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Background The multiple-indicator, multiple-cause model (MIMIC) incorporates covariates of interest in the factor analysis. It is a special case of structural equation modeling (SEM), which is modeled under latent variable framework. The MIMIC model provides rigorous results and becomes broadly available in multiple statistical software. The current study introduces the MIMIC model and how it can be implemented using statistical software packages SAS CALIS procedure, R lavaan package, and M plus version 8.0. Methods In this paper, we first discussed the formulation of the MIMIC model with regard to model specification and identification. We then demonstrated the empirical application of the MIMIC model with the Midlife in the United States II (MIDUS II) Study ( N  = 4109) using SAS CALIS procedure, R lavaan package and M plus version 8.0 to examine gender disparities in cognitive functioning. The input, output, and diagram syntaxes of the three statistical software packages were also presented. Results In terms of data structure, all three statistical programs can be conducted using both raw data and empirical covariance matrix. SAS and R are comprehensive statistical analytic packages and encompass numerous data manipulation capacities. M plus is designed primarily for latent variable modeling and has far more modeling flexibility compared to SAS and R, but limited in data manipulation. Differences in model results from the three statistical programs are trivial. Overall, the results show that while men show better performance in executive function than women, women demonstrate better episodic memory than men. Conclusions Our study demonstrates the utility of the MIMIC model in its empirical application, fitted with three popular statistical software packages. Results from our models align with empirical findings from previous research. We provide coding procedures and examples with detailed explanations in the hopes of providing a concise tutorial for researchers and methodologists interested in incorporating latent constructs with multiple indicators and multiple covariates in their research projects. Future researchers are encouraged to adopt this flexible and rigorous modeling approach.

\"Learn how Roslyn's new code generation capability will let you write software that is more concise, runs faster, and is easier to maintain. You will learn from real-world business applications to create better software by letting the computer write its own code based on your business logic already defined in lookup tables. Code Generation with Rosyln is the first book to cover this new capability. You will learn how these techniques can be used to simplify systems integration so that if one system already defines business logic through lookup tables, you can integrate a new system and share business logic by allowing the new system to write its own business logic based on already existing table-based business logic. One of the many benefits you will discover is that Roslyn uses an innovative approach to compiler design, opening up the inner workings of the compiler process. You will learn how to see the syntax tree that Roslyn is building as it compiles your code. Additionally, you will learn to feed it your own syntax tree that you create on the fly.\"-- Provided by publisher

Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting: planning the analysis, executing the analysis, interpreting the results, and reporting the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general.

Background Systematic review with meta-analysis integrates findings from multiple studies, offering robust conclusions on treatment effects and guiding evidence-based medicine. However, the process is often hampered by challenges such as inconsistent data reporting, complex calculations, and time constraints. Researchers must convert various statistical measures into a common format, which can be error-prone and labor-intensive without the right tools. Implementation Meta-Analysis Accelerator was developed to address these challenges. The tool offers 21 different statistical conversions, including median & interquartile range (IQR) to mean & standard deviation (SD), standard error of the mean (SEM) to SD, and confidence interval (CI) to SD for one and two groups, among others. It is designed with an intuitive interface, ensuring that users can navigate the tool easily and perform conversions accurately and efficiently. The website structure includes a home page, conversion page, request a conversion feature, about page, articles page, and privacy policy page. This comprehensive design supports the tool’s primary goal of simplifying the meta-analysis process. Results Since its initial release in October 2023 as Meta Converter and subsequent renaming to Meta-Analysis Accelerator, the tool has gained widespread use globally. From March 2024 to May 2024, it received 12,236 visits from countries such as Egypt, France, Indonesia, and the USA, indicating its international appeal and utility. Approximately 46% of the visits were direct, reflecting its popularity and trust among users. Conclusions Meta-Analysis Accelerator significantly enhances the efficiency and accuracy of meta-analysis of systematic reviews by providing a reliable platform for statistical data conversion. Its comprehensive variety of conversions, user-friendly interface, and continuous improvements make it an indispensable resource for researchers. The tool’s ability to streamline data transformation ensures that researchers can focus more on data interpretation and less on manual calculations, thus advancing the quality and ease of conducting systematic reviews and meta-analyses.

Background Estimating the health effects of multi-pollutant mixtures is of increasing interest in environmental epidemiology. Recently, a new approach for estimating the health effects of mixtures, Bayesian kernel machine regression (BKMR), has been developed. This method estimates the multivariable exposure-response function in a flexible and parsimonious way, conducts variable selection on the (potentially high-dimensional) vector of exposures, and allows for a grouped variable selection approach that can accommodate highly correlated exposures. However, the application of this novel method has been limited by a lack of available software, the need to derive interpretable output in a computationally efficient manner, and the inability to apply the method to non-continuous outcome variables. Methods This paper addresses these limitations by (i) introducing an open-source software package in the R programming language, the bkmr R package, (ii) demonstrating methods for visualizing high-dimensional exposure-response functions, and for estimating scientifically relevant summaries, (iii) illustrating a probit regression implementation of BKMR for binary outcomes, and (iv) describing a fast version of BKMR that utilizes a Gaussian predictive process approach. All of the methods are illustrated using fully reproducible examples with the provided R code. Results Applying the methods to a continuous outcome example illustrated the ability of the BKMR implementation to estimate the health effects of multi-pollutant mixtures in the context of a highly nonlinear, biologically-based dose-response function, and to estimate overall, single-exposure, and interactive health effects. The Gaussian predictive process method led to a substantial reduction in the runtime, without a major decrease in accuracy. In the setting of a larger number of exposures and a dichotomous outcome, the probit BKMR implementation was able to correctly identify the variables included in the exposure-response function and yielded interpretable quantities on the scale of a latent continuous outcome or on the scale of the outcome probability. Conclusions This newly developed software, integrated suite of tools, and extended methodology makes BKMR accessible for use across a broad range of epidemiological applications in which multiple risk factors have complex effects on health.

Go from total MATLAB newbie to plotting graphs and solving equations in a flash!  MATLAB is one of the most powerful and commonly used tools in the STEM field.But did you know it doesn't take an advanced degree or a ton of computer experience to learn it?  MATLAB For Dummies  is the roadmap you've been looking for to simplify and explain this.

The artificial intelligence (AI) framework is intended to solve a the problem of bias--variance tradeoff for supervised learning methodsin real-life applications. The AI framework It comprises of bootstrapping to create multiple training and testing datasetsdata sets with various characteristics, design, and analysis of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for machine learning (ML) methods, and data contamination to test for the robustness of the classifiers. Key Features: Using ML methods by itself doesn't does notensure building classifiers that generalize well for new data. Identifying optimal feature subsets and hyper-parameters of ML methods can be resolved using the design and analysis of statistical experiments. Using a bootstrapping approach to massive sampling of training and tests datasets with various data characteristics (e.g.: contaminated training sets) allows dealing with bias. Developing of anSAS-based table-driven environment allows managing the management of all meta-data related to the proposed AI framework and creating the creation of interoperability with R libraries to accomplish a variety of statistical and machine-learning tasks. Computer programs in R and SAS that create AI frameworks are available on GitHub.

Introduction Statistics helps medical students understand research. Without understanding statistics, students can’t choose the proper analysis in their research. We aimed to assess the attitude toward statistics, usage of statical software and associated factors for using statistical analysis software in Sudan. Method A cross-sectional online survey was distributed among undergraduate medical students across ten Sudanese universities. The study aimed to measure their attitude towards statistics using Survey of Attitudes Toward Statistics (SATS-36) scale. Results In total, 489 students were participated with a mean age of 21.94 ± 1.61 and a slight female preponderance (52%, n  = 256). The overall attitude towards statistics was 4.64 ± 0.91. The mean attitude scores for the components of SATS-36 scale was higher for students who were using statistical analysis software demonstrating significant difference in affect ( p  = 0.002), cognitive competence ( p  = 0.002), value ( p  = 0.002), Interest ( p  = 0.004) and Effort ( p  = 0.029). Almost half of the students (47%) had attended a biostatistics workshop with only 26% of them reported using statistical analysis software. Of the latter group, 72% ( n  = 91) used SPSS while 50% ( n  = 64) used excel. Univariate logistic regression showed students who had previously used an statistical software were more likely to be studying in their sixth year compared with second year (OR: 12.652, CI 95% 4.803– 33.332; p  < 0.001), older age (OR: 1.224, CI 95% 1.079– 1.388; p  = 0.002), attended a course in research methodology (OR: 3.383, CI 95% 2.120– 5.398; p  < 0.001) or biostatistics (OR: 1.886, CI 95% 1.252– 2.841; p  = 0.002), initiated or participated in a research project (OR:4.349, CI 95% 2.839 – 6.661; p  < 0.001) or published a paper (OR: 8.271, CI 95% 3.542 – 19.312; p  < 0.001). Conclusions The study showed an average attitude towards statistics among medical students. Being at higher years, participating or publishing research and attending research workshop are associated with the usage of statistical software. Also, few students were using statistical software.