12 PART 1 Getting Started with Biostatistics

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» ^{You may be fitting a theoretical formula to some data to estimate one of}

the parameters appearing in that formula. An example of such a problem

is determining how fast the kidneys can remove a drug from the body, which

is called a terminal elimination rate constant. This can be estimated from

measurements of drug concentration in the blood taken at various times

after taking a dose of the drug.

Regression analysis can manage all these tasks and many more. Regression is so

important in biological research that all the chapters in Part 5 are focused on some

aspect of regression.

If you have never learned correlation and regression analysis, read Chapter 15,

which introduces these topics. We cover simple straight-line regression in

Chapter  16, which includes one predictor variable. We extend that to cover

multiple regression with more than one predictor variable in Chapter 17. These

three chapters deal with ordinary linear regression, where you’re trying to predict

the value of a numerical outcome variable from one or more other variables. An

example would be trying to predict mean blood hemoglobin concentration using

variables like age, blood pressure level, and Type II diabetes status. Ordinary

linear regression uses a formula that’s a simple summation of terms, each of

which consists of a predictor variable multiplied by a regression coefficient.

But in real-world biological and epidemiologic research, you encounter more

complicated relationships. Chapter 18 describes logistic regression, where the out-

come is the occurrence or non-occurrence of an event (such as being diagnosed

with Type II diabetes), and you want to predict the probability that the event will

occur. You also find out about several other kinds of regression in Chapter 19:»

» ^{Poisson regression, where the outcome is the number of events that occur in}

an interval of time»

» ^{Nonlinear least-squares regression, where the relationship between the}

predictors and numerical outcome can be more complicated than a simple

summation of terms in a linear model»

» ^{LOWESS curve-fitting, where you fit a custom function to describe your data}

Finally, Part 5 ends with Chapter 20, which provides guidance on the mechanics

of regression modeling, including how to develop a modeling plan, and how to

choose variables to include in models.