Glossary of Statistical Terms

Editor-In-Chief: Sabina A. Murphy, M.P.H.; [mailto:smurphy@perfuse.org]


 * Absolute Risk: The difference between the probability of an event occurring in one group compared to the probability of the event occurring in another comparison group (see example below)
 * Confidence Interval [CI]: Related to the p-value, a range of values that have a specified probability of containing a specific value. For example, if a relative risk of MI with a study drug compared to placebo in a study is 0.8 with a 95% confidence interval of 0.7-0.9, this can be interpreted as meaning that the probability of observing these results is <5% if the "true" relative risk was outside of the range of 0.7-0.9.
 * Frequency: A proportion that describes how often an event occurs relative to the total study population. This is defined as the number of cases with an event, such as the primary endpoint outcome, divided by the total number of patients in the trial.
 * Hazard Ratio [HR]: Similar to the Relative risk and to the Odds Ratio, this is a ratio of the risk (or Hazard) of an event in one group compared to the risk in a comparison group. Hazard ratios are derived from Survival analyses, or analyses of events occurring over time, as in large longitudinal clinical trials, and take into account differences in duration of follow-up for individual patients.
 * Incidence: The number of new cases of a particular disease or condition over a defined period of time.
 * Mean: The average value, taken as the sum of all values divided by the total number of values.
 * Median: The "middle" value, or the value where half of the values are below this value, and half are above.
 * Number Needed to Treat [NNT]: The number of patients needed to be treated to prevent or avoid one event. This is defined as the reciprocal of the Absolute Risk Difference with a treatment or (1/Absolute Risk Difference) (see example below).
 * Odds Ratio [OR]: A ratio expressing the odds of an event occurring in one group divided by the odds of the event occurring in another comparison group (see example below). As a general rule, if the event is a rare occurrence, the odds ratio and the relative risk will yield very similar results.
 * Outlier: An "extreme" value that does not fit a normal (or bell-shaped) distribution curve. In other words, a value that is far away from the middle of all other values. Outlier values will contribute more to the mean than to the median, and therefore can skew results if means are being statistically compared.
 * P-value: When comparing two treatments in a trial, the probability that the results obtained would have occurred had there truly been no difference between treatments. The p-value is a measure of the "false positive rate" in a clinical trial.
 * Power: The probability of detecting a difference between two treatments within a trial if such a different truly exists. 1 minus power is a measure of the "false negative rate" in a clinical trial, or the probability that the study results will show that the treatments were not different, even if they truly are.
 * Primary Endpoint: The pre-specified event that will be used for testing differences between treatments in a trial. The primary endpoint reflects the primary hypothesis of the trial. Sample size is based upon expected differences in the primary endpoint of a clinical trial. Secondary endpoints (other pre-specified events or outcomes of interest) may also be used to change sample size in a trial.
 * Relative Risk (also known as risk ratio) [RR]: A ratio expressing the probability of an event occurring in one group divided by the probability of the event occurring in another comparison group (see example below).
 * Sample Size: The number of study subjects in a trial. Sample size is usually pre-specified before the start of a trial and is based upon assumptions regarding the expected differences between treatment groups, the power, and the statistical test that will try to identify the differences between treatment groups.
 * Standard Deviation: A measure of the "spread" or variability of values. When the distribution of values is "normal", or fits a bell-shaped curve, 95% of values will fall between ± 2 standard deviations from the mean value.
 * Statistical Significance: The p-value at which the results of a trial allow two treatments to be considered truly different. Statistical significance is typically assigned at a p < 0.05, or a <5% chance that the results of a study could have been obtained by chance alone.

Example
In a randomized trial of 100 patients given a Study Drug to prevent MI (50 given placebo and 50 given study drug)
 * Number of patients needed to treat to prevent 1 MI = 1 / 0.1 = 10 patients (If the 10 patients had been given placebo, there would have been 2 MI's; if they are given study drug, there is only 1 MI)