BACKGROUND: Residents in nursing homes are prone to cognitive decline affecting memory, visuospatial cognition, and executive functions. Cognitive decline can lead to dementia, necessitating prioritized intervention.
METHODS: The current study aimed to investigate whether an intervention using a digital game was effective for preserving and improving the cognitive function of residents in nursing homes. An intervention study was conducted using a single-case AB design with multiple baselines. The participants in the study were five older adults aged 65 and over who do not play digital games regularly. The study ran for 15 weeks, including a baseline (phase A) and an intervention phase (phase B). Phase A had five baselines (5 to 9 weeks) with random participant assignment. In phase B, participants engaged in a digital game (Space Invaders) individually. Cognitive function was assessed as the outcome, measured using the Brain Assessment (performed on a tablet through the Internet) at 16 measurement points. Four of five participants (two female and two male) were included in the analysis, using visual inspection and Bayesian statistics with multi-level modeling.
RESULTS: Visual inspection of the graphs revealed cognitive function score improvements after the intervention for most layers in terms of memory of numbers, memory of words, mental rotation test (visuospatial ability), and total scores in the Brain Assessment. These effects were also significant in the analysis by multi-level modeling.
CONCLUSIONS: The results suggest that the use of digital games may be effective for preserving and improving cognitive function among residents of nursing home.
BACKGROUND: This study was registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN000048677; public title: Effect of a Digital Game Intervention for Cognitive Functions in Older People; registration date: August 30, 2022).

Analytic methods development, like many other disciplines, relies on experimentation and data analysis. Determining the contribution of a paper or report on a study incorporating data analysis is typically left to the reviewer\'s experience and good sense, without reliance on structured guidelines. This is amplified by the growing role of machine learning driven analysis, where results are based on computer intensive algorithm applications. The evaluation of a predictive model where cross validation was used to fit its parameters adds challenges to the evaluation of regression models, where the estimates can be easily reproduced. This lack of structure to support reviews increases uncertainty and variability in reviews. In this paper, aspects of statistical assessment are considered. We provide checklists for reviewers of applied statistics work with a focus on analytic method development. The checklist covers six aspects relevant to a review of statistical analysis, namely: (1) study design, (2) algorithmic and inferential methods in frequentism analysis, (3) Bayesian methods in Bayesian analysis (if relevant), (4) selective inference aspects, (5) severe testing properties and (6) presentation of findings. We provide a brief overview of these elements providing references for a more elaborate treatment. The robustness analysis of an analytical method is used to illustrate how an improvement can be achieved in response to questions in the checklist. The paper is aimed at both engineers and seasoned researchers.

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The coronavirus disease spread out rapidly in China and then in the whole world. Kuwait is one of those countries which are positively affected by this pandemic. Objective : The current study aims to provide an appropriate and novel framework for the analysis of the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) infected patient\'s counts and rate of change in these counts with respect to time. Therefore, we considered the number of SARS- CoV-2 patients, i.e., confirmed cases, deaths, and recoveries for Kuwait, ranging from the 24th of February 2020 to the 25th of August 2020. Method : Here, we used the Markov Chain Monte Carlo (MCMC) simulation methods for the data analysis of SARS-CoV-2 to develop the Bayesian analysis of the Non-Homogeneous Poisson Process (NHPP). For this purpose, we used the two unique models of NHPP: the linear intensity function and the power law process. The discrimination methods are also discussed to select a better model for daily basis data of confirmed cases, deaths, and recoveries of SARS-CoV-2 patients. The appropriate model is selected based on the Deviance Information Criteria (DIC). Results : The value of DIC indicates that the power-law process performs better than the linear intensity functions for estimating and presenting all the study variables. The current study explored the usefulness and significance of the proposed research framework to analyze the SARS-CoV-2 new confirmed cases, recoveries, and deaths in a specific area. Conclusion : The findings of the study will be helpful for the health organizations or authorities to develop the approaches based on the current resources and situations due to the pandemic. The provided framework could be beneficial in analyzing the second and third layers of COVID-19 in the area. The analysis of the counts for each study variable and for each variable a comparative analysis of all the three layers is the aim of our future study.

Myoid gonadal stromal tumor represents a rare testicular neoplasm displaying smooth muscular and gonadal stromal differentiation. This entity has very few cases reported in the literature that describe heterogeneous clinical and pathological characteristics. Bayesian statistics provides a useful framework to combine information from diverse sources. We here presented a case series-the largest so far reported-of myoid gonadal stromal tumor (4 cases) with extensive morphologic, immunohistochemical, and molecular characterization, performed a systematic review of the literature (that identified 9 papers), and used a Bayesian data analysis to understand the characteristics of this disease. Our study collectively described 16 cases. This neoplasm is mainly found in adults (mean age about 40 years) and often has a size of about 3 cm. By morphology, the tumor can infiltrate testicular tubules and is composed of spindle cells; few mitoses can be seen (usually 2/10 HPF). Neoplastic cells are diffusely positive with α-smooth muscle actin with a tram-track staining pattern. S100 protein, FOXL2, and SF1 are also characteristically positive. Moreover, this neoplasm can display epithelial differentiation, in about half of the cases. In conclusion, we foresee the use of this statistical approach in pathology: our analysis allowed a more precise description of this rare entity.

Reconstructing reliable timescales for species evolution is an important and indispensable goal of modern biogeography. However, many factors influence the estimation of divergence times, and uncertainty in the inferred time trees remains a major issue that is often insufficiently acknowledged. We here focus on a fundamental problem of time tree analysis: the combination of slow-evolving (nuclear DNA) and fast-evolving (mitochondrial DNA) markers in a single time tree. Both markers differ in their suitability to infer divergences at different time scales (the \'genome-timescale-dilemma\'). However, strategies to infer shallow and deep divergences in a single time tree have rarely been compared empirically. Using Mediterranean amphibians as model system that is exceptional in its geographic and taxonomic completeness of available genetic information, we analyze 202 lineages of western Palearctic amphibians across the entire Mediterranean region. We compiled data of four nuclear and five mitochondrial genes and used twelve fossil calibration points widely acknowledged for amphibian evolution. We reconstruct time trees for an extensive lineage-level data set and compare the performances of the different trees: the first tree is based on primary fossil calibration and mitochondrial DNA, while the second tree is based on a combination of primary fossil and on secondary calibrations taken from a nuclear tree using mitochondrial DNA (two-step protocol). Focusing on a set of nodes that are most likely explained by vicariance, we statistically compare the reconstructed alternative time trees by applying a biogeographical plausibility test. Our two-step protocol outperformed the alternative approach in terms of spatial and temporal plausibility. It allows us to infer scenarios for Mediterranean amphibian evolution in eight geographic provinces. We identified several tectonic and climatic events explaining the majority of Mediterranean amphibian divergences, with Plio-Pleistocene climatic fluctuations being the dominant driver for intrageneric evolution. However, often more than one event could be invoked for a specific split. We give recommendations for the use of secondary calibrations in future molecular clock analyses at the community level.

BACKGROUND: Multicriteria decision analysis (MCDA) represents a promising method for benefit-risk assessment. Our goal was to develop features of pragmatic MCDA (EVIDEM [Evidence and Value: Impact on DEcisionMaking]) addressing real-life regulatory decision-making needs, incorporate advanced pharmacoepidemiology, and test the resulting benefit-risk framework using a case study.
METHODS: The Intervention Outcomes domain of EVIDEM was transformed into a generic benefit-risk framework including clinical efficacy, patient-reported outcomes, and adverse event (AE) criteria. The concept of relative benefit-risk balance (RBRB) was developed for comparability across products and therapeutic areas and over time. Evidence matrix was designed to include most relevant data from trials, observational studies, and models, including Bayesian and longitudinal modeling. The framework was tested with a panel of stakeholders using efalizumab for psoriasis as retrospective case study. Uncertainty was explored.
RESULTS: The MCDA benefit-risk tree was adapted with psoriasis-specific subcriteria. Panelists assigned similar weights to benefits (0.48; SD, 0.20-0.70) and risks (0.52; SD, 0.10-0.60), with large variations reflecting diverse perspectives. Panelist scores reflected higher efficacy versus placebo, lower efficacy versus active comparators, and serious and fatal AEs identified postlicensing. Efalizumab\'s RBRB was positive at licensing in 2004 (0.29, scale -1 to +1) and ranged from -0.41 (vs active comparators) to 0.01 (vs placebo) in 2009, when its market authorization was withdrawn. Retesting indicated good reproducibility. Panelists acknowledged good face validity and the importance of criteria beyond benefit-risk in real-life assessments.
CONCLUSIONS: The approach allows quantification and visualization of benefit-risk over time and across comparators. Combination of pragmatic MCDA designed to integrate criteria beyond benefit-risk and advanced statistics supports application of MCDA to further accountable benefit-risk assessments for real-life decision making.

All known populations of the Sardinian endemic Centaurea filiformis Viv. (Asteraceae) were studied in order to understand the impact of both geographic and ecological factors on the genetic structuring of this species. Fourteen populations and 234 individuals were sampled. The demographic structure of the populations and the reproductive ecology were estimated in 28 plots. Population genetic analyses were based on SSR markers. Genetic structure was investigated by spatial Bayesian methods. Average densities of 0.51 individuals m-2 were detected, with a prevalence of adults. Ten species of pollinators were identified; C. filiformis ability to self-pollinate and myrmecochory were demonstrated experimentally. The populations displayed an average heterozygosity value of He  = 0.576 and high genetic differentiation (overall FST  = 0.218). Bayesian analysis suggests that five is the most probable number of gene pools of origin. A strong correlation between geographic distances and genetic distances among populations was highlighted. The demographic population structure of C. filiformis is dominated by adults, suggesting that it is a stable-regressive or senile species, investing more in local persistence than colonisation ability. Despite the scattered distribution, the populations studied do not present evidence of genetic erosion. The analysis of genetic differentiation reveals very high differentiation levels among populations, thus indicating that effective barriers exist against gene flow. A general conclusion is that population distribution results in a clear genetic structure for the populations studied, and that geography and not ecology is shaping the present distribution of this species.

Count outcomes occur in virtually all disciplines, such as medicine, epidemiology or biology, but they often contain error. Recently, it has been shown that self-reported numbers of exacerbations of Chronic Obstructive Pulmonary Disease patients can be considerably miscounted. Motivated by this result, we reanalysed data from the Towards a Revolution in Chronic Obstructive Pulmonary Disease Health trial, a large randomized controlled trial with the self-reported number of exacerbations of Chronic Obstructive Pulmonary Disease patients as outcome. To adjust for miscounting error in the response of Poisson and (zero-inflated) negative binomial models, we introduce novel, general methodology. The key idea is to formulate a zero-inflated negative binomial model to capture the error mechanism. This parametric approach automatically circumvents drawbacks of previously suggested methodology that treats miscounted outcomes in the misclassification framework. Prior information for the response error model parameters was elicited from validation data of an external study and adaptively weighted to account for potential prior-data conflict. The results of the Bayesian hierarchical modelling approach indicated that the treatment effect has been overestimated in the original study. However, closer inspection revealed that this unexpected result was an artefact of an unaccounted time dependency of the treatment effect.

When recruitment into a clinical trial is limited due to rarity of the disease of interest, or when recruitment to the control arm is limited due to ethical reasons (eg, pediatric studies or important unmet medical need), exploiting historical controls to augment the prospectively collected database can be an attractive option. Statistical methods for combining historical data with randomized data, while accounting for the incompatibility between the two, have been recently proposed and remain an active field of research. The current literature is lacking a rigorous comparison between methods but also guidelines about their use in practice. In this paper, we compare the existing methods based on a confirmatory phase III study design exercise done for a new antibacterial therapy with a binary endpoint and a single historical dataset. A procedure to assess the relative performance of the different methods for borrowing information from historical control data is proposed, and practical questions related to the selection and implementation of methods are discussed. Based on our examination, we found that the methods have a comparable performance, but we recommend the robust mixture prior for its ease of implementation.