CHAPTER 1 Biostatistics 101 11

Comparing groups

In Part 4, we show you different ways to compare groups statistically.»

» ^{In Chapter 11, you see how to compare average values between two or}

more groups by using t tests and ANOVAs. We also describe their nonpara-

metric counterparts that can be used with skewed or other non-normally

distributed data.»

» ^{Chapter 12 shows how to compare proportions between two or more groups,}

such as the proportions of patients responding to two different drugs, using

the chi-square and Fisher Exact tests on cross-tabulated (cross-tab) data.»

» ^{Chapter 13 focuses on one specific kind of cross-tab called the fourfold table,}

which has exactly two rows and two columns. Because the fourfold table

provides the opportunity for some particularly insightful calculations, it’s

worth a chapter of its own.»

» ^{In Chapter 14, you discover how the terminology used in epidemiologic}

studies is applied to specifically formatted fourfold tables to calculate

incidence and prevalence rates.

Looking for relationships between variables

Epidemiology and biostatistics are interested in causal inference, which means try-

ing to figure out what causes particular outcomes in biological research. While it

is possible to look at the relationship between two variables in a bivariate analysis,

regression analysis is the part of statistics that enables you to explore the rela-

tionship between multiple variables and one outcome in the same model so you

can evaluate their relative cause of the outcome. Here are some use-cases for

regression:»

» ^{You may want to know whether there’s a statistically significant association}

between one or more variables and an outcome, even if there are other

variables in the model. You may ask: Does being overweight increase the

likelihood of getting liver cancer? Or: Is exercising fewer hours per week

associated with higher blood pressure measurements? In answering both

of those questions, you may want to control other variables known to

influence the outcome.»

» ^{You may want to develop a formula for predicting the value of a variable from}

the observed values of one or more other variables. For example, you may

want to predict how long a newly diagnosed cancer patient may survive

based on their age, obesity status, and medical history.