Hard learned lessons (November 25, 2005).

This page is moving to a new website.

It's been a busy month, as noted below, and in a rush to complete all my projects, I ended up doing some things that may have caused a few problems (nothing permanent, of course, but they did up delaying further some projects that were already behind schedule). I alluded to a bit of this in my weblog entry

but I have a few more lessons worth mentioning.

  1. It is very difficult to perform a good data analysis by email and telephone. A good data analysis requires a lot of dialog that can only be done in a face-to-face setting. Often I will produce a graph or table and have to ask a simple question like "Is this what you expected?" to make sure that I am on the right track. Sometimes I will need more information about a variable but will only realize this halfway through the analysis. I hate to ask busy people to sit with me as I work through a data analysis, but I am starting to realize that without this interaction, I cannot produce results quickly and accurately.
  2. Excel is a very fragile medium for the storage of data. On one project, I had to sort the data in order to identify dates of events that were within 30 days of other events in the data set. In Excel, it is easy to end up sorting just some of the columns or just some of the rows, leading to the production of a data set that has grossly incorrect results.
  3. You can't just jump in and run the final statistical model for a project without first laying a firm foundation of preliminary charts, tables, and graphs. This is a lesson I harp on in many of my classes, but one that I have ignored myself at my own peril. In particular, I had a data analysis that involved survival times with time varying covariates and with multiple events per patient. It's not that difficult an analysis to run, but it does require some care. But instead of starting with some simple analysis (estimating survival probabilities before incorporating time varying covariates, or estimating the time to the first event), I ended up jumping right straight in to the final statistical model. In the process, I ended up making some serious errors. These errors were fixable, but the time I spent fixing them was far more than the time I would have spent laying the proper foundation.