Pre- and post-test probability: Difference between revisions

Revision as of 04:31, 8 May 2014

Revision as of 17:07, 25 May 2014

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The subjectivity of the probabilities is based on the fact that, in reality, an individual either has the condition or not (with the probability always being either 0% or 100%), so pre- and post-test probabilities for individuals can rather be regarded as psychological phenomena in the minds of those involved in the [[diagnostics]] at hand.

Test, in this sense, can refer to any [[medical test]] (but usually in the sense of diagnostic tests), and in a broad sense also including questions and even assumptions (such as assuming that the target individual is a female or male). The ability to make a difference between pre- and post-test probabilities of various conditions is a major factor in the [[Medical_testMedical test#Indications|indication of medical tests]].

==Pre-test probability==

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| style="background: #ffdddd;"|'''''Positive'''''

| style="background: #eeeeff;"| <span style="color:#007700;"> '''True Positive'''

| <span style="color:#770000;"> '''False Positive'''<BR>([[Type_I_and_type_II_errorsType I and type II errors#False_positive_rateFalse positive rate|Type I error]])

| <span style="color:#770077;"> → [[Positive predictive value]]

|-

| style="background: #ffdddd;"|'''''Negative'''''

| style="background: #eeeeff;"|<span style="color:#770000;"> '''False Negative'''<BR>([[Type_I_and_type_II_errorsType I and type II errors#False_negative_rateFalse negative rate|Type II error]])

| <span style="color:#007700;"> '''True Negative'''

| <span style="color:#770077;"> → [[Negative predictive value]]

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To avoid such sources of inaccuracy by using likelihood ratios, the optimal method would be to gather a large reference group of equivalent individuals, in order to establish separate ''predictive values'' for use of the test in such individuals. However, with more knowledge of an individual's medical history, physical examination and previous test etc. that individual becomes more unique, with increasing difficulty to find a reference group to establish tailored predictive values, making an estimation of post-test probability by predictive values invalid.

Another method to overcome such inaccuracies is by evaluating the test result in the context of diagnostic criteracriteria, as described in the next section.

===By relative risk===

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Most major diseases have established [[diagnostic criteria]] and/or [[clinical prediction rule]]s. The establishment of diagnostic criteria or clinical prediction rules consists of a comprehensive evaluation of many tests that are considered important in estimating the probability of a condition of interest, sometimes also including how to divide it into subgroups, and when and how to treat the condition. Such establishment can include usage of predictive values, likelihood ratios as well as relative risks.

For example, the [[Systemic_lupus_erythematosusSystemic lupus erythematosus#Diagnostic_criteriaDiagnostic 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 online tool [http://hp2010.nhlbihin.net/atpiii/calculator.asp?usertype=prof] from the [[Framingham Heart Study]] for estimating the risk for coronary heart disease outcomes using multiple risk factors, including age, gender, blood lipids, blood pressure and smoking, being much more accurate than multiplying the individual relative risks of each risk factor.

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A major factor for such an absolute difference is the power of the test itself, such as can be described in terms of, for example, sensitivity and specificity or likelihood ratio. Another factor is the pre-test probability, with a lower pre-test probability resulting in a lower absolute difference, with the consequence that even very powerful tests achieve a low absolute difference for very unlikely conditions in an individual (such as [[rare disease]]s in the absence of any other indicating sign), but on the other hand, that even tests with low power can make a great difference for highly suspected conditions.

The probabilities in this sense may also need to be considered in context of conditions that are not primary targets of the test, such as [[Differential_diagnosisDifferential diagnosis#profile-relative probability|profile-relative probabilities in a differential diagnostic procedure]].

The absolute difference can be put in relation to the benefit for an individual that a [[medical test]] achieves, such as can roughly be estimated as: