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{{Short description|Probabilities of the presence of a condition}}
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'''Pre-test probability''' and '''post-test probability''' (alternatively spelled pretest and posttest probability) are the [[probabilities]] of the presence of a condition (such as a [[disease]]) before and after a [[diagnostic test]], respectively. ''Post-test probability'', in turn, can be ''positive'' or ''negative'', depending on whether the test falls out as a [[positive test|positive test or a negative test]], respectively. In some cases, it is used for the probability of developing the condition of interest in the future.
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*Posttest probability = Posttest odds / (Posttest odds + 1)
[[File:Fagan nomogram.svg|thumb|''Fagan nomogram''<ref>Parameters taken from [http://ard.bmj.com/content/65/10/1301/F4.large.jpg image] in: {{cite journal |vauthors=Zhang W, Doherty M, Pascual E, etal |title=EULAR evidence based recommendations for gout. Part I: Diagnosis. Report of a task force of the Standing Committee for International Clinical Studies Including Therapeutics (ESCISIT) |journal=Ann. Rheum. Dis. |volume=65 |issue=10 |pages=1301–11 |date=October 2006 |pmid=16707533 |pmc=1798330 |doi=10.1136/ard.2006.055251
The relation can also be estimated by a so-called ''Fagan nomogram'' (shown at right) by making a straight line from the point of the given ''pre-test probability'' to the given ''likelihood ratio'' in their scales, which, in turn, estimates the ''post-test probability'' at the point where that straight line crosses its scale.
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If only one risk factor of an individual is taken into account, the post-test probability can be estimated by multiplying the relative risk with the risk in the control group. The control group usually represents the unexposed population, but if a very low fraction of the population is exposed, then the prevalence in the general population can often be assumed equal to the prevalence in the control group. In such cases, the post-test probability can be estimated by multiplying the relative risk with the risk in the general population.
For example, the [[Incidence (epidemiology)|incidence]] of [[breast cancer]] in a woman in the United Kingdom at age 55 to 59 is estimated at 280 cases per 100.000 per year,<ref name=cancerresearchuk>[http://info.cancerresearchuk.org/prod_consump/groups/cr_common/@nre/@sta/documents/generalcontent/cases_crude_breast1_xls.xls Excel chart] for ''Figure 1.1: Breast Cancer (C50), Average Number of New Cases per Year and Age-Specific Incidence Rates, UK, 2006-2008'' at [http://info.cancerresearchuk.org/cancerstats/types/breast/incidence/ Breast cancer
--><ref name="acs bc facts 2005-6">{{cite web |author=ACS |year=2005 |title=Breast Cancer Facts & Figures 2005–2006 |url=http://www.cancer.org/downloads/STT/CAFF2005BrFacspdf2005.pdf |access-date=2007-04-26 |archive-url = https://web.archive.org/web/20070613192148/http://www.cancer.org/downloads/STT/CAFF2005BrFacspdf2005.pdf <!-- Bot retrieved archive --> |archive-date = 2007-06-13 |author-link= American Cancer Society}}</ref> compared to unexposed. Because a low fraction of the population is exposed, the prevalence in the unexposed population can be assumed equal to the prevalence in the general population. Subsequently, it can be estimated that a woman in the United Kingdom that is aged between 55 and 59 and that has been exposed to high-dose ionizing radiation should have a risk of developing breast cancer over a period of one year of between 588 and 1.120 in 100.000 (that is, between 0,6% and 1.1%).
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For example, the [[Systemic lupus erythematosus#Diagnostic criteria|ACR criteria for systemic lupus erythematosus]] defines the diagnosis as presence of at least 4 out of 11 findings, each of which can be regarded as a target value of a test with its own sensitivity and specificity. In this case, there has been evaluation of the tests for these target parameters when used in combination in regard to, for example, interference between them and overlap of target parameters, thereby striving to avoid inaccuracies that could otherwise arise if attempting to calculate the probability of the disease using likelihood ratios of the individual tests. Therefore, if diagnostic criteria have been established for a condition, it is generally most appropriate to interpret any post-test probability for that condition in the context of these criteria.
Also, there are risk assessment tools for estimating the combined risk of several risk factors, such as the
Still, an experienced physician may estimate the post-test probability (and the actions it motivates) by a broad consideration including criteria and rules in addition to other methods described previously, including both individual risk factors and the performances of tests that have been carried out.
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*''r<sub>i</sub>'' is the rate of how much ''probability differences'' are expected to result in ''changes in interventions'' (such as a change from "no treatment" to "administration of low-dose medical treatment").
*''b<sub>i</sub>'' is the benefit of ''changes in interventions'' for the individual
*''h<sub>i</sub>'' is the harm of ''changes in interventions'' for the individual, such as [[side
*''h<sub>t</sub>'' is the harm caused by the test itself
In this formula, what constitutes benefit or harm largely varies by [[Value (personal and cultural)|personal and cultural values]], but general conclusions can still be drawn. For example, if the only expected effect of a medical test is to make one disease more likely than another, but the two diseases have the same treatment (or neither can be treated), then ''r<sub>i</sub>'' = 0 and the test is essentially without any benefit for the individual.
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==References==
{{reflist
{{Medical research studies}}
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