Assessment of Diagnostic Technology in Health Care Rationale, Methods, Problems, and Directions (1989) / Chapter Skim
Currently Skimming:
2. The Use of Diagnostic Tests: A Probabilistic Approach
2. The Use of Diagnostic Tests: A Probabilistic Approach
Pages 23-54
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 23... ...
This chapter is a primer for applying probability theory to the interpretation of test results and deciding when to do a test rather than treat or do nothing.1 It is divided into five parts: (1) first ~ This chapter is adapted from an article written by one of Me authors (Sox 1986~.
|
From page 24... ...
These advantages are compeHing, and our approach to test evaluation is based on providing the information required to use probability theory to interpret and select diagnostic tests. Example: In a patient with chest pain, past history is very useful when trying to decide whether he or she has coronary artery disease.
|
From page 25... ...
In this situation, a test is only useful if, after it is performed, the probability of disease has changed so much that it has crossed from one side of the treatment threshold probability to the over. If the posttest probability were on the same side of the threshold as He pretest probability, the decision of whether or not to treat would be unaffected by the test results, and the test should not be ordered.
|
From page 26... ...
A test result may lead to a good outcome, such as improved longevity, but the increase in cost for each unit of increase in longevity may be so high that there is a consensus that the test should not be done. INTERPRETING TEST RESULTS: THE POSTTEST PROBABILITY The inte~pretation.of a test result is an important part of technology assessment.
|
From page 27... ...
Likelihood ratio probability of result in diseased patients probability of result in nondiseased patients . We can use this definition to define a positive test result and a negative test result.
|
From page 28... ...
A negative test result lowers Me probability of disease, and its likelihood ratio is between 0.0 and I.0. The likelihood ratio for a negative test result is abbreviated ER(-3.
|
From page 29... ...
This statement is inescapably true, because it is based on first pnnciples of probability theory. The effect of a test result depends on the pretest probability.
|
From page 30... ...
negative test has a considerable effect, and a positive test has lithe effect. This example shows Hat a test result that confirms one's prior judgment has little effect on the probability of disease.
|
From page 31... ...
1 . 0.0 0.2 0.4 0.6 0.8 1.0 PRETEST PROBABILITY Figure 2.lb The pastiest probability of disease corresponding to a negative test result was calculated win B ayes' theorem for ~1 values of Me pretest Probability.
|
From page 32... ...
The posttest probability of disease after a negative test may be so high that treatment is still indicated. Example: In a patient with typical angina pectoris, the posttest probability aher a negative exercise test is 0.76.
|
From page 33... ...
If the pretest probability is very low, as occurs in screening asymptomatic individuals, the clinician is likely to do nothing unless a positive test result raises concern. If, for example, the pretest probability is less than 0.001, the posttest probability may be less than 0.01.
|
From page 34... ...
Studies should be done by primary care physicians who keep track of everyone in their practice with a particular clinical complaint, eventually identifying ah patients as either having or not having a particular disease. Clinical prediction rules.
|
From page 35... ...
of a test is an important factor in detemurung die pastiest probability after a positive test. The falsepositive rate, however, has a very small effect on the pastiest probability after a negative test result, as seen in die lower family of curves.
|
From page 36... ...
has relatively little effect on the nostm~t nrohah;l:after a positive test, as seen ire the upper family of curves. probability after a negative test.
|
From page 37... ...
In one case, one may interpret a test result as indicating that disease is present if the test is positive and absent if the test is negative. Using data from another study, one cannot conclude anything from a test result, because the probability of disease is changed very little by the test results.
|
From page 38... ...
TABLE 2~2 Test Perfonnance Measurement Disease Disease Test Result Present Absent Positive A B Negative C D Total A+C B+D NOTE: True-positive rate (sensitivity)
|
From page 39... ...
Refernng physicians are much more apt to refer patients with an abnormal index test result and are unlikely to refer patients with a negative index test, because the latter is seen as presumptive evidence against the disease. When the index test is a referral criterion (workup bias)
|
From page 40... ...
Were the True-Positive Rate and False-Positcve Rate of the Test Measured in Clinically Relevant Subgroups of Patients? Most study populations contain a spectrum of padents, whose disease state varies in clinical seventy and in anatomic extent.
|
From page 41... ...
When sensitivity and specificity are known for each point on a continuous scale, the posttest probability can be calculated for any test result. The relationship between the true-positive rate and the false-positive rate of series of cutoff points may be expressed graphically.
|
From page 42... ...
The clinician should choose a cutoff point near the origin when the disease is rare or the treatment is dangerous; this choice will serve to minimize bow the number of false-positive results and the danger to nondiseased patients. The slope of the ROC curve is flat near the upper
|
From page 43... ...
This choice win minimize falsenegative results in a situation where Hey would be very harmful. EXPECTED-VALUE DECISIONMAKING Expected-value decisionmaking is the central idea behind quantitative approaches to decisionmaking when the outcomes are uncertain.
|
From page 44... ...
This patient's life expectancy was 20 years. Medical treatment: Because management associated with the medical option does not change, there are no chance nodes, and the patient's life expectancy is 20 years.
|
From page 45... ...
By assigning a value to each outcome and weighing it by the chance that it win occur, expected-value decisionmaking alBows one to integrate risks and benefits. THE CHOICE AMONG DOING NOTHING, TESTING, OR STARTING TREATMENT The art of medicine is making good decisions with inadequate data.
|
From page 46... ...
Note that when the costs of treating nondiseased patients equal the benefits of treating diseased patients, the treatment threshold is 0.5. Thus, for many treatments, the treatment threshold probability will be less than
|
From page 47... ...
If there is good reason to suspect disease, the pretest probability win be above the treatment threshold. In deciding whether to perform a test, Me clinician must ask whether the posttest probability after a negative test result would be below the treatment threshold probability.
|
From page 48... ...
48 ASSESSMENT OF DIAGNOSTIC TECHNOLOGY operative death p=03 U O no tumor SURGERY I, NO SURGERY 1 L tumor survive surgery U=.98 operative death p_.O3 U O I P[tumor] ~ Tsurvive ~ r tumor U=.25 , |p[tumorj| U=.98 p=.48 no cure U 23 |no tumor U 1 0 FIGURE 2.6 A decision Bee for choosing between treatment and no treatment when the clinician does not know whether He patient has the disease for which treatment is indicated.
|
From page 49... ...
, a positive test result could not increase the probability of disease enough to cross the treatment threshold, and both testing and treatment should be withheld. Above this threshold, the posttest probability will exceed the treatment threshold, and testing is indicated.
|
From page 50... ...
Past studies of diagnostic tests have measured only test performance. A complete evaluation should provide information about the treatment threshold and how to estimate the pretest probability of disease.
|
From page 51... ...
False-negative result: a negative result in a patient with a disease. False-positive rate: the likelihood of a positive test result in a patient without a disease (abbreviated FPR)
|
From page 52... ...
Sensitivity: the likelihood of a positive test result in a diseased person (synonym: true-positive rate, abbreviated TPR)
|
From page 53... ...
Below the threshold probability, treatment is withheld; above the threshold, treatment is given. True-negative result: a negative test result in a person with a disease.
|
From page 54... ...
Stason, W.B., and Fineberg, H.V. Implications of alternative strategies to diagnose coronary artery disease.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.