Statistical Power Analysis Research Articles

Sample Size Planning in the Design of Two-Level Randomized Cost-Effectiveness Trials

This study introduces recent advances in statistical power analysis methods and tools for designing and analyzing randomized cost-effectiveness trials (RCETs) to evaluate the causal effects and costs of social work interventions. The article focuses on two-level designs, where, for example, students are nested within schools, with interventions applied either at the school level (cluster design) or student level (multisite design). We explore three statistical modeling strategies—random-effects, constant-effects, and fixed-effects models—to assess the cost-effectiveness of interventions, and we develop corresponding power analysis methods and tools. Power is influenced by effect size, sample sizes, and design parameters. We developed a user-friendly tool, PowerUp!-CEA, to aid researchers in planning RCETs. When designing RCETs, it is crucial to consider cost variance, its nested effects, and the covariance between effectiveness and cost data, as neglecting these factors may lead to underestimated power.

Effects of selective serotonin reuptake inhibitors on platelet functions: a literature review.

Many epidemiological studies have evidenced an increased bleeding risk associated with selective serotonin reuptake inhibitors (SSRIs), yet the underlying mechanisms remain unclear. This review summarizes data on SSRIs' effects on platelet functions assessed with assays used in clinical practice and highlights the areas that deserve further investigation. Conflicting results of SSRI effects on platelet aggregation were observed irrespectively of the agonist used, the antidepressant drug or the study type. Alike, discrepant results were reported with flow-cytometry-based assays assessing either platelet surface glycoprotein levels, integrin activation, agonist-induced secretion of intraplatelet granule content or membrane anionic phospholipid exposure. Other tests may have detected a platelet function defect in SSRIs samples, however, results were largely inconsistent. Critical literature examination unveils very low certainty of evidence on potential SSRI effect on platelet functions. Findings are often inconsistent even when similar methods are used, most likely because of differences in study design, included patients (age, comorbid conditions), SSRIs' type and dose, uncontrolled confounding factors, and statistical analysis power. Further studies are needed to disentangle any intrinsic antiplatelet effect of SSRIs and the multiple confounding factors, mainly the depression control itself and the degree of platelet SERT inhibition.

The role of effect size and significance test in research design and analysis

Effect size is a straightforward method for measuring the difference between two groups and plays a critical role in research design and significance testing. This paper addresses the concept of effect size, explaining what it is, how it is calculated, and how it can be interpreted. It also explores related topics, such as statistical significance, meta-analysis, and power analysis. Effect sizes can be understood in the context of confidence intervals, which provide similar information to statistical significance tests but focus more on the magnitude of the effect rather than the size of the sample.

Poor sample size reporting quality and insufficient sample size in economic evaluations conducted alongside pragmatic trials: a cross-sectional survey

ObjectivesEconomic evaluations based on well-designed and -conducted pragmatic randomized controlled trials (pRCTs) can provide valuable evidence on the cost-effectiveness of interventions, enhancing the relevance and applicability of findings to healthcare decision-making. However, economic evaluation outcomes are seldom taken into consideration during the process of sample size calculation in pragmatic trials. The reporting quality of sample size and information on its calculation in economic evaluations that are well-suited to pRCTs remain unknown. This study aims to assess the reporting quality of sample size and estimate the power values of economic evaluations in pRCTs. Study Design and SettingWe conducted a cross-sectional survey using data of pRCTs available from PubMed and OVID from 1 January 2010 to 24 April 2022. Two groups of independent reviewers identified articles; three groups of reviewers each extracted the data. Descriptive statistics presented the general characteristics of included studies. Statistical power analyses were performed on clinical and economic outcomes with sufficient data. ResultsThe electronic search identified 715 studies and 152 met the inclusion criteria. Of these, 26 were available for power analysis. Only 9 out of 152 trials (5.9%) considered economic outcomes when estimating sample size, and only one adjusted the sample size accordingly. Power values for trial-based economic evaluations and clinical trials ranged from 2.56% to 100% and 3.21%–100%, respectively. Regardless of the perspectives, in 14 out of the 26 studies (53.8%), the power values of economic evaluations for quality-adjusted life years (QALYs) were lower than those of clinical trials for primary endpoints (PEs). In 11 out of the 24 (45.8%) and in 8 out of the 13 (61.5%) studies, power values of economic evaluations for QALYs were lower than those of clinical trials for PEs from the healthcare and societal perspectives, respectively. Power values of economic evaluations for non-QALYs from the healthcare and societal perspectives were potentially higher than those of clinical trials in 3 out of the 4 studies (75%). The power values for economic outcomes in Q1 were not higher than those for other journal impact factor quartile categories. ConclusionTheoretically, pragmatic trials with concurrent economic evaluations can provide real-world evidence for healthcare decision makers. However, in pRCT-based economic evaluations, limited consideration, and inadequate reporting of sample-size calculations for economic outcomes could negatively affect the results’ reliability and generalisability. We thus recommend that future pragmatic trials with economic evaluations should report how sample sizes are determined or adjusted based on the economic outcomes in their protocols to enhance their transparency and evidence quality.

Miesize: Effect-size calculation in imputed data

In this article, I describe the miesize command for the calculation of effect sizes in imputed data. There may be situations where an effect size needs to be estimated for an intervention, an exposure, or a group membership variable but data on the independent or dependent variable are missing. Such missing data are commonly dealt with by multiply imputing plausible values. However, in this circumstance, the estimated effect size and associated standard errors will need to be pooled and estimated from the imputed dataset. The miesize command automates this process and calculates effect sizes for a binary variable from multiply imputed data in wide format. The estimates and standard errors (used to calculate the confidence intervals) are recombined using Rubin’s (1987, Multiple Imputation for Nonresponse in Surveys [Wiley]) rules. These rules are applied such that the average point estimate for the effect size is calculated from the imputed datasets. The pooled standard error, and hence confidence intervals, is calculated to account for both the variance between the imputed datasets and the variance within them. Pooled effect sizes and confidence intervals for Cohen’s (1988, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. [Lawrence Erlbaum]) d, Hedges’s (1981, Journal of Educational Statistics 6: 107-128) g, and Glass’s ( Smith and Glass, 1977 , American Psychologist 32: 752-760) delta are provided by miesize.

Exploring medical and dental practitioner perspectives and developing a knowledge attitude and practice (KAP) evaluation tool for the common risk factor approach in managing non-communicable and periodontal diseases

BackgroundThe Common Risk Factor Approach (CRFA) is one of the methods to achieve medical-dental integration. CRFA addresses shared risk factors among major Non-communicable Diseases (NCDs). This study aimed to explore the perspectives of dental and medical practitioners concerning CRFA for managing NCDs and periodontal diseases and to create and validate a tool to evaluate the Knowledge, Attitude, and Practice (KAP) of medical and dental practitioners in relation to utilization of CRFA for management of NCDs and Periodontal diseases.MethodsThis research employed a concurrent mixed-method model and was carried out from January 2021 to February 2022, focusing on medical and dental practitioners in South India. In the qualitative phase, online interviews were conducted with dental and medical practitioners, recorded, and transcribed. Thematic analysis was applied after achieving data saturation. In the quantitative phase, a KAP questionnaire was developed. The sample size was determined by using the G power statistical power analysis program. A sample size of 220 in each group (dentists and medical practitioners) was estimated. Systematic random sampling was used to recruit the potential participants. The data obtained through the online dissemination of KAP tool was analysed and scores were standardized to categorize the KAP.ResultsQualitative thematic analysis identified four major themes: understanding of common risk factors, risk factor reduction and disease burden, integrating CRFA into clinical practice, and barriers to CRFA. In addition, thematic analysis revealed seventeen subthemes. For the quantitative phase, standardization was applied to a 14-item KAP questionnaire for medical practitioners and a 19-item KAP questionnaire for dental practitioners. The total KAP score for medical practitioners in the study was 21.84 ± 2.87, while dental practitioners scored 22.82 ± 3.21, which indicated a high level of KAP regarding CRFA. Meta integration of qualitative and quantitative data identified eight overarching themes: four were concordant, three were discordant, and one theme provided the explanatory component.ConclusionThe study’s structured, validated questionnaire showed that both medical and dental professionals had a high knowledge of CRFA. However, they were not appreciably aware of the risk factors that are shared between NCDs and periodontal disease. Both groups were interested in the idea of using CRFA in integrated medical and dental care.

Association of candidate gene (INSR & THADA) polymorphism with polycystic ovary syndrome: meta-analysis and statistical power analysis.

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder that impacts women before reaching menopause. In addition to notable features (irregular ovulation, elevated androgen levels, and the existence of numerous ovarian cysts), individuals with PCOS frequently encounter diverse metabolic, cardiovascular, and psychological conditions. The onset of PCOS is influenced by a combination of factors, and various genetic variations are believed to play a significant role in its progression. The objective of the current study was to explore the link between genetic variations in the candidate genes thyroid-adenoma-associated (THADA) gene and insulin receptor (INSR) and susceptibility to developing PCOS. We conducted an extensive search across various databases, including Google Scholar, PubMed, Science Direct, Scopus, and EMBASE, to compile relevant case-control studies and literature reviews for subsequent statistical analysis. In the present study, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was followed, a guideline for Systematic Reviews and Meta-Analysis. While a previous meta-analysis explored the correlation between INSR rs1799817 and THADA rs13429458 and their association with susceptibility to PCOS, our current study did not integrate any findings from these prior investigations. Our research encompassed articles published between 2017 and 2023, and we employed MetaGenyo software to assess the collected data. Statistical power analysis was performed using G*Power 3.1 software. Odds ratios and their corresponding 95% confidence intervals were calculated for each genetic model. Fifteen studies that met the criteria were analyzed. Out of these, ten studies, involving 1,189 cases and 1,005 controls, examined the INSR rs1799817 gene polymorphism, while five studies, including 783 cases and 553 controls, investigated the THADA rs13429458 gene polymorphism. The meta-analysis results indicated that there was no statistically significant association between the INSR rs1799817 gene polymorphism and the risk of PCOS (p>0.05). In contrast, the THADA rs13429458 gene polymorphism showed a significant association with PCOS risk under the over-dominant model (p<0.05). The present meta-analysis demonstrated a notable association between the THADA rs13429458 gene polymorphism and the likelihood of developing PCOS. Further rigorous studies with expanded sample sizes and diverse ethnic representation will be important to comprehensively evaluate and validate these findings.

Knowledge, Attitudes, and Practices towards Single-Use Plastic Bags in the United Arab Emirates

Due to their widespread misuse and inadequate waste management, single-use plastic bags represent the global plastic pollution crisis and ecosystem degradation. Comprehensive research on population knowledge, attitudes, and practices (KAP) regarding single-use plastic bags is lacking in the United Arab Emirates (UAE). This study aims to fill this gap by assessing the KAP levels among the UAE population and determining the relationships between these variables. A five-point Likert scale questionnaire from previous studies was used in a cross-sectional study, and the questionnaire was distributed via Google Forms. The UAE University Research Ethics Committee approved the study (ERSC_2024_4377). Non-probability sampling and G*power statistical analysis version 3.1.9.6 determined 385 UAE residents aged 18 years and older. The findings were verified through face-to-face interviews and Cronbach’s alpha tests. KAP variable associations were assessed using Chi-square tests, Pearson’s correlation coefficients, and a structural equation model with SPSS version 29.0. This test was chosen for robust categorical and continuous data. The study included 84.7% female, and 15.3% male participants aged 18 to 65 years, primarily aged 18 to 25 years, with most holding bachelor’s degrees (47.6%), and were students (51.4%), employed (33.1%), or unemployed (12.2%). The results indicated a favourable inclination towards sustainability, with mean ± standard deviation (SD) for seven knowledge questions (3.89 ± 0.7), seven attitude questions (3.99 ± 0.7), and five practice questions (3.42 ± 0.8) exceeding the midpoint. The correlation test showed that total knowledge was positively correlated with practice (r = 0.399; p &lt; 0.001) indicating the increasing knowledge regarding single-use plastic bags associated with increasing sustainable practices and attitudes (r = 0.648; p &lt; 0.001). There was also a significant correlation between the total attitude and practice (r = 0.487; p &lt; 0.001). The findings suggest targeted educational and policy interventions to promote sustainable behaviour.

Cypress: an R/Bioconductor package for cell-type-specific differential expression analysis power assessment.

Recent methodology advances in computational signal deconvolution have enabled bulk transcriptome data analysis at a finer cell-type level. Through deconvolution, identifying cell-type-specific differentially expressed (csDE) genes is drawing increasing attention in clinical applications. However, researchers still face a number of difficulties in adopting csDE genes detection methods in practice, especially in their experimental design. Here we present cypress, the first experimental design and statistical power analysis tool in csDE genes identification. This tool can reliably model purified cell-type-specific (CTS) profiles, cell-type compositions, biological and technical variations, offering a high-fidelity simulator for bulk RNA-seq convolution and deconvolution. cypress conducts simulation and evaluates the impact of multiple influencing factors, by various statistical metrics, to help researchers optimize experimental design and conduct power analysis. cypress is an open-source R/Bioconductor package at https://bioconductor.org/packages/cypress/.

An Assessment of the Knowledge, Attitude, and Practice of Probiotics and Prebiotics among the Population of the United Arab Emirates.

Probiotics and prebiotics offer a range of advantageous effects on human health. The knowledge, attitudes, and practices (KAP) of individuals can impact their inclination to consume probiotics and prebiotics. The main objective of this study was to examine the KAP of the people in the United Arab Emirates (UAE) about probiotics and prebiotics consumption. Additionally, the study aimed to assess the impact of KAP and sociodemographic factors on the use of probiotics and prebiotics. In order to accomplish this objective, a verified online questionnaire was used with a five-point Likert scale and distributed using an online platform (Google Forms). A cross-sectional research, non-probability sampling was implemented, and G*Power statistical power analysis was used to estimate a sample size of 385 participants. A total of 408 replies were gathered. The population under study consisted of residents in the UAE between the ages of 18 to 64 years old, excluding populations under the age of 18 and those living outside the UAE. A total of 392 participants met the criteria for inclusion in this study. The research ethics committees of UAE University granted the study approval (ERSC_2024_4359), and the validity of the findings was confirmed through face-to-face interviews with around 50 individuals and a Cronbach's alpha test with result of 0.84. The statistical software SPSS version 29.0 for Mac OS was utilized to examine the relationships between KAP variables, including Chi-square tests and Pearson's correlation coefficients. The tests were selected based on their capacity to handle categorical and continuous data, respectively. The female population was 85.2% of the total, while the male population accounts for 14.8%. The age distribution of participants shows that the largest proportion, 68.4%, falls within the 18-24 age range. Out of the participants, 61.5% held a bachelor's degree. Most of the participants, 56.4%, were students, while 29.1% were employees. The average results indicate a significant inclination towards probiotics and prebiotics, as demonstrated by the scores above the midpoint for the six knowledge questions (M = 2.70), six attitude questions (M = 3.10), and six practice questions (M = 3.04). Several studies have examined this phenomenon; however, additional research comparing individuals in the UAE is necessary to fully comprehend the influence of KAP on the consumption of probiotics and prebiotics in the UAE.

Statistical Significance is Desirable but not Absolute : In Search of the True Study

In the field of scientific research, the pursuit of robust and reliable results is paramount. Traditionally, the p-value has been the cornerstone of statistical analysis, used to determine the significance of study findings. However, there is a growing consensus that the calculation of effect size and statistical power analysis offer a more comprehensive and superior approach to data interpretation. While the p-value has its place in statistical analysis, the calculation of effect size and statistical power analysis provides a more nuanced and informative perspective on research findings. Embracing these methods will enhance the quality and impact of scientific research, leading to more robust and meaningful conclusions.

IMG-05. USING CONVOLUTIONAL NEURAL NETWORKS TO IDENTIFY COMMON MOLECULAR ALTERATIONS IN PEDIATRIC BRAIN TUMORS

Abstract BACKGROUND Artificial intelligence (AI) and deep learning have had increased uses in healthcare. Initial studies have shown it is possible to predict multiple tumor types in adults and children using convolutional neural networks (CNN), a form of deep learning, using MRIs. In pediatric CNS tumors, molecular alterations are used in both diagnosis and treatment decisions. This project aims to verify that we can use deep learning to help diagnose and molecularly classify primary pediatric CNS tumors. Previously, we used Recurrent Health’s platform to predict high vs. low grade CNS lesions to &amp;gt;95% recall and 85% precision in pediatric patients. The objective of this study is to validate the AI’s ability to predict common genetic alterations with 90% accuracy. METHODS This retrospective study evaluated patients (ages 0-21) with histologically confirmed primary CNS tumors that underwent BRAF molecular analysis. We collected demographic data: age, tumor location, and presence of BRAF mutations/fusions. The data was divided into a training and validation set (80%/20% split). To determine the viability of models, we will measure specificity, sensitivity, and F1 Score (Sorenson-Dice coefficient) for predicting genetic alterations. Model score confidence was achieved through K-fold cross-validation and statistical power analysis with a finite population coefficient to determine the appropriate sample size. RESULTS Our retrospective test included 136 patients with multiple scans for training, model validation and testing. The CNN was able to predict presence of BRAF fusions with &amp;gt;95% recall/sensitivity, &amp;gt;76% precision/specificity and F1/accuracy &amp;gt;87% while predicting BRAFV600 mutations with &amp;gt;75% recall/sensitivity, &amp;gt;95% precision/specificity and F1/accuracy &amp;gt;85%. These metrics indicate strong discrimination in classification. CONCLUSIONS Preliminary data suggests the accuracy of the model in identifying the presence of BRAF mutations, providing a potential opportunity for a non-invasive tool to help aid in diagnosis and help guide initiation of targeted therapy.

Causes and consequences of major depressive disorder: An encompassing Mendelian randomization study.

Major depressive disorder (MDD) is a prevalent and debilitating disorder that has been associated with a range of risk factors and outcomes. Causal pathways between MDD and other traits can be studied using genetic variants as instrumental variables. A literature review was conducted to identify 201 MDD-associated traits. For 115 traits, there were well-powered genome-wide association study (GWAS) results available that could be used to assess the genetic correlation with MDD. Of these, there were 89 meeting criteria for investigating causal associations in both directions using two-sample Mendelian randomization (TSMR). Of the traits that were not captured by GWAS, 43 could be included as outcomes of MDD using one-sample MR (OSMR). A range of methods and sensitivity tests was applied to gauge robustness of results, together with statistical power analyses to aid interpretation. Moderate to strong genetic overlap was found between MDD and most traits. Support for causal effects of MDD liability were found for circadian, cognitive, diet, medical disease, endocrine, functional, inflammatory, metabolic, mortality, physical activity, reproduction, risk behavior, social, socioeconomic, and suicide outcomes. Most associations were bidirectional, although there was less evidence for diet, disease, and endocrine traits causing MDD risk. Results were robust across sensitivity analyses. This study provides a systematic overview of traits putatively causally related to MDD, confirming previous findings as well as identifying new associations. Our results highlight the importance of MDD as a risk factor cross-cutting across medical, functional, and psychosocial domains and emphasize the need for concerted efforts at reducing this highly prevalent disorder.

Validation of Novel Microsurgical Vessel Anastomosis Techniques: A Systematic Review.

Thorough validation of novel microsurgical techniques is deemed essential before their integration into clinical practice. To achieve proper validation, the design of randomized controlled trials (RCTs) should be undertaken, accompanied by the execution of comprehensive statistical analyses, including confounder adjustment and power analysis. This systematic review aims to provide an encompassing overview of the validation methodologies employed in microsurgical studies, with a specific focus on innovative vessel anastomosis techniques. A literature search was conducted in PubMed for articles describing the validation of novel microsurgical vessel anastomosis techniques in animal or human subjects. The literature search yielded 6,658 articles. A total of 6,564 articles were excluded based on title and abstract. Ninety-four articles were assessed for full-text eligibility. Forty-eight articles were included in this systematic review. Out of 30 comparative studies, 9 studies validated novel modified interrupted suture techniques, 6 studies modified continuous techniques, 6 studies modified sleeve anastomosis techniques, 1 study a modified vesselotomy technique, 7 studies sutureless techniques, and 1 study a modified lymphaticovenular anastomosis technique. Twenty-eight studies contained animals (n = 1,998). Fifteen animal studies were RCTs. Two studies contained human/cadaveric subjects (n = 29). Statistical power analysis and confounder adjustment were performed in one animal study. Out of 18 noncomparative studies, 5 studies validated novel modified interrupted suture techniques, 1 study a modified continuous technique, 2 studies modified sleeve anastomosis techniques, 4 studies modified vesselotomy techniques, 4 studies sutureless techniques, and 2 studies modified lymphaticovenular anastomosis techniques. Ten studies contained animal subjects (n = 320), with two RCTs. Eight studies contained human subjects (n = 173). Statistical power analysis and confounder adjustment were performed in none of the animal or human studies. The current methods of microsurgical technique validation should be reconsidered due to poor study design. Statistical analysis including confounder adjustment and power analysis should be performed as a standard method of novel technique validation.

Education Experiments in Latin America: Empirical Evidence to Guide Evaluation Design.

Randomized experiments involving education interventions are typically implemented as cluster randomized trials, with schools serving as clusters. To design such a study, it is critical to understand the degree to which learning outcomes vary between versus within clusters (schools), specifically the intraclass correlation coefficient. It is also helpful to anticipate the benefits, in terms of statistical power, of collecting household data, testing students at baseline, or relying on administrative data on previous cohorts from the same school. We use data from multiple cluster-randomized trials in four Latin American countries to provide information on the intraclass correlations in early grade literacy outcomes. We also describe the proportion of variance explained by different types of covariates. These parameters will help future researchers conduct statistical power analysis, estimate the required sample size, and determine the necessity of collecting different types of baseline data such as child assessments, administrative data at the school level, or household surveys.

A Pilot Study Testing the Novel Use of a Snake Bite Compression Device for Field Hemostasis and Wound Repair in an Animal Model.

A commercially available snake bite device was pilot tested for novel use as a method of hemostasis and wound repair at a noncompressible site in a live swine model. The device is light, is plastic, uses a hook-and-loop strap attachment, and is easily deployed. The device could offer a method for the field repair of an actively bleeding laceration at a noncompressible site in an austere environment. This was an interventional, prospective, controlled study in a large animal model. The study was approved by the Rhode Island Hospital Institutional Review Board (IRB) and the Animal Welfare Committee/Institutional Animal Care and Use Committee and the Lifespan Research Conflict of Interest Committee. Each animal acted as its own control. Blood loss was measured and compared between repairs of standardized incisions with and without the device's application. The lacerations were sutured closed. Two proceduralists alternated tasks of wound repair versus blood collection. Blood loss was measured by using gauze sponges to capture the blood during a 30-second free-bleeding period and during the repair itself. Using a one sample t-test (the expected difference in blood loss between the two incision repair methods = 0 if the null hypothesis were true), we calculated the mean difference in the deltas between the repair methods. The mean delta difference was 3.1 g (SE ± 0.97). The t-test demonstrated that there was a significantly greater blood loss during the standard repair method, t(9) = 3.11, P < 0.01 than during the repair with the device in place (see Fig.2). A statistical power analysis conducted showed that with a sample size of 10 animals, there was sufficient statistical power to detect this significant effect (β = 0.82, α < 0.05, one-tailed). There was statistically significantly less blood loss during the repairs with the device's application. This feasibility experiment demonstrates that a commercially available snakebite device may be useful for hemostasis during laceration repair at anatomic sites not amenable to application of tourniquets or compressive dressings. Strengths of the study include the prospective controlled design, including the use of each animal as its own control; alternating proceduralists to account for any variability in suturing efficiency; and the statistical significance of the results despite the small number of subjects. One weakness is that the time required for each repair was not measured. The device's portability and reusability suggest applicability in austere medical environments. Future studies could include timing the repairs, using a skin stapler or wound adhesive instead of sutures, applying a hemostatic agent before the repair, and sequentially applying the device to wounds longer than the device.

Statistical power analysis and sample size planning for moderated mediation models.

Conditional process models, including moderated mediation models and mediated moderation models, are widely used in behavioral science research. However, few studies have examined approaches to conduct statistical power analysis for such models and there is also a lack of software packages that provide such power analysis functionalities. In this paper, we introduce new simulation-based methods for power analysis of conditional process models with a focus on moderated mediation models. These simulation-based methods provide intuitive ways for sample-size planning based on regression coefficients in a moderated mediation model as well as selected variance and covariance components. We demonstrate how the methods can be applied to five commonly used moderated mediation models using a simulation study, and we also assess the performance of the methods through the five models. We implement our approaches in the WebPower R package and also in Web apps to ease their application.

Advancing mouse models for transplantation research

Mouse models have been instrumental in understanding mechanisms of transplant rejection and tolerance, but cross-study reproducibility and translation of experimental findings into effective clinical therapies are issues of concern. The Mouse Models in Transplantation symposium gathered scientists and physician-scientists involved in basic and clinical research in transplantation to discuss the strengths and limitations of mouse transplant models and strategies to enhance their utility. Participants recognized that increased procedure standardization, including the use of prespecified, defined endpoints, and statistical power analyses, would benefit the field. They also discussed the generation of new models that incorporate environmental and genetic variables affecting clinical outcomes as potentially important. If implemented, these strategies are expected to improve the reproducibility of mouse studies and increase their translation to clinical trials and, ideally, new Food and Drug Administration–approved drugs.

Testing biasedness of self-reported microbusiness innovation in the annual business survey.

This study tests for potential bias in self-reported innovation due to the inclusion of a research and development (R&D) module that only microbusinesses (less than 10 employees) receive in the Annual Business Survey (ABS). Previous research found that respondents to combined innovation/R&D surveys reported innovation at lower rates than respondents to innovation-only surveys. A regression discontinuity design is used to test whether microbusinesses, which constitute a significant portion of U.S. firms with employees, are less likely to report innovation compared to other small businesses. In the vicinity of the 10-employee threshold, the study does not detect statistically significant biases for new-to-market and new-to-business product innovation. Statistical power analysis confirms the nonexistence of biases with a high power. Comparing the survey design of ABS to earlier combined innovation/R&D surveys provides valuable insights for the proposed integration of multiple Federal surveys into a single enterprise platform survey. The findings also have important implications for the accuracy and reliability of innovation data used as an input to policymaking and business development strategies in the United States.

Experimental and Statistical Study of Flexural Strength in Ternary Blended High-Performance Concrete using Alcofine

The primary aim of this research is to conduct a comprehensive comparative experimental and statistical study on the flexural strength of a novel ternary blended high-performance M30 grade concrete incorporating 20% Alcofine in comparison to traditional concrete.The components employed in the experimental investigation of high-performance M30 concrete incorporating Alcofine, in contrast to conventional concrete, comprise cement, fine aggregate, coarse aggregate, water, Alcofine, and additional cementitious materials like fly ash and silica fume. The Flexural Strength of high-performance M30 concrete containing Alcofine significantly influences the performance of concrete structures, rendering it a critical mechanical property for examination in the comparative analysis. The mean flexural strength of the Conventional Concrete group measured 8.1111 N/mm^2, with a standard deviation of 0.75840 and a standard error of the mean of 0.17876. In contrast, the Ternary Blended Concrete group exhibited a higher mean flexural strength of 12.5000 N/mm^2, coupled with a larger standard deviation of 2.09341 and a standard error of the mean of 0.49342. The statistical power analysis, involving parameters such as alpha (α) and beta (β), with commonly used values of 0.05 or 0.01, indicates a significance level of 5% or 1%, respectively. Further research could delve into refining the optimal percentage of Alcofine and exploring its long-term performance under varying environmental conditions. Keywords: Ternary Blended Concrete; Alcofine; Flexural Strength; Comparative Analysis; Statistical Study