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Overmatching, or post-treatment bias, is matching for an apparent mediator that actually is a result of the exposure. [12] If the mediator itself is stratified, an obscured relation of the exposure to the disease would highly be likely to be induced. [13] Overmatching thus causes statistical bias. [13]
Overmatching, matching for an apparent confounder that actually is a result of the exposure [clarification needed]. The control group becomes more similar to the cases in regard to exposure than does the general population. Survivorship bias, in which only "surviving" subjects are selected, ignoring those that fell out of view. For example ...
Within statistics, oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set (i.e. the ratio between the different classes/categories represented).
Overmatching is the opposite of undermatching, and is less common. Here the subjects response proportions are more extreme than reinforcement proportions. Overmatching may occur if there is a penalty for switching. A final deviation is bias, which occurs when subjects spend more time on one alternative than the matching equation predicts.
Detection bias occurs when a phenomenon is more likely to be observed for a particular set of study subjects. For instance, the syndemic involving obesity and diabetes may mean doctors are more likely to look for diabetes in obese patients than in thinner patients, leading to an inflation in diabetes among obese patients because of skewed detection efforts.
According to the US Army, the definition of overmatch is "the concept where my (insert lethality system here) can willfully and without prejudice or luck defeat your (insert your protective system here)." [4] According to Raytheon, overmatch is a verb which means "to defeat threats at every level – strategic, tactical and technological." [5]
Random variables are usually written in upper case Roman letters, such as or and so on. Random variables, in this context, usually refer to something in words, such as "the height of a subject" for a continuous variable, or "the number of cars in the school car park" for a discrete variable, or "the colour of the next bicycle" for a categorical variable.
Difference in differences (DID [1] or DD [2]) is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research design using observational study data, by studying the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. [3]