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In clinical practice, post-test probabilities are often just estimated or even guessed. This is usually acceptable in the finding of a pathognomonic sign or symptom, in which case it is almost certain that the target condition is present; or in the absence of finding a sine qua non sign or symptom, in which case it is almost certain that the target condition is absent.
When an individual being tested has a different pre-test probability of having a condition than the control groups used to establish the PPV and NPV, the PPV and NPV are generally distinguished from the positive and negative post-test probabilities, with the PPV and NPV referring to the ones established by the control groups, and the post-test ...
In statistics, econometrics, political science, epidemiology, and related disciplines, a regression discontinuity design (RDD) is a quasi-experimental pretest–posttest design that aims to determine the causal effects of interventions by assigning a cutoff or threshold above or below which an intervention is assigned.
After the patients were treated according to their assigned condition for some period of time, let’s say a month, they would be given a measure of depression again (post-test). This design would consist of one within-subject variable (test), with two levels (pre and post), and one between-subjects variable (therapy), with two levels ...
In fact, post-test probability, as estimated from the likelihood ratio and pre-test probability, is generally more accurate than if estimated from the positive predictive value of the test, if the tested individual has a different pre-test probability than what is the prevalence of that condition in the population.
The pairs are e.g. either one person's pre-test and post-test scores or between-pairs of persons matched into meaningful groups (for instance, drawn from the same family or age group: see table). The constant μ 0 is zero if we want to test whether the average of the difference is significantly different.
For example, Monte Carlo studies have shown that the rank transformation in the two independent samples t-test layout can be successfully extended to the one-way independent samples ANOVA, as well as the two independent samples multivariate Hotelling's T 2 layouts [2] Commercial statistical software packages (e.g., SAS) followed with ...
The calculation from likelihood ratio is better only if the pre-test probability is different from the prevalence in the population, but, as you pointed out, that was not the case in the example, and therefore the example is a bit overkill (the reason I took it was that it was easy to copy-paste from Positive predictive value. I'm now doing a ...