoV-2 pneumonia had a longer: time to clinical recovery (12·9 ± 4·4 vs 8·3 ± 4·7; P = 0·0011), duration of viral shedding (15·7 ± 6·7 vs 11·8 ± 5·0; P = 0·0183), and hospital stay (20·7 ± 1·2 vs 14·4 ± 4·3; P = 0·0211). Multivariate logistic regression indicated that lymphocyte count was significantly associated with the rate of converting to severe cases (odds ratio 1·28, 95%CI 1·06–1·54, per 0·1 × 109/L reduced; P = 0·007), while using of low-to-moderate doses of systematic corticosteroids was associated with reduced likelihood of converting to a severe case (odds ratio 0·14, 95%CI 0·02–0·80; P = 0·0275). Conclusions The low peripheral blood lymphocyte count was an independent risk factor for SARS-CoV-2 pneumonia patients converting to severe cases. However, this study was carried out right after the start of the pandemic with small sample size. Further prospective studies are warranted to confirm these findings. Trial registration Chinese Clinical Trial Registry, ChiCTR2000029839. Registered 15 February 2020 - Retrospectively registered.

Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. The AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different outcomes and data foundations. Moreover, superiority of AI methods over manual human evaluation is often claimed based on retrospective data, without any mentions of potential bias. In this paper, we provide a technical view on some of the major topics that divide how current AI models are trained, evaluated and compared. We explain and discuss the most common evaluation metrics and relate them to the two separate evaluation objectives, ranking and prediction. We also discuss when and how to compare AI models across studies and explain in detail how a selection bias is inevitable when comparing AI models against current embryo selection practice in retrospective cohort studies.

As the availability of real-world data sources (eg, EHRs, claims data, registries) and historical data has rapidly surged in recent years, there is an increasing interest and need from investigators and health authorities to leverage all available information to reduce patient burden and accelerate both drug development and regulatory decision making. Bayesian meta-analytic approaches are a popular historical borrowing method that has been developed to leverage such data using robust hierarchical models. The model structure accounts for various degrees of between-trial heterogeneity, resulting in adaptively discounting the external information in the case of data conflict. In this article, we propose to integrate the propensity score method and Bayesian meta-analytic-predictive (MAP) prior to leverage external real-world and historical data. The propensity score methodology is applied to select a subset of patients from external data that are similar to those in the current study with regards to key baseline covariates and to stratify the selected patients together with those in the current study into more homogeneous strata. The MAP prior approach is used to obtain stratum-specific MAP prior and derive the overall propensity score integrated meta-analytic predictive (PS-MAP) prior. Additionally, we allow for tuning the prior effective sample size for the proposed PS-MAP prior, which quantifies the amount of information borrowed from external data. We evaluate the performance of the proposed PS-MAP prior by comparing it to the existing propensity score-integrated power prior approach in a simulation study and illustrate its implementation with an example of a single-arm phase II trial.

Objective We propose a bidirectional GPS imputation method that can recover real-world mobility trajectories even when a substantial proportion of the data are missing. The time complexity of our online method is linear in the sample size, and it provides accurate estimates on daily or hourly summary statistics such as time spent at home and distance traveled. Materials and Methods To preserve a smartphone’s battery, GPS may be sampled only for a small portion of time, frequently Results We demonstrated that (1) the imputed trajectories mimic the real-world trajectories, (2) the confidence intervals of summary statistics cover the ground truth in most cases, and (3) our algorithm is much faster than existing methods if we have more than 3 months of observations; (4) we also provide guidelines on optimal sampling strategies. Conclusions Our approach outperformed existing methods and was significantly faster. It can be used in settings in which data need to be analyzed and acted on continuously, for example, to detect behavioral anomalies that might affect treatment adherence, or to learn about colocations of individuals during an epidemic.

For the cancer clinical trials with immunotherapy and molecularly targeted therapy, time-to-event endpoint is often a desired endpoint. In this paper, we present an event-driven approach for Bayesian one-stage and two-stage single-arm phase II trial designs. Two versions of Bayesian one-stage designs were proposed with executable algorithms and meanwhile, we also develop theoretical relationships between the frequentist and Bayesian designs. These findings help investigators who want to design a trial using Bayesian approach have an explicit understanding of how the frequentist properties can be achieved. Moreover, the proposed Bayesian designs using the exact posterior distributions accommodate the single-arm phase II trials with small sample sizes. We also proposed an optimal two-stage approach, which can be regarded as an extension of Simon's two-stage design with the time-to-event endpoint. Comprehensive simulations were conducted to explore the frequentist properties of the proposed Bayesian designs and an R package BayesDesign can be assessed via R CRAN for convenient use of the proposed methods.

Contemporary statistical publications rely on simulation to evaluate performance of new methods and compare them with established methods. In the context of random-effects meta-analysis of log-odds-ratios, we investigate how choices in generating data affect such conclusions. The choices we study include the overall log-odds-ratio, the distribution of probabilities in the control arm, and the distribution of study-level sample sizes. We retain the customary normal distribution of study-level effects. To examine the impact of the components of simulations, we assess the performance of the best available inverse–variance–weighted two-stage method, a two-stage method with constant sample-size-based weights, and two generalized linear mixed models. The results show no important differences between fixed and random sample sizes. In contrast, we found differences among data-generation models in estimation of heterogeneity variance and overall log-odds-ratio. This sensitivity to design poses challenges for use of simulation in choosing methods of meta-analysis.

Objective To comprehensively describe the quality of conduct, reporting, and publication integrity characteristics for all trials included in a large Cochrane review, comparing those published by presumed predatory publishers with those published by nonpredatory publishers. Design Cross-sectional meta-epidemiological study. Study selection Two hundred seventy-nine studies (25,704 participants) eligible for the recent update of the “Exercise therapy for chronic low back pain” Cochrane review were included. Data extraction Study and manuscript characteristics, including predatory publication status and other quality and integrity characteristics were extracted along with treatment effect. Results Nine percent of trials included were in presumed predatory publications; 12% in the period since 2010. We found frequency of other concerning characteristics to range from low (eg, plagiarism, 5%) to common (eg, lack of evidence of trial registration or protocol publication [75%]; insufficient sample size [84%]) in included studies. Studies published by presumed predatory publishers consistently had inferior conduct, reporting and publication integrity characteristics. Presumed predatory publication was associated with missing conflict of interest statement (OR 7.6, 95% CI 3.0–19.1), inadequate follow-up duration (OR 11.2, 95% CI 3.7–33.7), incomplete study methods (OR 12.1, 95% CI 2.8–52.2) and baseline reporting (OR 4.3, 95% CI 1.6–11.7), and high risk of bias (OR 2.7, 95% CI 1.2–6.3). All (100%) presumed predatory publications were missing trial registrations (vs. 72%) and had inadequate sample sizes (vs. 82%). Trials published in presumed predatory journals did not appear to have inflated effect sizes. Conclusions Predatory publishers pose a distinct challenge to the consumption and synthesis of randomized controlled trials. More work is needed in other clinical areas to understand the potential impact of randomized controlled trials published in predatory publications, and as a result, the potential impact on evidence from systematic reviews that include these studies.

International audience; Objectives: To estimate the prevalence of time-to-event (TTE) outcomes in cluster randomized trials (CRTs) and to examine their statistical management. Study design and setting: We searched PubMed to identify primary reports of CRTs published in six major general medical journals (2013-2018). Nature of outcomes and, for TTE outcomes, statistical methods for sample size, analysis, and measures of intracluster correlation were extracted.Results: A TTE analysis was used in 17% of the CRTs (32/184) either as a primary or secondary outcome analysis, or in a sensitivity analysis. Among the five CRTs with a TTE primary outcome, two accounted for both intracluster correlation and the TTE nature of the outcome in sample size calculation; one reported a measure of intracluster correlation in the analysis. Among the 32 CRTs with a least one TTE analysis, 44% (14/32) accounted for clustering in all TTE analyses. We identified 12 additional CRTs in which there was at least one outcome not analyzed as TTE for which a TTE analysis might have been preferred.Conclusion: TTE outcomes are not uncommon in CRTs but appropriate statistical methods are infrequently used. Our results suggest that further methodological development and explicit recommendations for TTE outcomes in CRTs are needed.

Meta-analysis of clinical trials targeting rare events face particular challenges when the data lack adequate number of events and are susceptible to high levels of heterogeneity. The standard meta-analysis methods (DerSimonian Laird (DL) and Mantel–Haenszel (MH)) often lead to serious distortions because of such data sparsity. Applications of the methods suited to specific incidence and heterogeneity characteristics are lacking, thus we compared nine available methods in a simulation study. We generated 360 meta-analysis scenarios where each considered different incidences, sample sizes, between-study variance (heterogeneity) and treatment allocation. We include globally recommended methods such as inverse-variance fixed/random-effect (IV-FE/RE), classical-MH, MH-FE, MH-DL, Peto, Peto-DL and the two extensions for MH bootstrapped-DL (bDL) and Peto-bDL. Performance was assessed on mean bias, mean error, coverage and power. In the absence of heterogeneity, the coverage and power when combined revealed small differences in meta-analysis involving rare and very rare events. The Peto-bDL method performed best, but only in smaller sample sizes involving rare events. For medium-to-larger sample sizes, MH-bDL was preferred. For meta-analysis involving very rare events, Peto-bDL was the best performing method which was sustained across all sample sizes. However, in meta-analysis with 20% or more heterogeneity, the coverage and power were insufficient. Performance based on mean bias and mean error was almost identical across methods. To conclude, in meta-analysis of rare binary outcomes, our results suggest that Peto-bDL is better in both rare and very rare event settings in meta-analysis with limited sample sizes. However, when heterogeneity is large, the coverage and power to detect rare events are insufficient. Whilst this study shows that some of the less studied methods appear to have good properties under sparse data scenarios, further work is needed to assess them against the more complex distributional-based methods to understand their overall performances.

Objective To evaluate the power of responder analyses in a randomized controlled trial. Study design and setting Simulations were based on the Chronic Kidney Disease Antidepressant Sertraline Trial (CAST), which compared sertraline to placebo for the treatment of depression in kidney disease. Baseline disease severity, placebo response, effect size, and the proportion of responders were varied across 72 scenarios. Power was assessed using a t-test for change scores, and the chi-square test for dichotomized outcomes of the minimal important difference (MID), improvement and remission in 10,000 datasets with a fixed sample size of 193. Results The t-test had >80% power except for scenarios with the lowest sertraline effect size. The chi-square test using the MID had 80% power only at higher effect sizes and/or when the proportion of responders was highest at 0.5. The chi-square test for improvement had marginal power increases compared to the t-test (4/72 scenarios = 5.6%) and that for remission did not outperform the t-test in any scenario. Conclusions The t-test outperforms the chi-square test for dichotomized outcomes regardless of baseline disease severity, placebo response, effect size and the proportion of responders to the intervention.

The role of medical expulsive treatment (MET) is controversial. Fragility index is an additional metric to assess randomized controlled trials (RCTs) outcome validity and indicates how many patients would be required to convert a trial from being statistically significant, to not significant. The larger is the FI, the better the trial’s data. The aim of this study is to assess FI of RCTs regarding MET for ureteral stones. A systematic literature search was performed. RCTs, reporting stone expulsion as a dichotomous outcome, showing statistical significance were eligible. FI (the number of patients needed to change from a non-event to event group, to lose statistical significance) and Fragility quotient (FI divided by total sample size), were calculated while Pearson’s correlation and Mann–Whitney U test were used as appropriate. Thirty-six RCTs were eligible, with median FI = 3.5 and fragility quotient = 0.042, median sample size = 81, median journal impact factor = 1.73 and median reported p value = 0.008. In 33.3% of the studies, number of patients lost during follow-up was larger than FI, while in 13.89% of the studies, FI was 0, indicating use of inappropriate statistical method. Pearson’s correlation showed significant positive association between FI and sample size (r = 0.981), number of events (r = 0.982) and impact factor (r = 0.731), while no association was found with p value or publication year. In this analysis, a calculated FI of 3.5 indicates that findings from RCTs on MET for ureteral stones are fragile and should be interpreted in combination with clinical thinking and expertise.

Objective The purpose of this study was to utilize fragility analysis to assess the robustness of randomized controlled trials (RCTs) evaluating the management of articular cartilage defects of the knee. We hypothesize that the cartilage restorative literature will be fragile with the reversal of only a few outcome events required to change statistical significance. Design RCTs from 11 orthopedic journals indexed on PubMed from 2000 to 2020 reporting dichotomous outcome measures relating to the management of articular cartilage defects of the knee were included. The Fragility Index (FI) for each outcome was calculated through the iterative reversal of a single outcome event until significance was reversed. The Fragility Quotient (FQ) was calculated by dividing each FI by study sample size. Additional statistical analysis was performed to provide median FI and FQ across subgroups. Results Nineteen RCTs containing 60 dichotomous outcomes were included for analysis. The FI and FQ of all outcomes was 4 (IQR 2-7) and 0.067 (IQR 0.034-0.096), respectively. The average number of patients lost to follow-up (LTF) was 3.9 patients with 15.8% of the included studies reporting LTF greater than or equal to 4, the FI of all included outcomes. Conclusions The orthopedic literature evaluating articular cartilage defects of the knee is fragile as the reversal of relatively few outcome events may alter the significance of statistical findings. We therefore recommend comprehensive fragility analysis and triple reporting of the P value, FI, and FQ to aid in the interpretation and contextualization of clinical findings reported in the cartilage restoration literature.

Effect size, α level, power, and sample size are misunderstood concepts that play a major role in the design and interpretation of studies. Effect size represents the magnitude of a change in an outcome or the strength of a relationship. Often, the effect size may be more important than just relying on the α level when interpreting results from a study because it informs a researcher of the actual magnitude of the difference or relationship. Confidence intervals can also assist in making this assessment. Power represents the probability of rejecting the null hypothesis when it is false. Effect size, power, and α level are all important in the calculation of sample size needed to conduct a study. Calculating the sample size a priori and including the required number of participants is essential. Studies with small sample sizes, relative to the needed number provided from a power analysis, may lead to false negative results. Studies with grossly large sample sizes may yield statistically significant findings with small effect sizes that may not be clinically relevant. It is beneficial when all four components are clearly presented in analytic studies. Failure to include these elements may limit the ability of other researchers to replicate the study's findings and lead to difficulty when interpreting the study's results.

The WiSE system is a novel, leadless endocardial system that can provide cardiac resynchronization therapy in patients who cannot be treated with a conventional epicardial left ventricular lead. Safety and efficacy were being evaluated in the pivotal, randomized, double-blind SOLVE-CRT Trial (Stimulation of the Left Ventricular Endocardium for Cardiac Resynchronization Therapy.) The trial was initiated in 2018; however, patient enrollment was significantly impacted by the COVID-19 pandemic necessitating a change in design. This article describes the revised trial and the scientific rationale for the specific changes in the protocol.

Summary Background Saliva iodine concentration (SIC) has been found to be a good indicator of iodine nutritional status. However, limited information is available regarding saliva iodine characteristics. Aims The study aimed to evaluate intra-day, intra-individual, and population SIC variation in order to provide information on optimal sample size and sampling time for assessing iodine nutritional status. Methods Twenty-nine healthy school-aged students were recruited. Iodine intake from diet and water, and iodine excretion through urine and feces were assessed over a three-day period. Saliva samples were collected six times a day. Results Diurnal variations were observed in SIC corresponding to iodine intake. The mean CV in SIC was lower than that for 24-hour urinary iodine concentration (24-h UIC) and 24-hour urinary iodine excretion (24-h UIE) not only at the individual level (42.73% vs. 47.71% and 49.69%) but also at the population level (71.29% vs. 100.43% and 72.49%). The number of saliva samples needed to estimate the iodine level with 95% CI within precision ranges of ±10%, ±20% was 55, 14 in an individual, and 180, 45 in a population. There was a good correlation between post-lunch SIC and total daily iodine intake. Conclusions Saliva iodine has utility for evaluating the recent iodine nutrition of individuals and populations. The variation in SIC was lower than that for 24-h UIC and 24-h UIE. Saliva may be preferred over urine because of its ease of collection. Fourteen samples are needed to assess individual iodine status and forty-five saliva samples for assessment of population iodine status with reasonable precision. We recommend that saliva samples be collected after 14:00 in a day.

Determination of a minimum sample size required for a study is a major consideration which all researchers are confronted with at the early stage of developing a research protocol. This is because the researcher will need to have a sound prerequisite knowledge of inferential statistics in order to enable him/her to acquire a thorough understanding of the overall concept of a minimum sample size requirement and its estimation. Besides type I error and power of the study, some estimates for effect sizes will also need to be determined in the process to calculate or estimate the sample size. The appropriateness in calculating or estimating the sample size will enable the researchers to better plan their study especially pertaining to recruitment of subjects. To facilitate a researcher in estimating the appropriate sample size for their study, this article provides some recommendations for researchers on how to determine the appropriate sample size for their studies. In addition, several issues related to sample size determination were also discussed.

PURPOSE Transfer learning is commonly used in deep learning for medical imaging to alleviate the problem of limited available data. In this work, we studied the risk of feature leakage and its dependence on sample size when using pretrained deep convolutional neural network (DCNN) as feature extractor for classification breast masses in mammography. METHODS Feature leakage occurs when the training set is used for feature selection and classifier modeling while the cost function is guided by the validation performance or informed by the test performance. The high-dimensional feature space extracted from pretrained DCNN suffers from the curse of dimensionality; feature subsets that can provide excessively optimistic performance can be found for the validation set or test set if the latter is allowed for unlimited reuse during algorithm development. We designed a simulation study to examine feature leakage when using DCNN as feature extractor for mass classification in mammography. Four thousand five hundred and seventy-seven unique mass lesions were partitioned by patient into three sets: 3222 for training, 508 for validation, and 847 for independent testing. Three pretrained DCNNs, AlexNet, GoogLeNet, and VGG16, were first compared using a training set in fourfold cross validation and one was selected as the feature extractor. To assess generalization errors, the independent test set was sequestered as truly unseen cases. A training set of a range of sizes from 10% to 75% was simulated by random drawing from the available training set in addition to 100% of the training set. Three commonly used feature classifiers, the linear discriminant, the support vector machine, and the random forest were evaluated. A sequential feature selection method was used to find feature subsets that could achieve high classification performance in terms of the area under the receiver operating characteristic curve (AUC) in the validation set. The extent of feature leakage and the impact of training set size were analyzed by comparison to the performance in the unseen test set. RESULTS All three classifiers showed large generalization error between the validation set and the independent sequestered test set at all sample sizes. The generalization error decreased as the sample size increased. At 100% of the sample size, one classifier achieved an AUC as high as 0.91 on the validation set while the corresponding performance on the unseen test set only reached an AUC of 0.72. CONCLUSIONS Our results demonstrate that large generalization errors can occur in AI tools due to feature leakage. Without evaluation on unseen test cases, optimistically biased performance may be reported inadvertently, and can lead to unrealistic expectations and reduce confidence for clinical implementation.

Background In acute myocardial infarction complicated by cardiogenic shock the use of mechanical circulatory support devices remains controversial and data from randomized clinical trials are very limited. Extracorporeal life support (ECLS) - venoarterial extracorporeal membrane oxygenation - provides the strongest hemodynamic support in addition to oxygenation. However, despite increasing use it has not yet been properly investigated in randomized trials. Therefore, a prospective randomized adequately powered clinical trial is warranted. Study Design The ECLS-SHOCK trial is a 420-patient controlled, international, multicenter, randomized, open-label trial. It is designed to compare whether treatment with ECLS in addition to early revascularization with percutaneous coronary intervention or alternatively coronary artery bypass grafting and optimal medical treatment is beneficial in comparison to no-ECLS in patients with severe infarct-related cardiogenic shock. Patients will be randomized in a 1:1 fashion to one of the two treatment arms. The primary efficacy endpoint of ECLS-SHOCK is 30-day mortality. Secondary outcome measures such as hemodynamic, laboratory, and clinical parameters will serve as surrogate endpoints for prognosis. Furthermore, a longer follow-up at 6 and 12 months will be performed including quality of life assessment. Safety endpoints include peripheral ischemic vascular complications, bleeding and stroke. Conclusions The ECLS-SHOCK trial will address essential questions of efficacy and safety of ECLS in addition to early revascularization in acute myocardial infarction complicated by cardiogenic shock.

Background Survival analyses methods (SAMs) are central to analysing time-to-event outcomes. Appropriate application and reporting of such methods are important to ensure correct interpretation of the data. In this study, we systematically review the application and reporting of SAMs in studies of tuberculosis (TB) patients in Africa. It is the first review to assess the application and reporting of SAMs in this context. Methods Systematic review of studies involving TB patients from Africa published between January 2010 and April 2020 in English language. Studies were eligible if they reported use of SAMs. Application and reporting of SAMs were evaluated based on seven author-defined criteria. Results Seventy-six studies were included with patient numbers ranging from 56 to 182,890. Forty-three (57%) studies involved a statistician/epidemiologist. The number of published papers per year applying SAMs increased from two in 2010 to 18 in 2019 (P = 0.004). Sample size estimation was not reported by 67 (88%) studies. A total of 22 (29%) studies did not report summary follow-up time. The survival function was commonly presented using Kaplan-Meier survival curves (n = 51, (67%) studies) and group comparisons were performed using log-rank tests (n = 44, (58%) studies). Sixty seven (91%), 3 (4.1%) and 4 (5.4%) studies reported Cox proportional hazard, competing risk and parametric survival regression models, respectively. A total of 37 (49%) studies had hierarchical clustering, of which 28 (76%) did not adjust for the clustering in the analysis. Reporting was adequate among 4.0, 1.3 and 6.6% studies for sample size estimation, plotting of survival curves and test of survival regression underlying assumptions, respectively. Forty-five (59%), 52 (68%) and 73 (96%) studies adequately reported comparison of survival curves, follow-up time and measures of effect, respectively. Conclusion The quality of reporting survival analyses remains inadequate despite its increasing application. Because similar reporting deficiencies may be common in other diseases in low- and middle-income countries, reporting guidelines, additional training, and more capacity building are needed along with more vigilance by reviewers and journal editors.

Heart failure (HF) and atrial fibrillation (AF) are 2 cardiac conditions that are increasing in prevalence and incidence. The 2 conditions frequently coexist, and are associated with increased morbidity and mortality. Catheter ablation of AF has been successfully performed in patients with HF, with an improvement in HF and AF, when compared to amiodarone, but further data is required to compare this to rate control. OBJECTIVES: The primary objective is to determine whether AF treated by catheter ablation, with or without antiarrhythmic drugs reduces all-cause mortality and hospitalizations for HF as compared with rate control in patients with HF and a high burden AF. METHODS: This is a multi-center prospective randomized open blinded endpoint (PROBE) study. Patients with NYHA class II-III HF (HF with reduced ejection fraction (

Purpose To determine how changing the P value threshold of statistical significance from .05 to .005 could affect the statistical significance of findings in previously published orthopaedic sports medicine randomized controlled trials (RCTs). Methods The authors searched PubMed from January 1, 2016, to December 31, 2017, for RCTs published in the American Journal of Sports Medicine, Arthroscopy, and Knee Surgery, Sports Traumatology, Arthroscopy. Data were extracted blinded and in duplicate fashion by 2 of us. The authors then extracted P value data for primary end points, since RCTs are most often powered for these end points. Discrepancies were resolved by consensus. Google Forms were used for data extraction and STATA 15.1 for the data analysis. Results In total, 275 primary end points were identified from 132 trials. Analysis of primary end points found 45.8% (126/275) had a P value less than .05 and were classified as statistically significant under the current threshold, whereas 54.2% (149/275) had a P value greater than .05 and were not classified as suggestive. Of those end points that were previously considered statistically significant, 38.9% (49/126) were less than .005, whereas 61.1% (77/126) were between .005 and .05 and thereby would be reclassified as suggestive rather than statistically significant under the proposed threshold. Overall, when analyzing the 275 primary end points, we found only 49 (17.8%) of the end points were less than .005 and would hold statistical significance with the proposed threshold. Conclusions The results suggest that if the threshold of statistical significance were to change to .005, the significance of orthopaedic sports medicine RCTs would be heavily altered. The authors also acknowledge the many issues research faces in regard to P value reliability and therefore interpretation of study results. Because P values from RCTs can often influence the ways physicians choose interventions, it is important to implement methodology that decreases incidence of bias and misrepresentation of these results. However, the authors also understand that lowering the P value could increase the needed sample size and by consequence increase study costs as well, while not directly correlating to clinical significance. Thus, the authors recommend that this proposed threshold should be further evaluated and cautiously interpreted. Clinical Relevance If the statistical significance threshold is changed, clinical practice guideline recommendations also may be affected.

A major emphasis in precision medicine is to optimally treat subgroups of patients who may benefit from certain therapeutic agents. And as such, enormous resources and innovative clinical trials designs in oncology are devoted to identifying predictive biomarkers. Predictive biomarkers are ones that will identify patients that are more likely to respond to specific therapies and they are usually discovered through retrospective analysis from large randomized phase II or phase III trials. One important design to consider is the stratified biomarker design, where patients will have their specimens obtained at baseline and the biomarker status will be assessed prior to random assignment. Regardless of their biomarker status, patients will be randomized to either an experimental arm or the standard of care arm. The stratified biomarker design can be used to test for a treatment-biomarker interaction in predicting a time-to event outcome. Many biomarkers, however, are derived from tissues from patients, and their levels may be heterogeneous. As a result, biomarker levels may be measured with error and this would have an adverse impact on the power of a stratified biomarker clinical trial. We present a trial design and an analysis framework for the stratified biomarker design. We show that the naïve test is biased and provide bias-corrected estimators for computing the sample size and the 95% confidence interval when testing for a treatment-biomarker interaction in predicting a time to event outcome. We propose a sample size formula that adjusts for misclassification and apply it in the design of a phase III clinical trial in renal cancer.

Background Cardiogenic shock (CS) is a systemic disorder associated with dismal short-term prognosis. Given its time-dependent nature, mechanical circulatory support may improve survival. Intra-aortic balloon pump (IABP) had gained widespread use because of the easiness to implant and the low rate of complications; however, a randomized trial failed to demonstrate benefit on mortality in the setting of acute myocardial infarction. Acute decompensated heart failure with cardiogenic shock (ADHF-CS) represents a growing resource-intensive scenario with scant data and indications on the best management. However, a few data suggest a potential benefit of IABP in this setting. We present the design of a study aimed at addressing this research gap. Methods and design The Altshock-2 trial is a prospective, randomized, multicenter, open-label study with blinded adjudicated evaluation of outcomes. Patients with ADHF-CS will be randomized to early IABP implantation or to vasoactive treatments. The primary end point will be 60 days patients’ survival or successful bridge to heart replacement therapy. The key secondary end point will be 60-day overall survival; 60-day need for renal replacement therapy; in-hospital maximum inotropic score, maximum duration of inotropic/vasopressor therapy, and maximum sequential organ failure assessment score. Safety end points will be in-hospital occurrence of bleeding events (Bleeding Academic Research Consortium >3), vascular access complications and systemic (noncerebral) embolism. The sample size for the study is 200 patients. Implications The Altshock-2 trial will provide evidence on whether IABP should be implanted early in ADHF-CS patients to improve their clinical outcomes.

Background In a cluster randomised controlled trial (CRCT), randomisation units are “clusters” such as schools or GP practices. This has methodological implications for study design and statistical analysis, since clustering often leads to correlation between observations which, if not accounted for, can lead to spurious conclusions of efficacy/effectiveness. Bayesian methodology offers a flexible, intuitive framework to deal with such issues, but its use within CRCT design and analysis appears limited. This review aims to explore and quantify the use of Bayesian methodology in the design and analysis of CRCTs, and appraise the quality of reporting against CONSORT guidelines. Methods We sought to identify all reported/published CRCTs that incorporated Bayesian methodology and papers reporting development of new Bayesian methodology in this context, without restriction on publication date or location. We searched Medline and Embase and the Cochrane Central Register of Controlled Trials (CENTRAL). Reporting quality metrics according to the CONSORT extension for CRCTs were collected, as well as demographic data, type and nature of Bayesian methodology used, journal endorsement of CONSORT guidelines, and statistician involvement. Results Twenty-seven publications were included, six from an additional hand search. Eleven (40.7%) were reports of CRCT results: seven (25.9%) were primary results papers and four (14.8%) reported secondary results. Thirteen papers (48.1%) reported Bayesian methodological developments, the remaining three (11.1%) compared different methods. Four (57.1%) of the primary results papers described the method of sample size calculation; none clearly accounted for clustering. Six (85.7%) clearly accounted for clustering in the analysis. All results papers reported use of Bayesian methods in the analysis but none in the design or sample size calculation. Conclusions The popularity of the CRCT design has increased rapidly in the last twenty years but this has not been mirrored by an uptake of Bayesian methodology in this context. Of studies using Bayesian methodology, there were some differences in reporting quality compared to CRCTs in general, but this study provided insufficient data to draw firm conclusions. There is an opportunity to further develop Bayesian methodology for the design and analysis of CRCTs in order to expand the accessibility, availability, and, ultimately, use of this approach.

In patients with atrial fibrillation, incomplete adherence to anticoagulants increases risk of stroke. Non-warfarin oral anticoagulants (NOACs) are expensive; we evaluated whether higher copayments are associated with lower NOAC adherence.Using a national claims database of commercially-insured patients, we performed a cohort study of patients with atrial fibrillation who newly initiated a NOAC from 2012 to 2018. Patients were stratified into low ($35), medium ($35-$59), or high (≥$60) copayments and propensity-score weighted based on demographics, insurance characteristics, comorbidities, prior health care utilization, calendar year, and the NOAC received. Follow-up was 1 year, with censoring for switching to a different anticoagulant, undergoing an ablation procedure, disenrolling from the insurance plan, or death. The primary outcome was adherence, measured by proportion of days covered (PDC). Secondary outcomes included NOAC discontinuation (no refill for 30 days after the end of NOAC supply) and switching anticoagulants. We compared PDC using a Kruskal-Wallis test and rates of discontinuation and switching using Cox proportional hazards models.After weighting patients across the 3 copayment groups, the effective sample size was 17,558 patients, with balance across 50 clinical and demographic covariates (standardized differences0.1). Mean age was 62 years, 29% of patients were female, and apixaban (43%), and rivaroxaban (38%) were the most common NOACs. Higher copayments were associated with lower adherence (P.001), with a PDC of 0.82 (Interquartile range [IQR] 0.36-0.98) among those with high copayments, 0.85 (IQR 0.41-0.98) among those with medium copayments, and 0.88 (IQR 0.41-0.99) among those with low copayments. Compared to patients with low copayments, patients with high copayments had higher rates of discontinuation (hazard ratio [HR] 1.13, 95% confidence interval [CI] 1.08-1.19; P.001).Among atrial fibrillation patients newly initiating NOACs, higher copayments in commercial insurance were associated with lower adherence and higher rates of discontinuation in the first year. Policies to lower or limit cost-sharing of important medications may lead to improved adherence and better outcomes among patients receiving NOACs.

OBJECTIVE Selective biliary cannulation is a difficult part of endoscopic retrograde cholangiopancreatography (ERCP). Therefore, endoscopists have focused on how to improve the cannulation success rate and reduce the postoperative complication rate. This study aims to provide practical advice on safe operation for endoscopists. MATERIALS AND METHODS Data from the ERCP database of our medical center from January 2008 to December 2017 were retrospectively reviewed. All patients underwent ERCP for the first time. Data pertaining to diseases, anatomic factors, difficult cannulation rates, cannulation success rates, and postoperative complication rates for various assistive cannulation techniques were analyzed by using the Pearson χ2 tests and Fisher exact tests. RESULTS The difficult cannulation rate was 8.1%. The main influencing factors were benign and malignant biliary stricture, duodenal papillae or ampullary tumors, thick and long duodenal papillae, peridiverticular papillae, ectopic papillae, edematous papillae, ulcerative papillae, anomalous pancreaticobiliary junction, and Billroth II gastrectomy. The postoperative complication rates and overall cannulation success rates of 7 assistive techniques were not statistically different (P>0.05), but the overall complication rates were statistically different (P