Describe basic statistical methodology and concepts
Describe how basic statistical methodology may be used in pharmacokinetic and pharmacodynamics study designs
Describe how basic statistical methodology may be used in critically evaluating data
Describe how basic statistical methodology may be used to help minimize error, bias, and confounding, and therefore promote safe and efficacious drug therapy
Provide examples of how basic statistical methodology may be used for study design and data evaluation
Several types of variables will be discussed throughout this text1. A random variable is “a variable whose observed values may be considered as outcomes of an experiment and whose values cannot be anticipated with certainty before the experiment is conducted” (Herring, 2015). There are two types of random variables: discrete and continuous. Discrete variables are also known as counting or nonparametric variables (Glasner, 1995). Continuous variables are also known as measuring or parametric variables (Glasner, 1995). We will explore this further in the next section, but a basic example of a discrete variable would be hospitalization or death. Was the patient hospitalized: yes or no? Did the patient die: yes or no? Whereas a continuous variable would be something like drug concentration since these have a consistent difference between data points; the difference between 1 mcg/mL and 6 mcg/mL is the same as the difference between 2 mcg/mL and 7 mcg/mL. The difference for both is 5 mcg/mL.
An independent variable is defined as the “intervention or what is being manipulated” in a study (eg, the drug or dose of the drug being evaluated) (Herring, 2015). “The number of independent variables determines the category of statistical methods that are appropriate to use” (Herring, 2015). A dependent variable is the “outcome of interest within a study.” In bioavailability and bioequivalence studies, examples include the maximum concentration of the drug in circulation, the time to reach that maximum level, and the area under the curve (AUC) of drug level-versus-time curve. These are “the outcomes that one intends to explain or estimate” (Herring, 2015). There may be multiple dependent (ie, outcome) variables. For example, in a study determining the half-life (t½), clearance, and plasma protein binding of a new drug following an oral dose, the independent variable is the oral dose of the new drug. The dependent variables are the t½, clearance, and plasma protein binding of the drug because these variables “depend upon” the oral dose given.
TYPES OF DATA (NONPARAMETRIC VERSUS PARAMETRIC)
There are two types of nonparametric data, nominal and ordinal. For nominal data, numbers are purely arbitrary or without regard to any order or ...