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Such a small difference could be irrelevant (i.e., of no clinical importance) to patients or clinicians. Thus, statistical significance does not necessarily imply clinical importance. Over the years clinicians and researchers have moved away from physical and radiological endpoints towards patient-reported outcomes.
In broad usage, the "practical clinical significance" answers the question, how effective is the intervention or treatment, or how much change does the treatment cause. In terms of testing clinical treatments, practical significance optimally yields quantified information about the importance of a finding, using metrics such as effect size, number needed to treat (NNT), and preventive fraction ...
Many clinical trials, for instance, have low statistical power to detect differences in adverse effects of treatments, since such effects may only affect a few patients, even if this difference can be important. [8]
Medical statistics (also health statistics) deals with applications of statistics to medicine and the health sciences, including epidemiology, public health, forensic medicine, and clinical research. [1] Medical statistics has been a recognized branch of statistics in the United Kingdom for more than 40 years, but the term has not come into ...
In 2016, the American Statistical Association (ASA) published a statement on p-values, saying that "the widespread use of 'statistical significance' (generally interpreted as 'p ≤ 0.05') as a license for making a claim of a scientific finding (or implied truth) leads to considerable distortion of the scientific process". [57]
In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true ...
A very simple equivalence testing approach is the ‘two one-sided t-tests’ (TOST) procedure. [11] In the TOST procedure an upper (Δ U) and lower (–Δ L) equivalence bound is specified based on the smallest effect size of interest (e.g., a positive or negative difference of d = 0.3).
The investigators then obtain a standard set of clinical observations on each patient and a test or clinical follow-up to define the true state of the patient. They then use statistical methods to identify the best clinical predictors of the patient's true state. The probability of disease will depend on the patient's key clinical predictors.