The regulatory EMA\'s reference scaled average bioequivalence (RSABE) approach for highly variable drugs suffers from some type I error control problems at the neighborhood of the 30% coefficient of variation (CV), where the bioequivalence (BE) limits change from constant to linearly scaled. This paper analyses BE inference methods based on the \"Leveling-off\" (LO) soft sigmoid expanding BE limits that were proposed as an appealing surrogate for the EMA\'s limits and compares both approaches, on the replicated and partially replicated crossover designs. The initially proposed version of the LO method also has type I error inflation problems, albeit attenuated. But given its more mathematically regular character, it is more suitable for analytical corrections. Here we introduce two improvements over LO, one based on the application of Howe\'s method and the other based on correcting the estimation error. They further reduce the type I error inflation, although it does not disappear completely. Finally, the effect of heteroscedasticity on the above results is studied. It leads to inflation or deflation of the type I error, depending on the design and the type of heteroscedasticity (variability of the test product greater than that of the reference product or the opposite). The replicated design is much more stable against these effects than the partially replicated design and maintains these improvements much better.

In many organisms, especially those of conservation concern, traditional lines of evidence for taxonomic delineation, such as morphological data, are often difficult to obtain. In these cases, genetic data are often the only source of information available for taxonomic studies. In particular, population surveys of mitochondrial genomes offer increased resolution and precision in support of taxonomic decisions relative to conventional use of the control region or other gene fragments of the mitochondrial genome. To improve quantitative guidelines for taxonomic decisions in cetaceans, we build on a previous effort targeting the control region and evaluate, for whole mitogenome sequences, a suite of divergence and diagnosability estimates for pairs of recognized cetacean populations, subspecies, and species. From this overview, we recommend new guidelines based on complete mitogenomes, combined with other types of evidence for isolation and divergence, which will improve resolution for taxonomic decisions, especially in the face of small sample sizes or low levels of genetic diversity. We further use simulated data to assist interpretations of divergence in the context of varying forms of historical demography, culture, and ecology.

BACKGROUND: The leukodystrophy \"Vanishing White Matter\" (VWM) is an orphan disease with neurological decline and high mortality. Currently, VWM has no approved treatments, but advances in understanding pathophysiology have led to identification of promising therapies. Several investigational medicinal products are either in or about to enter clinical trial phase. Clinical trials in VWM pose serious challenges, as VWM has an episodic disease course; disease phenotype is highly heterogeneous and predictable only for early onset; and study power is limited by the small patient numbers. To address these challenges and accelerate therapy delivery, the VWM Consortium, a group of academic clinicians with expertise in VWM, decided to develop a core protocol to function as a template for trials, to improve trial design and facilitate sharing of control data, while permitting flexibility regarding other trial details. Overall aims of the core protocol are to collect safety, tolerability, and efficacy data for treatment assessment and marketing authorization.
METHODS: To develop the core protocol, the VWM Consortium designated a committee, including clinician members of the VWM Consortium, family and patient group advocates, and experts in statistics, clinical trial design and alliancing with industries. We drafted three age-specific protocols, to stratify into more homogeneous patient groups, of ages ≥ 18 years, ≥ 6 to < 18 years and < 6 years. We chose double-blind, randomized, placebo-controlled design for patients aged ≥ 6 years; and open-label non-randomized natural-history-controlled design for patients < 6 years. The protocol describes study populations, age-specific endpoints, inclusion and exclusion criteria, study schedules, sample size determinations, and statistical considerations.
CONCLUSIONS: The core protocol provides a shared uniformity across trials, enables a pool of shared controls, and reduces the total number of patients necessary per trial, limiting the number of patients on placebo. All VWM clinical trials are suggested to adhere to the core protocol. Other trial components such as choice of primary outcome, pharmacokinetics, pharmacodynamics, and biomarkers are flexible and unconstrained by the core protocol. Each sponsor is responsible for their trial execution, while the control data are handled by a shared research organization. This core protocol benefits the efficiency of parallel and consecutive trials in VWM, and we hope accelerates time to availability of treatments for VWM.
BACKGROUND: NA. From a scientific and ethical perspective, it is strongly recommended that all interventional trials using this core protocol are registered in a clinical trial register.

Treatment guidelines in neurosurgery are often based on evidence obtained from randomized controlled trials (RCTs).
To evaluate the robustness of RCTs supporting current central nervous tumor and cerebrovascular disease guidelines by calculating their fragility index (FI)-the minimum number of patients needed to switch from an event to nonevent outcome to change significant trial primary outcome.
We analyzed RCTs referenced in the Congress of Neurological Surgeons and American Association of Neurological Surgeons guidelines on central nervous tumor and cerebrovascular disease management. Trial characteristics, finding of a statistically significant difference in the primary endpoint favoring the experimental intervention, the FI, and FI minus number lost to follow-up were assessed.
Of 312 RCTs identified, 158 (50.6%) were published from 2000 to 2010 and 106 (34%) after 2010. Sixty-three trials (19.2%) were categorized as surgical trials, and the rest studied medical treatment (82.0%) or percutaneous intervention (8.33%). The trials had a median power of 80.0% (IQR 80.0-90.0). Of these, 120 trials were eligible for FI calculation. The median FI was 7.0 (IQR 2.0-16.25). Forty-four (36.6%) trials had FI ≤ 3 indicating very low robustness. After adjusting for covariates, recently published trials and trials studying percutaneous interventions were associated with significantly higher FI compared with older trials and trials comparing surgical approaches, respectively. Trials limited to single centers were associated with significantly lower FI.
Trials supporting current guidelines on neuro-oncological and neurovascular surgical interventions have low robustness. While the robustness of trials has improved over time, future guidelines must take into consideration this metric in their recommendations.

To use the fragility index (FI) and fragility quotient (FQ) to assess the strength of statistically significant findings for randomized controlled trials (RCTs) cited in the American Urological Association (AUA) guidelines for benign prostatic hyperplasia.
Two investigators independently screened the AUA guidelines for management of benign prostatic hyperplasia for RCTs cited as evidence for recommendations. Investigators extracted data related to event rate per group and loss to follow-up which was compared against the FI. Stata 17.0 was used to calculate the FI and FQ which was then summarized and reported according to primary or secondary endpoints.
Among the 373 citations in the AUA guidelines, 24 RCTs met inclusion criteria with 29 distinct outcomes analyzed. The median fragility index was 12 (IQR = 4-38), indicating that twelve alternative events to either study arm would nullify statistical significance. Six studies had a FI of ≤2, indicating that only 1-2 outcomes would need to be changed in order to render nonsignificance of results. In 10/24 RCTs, the number of patients lost to follow-up was greater than the FI.
The AUA Clinical Practice Guidelines for management of benign prostatic hyperplasia cite RCTs with more robust findings when compared to previous studies assessing fragility in the field of Urology. While several included studies had high fragility, the median FI in our analysis was approximately 4-5 times higher than comparable studies of urologic RCTs. However, there are areas where improvement is necessary to support the highest quality of evidence-based medicine.

OBJECTIVE: In randomized clinical trials (RCTs) rejecting the null hypothesis, the fragility index (FI) yields the minimum number of participants who would need to have had a different outcome for the results of the trial to become non-significant. We evaluated the robustness of RCTs supporting American College of Cardiology/American Heart Association (ACC/AHA) and European Society of Cardiology (ESC) clinical practice guidelines (CPGs) for ST-elevation myocardial infarction (STEMI) and non-ST-elevation acute coronary syndrome (NSTE-ACS) using the FI.
RESULTS: There were 407 RCTs among the 2128 studies cited in the 2013 and 2014 ACC/AHA and 2017 and 2020 ESC CPGs for STEMI and NSTE-ACS, respectively. The FI could be calculated in 132 RCTs (32.4%) meeting the needed criteria for its estimation (two-arm RCT, 1:1 allocation, binary outcome, P < 0.05). The median FI was 12 (interquartile range: 4-29). Hence, a change in the outcome status of 12 patients would be needed to reverse the statistical significance of the primary endpoint in 50% of the RCTs. The FI was ≤1% than their sample size in 55.7% RCTs, whereas in 47% of RCTs, the FI was lower than the number of patients lost to follow-up. Some study design features were associated with a higher FI (international, multicentre, private funding; all P < 0.05), whilst baseline patient characteristics were not substantially different by FI (e.g. age, female sex, white study participants; all P > 0.05), except for geographic enrolment (P = 0.042).
CONCLUSIONS: The FI might be useful to evaluate the robustness of those RCTs with statistically significant findings for the primary endpoint that have an impact on key guideline recommendations.

Although survey reports are common, fewer than 10% of medical journals provide clear guidelines to investigators for survey research. In this special article, we provide guidance on minimum recommendations in the form of a CHecklist for Allergy and Immunology Reporting of Survey research (CHAIRS). Key components to consider include providing background information, such as a clear statement of the research hypothesis and question, and rationale for the study. When considering sample selection, a clear understanding of the relationship between the target population, sampling frame, sample scheme, representativeness, and sample size is needed. Review of the survey tool by content experts and assessment of threats to survey validity should occur early in questionnaire development with consideration of cognitive interviews and pretesting to facilitate accurate measurement. Last, a transparent description of data collection and qualitative and quantitative characteristics of response rate is needed to ensure that appropriate inferences and conclusions can be drawn from the survey research.

Standard-of-care HIV pre-exposure prophylaxis (PrEP) is highly efficacious, but uptake of and persistence on a daily oral pill is low in many settings. Evaluation of alternate PrEP products will require innovation to avoid the unpractically large sample sizes in noninferiority trials. We propose estimating HIV incidence in people not on PrEP as an external counterfactual to which on-PrEP incidence in trial subjects can be compared. HIV recent infection testing algorithms (RITAs), such as the limiting antigen avidity assay plus viral load used on specimens from untreated HIV positive people identified during screening, is one possible approach. Its feasibility is partly dependent on the sample size needed to ensure adequate power, which is impacted by RITA performance, the number of recent infections identified, the expected efficacy of the intervention, and other factors. Screening sample sizes to support detection of an 80% reduction in incidence for 3 key populations are more modest, and comparable to the number of participants in recent phase III PrEP trials. Sample sizes would be significantly larger in populations with lower incidence, where the false recency rate is higher or if PrEP efficacy is expected to be lower. Our proposed counterfactual approach appears to be feasible, offers high statistical power, and is nearly contemporaneous with the on-PrEP population. It will be important to monitor the performance of this approach during new product development for HIV prevention. If successful, it could be a model for preventive HIV vaccines and prevention of other infectious diseases.

Changes in statistical practices and reporting have been documented by Giofrè et al. PLOS ONE 12(4), e0175583 (2017), who investigated ten statistical and open practices in two high-ranking journals (Psychological Science [PS] and Journal of Experimental Psychology-General [JEPG]): null hypothesis significance testing; confidence or credible intervals; meta-analysis of the results of multiple experiments; confidence interval interpretation; effect size interpretation; sample size determination; data exclusion; data availability; materials availability; and preregistered design and analysis plan. The investigation was based on an analysis of all papers published in these journals between 2013 and 2015. The aim of the present study was to follow up changes in both PS and JEPG in subsequent years, from 2016 to 2020, adding code availability as a further open practice. We found improvement in most practices, with some exceptions (i.e., confidence interval interpretation and meta-analysis). Despite these positive changes, our results indicate a need for further improvements in statistical practices and adoption of open practices.

The Gaussian graphical model (GGM) has recently grown popular in psychological research, with a large body of estimation methods being proposed and discussed across various fields of study, and several algorithms being identified and recommend as applicable to psychological data sets. Such high-dimensional model estimation, however, is not trivial, and algorithms tend to perform differently in different settings. In addition, psychological research poses unique challenges, including placing a strong focus on weak edges (e.g., bridge edges), handling data measured on ordered scales, and relatively limited sample sizes. As a result, there is currently no consensus regarding which estimation procedure performs best in which setting. In this large-scale simulation study, we aimed to overcome this gap in the literature by comparing the performance of several estimation algorithms suitable for Gaussian and skewed ordered categorical data across a multitude of settings, as to arrive at concrete guidelines from applied researchers. In total, we investigated 60 different metrics across 564,000 simulated data sets. We summarized our findings through a platform that allows for manually exploring simulation results. Overall, we found that an exchange between discovery (e.g., sensitivity, edge weight correlation) and caution (e.g., specificity, precision) should always be expected, and achieving both-which is a requirement for perfect replicability-is difficult. Further, we identified that the estimation method is best chosen in light of each research question and have highlighted, alongside desirable asymptotic properties and low sample size discovery, results according to most common research questions in the field. (PsycInfo Database Record (c) 2023 APA, all rights reserved).