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Matching is a statistical technique that evaluates the effect of a treatment by comparing the treated and the non-treated units in an observational study or quasi-experiment (i.e. when the treatment is not randomly assigned).
The insights behind the use of matching still hold but should be applied with other matching methods; propensity scores also have other productive uses in weighting and doubly robust estimation. Like other matching procedures, PSM estimates an average treatment effect from observational data. The key advantages of PSM were, at the time of its ...
Immediate-spin cross-matching (ISCM) is an abbreviated form of cross-matching that is faster, but less sensitive; its primary use is to detect a mismatch between ABO blood types. It is an immediate test that involves combining the patient's serum and donor's red blood cells at room temperature, then centrifuging the sample and observing for ...
TDA is a powerful program, offering access to some of the latest developments in transition data analysis. STATA has implemented a package to run optimal matching analysis. TraMineR is an open source R-package for analyzing and visualizing states and events sequences, including optimal matching analysis.
This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables. General tests [ edit ]
Repeated measures analysis of variance (rANOVA) is a commonly used statistical approach to repeated measure designs. [3] With such designs, the repeated-measure factor (the qualitative independent variable) is the within-subjects factor, while the dependent quantitative variable on which each participant is measured is the dependent variable.
Convergent Cross Mapping (CCM) leverages a corollary to the Generalized Takens Theorem [2] that it should be possible to cross predict or cross map between variables observed from the same system. Suppose that in some dynamical system involving variables X {\displaystyle X} and Y {\displaystyle Y} , X {\displaystyle X} causes Y {\displaystyle Y} .
It allows users to apply baseline correction, normalization, smoothing, peak detection and peak matching. In addition, it allows the application of different machine learning and statistical methods to the pre-processed data for biomarker discovery, unsupervised clustering and supervised sample classification. [56] massXpert Open source GPL