This study investigates the effectiveness of multiple COVID-19 vaccinations on daily confirmed cases in Seoul City. Utilizing comprehensive data on vaccinated individuals and confirmed cases sourced from the official website of the Korean Ministry of the Interior and Safety, we conducted detailed statistical analyses to assess the impact of each vaccination dose. The study covers data from April 21, 2021, to September 29, 2022. Statistical multiple linear regression was employed to analyze the relationship between daily confirmed cases (positive outcomes from PCR tests) and multiple vaccine doses, using p-values as the criteria for determining the effectiveness of each dose. The analysis included data from four vaccination doses. The analysis reveals that the first, second, and third doses of the COVID-19 vaccines have a statistically significant positive effect associated with the daily confirmed cases. However, the study finds that the fourth dose does not show a statistically significant impact on the reduction of daily confirmed cases. This suggests that while the initial three doses are crucial for establishing and maintaining high levels of immunity, the incremental benefit of subsequent doses may diminish.

OBJECTIVE: Temporomandibular disorder (TMD) is the term used to describe a pathology (dysfunction and pain) in the masticatory muscles and temporomandibular joint (TMJ). There is an apparent upward trend in the publication of dental research and a need to continually improve the quality of research. Therefore, this study was conducted to analyse the use of sample size and effect size calculations in a TMD randomised controlled trial.
METHODS: The period was restricted to the full 5 years, i.e., papers published in 2019, 2020, 2021, 2022, and 2023. The filter article type-\"Randomized Controlled Trial\" was used. The studies were graded on a two-level scale: 0-1. In the case of 1, sample size (SS) and effect size (ES) were calculated.
RESULTS: In the entire study sample, SS was used in 58% of studies, while ES was used in 15% of studies.
CONCLUSIONS: Quality should improve as research increases. One factor that influences quality is the level of statistics. SS and ES calculations provide a basis for understanding the results obtained by the authors. Access to formulas, online calculators and software facilitates these analyses. High-quality trials provide a solid foundation for medical progress, fostering the development of personalized therapies that provide more precise and effective treatment and increase patients\' chances of recovery. Improving the quality of TMD research, and medical research in general, helps to increase public confidence in medical advances and raises the standard of patient care.

The proposal of improving reproducibility by lowering the significance threshold to 0.005 has been discussed, but the impact on conducting clinical trials has yet to be examined from a study design perspective. The impact on sample size and study duration was investigated using design setups from 125 phase II studies published between 2015 and 2022. The impact was assessed using percent increase in sample size and additional years of accrual with the medians being 110.97% higher and 2.65 years longer respectively. The results indicated that this proposal causes additional financial burdens that reduce the efficiency of conducting clinical trials.

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.

Storey\'s estimator for the proportion of true null hypotheses, originally proposed under the continuous framework, has been modified in this work under the discrete framework. The modification results in improved estimation of the parameter of interest. The proposed estimator is used to formulate an adaptive version of the Benjamini-Hochberg procedure. Control over the false discovery rate by the proposed adaptive procedure has been proved analytically. The proposed estimate is also used to formulate an adaptive version of the Benjamini-Hochberg-Heyse procedure. Simulation experiments establish the conservative nature of this new adaptive procedure. Substantial amount of gain in power is observed for the new adaptive procedures over the standard procedures. For demonstration of the proposed method, two important real life gene expression data sets, one related to the study of HIV and the other related to methylation study, are used.

Introduction Monosodium glutamate (MSG), a common global food additive in processed foods, influences flavors and textures due to its chemical complexity and nutritional intricacy. Despite an annual production of 1.9 million tons and historical safety concerns, the multifaceted impact on health, ranging from metabolic disorders to neurological and cardiovascular implications, necessitates ongoing research for informed consumption and balanced dietary practices. Materials and methods This cross-sectional study investigates MSG-associated intricacies among Saudi Arabia\'s urban population. The research included questionnaire development, translation, and cultural adaptation, and was validated by nutrition experts. A sample size of 420 was calculated for a 95% confidence level. Data collection occurred from September 13 to October 31, 2023, and ethical considerations were ensured. Statistical analysis, including chi-square tests, regression analysis, and SPSS, explored intricacy relationships. Results The MSG intricacy study in Saudi Arabia\'s urban population, involving 420 respondents, showed statistically significant correlations (P < 0.05) in demographics. The key findings indicate an awareness of the impact of MSG on health, its associations with various conditions, and strong support for its exclusion from foods. Region, gender, age, and social status correlations highlighted diverse perspectives. The Western province showed the highest response rate at 42.61%, prompting regional awareness questions. Gender dynamics showed that 90.47% of the respondents were females, emphasizing potential gender-specific concerns. Concentration among ages 20-30 (61.9%) underscored generational factors. While commendable baseline awareness was noted, 73.09% of the participants believing MSG is harmful prompts further investigation. Emotional responses, including happiness (25.95%) and frustration (18.33%), highlight the complexity of the individuals\' experiences, emphasizing the need for tailored communication strategies. Conclusion The MSG intricacy study in Saudi Arabia\'s urban population reveals insights into knowledge, attitudes, and behaviors, emphasizing the need for nuanced interventions considering regional and emotional differences. The findings underscore health concerns, supporting regulations, and knowledge impact on behavior. This survey serves as a valuable tool for informed public discourse and decision-making in the unique socio-cultural context of urban Saudi Arabia.

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High content screening (HCS) is becoming widely adopted as a high throughput screening modality, using hundred-of-thousands compounds library. The use of machine learning and artificial intelligence in image analysis is amplifying this trend. Another factor is the recognition that diverse cell phenotypes can be associated with changes in biological pathways relevant to disease processes. There are numerous challenges in HCS campaigns. These include limited ability to support replicates, low availability of precious and unique cells or reagents, high number of experimental batches, lengthy preparation of cells for imaging, image acquisition time (45-60 min per plate) and image processing time, deterioration of image quality with time post cell fixation and variability within wells and batches. To take advantage of the data in HCS, cell population based rather than well-based analyses are required. Historically, statistical analysis and hypothesis testing played only a limited role in non-high content high throughput campaigns. Thus, only a limited number of standard statistical criteria for hit selection in HCS have been developed so far. In addition to complex biological content in HCS campaigns, additional variability is impacted by cell and reagent handling and by instruments which may malfunction or perform unevenly. Together these can cause a significant number of wells or plates to fail. Here we describe an automated approach for hit analysis and detection in HCS. Our approach automates HCS hit detection using a methodology that is based on a documented statistical framework. We introduce the Virtual Plate concept in which selected wells from different plates are collated into a new, virtual plate. This allows the rescue and analysis of compound wells which have failed due to technical issues as well as to collect hit wells into one plate, allowing the user easier access to the hit data.

A great deal of interest has recently focused on conducting inference on the parameters in a high-dimensional linear model. In this paper, we consider a simple and very naïve two-step procedure for this task, in which we (i) fit a lasso model in order to obtain a subset of the variables, and (ii) fit a least squares model on the lasso-selected set. Conventional statistical wisdom tells us that we cannot make use of the standard statistical inference tools for the resulting least squares model (such as confidence intervals and p-values), since we peeked at the data twice: once in running the lasso, and again in fitting the least squares model. However, in this paper, we show that under a certain set of assumptions, with high probability, the set of variables selected by the lasso is identical to the one selected by the noiseless lasso and is hence deterministic. Consequently, the naïve two-step approach can yield asymptotically valid inference. We utilize this finding to develop the naïve confidence interval, which can be used to draw inference on the regression coefficients of the model selected by the lasso, as well as the naïve score test, which can be used to test the hypotheses regarding the full-model regression coefficients.

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power. We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as \"prior information\" into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels. Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients. Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.