John C. Pezzullo

To estimate sample size in biostatistics, you must state the effect size of importance, or the effect size worth knowing about. If the true effect size is less than the “important” size, you don’t care if the test comes out nonsignificant. With a few shortcuts, you can pick an important effect size and find out how many participants you need, based on that effect size, for several common statistical tests.

After the Phase I trials of human drug testing, you'll have a good estimate of the maximum tolerated dose (MTD) for the drug. The next step is to find out about the drug's safety and efficacy at various doses. You may also be looking at several different dosing regimens, including the following options: What route (oral or intravenous, for example) to give the drug How frequently to give the drug For how long (or for what duration) to give the drug Generally, you have several Phase II studies, with each study testing the drug at several different dose levels up to the MTD to find the dose that offers the best tradeoff between safety and efficacy.

As you dive deeper into the field of biostatistics, you'll need to develop a firm understanding of pharmacokinetics (PK) and pharmacodynamics (PD) and the differences between the two. The term pharmacokinetics (PK) refers to the study of How fast and how completely the drug is absorbed into the body (from the stomach and intestines if it's an oral drug) How the drug becomes distributed through the various body tissues and fluids, called body compartments (blood, muscle, fatty tissue, cerebrospinal fluid, and so on) To what extent (if any) the drug is metabolized (chemically modified) by enzymes produced in the liver and other organs How rapidly the drug is eliminated from the body (usually via urine, feces, and other routes) The term pharmacodynamics (PD) refers to the study of The relationship between the concentration of the drug in the body and the biological and physiological effects of the drug on the body or on other organisms (bacteria, parasites, and so forth) on or in the body.

Biostatistics, in its present form, is the cumulative result of four centuries of contributions from many mathematicians and scientists. Some are well known, and some are obscure; some are famous people you never would’ve suspected of being statisticians, and some are downright eccentric and unsavory characters.

One of the reasons (but not the only reason) for running a multiple regression analysis is to come up with a prediction formula for some outcome variable, based on a set of available predictor variables. Ideally, you’d like this formula to be parsimonious — to have as few variables as possible, but still make good predictions.

Two quite different ideas about probability have coexisted for more than a century. These probability approaches, which differ in several important ways, are as follows: The frequentist view defines probability of some event in terms of the relative frequency with which the event tends to occur. The Bayesian view defines probability in more subjective terms — as a measure of the strength of your belief regarding the true situation.

Modern statistical software makes it easy for you to analyze your data in most of the situations that you’re likely to encounter (summarize and graph your data, calculate confidence intervals, run common significance tests, do regression analysis, and so on). But occasionally you may run into a problem for which no preprogrammed solution exists.

You can calculate the standard error (SE) and confidence interval (CI) of the more common sample statistics (means, proportions, event counts and rates, and regression coefficients). But an SE and CI exist (theoretically, at least) for any number you could possibly wring from your data — medians, centiles, correlation coefficients, and other quantities that might involve complicated calculations, like the area under a concentration-versus-time curve (AUC) or the estimated five-year survival probability derived from a survival analysis.

While many programs, apps, and web pages are available to perform power and sample-size calculations, they aren’t always easy or intuitive to use. Because spreadsheets like Excel are readily available and intuitive, it’s convenient to have a single spreadsheet that can perform power and sample-size calculations for the situations that arise most frequently in biological and clinical research.