Biostatistics was very confusing for me at first but I made it a point to understand it. It was easy enough to memorize the equations but I really wanted to know what they all meant, how it all came together. Getting to that place of really understanding and feeling comfortable with the material took a combination of videos (from Kaplan and from YouTube), High Yield Biostatistics by Glaser, along with the new Subject Review Series that UsmleWorld came out with. Throughout all this I was doing Biostat questions from the UsmleWorld Step 1 Qbank. I did it in this order (roughly from simplest to more challenging):
1. Kaplan Biostats Videos/YouTube Videos
2. HY Biostats
3. UsmleWorld Biostats Subject review
4. UsmleWorld Step 1 Qbank
The UW Biostat subject review was by far the one that brought it all home for me. Granted this was probably because I had gained some basic understanding already from the previous videos and Glaser’s book. The subject review is nicely organized by main sections and organized in order that builds on itself. I definitely recommend purchasing it. If you only pick one thing to do I suggest doing that, because honestly the Kaplan books and videos do not cover everything you need to know for potential Step 1 questions.
Here’s an example of a video that was helpful for me. Khan academy actually has several videos out on YouTube for Statistics. I would watch these during my breaks and found that the presenter clarified some things I never really understood. You might or might not like his style of teaching. Enjoy!
Here are some topics I feel are high yield for the Step 1 exam:
- Sensitivity (snout)
- Specificity (spin)
- Positive predictive value, PPV (remember this depends on prevalence)
- Negative predictive value, NPV (also depends on prevalence)
- Relative Risk (remember to use in cohort studies)
- Odds Ratio (remember to use in case-control studies)
- Confidence Intervals
- Setting a cutoff point on normal distributions (classic example is the fasting blood glucose cutoff for diabetes)
- Attributable Risk
- Number Needed to Treat
- P value (probability that the null hypothesis is correct)
- Correlation coefficient (describes a linear association does NOT necessarily imply causation)
- Variability or the percent of variability (remember to square the correlation coefficient)
- Which test to use chi-square? correlation? t-test? ANOVA?
- The biases: length-time, lead-time, confounding, selection, etc.
All that said, I am sure I left out some potential test question topics. What I left out however, I’m sure the UW subject review will cover. One thing I do want to cover is something I personally had difficulty understanding for the longest time and it was only recently that it became clear and that is the difference between relative risk and odds ratio. Risk and Odds, they always sounded like the same thing.