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Minimal Clinically Important Difference
The minimal important difference (MID) or minimal clinically important difference (MCID) is the smallest change in a treatment outcome that an individual patient would identify as important and which would indicate a change in the patient's management. Purpose Over the years great steps have been taken in reporting what really matters in clinical research. A clinical researcher might report: "in my own experience treatment X does not do well for condition Y". The use of a P value cut-off point of 0.05 was introduced by R.A. Fisher; this led to study results being described as either statistically significant or non-significant. Although this ''p''-value objectified research outcome, using it as a rigid cut off point can have potentially serious consequences: (i) clinically important differences observed in studies might be statistically non-significant ( a type II error, or false negative result) and therefore be unfairly ignored; this often is a result of having a small number ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Therapy
A therapy or medical treatment (often abbreviated tx, Tx, or Tx) is the attempted remediation of a health problem, usually following a medical diagnosis. As a rule, each therapy has indications and contraindications. There are many different types of therapy. Not all therapies are effective. Many therapies can produce unwanted adverse effects. ''Medical treatment'' and ''therapy'' are generally considered synonyms. However, in the context of mental health, the term ''therapy'' may refer specifically to psychotherapy. History Before the creating of therapy as a formal procedure, people told stories to one another to inform and assist about the world. The term "healing through words" was used over 3,500 years ago in Greek and Egyptian writing. The term psychotherapy was invented in the 19th century, and psychoanalysis was founded by Sigmund Freud under a decade later. Semantic field The words ''care'', ''therapy'', ''treatment'', and ''intervention'' overlap in a s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
P Value
In null-hypothesis significance testing, the ''p''-value is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct. A very small ''p''-value means that such an extreme observed outcome would be very unlikely under the null hypothesis. Reporting ''p''-values of statistical tests is common practice in academic publications of many quantitative fields. Since the precise meaning of ''p''-value is hard to grasp, misuse is widespread and has been a major topic in metascience. Basic concepts In statistics, every conjecture concerning the unknown probability distribution of a collection of random variables representing the observed data X in some study is called a ''statistical hypothesis''. If we state one hypothesis only and the aim of the statistical test is to see whether this hypothesis is tenable, but not to investigate other specific hypotheses, then such a test is called a null ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Type II Error
In statistical hypothesis testing, a type I error is the mistaken rejection of an actually true null hypothesis (also known as a "false positive" finding or conclusion; example: "an innocent person is convicted"), while a type II error is the failure to reject a null hypothesis that is actually false (also known as a "false negative" finding or conclusion; example: "a guilty person is not convicted"). Much of statistical theory revolves around the minimization of one or both of these errors, though the complete elimination of either is a statistical impossibility if the outcome is not determined by a known, observable causal process. By selecting a low threshold (cut-off) value and modifying the alpha (α) level, the quality of the hypothesis test can be increased. The knowledge of type I errors and type II errors is widely used in medical science, biometrics and computer science. Intuitively, type I errors can be thought of as errors of ''commission'', i.e. the researcher unluck ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Standard Deviation
In statistics, the standard deviation is a measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the values are spread out over a wider range. Standard deviation may be abbreviated SD, and is most commonly represented in mathematical texts and equations by the lower case Greek letter σ (sigma), for the population standard deviation, or the Latin letter '' s'', for the sample standard deviation. The standard deviation of a random variable, sample, statistical population, data set, or probability distribution is the square root of its variance. It is algebraically simpler, though in practice less robust, than the average absolute deviation. A useful property of the standard deviation is that, unlike the variance, it is expressed in the same unit as the data. The standard deviation of a popu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Effect Size
In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of a parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size value. Examples of effect sizes include the correlation between two variables, the regression coefficient in a regression, the mean difference, or the risk of a particular event (such as a heart attack) happening. Effect sizes complement statistical hypothesis testing, and play an important role in power analyses, sample size planning, and in meta-analyses. The cluster of data-analysis methods concerning effect sizes is referred to as estimation statistics. Effect size is an essential component when evaluating the strength of a statistical claim, and it is the first item (magnitude) in the MAGI ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Diabetes
Diabetes, also known as diabetes mellitus, is a group of metabolic disorders characterized by a high blood sugar level ( hyperglycemia) over a prolonged period of time. Symptoms often include frequent urination, increased thirst and increased appetite. If left untreated, diabetes can cause many health complications. Acute complications can include diabetic ketoacidosis, hyperosmolar hyperglycemic state, or death. Serious long-term complications include cardiovascular disease, stroke, chronic kidney disease, foot ulcers, damage to the nerves, damage to the eyes, and cognitive impairment. Diabetes is due to either the pancreas not producing enough insulin, or the cells of the body not responding properly to the insulin produced. Insulin is a hormone which is responsible for helping glucose from food get into cells to be used for energy. There are three main types of diabetes mellitus: * Type 1 diabetes results from failure of the pancreas to produce enough insulin due to lo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hypoglycemia
Hypoglycemia, also called low blood sugar, is a fall in blood sugar to levels below normal, typically below 70 mg/dL (3.9 mmol/L). Whipple's triad is used to properly identify hypoglycemic episodes. It is defined as blood glucose below 70 mg/dL (3.9 mmol/L), symptoms associated with hypoglycemia, and resolution of symptoms when blood sugar returns to normal. Hypoglycemia may result in headache, tiredness, clumsiness, trouble talking, confusion, fast heart rate, sweating, shakiness, nervousness, hunger, loss of consciousness, seizures, or death. Symptoms typically come on quickly. The most common cause of hypoglycemia is medications used to treat diabetes such as insulin, sulfonylureas, and biguanides. Risk is greater in diabetics who have eaten less than usual, recently exercised, or consumed alcohol. Other causes of hypoglycemia include severe illness, sepsis, kidney failure, liver disease, hormone deficiency, tumors such as insulinomas or non-B cell tumo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Medical Statistics
Medical statistics deals with applications of statistics to medicine and the health sciences, including epidemiology, public health, forensic medicine, and clinical research. 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 general use in North America, where the wider term 'biostatistics' is more commonly used.Dodge, Y. (2003) ''The Oxford Dictionary of Statistical Terms'', OUP. However, "biostatistics" more commonly connotes all applications of statistics to biology. Medical statistics is a subdiscipline of statistics. "It is the science of summarizing, collecting, presenting and interpreting data in medical practice, and using them to estimate the magnitude of associations and test hypotheses. It has a central role in medical investigations. It not only provides a way of organizing information on a wider and more formal basis than relying on the exchange of anecdotes and personal experien ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Clinical Significance
In medicine and psychology, clinical significance is the practical importance of a treatment effect—whether it has a real genuine, palpable, noticeable effect on daily life. Types of significance Statistical significance Statistical significance is used in hypothesis testing, whereby the null hypothesis (that there is no relationship between variables) is tested. A level of significance is selected (most commonly ''α'' = 0.05 or 0.01), which signifies the probability of incorrectly rejecting a true null hypothesis. If there is a significant difference between two groups at ''α'' = 0.05, it means that there is only a 5% probability of obtaining the observed results under the assumption that the difference is entirely due to chance (i.e., the null hypothesis is true); it gives no indication of the magnitude or clinical importance of the difference. When statistically significant results are achieved, they favor rejection of the null hypothesis, but they do not prove that the nul ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Significance
In statistical hypothesis testing, a result has statistical significance when it is very unlikely to have occurred given the null hypothesis (simply by chance alone). More precisely, a study's defined significance level, denoted by \alpha, is the probability of the study rejecting the null hypothesis, given that the null hypothesis is true; and the ''p''-value of a result, ''p'', is the probability of obtaining a result at least as extreme, given that the null hypothesis is true. The result is statistically significant, by the standards of the study, when p \le \alpha. The significance level for a study is chosen before data collection, and is typically set to 5% or much lower—depending on the field of study. In any experiment or observation that involves drawing a sample from a population, there is always the possibility that an observed effect would have occurred due to sampling error alone. But if the ''p''-value of an observed effect is less than (or equal to) the significanc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Just-noticeable Difference
In the branch of experimental psychology focused on sense, sensation, and perception, which is called psychophysics, a just-noticeable difference or JND is the amount something must be changed in order for a difference to be noticeable, detectable at least half the time. This limen is also known as the difference limen, difference threshold, or least perceptible difference. Quantification For many sensory modalities, over a wide range of stimulus magnitudes sufficiently far from the upper and lower limits of perception, the 'JND' is a fixed proportion of the reference sensory level, and so the ratio of the JND/reference is roughly constant (that is the JND is a constant proportion/percentage of the reference level). Measured in physical units, we have: \frac = k, where I\! is the original intensity of the particular stimulation, \Delta I\! is the addition to it required for the change to be perceived (the JND), and ''k'' is a constant. This rule was first discovered by Erns ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
