A conclusion that an intervention has an effect that is of practical meaning to older persons and health care providers. Even though an intervention is found to have a statistically significant effect, this effect may not be clinically significant. In a trial with a large number of participants, a small difference between treatment and control groups may be statistically significant, but clinically unimportant. In a trial with few participants, an important clinical difference may be observed that does not achieve statistical significance. (A larger trial may be needed to confirm that this is a statistically significant difference).... clinical significance
A list of diagnoses and identifying codes used by medical practitioners and other health care providers. The coding and terminology provide a uniform language that permits consistent communication on claim forms. Data from earlier time periods were coded using the appropriate revision of the ICD for that time period. Changes in classification of causes of death in successive revisions of the ICD may introduce discontinuities in cause of death statistics over time.... international statistical classification of diseases and related health problems, tenth revision (icd-10)
A mathematical formula (or function) that is used to determine if the difference between outcomes of a treatment or intervention and a control group is great enough to conclude that the difference is statistically significant. Statistical tests generate a value that is associated with a particular P value. Among the variety of common statistical tests are: F, t, Z, and chi-square. The choice of a test depends upon the conditions of the study, e.g. what type of outcome variable is used, whether or not the subjects are randomly selected from a larger population, and whether it can be assumed that the outcome values of the population have a normal distribution or other type of distribution.... statistical test
n. (in statistics) the degree to which an observed relationship between two test groups is unlikely to have occurred by chance alone. An initial assumption is made that there is no meaningful difference between the groups or conditions under investigation (the null hypothesis). This can be tested using various statistical procedures, and a calculation that there is a probability of less than 5% (P value <0.05) is usually considered sufficient to reject the null hypothesis: the observed difference is statistically significant. Some tests are parametric, based on the assumption that observations will be distributed in a normal or Gaussian *distribution, where 95% of observations lie within two *standard deviations of the mean (Student’s t test to compare means). Nonparametric tests (Mann–Whitney U tests) make no assumptions about distribution patterns. See also standard error of the mean.... significance
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