StatiCAL: an interactive tool for statistical analysis of biomedical data and scientific valorization.

BACKGROUND: In the realm of biomedical research, the growing volume, diversity and quantity of data has escalated the demand for statistical analysis as it is indispensable for synthesizing, interpreting, and publishing data. Hence the need for accessible analysis tools drastically increased. StatiCAL emerges as a user-friendly solution, enabling researchers to conduct basic analyses without necessitating extensive programming expertise. RESULTS: StatiCAL includes divers functionalities: data management, visualization on variables and statistical analysis. Data management functionalities allow the user to freely add or remove variables, to select sub-population and to visualise selected data to better perform the analysis. With this tool, users can freely perform statistical analysis such as descriptive, graphical, univariate, and multivariate analysis. All of this can be performed without the need to learn R coding as the software is a graphical user interface where all the action can be performed by clicking a button. CONCLUSIONS: StatiCAL represents a valuable contribution to the field of biomedical research. By being open-access and by providing an intuitive interface with robust features, StatiCAL allow researchers to gain autonomy in conducting their projects.

IsoBayes: a Bayesian approach for single-isoform proteomics inference.

Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms. Here, we introduce IsoBayes, a novel statistical method to perform inference at the isoform level. Our method enhances the information available, by integrating mass spectrometry proteomics and transcriptomics data in a Bayesian probabilistic framework. To account for the uncertainty in the measurement process, we propose a two-layer latent variable approach: first, we sample if a peptide has been correctly detected (or, alternatively filter peptides); second, we allocate the abundance of such selected peptides across the protein(s) they are compatible with. This enables us, starting from peptide-level data, to recover protein-level data; in particular, we: i) infer the presence/absence of each protein isoform (via a posterior probability), ii) estimate its abundance (and credible interval), and iii) target isoforms where transcript and protein relative abundances significantly differ. We benchmarked our approach in simulations, and in two multi-protease real datasets: our method displays good sensitivity and specificity when detecting protein isoforms, its estimated abundances highly correlate with the ground truth, and can detect changes between protein and transcript relative abundances. IsoBayes is freely distributed as a Bioconductor R package, and is accompanied by an example usage vignette.

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.

Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g. healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, ie reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, vs. state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package.

From significance testing to estimation and Open Science: How esci can help.

We argue that researchers should test less, estimate more, and adopt Open Science practices. We outline some of the flaws of null hypothesis significance testing and take three approaches to demonstrating the unreliability of the p value. We explain some advantages of estimation and meta-analysis ("the new statistics"), especially as contributions to Open Science practices, which aim to increase the openness, integrity, and replicability of research. We then describe esci (estimation statistics with confidence intervals): a set of online simulations and an R package for estimation that integrates into jamovi and JASP. This software provides (a) online activities to sharpen understanding of statistical concepts (e.g., "The Dance of the Means"); (b) effects sizes and confidence intervals for a range of study designs, largely by using techniques recently developed by Bonett; (c) publication-ready visualisations that make uncertainty salient; and (d) the option to conduct strong, fair hypothesis evaluation through specification of an interval null. Although developed specifically to support undergraduate learning through the 2nd edition of our textbook, esci should prove a valuable tool for graduate students and researchers interested in adopting the estimation approach. Further information is at ( https://thenewstatistics.com).

Analysing postprandial amino acid responses in crossover studies with the aaresponse package for R.

Nowadays, a healthier and more sustainable lifestyle is the subject of much research. One example is the use of crossover trials to investigate the uptake of proteins, usually from alternatives to animal-based sources, by healthy volunteers. The data analysis is complex and requires many decisions on the part of the scientists involved. Such a process can be streamlined and made more objective and reproducible through bespoke software. This paper describes such a software package, aaresponse , for the R environment, available as open source. It features ample visualization functions, supports consistent curation strategies, and compares amino acid uptake of different protein meals (interventions) through the use of mixed models analysing parameters of interest like the area under the curve (AUC). The defining feature is the use of parametric curves to fit the amino acid levels over time, increasing the robustness of the approach and allowing for more strict quality control strategies.

A practical guide to adopting Bayesian analyses in clinical research.

Background: Bayesian statistical approaches are extensively used in new statistical methods but have not been adopted at the same rate in clinical and translational (C&T) research. The goal of this paper is to accelerate the transition of new methods into practice by improving the C&T researcher's ability to gain confidence in interpreting and implementing Bayesian analyses. Methods: We developed a Bayesian data analysis plan and implemented that plan for a two-arm clinical trial comparing the effectiveness of a new opioid in reducing time to discharge from the post-operative anesthesia unit and nerve block usage in surgery. Through this application, we offer a brief tutorial on Bayesian methods and exhibit how to apply four Bayesian statistical packages from STATA, SAS, and RStan to conduct linear and logistic regression analyses in clinical research. Results: The analysis results in our application were robust to statistical package and consistent across a wide range of prior distributions. STATA was the most approachable package for linear regression but was more limited in the models that could be fitted and easily summarized. SAS and R offered more straightforward documentation and data management for the posteriors. They also offered direct programming of the likelihood making them more easily extendable to complex problems. Conclusion: Bayesian analysis is now accessible to a broad range of data analysts and should be considered in more C&T research analyses. This will allow C&T research teams the ability to adopt and interpret Bayesian methodology in more complex problems where Bayesian approaches are often needed.

Free software applications for authors for writing a research paper.

Basic computer skills are essential for authors writing research papers as it has the potential to make the task easier for a researcher. This article provides a glimpse about the essential software programs for a novice author writing a research paper. These software applications help streamline the writing process, improve the quality of work, and ensure that papers are formatted correctly. It covers word processing software, grammar correction software, bibliography management software, paraphrasing tool, writing tools, and statistical software. All of the tools described are free to use. Hence, it would help researchers from resource-limited settings or busy physicians who get lesser time for research writing. We presume this review paper would help provide valuable insights and guidance for novice authors looking to write a high-quality research paper.

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.

Motivation: Although transcriptomics data is typically used to analyse mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g., healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, i.e., reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Results: Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, versus state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. Availability and implementation: DifferentialRegulation is distributed as a Bioconductor R package.

Avoiding Blunders When Analyzing Correlated Data, Clustered Data, or Repeated Measures.

Rheumatology research often involves correlated and clustered data. A common error when analyzing these data occurs when instead we treat these data as independent observations. This can lead to incorrect statistical inference. The data used are a subset of the 2017 study from Raheel et al consisting of 633 patients with rheumatoid arthritis (RA) between 1988 and 2007. RA flare and the number of swollen joints served as our binary and continuous outcomes, respectively. Generalized linear models (GLM) were fitted for each, while adjusting for rheumatoid factor (RF) positivity and sex. Additionally, a generalized linear mixed model with a random intercept and a generalized estimating equation were used to model RA flare and the number of swollen joints, respectively, to take additional correlation into account. The GLM's ß coefficients and their 95% confidence intervals (CIs) are then compared to their mixed-effects equivalents. The ß coefficients compared between methodologies are very similar. However, their standard errors increase when correlation is accounted for. As a result, if the additional correlations are not considered, the standard error can be underestimated. This results in an overestimated effect size, narrower CIs, increased type I error, and a smaller P value, thus potentially producing misleading results. It is important to model the additional correlation that occurs in correlated data.

prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis.

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.

Weibull Regression and Machine Learning Survival Models: Methodology, Comparison, and Application to Biomedical Data Related to Cardiac Surgery.

In this article, we propose a comparative study between two models that can be used by researchers for the analysis of survival data: (i) the Weibull regression model and (ii) the random survival forest (RSF) model. The models are compared considering the error rate, the performance of the model through the Harrell C-index, and the identification of the relevant variables for survival prediction. A statistical analysis of a data set from the Heart Institute of the University of São Paulo, Brazil, has been carried out. In the study, the length of stay of patients undergoing cardiac surgery, within the operating room, was used as the response variable. The obtained results show that the RSF model has less error rate for the training and testing data sets, at 23.55% and 20.31%, respectively, than the Weibull model, which has an error rate of 23.82%. Regarding the Harrell C-index, we obtain the values 0.76, 0.79, and 0.76, for the RSF and Weibull models, respectively. After the selection procedure, the Weibull model contains variables associated with the type of protocol and type of patient being statistically significant at 5%. The RSF model chooses age, type of patient, and type of protocol as relevant variables for prediction. We employ the randomForestSRC package of the R software to perform our data analysis and computational experiments. The proposal that we present has many applications in biology and medicine, which are discussed in the conclusions of this work.

Statistical Analysis for Understanding PM2.5 Air Quality and the Impacts of COVID-19 Social Distancing in Several Provinces and Cities in Vietnam.

Air pollution, especially in urban regions, is receiving increasing attention in Vietnam. Consequently, this work aimed to study and analyze the air quality in several provinces and cities in the country focusing on PM2.5. Moreover, the impacts of COVID-19 social distancing on the PM2.5 level were investigated. For this purpose, descriptive statistic, Box and Whisker plot, correlation matrix, temporal variation, and trend analysis were conducted. R-based program and the R package "openair" were employed for the calculations. Hourly PM2.5 data were obtained from 8 national air quality monitoring sites. The study results indicated that provinces and cities in the North experienced more PM2.5 pollution compared to the Central and South. PM2.5 concentrations at each monitoring site varied significantly. Among monitoring sites, the northern sites showed high PM2.5 correlations with each other than the other sites. Seasonal variation was observed with high PM2.5 concentration in the dry season and low PM2.5 concentration in the wet season. PM2.5 concentration variation during the week was not so different. Diurnal variation showed that PM2.5 concentration rose at peak traffic hours and dropped in the afternoon. There was mainly a decreasing trend in PM2.5 concentration over the studied period. The COVID-19 pandemic has contributed to PM2.5 reduction. In the months implemented social distancing for preventing the epidemic, PM2.5 concentration declined but it would mostly increase in the following months. This study provided updated and valuable assessments of recent PM2.5 air quality in Vietnam.

Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar.

Prostar is a software tool dedicated to the processing of quantitative data resulting from mass spectrometry-based label-free proteomics. Practically, once biological samples have been analyzed by bottom-up proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, notably by means of precursor ion chromatogram integration. From that point, the classical workflows aggregate these pieces of peptide-level information to infer protein-level identities and amounts. Finally, protein abundances can be statistically analyzed to find out proteins that are significantly differentially abundant between compared conditions. Prostar original workflow has been developed based on this strategy. However, recent works have demonstrated that processing peptide-level information is often more accurate when searching for differentially abundant proteins, as the aggregation step tends to hide some of the data variabilities and biases. As a result, Prostar has been extended by workflows that manage peptide-level data, and this protocol details their use. The first one, deemed "peptidomics," implies that the differential analysis is conducted at peptide level, independently of the peptide-to-protein relationship. The second workflow proposes to aggregate the peptide abundances after their preprocessing (i.e., after filtering, normalization, and imputation), so as to minimize the amount of protein-level preprocessing prior to differential analysis.

Conducting Bayesian-Classical Hybrid Power Analysis with R Package Hybridpower.

There are several approaches to incorporating uncertainty in power analysis. We review these approaches and highlight the Bayesian-classical hybrid approach that has been implemented in the R package hybridpower. Calculating Bayesian-classical hybrid power circumvents the problem of local optimality in which calculated power is valid if and only if the specified inputs are perfectly correct. hybridpower can compute classical and Bayesian-classical hybrid power for popular testing procedures including the t-test, correlation, simple linear regression, one-way ANOVA (with equal or unequal variances), and the sign test. Using several examples, we demonstrate features of hybridpower and illustrate how to elicit subjective priors, how to determine sample size from the Bayesian-classical approach, and how this approach is distinct from related methods. hybridpower can conduct power analysis for the classical approach, and more importantly, the novel Bayesian-classical hybrid approach that returns more realistic calculations by taking into account local optimality that the classical approach ignores. For users unfamiliar with R, we provide a limited number of RShiny applications based on hybridpower to promote the accessibility of this novel approach to power analysis. We end with a discussion on future developments in hybridpower.

Web based resource for Statistical Consultants in the Pharmaceutical Industry.

The intention of this article is to highlight sources of web-based reference material and software that will aid consulting statisticians when designing clinical trials. The article includes websites that provide links to explanation of statistical concepts for non-statisticians, regulatory guidelines, and free statistical study design software.

Researcher degrees of freedom in statistical software contribute to unreliable results: A comparison of nonparametric analyses conducted in SPSS, SAS, Stata, and R.

Researcher degrees of freedom can affect the results of hypothesis tests and consequently, the conclusions drawn from the data. Previous research has documented variability in accuracy, speed, and documentation of output across various statistical software packages. In the current investigation, we conducted Pearson's chi-square test of independence, Spearman's rank-ordered correlation, Kruskal-Wallis one-way analysis of variance, Wilcoxon Mann-Whitney U rank-sum tests, and Wilcoxon signed-rank tests, along with estimates of skewness and kurtosis, on large, medium, and small samples of real and simulated data in SPSS, SAS, Stata, and R and compared the results with those obtained through hand calculation using the raw computational formulas. Multiple inconsistencies were found in the results produced between statistical packages due to algorithmic variation, computational error, and statistical output. The most notable inconsistencies were due to algorithmic variations in the computation of Pearson's chi-square test conducted on 2 × 2 tables, where differences in p-values reported by different software packages ranged from .005 to .162, largely as a function of sample size. We discuss how such inconsistencies may influence the conclusions drawn from the results of statistical analyses depending on the statistical software used, and we urge researchers to analyze their data across multiple packages to check for inconsistencies and report details regarding the statistical procedure used for data analysis.

Level of attitude, knowledge and practice of nurses toward postoperative pain management, cross-sectional study.

Background: Patients still experience unnecessary pain in many hospitals, especially after surgery, despite increasing awareness of pain management in many healthcare settings. Unrelieved postoperative pain has been shown to increase the incidence of postoperative complications (such as atelectasis, pneumonia, thromboembolism, and impaired immune function. Little known and not known, evidence to understand gaps in nurses' attitudes and practices regarding postoperative pain management in our hospital. Methods: A descriptive cross-sectional study was performed with 144 nurses A systematic random sampling technique was used to select study participants. Data were collected using a self-administered and verified structured questionnaire; Data were entered and analyzed using SPSS software version 24. Descriptive results are presented by frequency, percentage, mean, bar graph, and pie chart. Results: Overall results from 144 study participants showed that nurses had good knowledge 78 (54.2%), favorable attitudes 67 (60.4%), and practice 81 (56). %) on pain management after surgery. In terms of nurse training, 60 (41.7%) have a bachelor's degree holders and only 34 (23.6%) nurses are trained in postoperative pain management. Conclusions: In this study, the nurses' overall knowledge of postoperative management was good, with favorable attitudes and good practices. But the level of knowledge, attitude, and practice according to the research are only average; therefore, it will make it possible to recommends to the responsible authorities of universities, hospitals, and nurses to organize continuing education.

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study.

Background: Surgical site infections (SSIs) have a major role in the evolution of medical care. Despite centuries of medical progress, the management of surgical infection remains a pressing concern. Nowadays, the SSIs continue to be an important factor able to increase the hospitalization duration, cost, and risk of death, in fact, the SSIs are a leading cause of morbidity and mortality in modern health care. Methods: A study based on statistical test and logistic regression for unveiling the association between SSIs and different risk factors was carried out. Successively, a predictive analysis of SSIs on the basis of risk factors was performed. Results: The obtained data demonstrated that the level of surgery contamination impacts significantly on the infection rate. In addition, data also reveals that the length of postoperative hospital stay increases the rate of surgical infections. Finally, the postoperative length of stay, surgery department and the antibiotic prophylaxis with 2 or more antibiotics are a significant predictor for the development of infection. Conclusions: The data report that the type of surgery department and antibiotic prophylaxis there are a statistically significant predictor of SSIs. Moreover, KNN model better handle the imbalanced dataset (48 infected and 3983 healthy), observing highest accuracy value.

The regression discontinuity design: Methods and implementation with a worked example in health services research.

BACKGROUND: The randomized controlled trial (RCT) is the gold standard in evidence-based medicine. However, this design may not be appropriate in every setting, so other methods or designs such as the regression discontinuity design (RDD) are required. METHOD: The aim of this article is to introduce the RDD, summarise methodology in the context of health services research and present a worked example using the statistic software SPSS (Examples for R and Stata in the Appendix A). The mathematical notations of sharp and fuzzy RDD as well as their distinction are presented. Furthermore, examples from the literature and recent studies are highlighted, and both advantages and disadvantages of the design are discussed. APPLICATION: The RDD consists of four essential steps: 1. Determine feasibility; 2. Note possible treatment manipulation, 3. Check for the treatment effect, and 4. Fit the regression models to measure the treatment effect. CONCLUSION: The RDD comes as an alternative for studies in health service research where an RCT cannot be conducted, but a threshold-based comparison can be made.

Knowledge of intensive care nurses' towards prevention of ventilator-associated pneumonia in North West Ethiopia referral hospitals, 2021: A multicenter, cross-sectional study.

Introduction: Ventilator-associated pneumonia is a common nosocomial infection that occurs in critically ill patients who are on intubation and mechanical ventilation. Nurses' lack of knowledge may be a barrier to adherence to evidence-based guidelines for preventing ventilator-associated pneumonia. This study aimed to assess the knowledge of intensive care nurses' towards the prevention of ventilator-associated pneumonia. Methods: A multicenter cross-sectional study was conducted among nurses working in the intensive care unit from April to July 2021. A pre-tested and structured questionnaire was used to collect data. All intensive care nurses working in the study area were included in the study. Data was entered into Epi-data 4.1 version (EpiData Association, Denmark) and transferred to STATA version 14 (College Station, Texas 77845-4512 USA) statistical software for analysis. Both bi-variable and multivariable binary logistic regression analysis was used to identify factors associated with knowledge of intensive care unit nurse. Variables with a p-value less than <0.2 in the bi-variable analysis were fitted into the multivariable logistic regression analysis. Both Crude and Adjusted Odds Ratio with the corresponding 95% Confidence Interval was calculated to show the strength of association. In multivariable analysis, variables with a p-value of <0.05 were considered statistically significant. Result: A total of 213 intensive care nurses were included in the study, with a response rate of 204(95.77%). The mean knowledge score of intensive care nurses regarding the prevention of ventilator-associated pneumonia out of 20 questions is (10.1 ± 2.41). There are 98 (48.04%) of the participants have been found to have good knowledge and 106 (51.96%) of them are rendered poor knowledge about the overall knowledge related to the prevention of ventilator-associated pneumonia. Higher academic qualifications and taking intensive care unit training were significantly associated with good knowledge of ventilator-associated pneumonia prevention in multi-variable logistic regression. Conclusion: Our study indicates that the knowledge of intensive care nurses about ventilator-associated pneumonia prevention is not sufficient. Higher academic qualifications and taking intensive care unit training are significantly associated with a good level of knowledge. Therefore it shows the necessity for thorough training and education.