Rex: R-linked EXcel add-in for statistical analysis of medical and bioinformatics data

BACKGROUND

Microsoft Excel has substantial functionalities for data management and analyses, and has been the most popular software in this field. However, in spite of Excel's user-friendly interface and functionality for data management, it provides very few functions for in-depth statistical analyses, which has limited its wider application for this purpose.

OBJECTIVE

Here, we introduce Rex, an Excel add-in software implementing the powerful analytical and graphical functions of R within Excel.

METHODS

Rex was implemented using three types of programming software: R, JavaScript, and Microsoft VB.Net.

RESULTS

Rex provides a graphical user interface (GUI) through Excel, and statistical analysis can be conducted by pointing and clicking the menu without programming R. Rex covers a wide range of analyses from basic statistics to advanced analysis, including structural equation modeling, complex sampling design, and machine learning models, making it possible for researchers not skilled in using a command-line interface to conduct in-depth statistical analyses. Most Rex modules are available in a free version for non-commercial use, and it can be used for educational and public purposes.

CONCLUSION

In this article, we introduce the framework and features of Rex with illustrative examples of its implementation.

Statistics for undergraduate medical students in Sudan: associated factors for using statistical analysis software and attitude toward statistics among undergraduate medical students in Sudan

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.