Bob Muenchen has a series of articles on the Popularity of Data Science Software. He found that SPSS is the most used software, followed by R, SAS, Stata, GraphPad Prism, and MATLAB, by looking at scholarly articles in Google Scholar. He presents his methodology here. While he’s showing popularity (or market share) of several softwares for data science, statistical analysis, machine learning, artificial intelligence, predictive analytics, business analytics, and business intelligence, I was always wondering what the results would be like for my specific field, epidemiology.
Bug fixes were brought to episensr R package, regarding the use of distributions and computations of odds and risk ratios in probsens.conf function for the probabilistic sensitivity analysis for unmeasured confounder. Improvement on the use of distributions was also brought to other probsens series of functions. Let’s run the example from Modern Epidemiology by Rothman, Greenland & Lash, on page 365-366.
This example is taken from a paper by Greenland et al.
My R package episensr can now also be used through a new Shiny application, episensr_shiny, to more easily assess the effect of biases on epidemiological results.
Not all functions and options are available yet, only selection bias and misclassification of exposure or outcome can be specified. But direct consequences of modifying the bias parameters by moving the sliders can be checked on the 2-by-2 tables and measures of association.
This is therefore still in “beta” but more will come.
I was curious to see if I could identify some patterns of collaboration and research topics in cow health research made in Canada. For this, I’m trying the R package bibliometrix. This package allows quantitative research in scientometrics and bibliometrics by providing different routines for importing bibliographic data from Scopus and ISI Web of Knowledge databases, and performing various bibliometric analyses. The Bibliometrix website provides a good tutorial that I will mainly follow, with the sole objective to satisfy my curiosity and have fun!
A small update for my episensr R package is now available on CRAN. The update focus on misclassification.
First, covariate misclassification is now available, via the function misclassification_cov. For example, the paper by Berry et al. looked if misclassification of the confounding variable in vitro fertilization (IVF), a confounder, resulted wrongly on an association between increase folic acid and having twins. IVF increases the risk of twins, and women undergoing IVF might be more likely to take folic acid supplements, i.