Introduction to Clinical Data Science at TTSI This is a demonstration of how R Markdown can be used to generate reports and visualizations relevant to clinical data science. At TTSI, we specialize in study simulations, sample size estimation, and live anomaly detection. Markdown allows us to format reports that are dynamic and reproducible. You can embed an R code chunk like this to perform basic data analysis, for example: # Load the ToothGrowth dataset data(ToothGrowth) # Inspect the first few rows head(ToothGrowth) ## len supp dose ## 1 4.2 VC 0.5 ## 2 11.5 VC 0.5 ## 3 7.3 VC 0.5 ## 4 5.8 VC 0.5 ## 5 6.4 VC 0.5 ## 6 10.0 VC 0.5 # Fit a linear model to predict tooth length based on the dose of vitamin C fit <- lm(len ~ dose, data = ToothGrowth) fit |> broom::tidy() |> knitr::kable(digits = 2) term estimate std.error statistic p.value (Intercept) 7.42 1.26 5.89 0 dose 9.76 0.95 10.25 0 Including Visualizations At TTSI, visualizations are essential for interpreting complex data from clinical trials. Below is a sample pie chart (Figure ??) demonstrating a common method for displaying categorical data distributions, like treatment groups in a clinical study. ...