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{{Short description|Probabilities of the presence of a condition}}
{{Use dmy dates|date=
'''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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The post-test probability of disease given a negative result is calculated as:
Negative posttest probability =
The validity of the equations above also depend on that the sample from the population does not have substantial [[sampling bias]] that make the groups of those who have the condition and those who do not substantially disproportionate from corresponding prevalence and "non-prevalence" in the population. In effect, the equations above are not valid with merely a [[case-control study]] that separately collects one group with the condition and one group without it.
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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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</gallery>
It is possible to do a calculation of likelihood ratios for tests with continuous values or more than two outcomes which is similar to the calculation for dichotomous outcomes. For this purpose, a separate likelihood ratio is calculated for every level of test result and is called interval or stratum specific likelihood ratios.<ref>{{cite journal | doi = 10.1067/mem.2003.274 |vauthors=Brown MD, Reeves MJ | year = 2003 | title = Evidence-based emergency medicine/skills for evidence-based emergency care. Interval likelihood ratios: another advantage for the evidence-based diagnostician | journal =Ann Emerg Med | volume = 42 | issue = 2| pages = 292–297 | pmid = 12883521 | doi-access = free }}</ref>
====Example====
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The [[prevalence]] in the population sample is calculated to be:
*Prevalence = (2 + 1) / 203 = 0.0148 or 1.48%
The individual's pre-test probability was more than twice
====Specific sources of inaccuracy====
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=====Interference with test=====
''Post-test probability'', as estimated from the ''pre-test probability'' with ''likelihood ratio'', should be handled with caution in individuals with other determinants (such as risk factors) than the general population, as well as in individuals that have undergone previous tests, because such determinants or tests may also influence the test itself in unpredictive ways, still causing inaccurate results. An example with the risk factor of [[obesity]] is that additional abdominal fat can make it difficult to palpate abdominal organs and decrease the resolution of [[abdominal ultrasonography]], and similarly, remnant [[barium contrast]] from a previous radiography can interfere with subsequent abdominal examinations,<ref>[https://books.google.com/books?id=CQuBkXDspBkC&pg=PA750 Page 750] (Chapter 10) in: {{cite book |author1=Dunning, Marshall Barnett |author2=Fischbach, Frances Talaska |title=A manual of laboratory and diagnostic tests [electronic resource] |publisher=Wolters Kluwer Health/Lippincott Williams & Wilkins |___location=Philadelphia |year=2009 |isbn=978-0-7817-7194-
=====Overlap of tests=====
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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
====Multiple risk factors====
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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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