When you do meta-analysis you usually visualize your results using a so-called forest plot. The plot is automatically available in the packages denoted for meta-analysis (such as meta), but there are times you would like to plot it “from the scratch” and completely customize it. After struggling a bit with ggplot2 you discover forestplot package Read more about Forestplot R package – what you CAN’T do with it.[…]

Where the interpretation of the interaction comes from? Let us assume that we have patients in out sample and a linear model (simple or mixed). For the linear model we have an equation of the form: Y=A0+A1*X1+A2*X2+A3*X1*X2, where A0 is the intercept and A1 and A2 are the main effects of X1 and X2 respectively. Read more about Interactions and why we all get them wrong?[…]

Let’s say we need to calculate a sample size for a single proportion estimation (testing). Omitting now maths and some variability in the used formulas we can do it two in two ways: First: using power of a single test and then we just require the test is rejecting or not the hypothesis of a Read more about Few thoughts about sample size calculation for a single proportion[…]