Handbook of Clinical Anesthesia
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 Clinical Anesthesia. Edited by Barash PG, Cullen BF, Stoelting RK, Cahalan MK, Stock MC. Philadelphia: Lippincott Williams & Wilkins, 2009, pp 192–206).
- Design of Research Studies
A case report engenders interest and the desire to experiment but does not provide sufficient evidence to advance scientific medicine.
- A sampleis 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 groupsmay 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 groupsis helpful to avoid research bias in entering patients into specific study groups. Random allocation is most commonly accomplished by computer-generated random numbers.
- Blindingrefers 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 studiesevaluate changes over time using research subjects chosen prospectively (cohort) or retrospectively (case-control). Retrospective studies are a primary tool of epidemiology.
- Cross-sectional studiesevaluate changes at a certain point in time.
- 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 statisticsare 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).
III. Hypotheses and Parameters
- Hypothesis Formulation
- The researcher starts the work with some intuitive feel for the phenomenon to be studied (biologic hypothesis).
- The biologic hypothesis becomes a statistical hypothesis during research planning.
- Logic of Proof
- If sample values are sufficiently unlikely to have occurred by chance (alpha [p] < 0.05), the null hypothesis
(which assumes there is no difference) is rejected.
Table 9-1 Data Types
- Because statistics deal with probabilities rather than certainties, there is a chance that decisions made concerning the null hypothesis are erroneous.
- A type I (alpha) erroris wrongly rejecting the null hypothesis (false-positive). The smaller the chosen alpha, the smaller the risk of a type I error.
- A type II (beta) erroris failing to reject the null hypothesis (false-negative). Variability in the
population increases the chance of type II error. Increasing the number of subjects (which is very important in research design for controlled clinical trials), raising the alpha value, and dealing with large differences between two conditions decrease the chances of a type II error.
Table 9-2 Descriptive Statistics
Table 9-3 Information Necessary to Accept or Reject the Null Hypothesis
- Inferential Statistics.The testing of hypotheses or significance testing is the main focus of inferential statistics (Table 9-3).
- Statistical Tests and Models
- General guidelines relate the variable type and the experimental design to the choice of statistical test (Table 9-4).
- tTest. The Student's t test is used to compare the values of the means of two populations. The paired t test is used when each subject serves as his or her own control (before and after measurements in the same patient decrease variability and increase statistical power). An unpaired t test is used when measurements are taken on two groups of subjects.
- Analysis of Variance
- The most versatile approach for handling comparisons of means among more than two groups is called the analysis of variance(ANOVA).
- For parametric statistics (ttests and ANOVA), it is assumed that the populations follow the normal distribution.
Table 9-4 Guidelines for Which Statistical Test to Use
- Robustness and nonparametric tests canbe used when there is concern that the populations do not follow a normal distribution.
- Systematic Reviews and Meta-Analyses.To answer the experimental question, data are obtained from controlled trials (usually randomized) in the medical literature rather than from newly conducted clinical trials.
- The American Society of Anesthesiologists has developed a process for the creation of practice parameters that include a variant form of systematic reviews.
- Linear Regression.Often the goal of the experiment is to predict the value of one characteristic from knowledge of another characteristic using regression analysis.
- Interpretation of Results
- Scientific studies do not end with the statistical test. (Statistical significance does not always equate with biologic relevance.)
- Even small, clinically unimportant differences between groups can be detected if the sample size is sufficiently large. If the sample size is small, there is a greater chance that confounding variables may explain any difference.
- If the experimental groups in a properly designed study are given three or more doses of a drug, the reader should expect to observe a steadily increasing or decreasing dose–response relationship.
Table 9-5 Strength of Evidence (Increasing Order) Concerning Efficacy
- In comparing alternative therapies, the confidence that a claim for a superior therapy is true depends on the study design (Table 9-5).
- Guidelines for Reading Journal Articles
- Clinicians with limited time should select journal articles to read that are relevant (determined by the specifics of one's anesthetic practice) and credible (function of the merits of the research methods).
- Although the statistical knowledge of most physicians is limited, these skills of critical appraisal of the literature can be learned and can greatly increase the efficiency and benefit of journal reading.
- Statistics and Anesthesia.Understanding the principles of experimental design can prevent premature acceptance of new therapies from faulty studies.
Editors: Barash, Paul G.; Cullen, Bruce F.; Stoelting, Robert K.; Cahalan, Michael K.; Stock, M. Christine
Title: Handbook of Clinical Anesthesia, 6th Edition
Copyright ©2009 Lippincott Williams & Wilkins
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