Experimental Design and Statistics
Practitioners of scientific medicine must be able to read the language of science to independently assess and interpret the scientific literature and the increasing emphasis on statistical methods (Pace NL. Experimental design and statistics. In: Barash PG, Cullen BF, Stoelting RK, Cahalan MK, Ortega R, Stock MC, eds. Clinical Anesthesia. Philadelphia: Lippincott Williams & Wilkins; 2013: 219–236).
I. Design of Research Studies
A case report engenders interest and the desire to experiment but does not provide sufficient evidence to advance scientific medicine.
Sampling
A sample is a subset of a target population that is intended to allow the researcher to generalize the results of the small sample to the entire population. The elements of experimental design are intended to prevent and minimize the possibility of bias.
The best hope for a representative sample of the population would be realized if every subject in the population had the same chance of being in the experiment (random sampling). However, most clinical anesthesia studies are limited to using patients who are available (convenience sampling).
Control groups may be self-control or parallel control groups versus historical control groups. (Studies using historical controls are more likely than those using self-controls or parallel controls to show a benefit from a new therapy.)
Random allocation of treatment groups is helpful to avoid research bias in entering patients into specific study groups. Random allocation is most commonly accomplished by computer-generated random numbers.
Blinding refers to masking from the view of both the patient and experimenter the experimental group to which the subject has been assigned.
In a single-blind study, the patient is unaware of the treatment given. (Patient expectations from a treatment could influence results.)
In a double-blind study, the subject and the data collector are unaware of the treatment group. This is the best way to test a new therapy.
Types of Research Design
Longitudinal studies evaluate changes over time using research subjects chosen prospectively (cohort) or retrospectively (case control). Retrospective studies are a primary tool of epidemiology.
Cross-sectional studies evaluate changes at a certain point in time.
II. Data and Descriptive Statistics
Statistics is a method for working with sets of numbers (X and Y) and determining if the values are different. Statistical methods are necessary because there are sources of variation in any data set, including random biologic variation and measurement error. These errors make it difficult to avoid bias and to be precise.
Data Structure. Properly assigning a variable to the correct data type is essential for choosing the correct statistical technique (Table 9-1).
Descriptive statistics are intended to describe the sample of numbers obtained and to characterize the population from which the sample was obtained. The two summary statistics most frequently used are the central location and spread or variability (Table 9-2).
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