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Matching is the method of making a study group and a comparison group comparable with respect to the extraneous factors. Generally used in the retrospective studies when selecting cases and controls to control variation in a response variable due to sources other than those which are taken immediately under investigation. Numerous kinds of matching can be recognized, the most common of which is when each case is individually matched with the control subject on the matching variables, for instance sex, age, occupation, etc. When the variable on which the matching takes place is continuous it is generally transformed into a series of categories (such as age), but the second process is to say that two values of the variable match if their difference lies between the defined limits.
This technique is known as caliper matching. Also significant is group matching in which distributions of the extraneous factors are made similar in the groups to be compared.
Collapsing categories : A procedure generally applied to contingency tables in which the two or more row or column categories are combined, in number of cases so as to yield the re
Randomized consent design is the design at first introduced to overcome some of the perceived ethical problems facing clinicians entering patients in the clinical trials including
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A unified approach to all problems of prediction, estimation, and hypothesis testing. It is based on concept of the decision function, which tells the performer of experiment how t
This is an attempt to measure the suffering caused by the illness which takes into the account both the years of the potential life lost due to the premature mortality as well as t
Random allocation is a technique for creating the treatment and control groups particularly in accordance of the clinical trial. Subjects receive the active treatment or the place
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It is an informal method of assessing the effect of the publication bias, generally in the context of the meta-analysis. The effect measures from each of the reported study are plo
An approach to decrease the size of very large data sets in which the data are first 'binned' and then statistics such as the mean and variance/covariance are calculated on each bi
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