Simulation and data analysis are two of the principle parts of health economics research. Most Master’s courses teach both, although many focus more heavily on the former. There are many programs and packages available to a researcher, each with their own strengths and weaknesses, however some have more weaknesses than others.
Microsoft Excel continues to be the weapon of choice for a huge number of health economists, particularly in industry, despite the fact it’s one of the slowest, least well equipped packages available. Ok, it’s not as bad as all that, but it is certainly trumped by many other programs out there. Despite a steeper learning curve, and a less attractive user interface, programs such as Stata and R should be embraced. To quote another blog, ‘R is very speedy statistical package that’s like an F-18A Hornet, versus Excel which is like a paper airplane.’
Personally, I use R with other software such as WinBUGS and C++ to facilitate computation. These programs are daunting to the novice and take a while to master, which is why they should be taught more widely to alleviate this fear and encourage companies to embrace them. Programs like R are a collaborative effort; users contribute packages of code which can be used by the rest of the community, so there is no limit to the possibilities. They are more flexible and allow a greater range of models to be created. Not to mention R is free.
In Excel much work consists of dragging a mouse continually over thousands of rows and copying and pasting and trying to work out how to do more than just the basic statistics such as =mean() and =sd(). One line of code in another program will do all this and more. Oh, and the graphs in Excel are rubbish.
