BACKGROUND: The proper interpretation of a study\'s results requires both excellent understanding of good methodological practices and deep knowledge of prior results, aided by the availability of effect sizes.
METHODS: This review takes the form of an expository essay exploring the complex and nuanced relationships among statistical significance, clinical importance, and effect sizes.
RESULTS: Careful attention to study design and methodology will increase the likelihood of obtaining statistical significance and may enhance the ability of investigators/readers to accurately interpret results. Measures of effect size show how well the variables used in a study account for/explain the variability in the data. Studies reporting strong effects may have greater practical value/utility than studies reporting weak effects. Effect sizes need to be interpreted in context. Verbal summary characterizations of effect sizes (e.g., \"weak\", \"strong\") are fundamentally flawed and can lead to inappropriate characterization of results. Common language effect size (CLES) indicators are a relatively new approach to effect sizes that may offer a more accessible interpretation of results that can benefit providers, patients, and the public at large.
CONCLUSIONS: It is important to convey research findings in ways that are clear to both the research community and to the public. At a minimum, this requires inclusion of standard effect size data in research reports. Proper selection of measures and careful design of studies are foundational to the interpretation of a study\'s results. The ability to draw useful conclusions from a study is increased when investigators enhance the methodological quality of their work.

The genomic evaluation process relies on the assumption of linkage disequilibrium between dense single-nucleotide polymorphism (SNP) markers at the genome level and quantitative trait loci (QTL). The present study was conducted with the aim of evaluating four frequentist methods including Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net, and Genomic Best Linear Unbiased Prediction (GBLUP) and five Bayesian methods including Bayes Ridge Regression (BRR), Bayes A, Bayesian LASSO, Bayes C, and Bayes B, in genomic selection using simulation data. The difference between prediction accuracy was assessed in pairs based on statistical significance (p-value) (i.e., t test and Mann-Whitney U test) and practical significance (Cohen\'s d effect size) For this purpose, the data were simulated based on two scenarios in different marker densities (4000 and 8000, in the whole genome). The simulated data included a genome with four chromosomes, 1 Morgan each, on which 100 randomly distributed QTL and two different densities of evenly distributed SNPs (1000 and 2000), at the heritability level of 0.4, was considered. For the frequentist methods except for GBLUP, the regularization parameter λ was calculated using a five-fold cross-validation approach. For both scenarios, among the frequentist methods, the highest prediction accuracy was observed by Ridge Regression and GBLUP. The lowest and the highest bias were shown by Ridge Regression and GBLUP, respectively. Also, among the Bayesian methods, Bayes B and BRR showed the highest and lowest prediction accuracy, respectively. The lowest bias in both scenarios was registered by Bayesian LASSO and the highest bias in the first and the second scenario were shown by BRR and Bayes B, respectively. Across all the studied methods in both scenarios, the highest and the lowest accuracy were shown by Bayes B and LASSO and Elastic Net, respectively. As expected, the greatest similarity in performance was observed between GBLUP and BRR ( d = 0.007 , in the first scenario and d = 0.003 , in the second scenario). The results obtained from parametric t and non-parametric Mann-Whitney U tests were similar. In the first and second scenario, out of 36 t test between the performance of the studied methods in each scenario, 14 ( P < . 001 ) and 2 ( P < . 05 ) comparisons were significant, respectively, which indicates that with the increase in the number of predictors, the difference in the performance of different methods decreases. This was proven based on the Cohen\'s d effect size, so that with the increase in the complexity of the model, the effect size was not seen as very large. The regularization parameters in frequentist methods should be optimized by cross-validation approach before using these methods in genomic evaluation.

UNASSIGNED: In this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics.
UNASSIGNED: Spatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male-female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis.
UNASSIGNED: The proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients.
UNASSIGNED: The proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.

This article traces the development of Creative Nursing from its origin in 1981 as a newsletter about Primary Nursing to its current position as a quarterly international, interdisciplinary, peer-reviewed, indexed, themed journal that continues to nurture novice authors, welcome international submissions, review articles that other journals won\'t consider, and address subjects that many journals avoid. Future directions include content in multiple languages, new author guidelines that invite submissions of research methods papers, moving beyond statistical significance based on p-value thresholds, asking authors to make explicit the implications for knowledge translation in their papers, and thinking creatively about how artificial intelligence can be leveraged for research, education, and practice.

This research analyzed the real-world NOx and particle number (PN) emissions of 21 China VI heavy-duty diesel trucks (HDDTs). On-road emission conformity was first evaluated with portable emission measurement system (PEMS). Only 76.19 %, 71.43 % and 61.90 % of the vehicles passed the NOx test, PN test and both tests, respectively. The impacts of vehicle features including exhaust gas recirculation (EGR) equipment, mileage and tractive tonnage were then assessed. Results demonstrated that EGR helped reducing NOx emission factors (EFs) while increased PN EFs. Larger mileages and tractive tonnages corresponded to higher NOx and PN EFs, respectively. In-depth analyses regarding the influences of operating conditions on emissions were conducted with both numerical comparisons and statistical tests. Results proved that HDDTs generated higher NOx EFs under low speeds or large vehicle specific powers (VSPs), and higher PN EFs under high speeds or small VSPs in general. In addition, unqualified vehicles generated significantly higher NOx EFs than qualified vehicles on freeways or under speed≥40 km/h, while significant higher PN EFs were generated on suburban roads, freeways or under operating modes with positive VSPs by unqualified vehicles. The reliability and accuracy of on-board diagnostic (OBD) NOx data were finally investigated. Results revealed that 43 % of the test vehicles did not report reliable OBD data. Correlation analyses between OBD NOx and PEMS measurements further demonstrated that the consistency of instantaneous concentrations were generally low. However, sliding window averaged concentrations show better correlations, e.g., the Pearson correlation coefficients on 20s-window averaged concentrations exceeded 0.85 for most vehicles. The research results provide valuable insights into emission regulation, e.g., focusing more on medium- to high-speed operations to identify unqualified vehicles, setting higher standards to improve the quality of OBD data, and adopting window averaged OBD NOx concentrations in evaluating vehicle emission performance.

Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data. A common understanding is that its performance scales up with the amount of training data. However, the data must also exhibit variety to enable improved learning. In medical imaging data, semantic redundancy, which is the presence of similar or repetitive information, can occur due to the presence of multiple images that have highly similar presentations for the disease of interest. Also, the common use of augmentation methods to generate variety in DL training could limit performance when indiscriminately applied to such data. We hypothesize that semantic redundancy would therefore tend to lower performance and limit generalizability to unseen data and question its impact on classifier performance even with large data. We propose an entropy-based sample scoring approach to identify and remove semantically redundant training data and demonstrate using the publicly available NIH chest X-ray dataset that the model trained on the resulting informative subset of training data significantly outperforms the model trained on the full training set, during both internal (recall: 0.7164 vs 0.6597, p<0.05) and external testing (recall: 0.3185 vs 0.2589, p<0.05). Our findings emphasize the importance of information-oriented training sample selection as opposed to the conventional practice of using all available training data.

Null hypothesis significant testing (NHST) is the dominant statistical approach in the geriatric and rehabilitation fields. However, NHST is routinely misunderstood or misused. In this case, the findings from clinical trials would be taken as evidence of no effect, when in fact, a clinically relevant question may have a \"non-significant\" p-value. Conversely, findings are considered clinically relevant when significant differences are observed between groups. To assume that p-value is not an exclusive indicator of an association or the existence of an effect, researchers should be encouraged to report other statistical analysis approaches as Bayesian analysis and complementary statistical tools alongside the p-value (eg, effect size, confidence intervals, minimal clinically important difference, and magnitude-based inference) to improve interpretation of the findings of clinical trials by presenting a more efficient and comprehensive analysis. However, the focus on Bayesian analysis and secondary statistical analyses does not mean that NHST is less important. Only that, to observe a real intervention effect, researchers should use a combination of secondary statistical analyses in conjunction with NHST or Bayesian statistical analysis to reveal what p-values cannot show in the geriatric and rehabilitation studies (eg, the clinical importance of 1kg increase in handgrip strength in the intervention group of long-lived older adults compared to a control group). This paper provides potential insights for improving the interpretation of scientific data in rehabilitation and geriatric fields by utilizing Bayesian and secondary statistical analyses to better scrutinize the results of clinical trials where a p-value alone may not be appropriate to determine the efficacy of an intervention.

BACKGROUND: Fragility analysis is a method of further characterizing outcomes in terms of the stability of statistical findings. This study assesses the statistical fragility of recent randomized controlled trials (RCTs) evaluating robotic-assisted versus conventional total knee arthroplasty (RA-TKA versus C-TKA).
METHODS: We queried PubMed for RCTs comparing alignment, function, and outcomes between RA-TKA and C-TKA. Fragility index (FI) and reverse fragility index (RFI) (collectively, \"FI\") were calculated for dichotomous outcomes as the number of outcome reversals needed to change statistical significance. Fragility quotient (FQ) was calculated by dividing the FI by the sample size for that outcome event. Median FI and FQ were calculated for all outcomes collectively as well as for each individual outcome. Subanalyses were performed to assess FI and FQ based on outcome event type and statistical significance, as well as study loss to follow-up and year of publication.
RESULTS: The overall median FI was 3.0 (interquartile range, [IQR] 1.0 to 6.3) and the median reverse fragility index was 3.0 (IQR 2.0 to 4.0). The overall median FQ was 0.027 (IQR 0.012 to 0.050). Loss to follow-up was greater than FI for 23 of the 38 outcomes assessed.
CONCLUSIONS: A small number of alternative outcomes is often enough to reverse the statistical significance of findings in RCTs evaluating dichotomous outcomes in RA-TKA versus C-TKA. We recommend reporting FI and FQ alongside P values to improve the interpretability of RCT results.

UNASSIGNED: Evidence-based care relies on robust research. The fragility index (FI) is used to assess the robustness of statistically significant findings in randomized controlled trials (RCTs). While the traditional FI is limited to dichotomous outcomes, a novel tool, the continuous fragility index (CFI), allows for the assessment of the robustness of continuous outcomes.
UNASSIGNED: To calculate the CFI of statistically significant continuous outcomes in RCTs evaluating interventions for managing anterior shoulder instability (ASI).
UNASSIGNED: Meta-analysis; Level of evidence, 2.
UNASSIGNED: A search was conducted across the MEDLINE, Embase, and CENTRAL databases for RCTs assessing management strategies for ASI from inception to October 6, 2022. Studies that reported a statistically significant difference between study groups in ≥1 continuous outcome were included. The CFI was calculated and applied to all available RCTs reporting interventions for ASI. Multivariable linear regression was performed between the CFI and various study characteristics as predictors.
UNASSIGNED: There were 27 RCTs, with a total of 1846 shoulders, included. The median sample size was 61 shoulders (IQR, 43). The median CFI across 27 RCTs was 8.2 (IQR, 17.2; 95% CI, 3.6-15.4). The median CFI was 7.9 (IQR, 21; 95% CI, 1-22) for 11 studies comparing surgical methods, 22.6 (IQR, 16; 95% CI, 8.2-30.4) for 6 studies comparing nonsurgical reduction interventions, 2.8 for 3 studies comparing immobilization methods, and 2.4 for 3 studies comparing surgical versus nonsurgical interventions. Significantly, 22 of 57 included outcomes (38.6%) from studies with completed follow-up data had a loss to follow-up exceeding their CFI. Multivariable regression demonstrated that there was a statistically significant positive correlation between a trial\'s sample size and the CFI of its outcomes (r = 0.23 [95% CI, 0.13-0.33]; P < .001).
UNASSIGNED: More than a third of continuous outcomes in ASI trials had a CFI less than the reported loss to follow-up. This carries the significant risk of reversing trial findings and should be considered when evaluating available RCT data. We recommend including the FI, CFI, and loss to follow-up in the abstracts of future RCTs.

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