Likelihood ratios in diagnostic testing
this wiki
Ad blocker interference detected!
Wikia is a freetouse site that makes money from advertising. We have a modified experience for viewers using ad blockers
Wikia is not accessible if you’ve made further modifications. Remove the custom ad blocker rule(s) and the page will load as expected.
Assessment 
Biopsychology 
Comparative 
Cognitive 
Developmental 
Language 
Individual differences 
Personality 
Philosophy 
Social 
Methods 
Statistics 
Clinical 
Educational 
Industrial 
Professional items 
World psychology 
Clinical: Approaches · Group therapy · Techniques · Types of problem · Areas of specialism · Taxonomies · Therapeutic issues · Modes of delivery · Model translation project · Personal experiences ·
 Not to be confused with Likelihoodratio test.
In evidencebased medicine, likelihood ratios are used for assessing the value of performing a diagnostic test. They use the sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition (such as a disease state) exists.
CalculationEdit
Two versions of the likelihood ratio exist, one for positive and one for negative test results. Respectively, they are known as the likelihood ratio positive (LR+) and likelihood ratio negative (LR–).
The likelihood ratio positive is calculated as
which is equivalent to
or "the probability of a person who has the disease testing positive divided by the probability of a person who does not have the disease testing positive." Here "T+" or "T−" denote that the result of the test is positive or negative, respectively. Likewise, "D+" or "D−" denote that the disease is present or absent, respectively. So "true positives" are those that test positive (T+) and have the disease (D+), and "false positives" are those that test positive (T+) but do not have the disease (D−).
The likelihood ratio negative is calculated as^{[1]}
which is equivalent to^{[1]}
or "the probability of a person who has the disease testing negative divided by the probability of a person who does not have the disease testing negative."
The pretest odds of a particular diagnosis, multiplied by the likelihood ratio, determines the posttest odds. This calculation is based on Bayes' theorem. (Note that odds can be calculated from, and then converted to, probability.)
Application to medicineEdit
A likelihood ratio of greater than 1 indicates the test result is associated with the disease. A likelihood ratio less than 1 indicates that the result is associated with absence of the disease. Tests where the likelihood ratios lie close to 1 have little practical significance as the posttest probability (odds) is little different from the pretest probability, and as such is used primarily for diagnostic purposes, and not screening purposes. When the positive likelihood ratio is greater than 5 Template:Why or the negative likelihood ratio is less than 0.2 (i.e. 1/5) then they can be applied to the pretest probability of a patient having the disease tested for to estimate a posttest probability of the disease state existing.^{[2]} In summary, the pretest probability refers to the chance that an individual has a disorder or condition prior to the use of a diagnostic test. It allows the clinician to better interpret the results of the diagnostic test and helps to predict the likelihood of a true positive (T+) result.^{[3]}
Research suggests that physicians rarely make these calculations in practice, however,^{[4]} and when they do, they often make errors.^{[5]} A randomized controlled trial compared how well physicians interpreted diagnostic tests that were presented as either sensitivity and specificity, a likelihood ratio, or an inexact graphic of the likelihood ratio, found no difference between the three modes in interpretation of test results.^{[6]}
ExampleEdit
A medical example is the likelihood that a given test result would be expected in a patient with a certain disorder compared to the likelihood that same result would occur in a patient without the target disorder.
Some sources distinguish between LR+ and LR−.^{[7]} A worked example is shown below. Template:SensSpecPPVNPV
Confidence intervals for all the predictive parameters involved can be calculated, giving the range of values within which the true value lies at a given confidence level (e.g. 95%).^{[8]}
Estimation of pre and posttest probabilityEdit
 Further information: Pre and posttest probability
The likelihood ratio of a test provides a way to estimate the pre and posttest probabilities of having a condition.
With pretest probability and likelihood ratio given, then, the posttest probabilities can be calculated by the following three steps:^{[9]}
 Pretest odds = (Pretest probability / (1  Pretest probability)
 Posttest odds = Pretest odds * Likelihood ratio
In equation above, positive posttest probability is calculated using the likelihood ratio positive, and the negative posttest probability is calculated using the likelihood ratio negative.
 Posttest probability = Posttest odds / (Posttest odds + 1)
In fact, posttest probability, as estimated from the likelihood ratio and pretest probability, is generally more accurate than if estimated from the positive predictive value of the test, if the tested individual has a different pretest probability than what is the prevalence of that condition in the population.
ExampleEdit
Taking the medical example from above (20 true positives, 10 false negatives, and 2030 total patients), the positive pretest probability is calculated as:
 Pretest probability = (20 + 10) / 2030 = 0.0148
 Pretest odds = 0.0148 / (1  0.0148) =0.015
 Posttest odds = 0.015 * 7.4 = 0.111
 Posttest probability = 0.111 / (0.111 + 1) =0.1 or 10%
As demonstrated, the positive posttest probability is numerically equal to the positive predictive value; the negative posttest probability is numerically equal to (1  negative predictive value).
ReferencesEdit
 ↑ ^{1.0} ^{1.1} Gardner, M.; Altman, Douglas G. (2000). Statistics with confidence: confidence intervals and statistical guidelines, London: BMJ Books.
 ↑ Beardsell A, Bell S, Robinson S, Rumbold H. MCEM Part A:MCQs, Royal Society of Medicine Press 2009
 ↑ Harrell F, Califf R, Pryor D, Lee K, Rosati R (1982). Evaluating the Yield of Medical Tests. JAMA 247 (18): 2543–2546.
 ↑ Reid MC, Lane DA, Feinstein AR (1998). Academic calculations versus clinical judgments: practicing physicians’ use of quantitative measures of test accuracy. Am. J. Med. 104 (4): 374–80.
 ↑ Steurer J, Fischer JE, Bachmann LM, Koller M, ter Riet G (2002). Communicating accuracy of tests to general practitioners: a controlled study. BMJ 324 (7341): 824–6.
 ↑ Puhan MA, Steurer J, Bachmann LM, ter Riet G (2005). A randomized trial of ways to describe test accuracy: the effect on physicians' posttest probability estimates. Ann. Intern. Med. 143 (3): 184–9.
 ↑ Likelihood ratios. URL accessed on 20090404.
 ↑ Online calculator of confidence intervals for predictive parameters
 ↑ Likelihood Ratios, from CEBM (Centre for EvidenceBased Medicine). Page last edited: 01 February 2009
Biomedical research: Clinical study design / Design of experiments  

Overview  
Controlled study (EBM I to II1; A to B)  
Observational study (EBM II2 to II3; B to C)  
Epidemiology/ methods 

Trial/test types  
Analysis of clinical trials  
Interpretation of results  
* Category 