Biostatistics Part II

Welcome back to biostatistics! Today we spend some time on study design and study-specific statistical calculations.

If you have more time, check out the Khan Academy series of videos and infographics on statistics and study design. Their resources are phenomenal and can really help with both understanding CREOG questions as well as helping you out in your own research design!

And for a concise review, check out our own quick notes on the subject.

Biostatistics Part I

On today’s episode, we try to tackle the highly testable, last-minute-cram topic of biostatistics! This will be the first in a two part series. Sorry about the sound issues — had some problems with Nick’s microphone, but should be fixed after this series!

Below is the official cheat sheet of equations from us for this episode. Hopefully this is helpful in guiding your studying! And stay tuned for next week when we talk more about study design and study-specific statistics.

We also talk about a few other statistical points today:

Prevalence represents the number of people in a population who have a disease. From the above table, this could be calculated as (A+C) / (A+B+C+D).

Likelihood ratio is a value that can represent the significance or utility of a diagnostic test, and is calculated as Sensitivity / 1 - Specificity. In other words, the true positive rate divided by the false positive rate.

An LR > 1 signifies the test is associated with the disease.
An LR < 1 signifies the test is associated with absence of a disease.
An LR that is close to 1 demonstrates the test doesn’t have a strong association with either presence or absence of disease.

Why use LR? If you know the prevalence of disease in a population, you know the pre-test probability of the patient in front of you having the disease. An LR away from 1 demonstrates that your post-test probability is more likely to make you certain of diagnosis. LR of close to 1 doesn’t change your pre-test probability.