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Diagnostic testing revisited:pathways through uncertainty

Martin T. Schechter, MD, MSc,PhD

Samuel B. Sheps, MD, FRCPC

To aid physicians who may be havingdifficulty applying the principles ofdecision analysis to diagnostic dataaccording to the methods publishedin the past several years, the authorsof this paper set out a few principlesand schemes for using and interpret-ing diagnostic data obtained fromdichotomous tests. They also presenta simple BASIC program for cal-culating post-test probabilities fromlikelihood ratios and pretest proba-bilities that a particular disease ispresent in a particular patient; theprogram can be adapted for use onmicrocomputers.

Certains medecins peuvent eprouverdes difficultes dans l'application audiagnostic des principes de l'analysedecisionnelle selon les methodes pu-bliees depuis quelques annees. A leurintention, les auteurs proposent desprincipes et des schemas pour l'utili-sation et l'interpretation des resul-tats d'epreuves reposant sur une di-chotomie. Ils presentent aussi unprogramme BASIC simple pour lecalcul de la probabilite finale del'existence d'une maladie donnee,chez un malade donne, a partir de laprobabilite de depart et des coeffi-

From the Clinical Research Support Group,Department of Health Care and Epidemiolo-gy, Faculty of Medicine, University of BritishColumbia, Vancouver

Reprint requests to: Dr. Martin T. Schechter,Department of Health Care and Epidemiolo-gy, University of British Columbia, MatherBuilding, 5804 Fairview Cres., Vancouver,BC V6T lW5

cients de sensibilite et dedes epreuves utilisees. Ces'adapte aux micro-ordina

Life is shortAnd the art longThe occasion instantExperiment perilousDecision difficult

Nature is probabilisticAnd information incompleteOutcomes are valuedResources limitedDecisions unavoidable

Weinstein a

Physicians have at theirvast arsenal of diagnostiicontinues to expand at arate. The use and costtests, both the financiehealth costs, increase irfashion.2 Despite the ncstraint in the face of 1trolled growth, diagnostcontinues to be excessivinstances the test is orderly and its results may bemisinterpreted.3 Lundberened the process of test oform of perseveration,and Lincoln' have aptl'the sequence of events irtest utilization as "Read~Aim!".

Fortunately, as evidencarticles, there is at thethe growing recognition 1rigour must be applied tdiagnostic tests and thetion of their results and tindividualized testing muthe traditional processtesting. Along these linerecently elucidated sometors that cause us to carr'

specificite testing beyond what is necessary forprogramme optimal patient management, callingiteurs. upon academic generalists and

epidemiologists to "take an active,even aggressive, role in teachingstudents, clinicians, and teachers touse tests more discriminately".

Methods for the use and interpre-tation of diagnostic data have been

Hippocrates developed over the past severalyears7-'4 and have received consider-able attention. In general thesemethods centre on the application ofprinciples of decision analysis, in-cluding the use of concepts such as

[nd Fineberg' predictive values, to diagnostic data.Despite these efforts, it is our expe-rience that the medical community

disposal a continues to have difficulty under-c tests that standing and using these methods.i geometric As Reuben6 stated: "Although theLs of these influence of such researchers isal and the spreading, they still represent ann a similar eclectic minority. Unfortunately, theeed for re- statistics and epidemiology thatthis uncon- have become tools of their trade aretic test use beyond the grasp of many clini-e; in many cians." Regrettably, some cliniciansed careless- tend to conclude from their lack ofignored or comprehension that these concepts

rg4 has lik- must be esoteric and clinically irrel-rdering to a evant. Nothing could be furtherand Wong from the truth. For example, Mor-y described gan'3 recently applied epidemiologici diagnostic concepts to suggest ways of dealingy! Fire! . . . with an issue of crucial importance

to everyday clinical practice; that is,-ed by these the waste associated with ruling outsame time unlikely diagnoses.

that greater To help those who may have hado the use of difficulty grasping the basics of theinterpreta- use and interpretation of diagnostic

hat rational data, we set out here a few princi-ist supplant ples and schemes. We hope thatof routine they can serve both as a guide for

es, Reuben6 utilization in clinical practice and asof the fac- a foundation for further study. For

y diagnostic simplicity we will concentrate our

CAN MED ASSOC J, VOL. 132, APRIL 1, 1985 755

attention on dichotomous tests; thatis, those whose result is simply posi-tive or negative. Readers who wish amore detailed review or who wish tocarry out further study should referto an excellent and comprehensiveseries on this topic that appearedrecently in CMAJ.'4

Principles (Table I) and schemes

Principle 1: In the diagnostic con-text,. patients do not have disease,only a probability of disease.

To understand this statement, letus consider a patient who is toundergo a diagnostic test meant todetect a certain target disease. Ifone is absolutely certain that thedisease is present prior to the test(i.e., the pretest probability is 1),then the test need not be done.Conversely, if one is absolutely cer-tain that the disease is not presentprior to the test (i.e., the pretestprobability is 0), then again the testneed not be done. Since in mostinstances the pretest probability isthus not 0 or 1, it must lie some-where in between. Thus, the princi-ple asserts that prior to diagnostictesting, patients should not be con-sidered to have or not to have dis-ease but simply to have a pretestprobability of disease. To utilizeprinciple 1 effectively, one need onlyassign to patients a pretest probabil-ity of disease that reflects one's levelof confidence that the target diseaseis actually present. This can bebased on the history and the resultsof physical examination, previoustests and consultations, combinedwith one's cumulative clinical expe-rience in similar situations.

Principle 2: Diagnostic tests aremerely revisions ofprobabilities.

One of the great failings of cur-rent medical training has resulted inthe widespread misconception that

diagnostic tests confer certainty withtheir results; that is, that positivetest results imply that patients arediseased and that negative onesimply that they are not. Since virtu-ally all tests have false-positive andfalse-negative results, a moment'sreflection should persuade the read-er that this cannot be so. In fact,even after diagnostic tests, principle1 still applies: patients still do nothave disease, only a probability ofdisease.What the test has accomplished is

to revise the probability of disease asfollows: The patient enters the testwith a pretest probability (P) asdiscussed earlier. If the test result ispositive, the probability of diseaseshould now be higher than prior tothe test; in fact, the probability isrevised upwards to the post-testprobability of a positive test result,denoted as PTL(+). If the test re-sult is negative, the probability isrevised downwards to the post-testprobability of a negative test result,denoted as PTL( -).The diagnostic test and its results

may be pictorially represented by atree diagram, as shown in Fig. 1.This type of representation is afundamental tool in the science ofstructuring clinical decisions, knownas clinical decision analysis.'5The amount by which the proba-

bility rises in the case of a positivetest result is a measure of how muchmore confident one can now beabout the presence of disease. Themagnitude of this rise measures, in asense, what a positive result accom-plishes in terms of "ruling in" thedisease. Similarly, the amount theprobability falls in the case of anegative result is a measure of howmuch more confident one can nowbe about the absence of disease. Themagnitude of this fall measureswhat a negative result accomplishesin terms of "ruling out" the disease.

Consider, for example, a hypo-thetical diagnostic test with a sensi-

I atble I -P-Trinciples of diagnostic deci si v) n IYt1 i ik

Principle I: In the diagnostic context. patients do not have disease. oni' -

probability of disease.Principle 2: [Diagnostic tests are mereli re isions of probabilities.Principle 3: lest interpretation should precede test ordering.Principle 4: In general, if the re%isions in prohahilities caLused bN a diagnostic tesk

do iniot entail a change in subsequencot management. use of thle testshould be reconsidered.

tivity of 0.65 and a specificity of0.90, and suppose this test is appliedto patients with a pretest probabilityof the target disease of 0.20. Adiagnostic tree for this situation isshown in Fig. 2. If the test result ispositive, the probability of diseaserises from 0.20 to 0.62. If the resultis negative, the probability fallsfrom 0.20 to 0.09. Methods for thecalculation of the post-test probabil-ities will be discussed shortly. Asensitivity and a specificity of 0.65and 0.90 respectively suggest thatthe diagnostic test will yield anabnormal result in about 65 of every100 diseased patients and a normalresult in about 90 of every 100nondiseased patients. It is difficultto see how these observations canhelp one directly to make predic-

+

PTL(+)

PTL( -)

Fig. I-Diagnostic "tree" for a diagnos-tic test. The patient enter the test, at theleft, with a pretest probability of thetarget disease of P. If the test result ispositive, the probability rises to PTL(+),the post-test probability of a positiveresult. If the result is negative, the proba-bility falls to PTL( -), the post-testprobability of a negative result.

0.

0.62

0.09

Fig. 2-Diagnostic tree for a test with asensitivity of 0.65 and a specificity of0.90 in a patient with a pretest probabili-ty of the target disease of 0.20.

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tions about patients in whom thepresence or absence of the targetdisease is still uncertain. That is whyone considers post-test probabilities.

It is worth while at this point tointroduce the likelihood ratios as anaid to the interpretation of diagnos-tic data. The positive likelihood ratioand the negative likelihood ratio,which we shall denote as LR(+) andLR( -) respectively, have severaladvantages in describing the capa-bilities of a diagnostic test. First,they are simple to calculate:* LR(+) sensitivity

(1 - specificity)

* LR(-) = (1 -sensitivity)specificity

Second, and most important, theyprovide clear measures of the "rul-ing-in" and "ruling-out" capabilitiesof a test.

LR(+), for example, is a quantitygreater than or equal to 1.0, and themagnitude by which it exceeds 1.0 isa measure of the test's ability torevise probabilities upward when thetest result is positive. An LR(+) of2.0 to 5.0 should be considered aspoor to fair, while one exceeding10.0 might be considered good. Con-versely, LR(-) is a quantity lessthan or equal to 1.0, and the magni-tude by which it falls below 1.0 is ameasure of the test's ability to reviseprobabilities downward when thetest result is negative. An LR( -) of0.5 to 0.2 should be considered aspoor to fair, while one below 0.1might be considered good.The third advantage of the likeli-

hood ratios lies in their use in thecalculations of post-test probabili-

O..

0.5

0.5

Fig. 3-Diagnostic tree for a test with asensitivity of 0.8 and a specificity of 0.2in a patient with a pretest probability ofthe target disease of 0.5.

ties. To derive PTL(+) andPTL(-) one need only employ thepretest probability P in the followingpaired formulas:

* PTL(+)= P- LR(+)(1 - P) + P . LR(+)

* PTL(-)= P*LR(-)(1 -P) + P- LR(-)

This is a form of Bayes' theorem.Several other methods for calculat-ing post-test likelihoods have alsobeen described;'4 these include asimple nomogram, first proposed byFagan,'6 that allows direct readingof post-test probabilities from ascale using likelihood ratios andavoids the need for the calculationsentailed by the use of these for-mulas. As an additional aid we haveprovided a simple BASIC program(see the Appendix) that performsthese calculations and can be adapt-ed for use on microcomputers.

Clearly, the ability of a test torevise probabilities up or down is thekey to the test's potential contribu-tion to the clinical situation. Unfor-tunately, the concepts of sensitivityand specificity that have been adopt-ed by convention as the measures oftest validity do not directly describethis ability. To see this readily, con-sider the example of a test with aspecificity of only 0.2 but a sensitivi-ty of 0.8. While the test is not veryspecific, it possesses fairly good sen-sitivity. Is this an excellent test thatshould always be used, a good testthat should often be used, a medio-cre test that may sometimes be usedor a worthless test that should neverbe used? Choose one of these fouroptions before reading further. Fromour informal survey of over 200faculty and residents it appears thatthe vast majority of physicianschoose the third option, feeling thatsuch a test could be of value inselected clinical situations.

Fig. 3 shows that the test is, infact, worthless. A diagnostic tree forthis test for a patient with a pretestprobability of the target disease of0.5 is shown. The probability ofdisease remains 0.5 regardless ofwhether the test result is positive ornegative, or whether the test is evenperformed at all. The three proba-bilities P, PTL(+) and PTL(-) willbe identical no matter what thepretest probability is assumed to be.

This can be seen immediately bynoting that in this case both LR(+)and LR(-) are equal to 1.0. De-spite a sensitivity of 80%, the testprovides absolutely no information.In general, consideration of the sen-sitivity and specificity alone in theassessment of a diagnostic test, as isso often the case, will not suffice todetermine the test's clinical utility.

Let us, by way of illustration,consider the example of radionuclideangiography (RNA) for the detec-tion of coronary artery disease(CAD). A recent assessment byAustin and colleagues'7 demonstrat-ed a sensitivity and a specificity forthe RNA test of 0.87 and 0.54respectively. One can now obtain anLR(+) of 1.9 and an LR(-) of 0.24for this test. The LR(+) of only 1.9indicates immediately that the testwill perform poorly at increasing theprobability of disease when its resultis positive. The LR(-) of 0.24 sug-gests that the test is only somewhatbetter at lowering the probability ofdisease when its result is negative.To see this, let us consider the test

as applied to several patients. Thefirst is a 40-year-old woman with achest pain syndrome that is notconsistent with angina, so that herpretest probability of CAD is low,say 0.1. The second patient is anelderly man with a classic history ofexertional angina responsive to ni-trates who has several coronary riskfactors, so that the pretest probabili-ty of CAD is extremely high, say0.9. Finally, let us consider a55-year-old man with a syndrome ofatypical angina during a period ofhigh stress. He is a smoker and hasa 10-year history of well controlledhypertension, so he is at an elevatedrisk of CAD, but the history issomewhat suspicious. For purposesof illustration we estimate his pre-test probability to be 0.5.

Diagnostic trees for these threepatients are presented in Fig. 4. Thepost-test probabilities were calculat-ed with the formulas presented earli-er involving the likelihood ratios andthe pretest probability. Just as pre-dicted from the likelihood ratios, theRNA test does perform better atrevising probabilities downwardswhen its result is negative than itdoes at revising them upwards whenits result is positive.

Principle 2 implies that diagnostic

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tests should not be considered asproviding absolute conclusions aboutthe presence or absence of diseasedepending on their results; rather,they should be viewed as adjust-ments of our level of confidence inmaking conclusions about individualpatients. While the likelihood ratiosmeasure the capabilities of the testat performing such adjustments, it isthe diagnostic tree that maps a par-ticular patient's pathway throughuncertainty. As shown by Fig. 4, theconclusions provided by a test de-pend not only on the test's capabili-ties but also, and much more heavi-ly, on the individual patient towhom the test is applied.

Principle 3: Test interpretation shouldprecede test ordering.

In considering the interpretationof diagnostic data discussed thusfar, it is important to recognize thatthere is nothing to dictate that thismethod must be applied after thediagnostic test. Since the likelihoodratios and pretest probability areknown prior to the test, the diagnos-tic tree can be constructed prior to itas well. This can be done not onlyfor purposes of interpretation butalso in consideration of whether thetest should be ordered at all. In thelanguage of Wong and Lincoln' thiswould correspond to "aiming" thetest before "firing". If one views thediagnostic tree as the patient's "re-sponse" to a diagnostic test, thenFig. 4 illustrates that patients "re-spond" to a given diagnostic test inas varied a fashion as they mightrespond to a specific therapy. In thetest situation, however, the "re-sponse" is not idiosyncratic: it canbe mapped out in advance of the testby using the methods we have out-lined.

Let us, therefore, interpret theRNA test results for our three pa-tients with CAD (Fig. 4) prior toordering the test. For the first pa-tient the "response" to the test willbe poor, and the test's use in thiscase would be inappropriate andpotentially harmful. A positive re-sult will move the probability from0.10 to only 0.17, hardly a majorboost to our confidence in the pres-ence of CAD. It is difficult to arguethat the further management of thepatient would be different if the

probability of CAD was 17% asopposed to 10%, so that a positivetest result should entail no change insubsequent management. Unfortu-nately, the reality of current clinicalpractice is such that this type ofinterpretation prior to test orderingis not often carried out. Many clini-cians will order such a test in justthis situation and worry about theresults later. Often this approachreflects a desire to be thorough orsimply to follow a test-ordering pro-tocol; in addition, there is often themotivation that one "might just pickup" a case of CAD. Once a test iscarried out, unfortunately, one isinvariably confronted with the re-sults and must deal with them. Forpatients who do not have CAD,about half (46%) will have abnor-mal RNA test results since the spec-ificity is 0.54, and these abnormalresults must be explained. Thinkingthat he or she has "picked up" acase of CAD, the physician may soadvise the patient, causing unneces-sary anxiety, and may feel com-pelled to order further, more inva-sive testing. In fact, all that hasbeen "picked up" is about sevenpercentage points of chance that thepatient truly has CAD. Examplessuch as this illustrate the pitfallsassociated with the practice of rul-ing out unlikely diagnoses.3

For the second patient there areseveral reasons why the RNA testwould be inappropriate for diagnos-tic purposes. First, setting a pretestprobability of 0.9 is equivalent tostating that one is virtually certainof the diagnosis and that furtherdiagnostic maneuvers are unneces-sary. Such a patient can go directlyto treatment or to staging proce-dures such as coronary angiographyin anticipation of possible bypasssurgery. Second, the RNA test willnot lead to any change of manage-ment anyway. Even if the result isnegative, the probability of diseaseis still 68%. Although this is anappreciable drop from 90%, it isprobably not enough to warrant achange in management. Unfortu-nately, clinicians may overestimatethe meaning of a negative result andthink that CAD is unlikely, when, infact, it is probably still present. Thisillustrates the problems associatedwith the practice of ruling in likelydiagnoses.

It is only for the third patient thatthere appears to be a rationale forthe use of the RNA test. Here, inplanning whether or not to order it,we see that the results will lead tochanges in management. A positiveresult will cause a modest rise inprobability, from 50% to 66%,where further testing is clearly war-ranted, while a negative result en-tails a significant drop in probabili-ty, from 50% to 19%, a level atwhich one may feel sufficiently con-fident to just observe the patient.

Thus, the diagnostic test serves ameaningful discriminatory functionhere, separating patients into twogroups that should be managed indifferent ways. Only for the thirdpatient does the test appear to satis-fy the final principle.

I. 0.19JFig. 4-Diagnostic trees for radionuclideangiography for three patients with pos-sible coronary artery disease.

CAN MED ASSOC J, VOL. 132, APRIL 1, 1985758

Principle 4: In general, if the revi-sions in probabilities caused by adiagnostic test do not entail achange in subsequent management,use of the test should be reconsid-ered.

Conclusion

If one synthesizes the principlesdiscussed thus far, one can envisagethe following type of diagnostic pro-cedure: Given an individual patientand a target disease, one begins byestimating the probability that thetarget disease is present. In thescreening situation, for example,when the patient is asymptomatic,one might very well use the preva-lence of the target disease in thesource population as an estimate ofthe pretest probability. In the diag-nostic situation, where additional ev-idence for the presence of the targetdisease exists, one must rely on thehistory and the results of physicalexamination, previous tests and con-sultations, together with one's clini-cal experience. In addition, with theincreasing availability and decreas-ing cost of computing facilities, it ishoped that more data will be collect-ed, stored and thus available foranalysis to provide better estimatesof these probabilities.'8 Instead ofordering a panel of tests for thepatient and then interpreting theresults, one can first consider thecapabilities of the various- testsavailable and map out diagnostictrees of the patient's response tothese tests. The likelihood ratios, aswe have discussed, provide an imme-diate measure of the tests' capabili-ties at revising probabilities. Foreach test and each post-test proba-bility, one can then consider whatthe appropriate management shouldbe in that eventuality. Only thosetests that significantly alter subse-quent management need be consid-ered, and a strategy for serial testingcan be developed in advance.

In summary, we have outlinedseveral principles (Table I) andschemes that we hope will serve asan introduction to clinical decisionanalysis and as a stimulus for fur-ther reading. Owing to inadequaciesin medical training and to a generallack of understanding in the medicalcommunity, most clinicians have se-rious misconceptions about the use

and interpretation of diagnosticdata, including an inability to quan-tify and judge the impact of diag-nostic test results in individual pa-tients."9 Diagnostic tests must beviewed not as infallible technologictools providing definitive answers forall patients, but as aids with whichwe may revise probabilities in indi-vidual patients. It is the former viewthat gives rise to the reliance ontest-ordering protocols and on condi-tioned test-ordering behaviour. Thelatter view encourages us to tailordiagnostic procedures to the individ-ual patient. To do this, we need touse elementary probability as a wayof quantifying and not mystifyingdiagnostic uncertainty. The calcula-tions and concepts involved, whileforeign to some, are really no moreinvolved than many physiologicalderivations in current clinical use(e.g., sodium replacement calcula-tions and the arterial-alveolar gra-dient) and are clearly of equal orgreater importance. This methodwould, in fact, be trivial if it wereintroduced in a fundamental way atan early stage of the medical curric-ulum.The continued excessive and often

inappropriate use of diagnostic testsshould be a matter of concern to theentire medical community. From awider perspective, these practicesrepresent an important source ofwaste of limited resources in a timewhen such waste can no longer betolerated. More important, from theperspective of individual patientcare these behaviours lead to unnec-essary morbidity and mortality, aris-ing both from the tests themselvesand from the misinterpretation oftheir results.

References

1. Weinstein MC, Fineberg HV: ClinicalDecision Analysis, Saunders, Philadel-phia, 1980: 293

2. Fineberg HV: Clinical chemistries: thehigh cost of low cost diagnostic tests. InAltman S, Blendon R (eds): MedicalTechnology: the Culprit Behind HealthCare Costs? (DHEW publ no [PHS]79-3216), US Govt Printing Office,Washington, 1979: 144-165

3. Griner PF, Glaser RJ: Misuse of labora-tory tests and diagnostic procedures. NEngl J Med 1982; 307: 1336-1339

4. Lundberg GD: Perseveration of laborato-ry test ordering: a syndrome affectingclinicians [E]. JAMA 1983; 249: 639

5. Wong ET, Lincoln TL: Ready! Fire! . . .

Aim!: an inquiry into laboratory testordering. JAMA 1983; 250: 2510-2513

6. Reuben DB: Learning diagnostic re-straint. N Engl J Med 1984; 310: 591-593

7. Vecchio TJ: Predictive value of a singlediagnostic test in unselected populations.N Engl J Med 1966; 274: 1171-1173

8. McNeil BJ, Keeler E, Adelstein SJ:Primer on certain elements of medicaldecision making. N Engl J Med 1975;293:211-215

9. Galen RS, Gambino SR: Beyond Nor-mality.' the Predictive Value and Effi-ciency of Medical Diagnoses, Wiley,New York, 1975

10. Feinstein AR: On the sensitivity, specific-ity and discrimination of diagnostic tests.In Clinical Biostatistics, Mosby, St Lou-is, 1977: 214-216

11. Department of Clinical Epidemiologyand Biostatistics, McMaster UniversityHealth Sciences Centre: How to readclinical journals: I. To learn about adiagnostic test. Can Med Assoc J 1981;124: 703-710

12. Griner PF, Mayewski RJ, Mushlin Al etal: Selection and interpretation of diag-nostic tests and procedures: principlesand applications. Ann Intern Med 1981;94 (4, pt 2): 557-592

13. Morgan PP: Why rule out unlikely diag-noses'? [El. Can Med Assoc J 1984; 130:1424

14. Department of Clinical Epidemiologyand Biostatistics, McMaster University,Hamilton, Ont.: Interpretation of diag-nostic data [six parts]. Can Med Assoc J1983; 129: 429-432; 559-564, 587; 705-710; 832-835; 947-954; 1093-1099

15. Weinstein MC, Fineberg HV: ClinicalDecision Analysis, Saunders, Philadel-phia, 1980

16. Fagan TJ: Nomogram for Bayes's theo-rem [C]. N Engl J Med 1975; 293: 257

17. Austin EH, Cobb FR, Coleman RE et al:Prospective evaluation of radionuclideangiocardiography for the diagnosis ofcoronary artery disease. Am J Cardiol1982; 50: 1212-1216

18. Diamond GA, Forrester JS: Analysis ofprobability as an aid in the clinicaldiagnosis of coronary artery disease. NEngl J Med 1979; 300: 1350-1358

19. Casscells W, Schoenberger A, GraboysTB: Interpretation by physicians of clini-cal laboratory results. N Engl J Med1978; 299: 999-1001

CAN MED ASSOC J, VOL. 132, APRIL 1, 1985 759

A_x,ientered as percentages (e.g., 80) and In the second phase the program

Below is a program listing for a not as decimals (e.g., 0.8). The pro- produces a three-column table contain-simple interactive MS-BASIC pro- gram then responds with a listing of ing the post-test probabilities of bothgram that performs a likelihood analy- these values together with the positive negative and positive test results, de-sis. and negative likelihood ratios, LR(+) noted as PTL(-) and PTL(+), for a

In the first phase the program elicits and LR(-), followed by a display of range of pretest probabilities that in-from the user the sensitivity, specificity the diagnostic tree diagram similar in cludes 5%, 10%, 15%, 20% and so onand pretest probability, which must be format to those in Figs. 1 to 4. up to 95%.

100 DEFINT A-J110 DEFSNG L-S120 DIM B(19), BP(19), BN(19)130 PRINT:PRINT "DIAGNOSTIC LIKELIHOOD ANALYSIS"140 PRINT:PRINT "ENTER SENSITIVITY(%), SPECIFICITY(%), PRE-TEST PROBABILITY(%)"150 PRINT:PRINT "e.g. 90,60,15"160 PRINT:INPUT SE,SP,P170 LP=SE/(100! -SP)180 LN=(100! -SE)/SP190 DEF FNPOST(Q,L)=(100!*Q*L)/(100!-Q+Q*L)200 PTLP=FNPOST(P,LP)210 PTLN=FNPOST(P,LN)220 PRINT "SENSITIVITY", "SPECIFICITY",SPC( 1) "LR(+)",SPC(2) "LR(-)",SPC(2) "P"230 PRINT SE, SP, LP, LN,P240 PRINT:PRINT TAB(15) "DIAGNOSTIC TREE DIAGRAM"250 PRINT TAB(15) "-----------------------"260 PRINT TAB(50) PTLP270 FOR I=44 TO 20 STEP -6280 PRINT TAB(I) "*"290 NEXT I300 PRINT SPC(10) P310 FOR I=20 TO 44 STEP 6320 PRINT TAB(I) "*"330 NEXT I340 PRINT TAB(50) PTLN350 PRINT "TO CONTINUE, HIT ANY KEY"360 X$=INKEY$: IF LEN(X$)=0 THEN 360370 PRINT TAB(15) "PTL(-)", TAB(33) "P", TAB(45) "PTL(+)"380 PRINT TAB(15) "-----", TAB(33) "-", TAB(45) "-390 FOR J=1 TO 19400 B(J) = 5*J410 BP(J)=-FNPOST(B(J),LP)420 BN(J)=FNPOST(B(J),LN)430 PRINT TAB(17) BN(J), TAB(32) B(J), TAB(47) BP(J)440 NEXT J450 PRINT "TO START AGAIN, TYPE 'YES' - TO STOP, TYPE 'NO"'460 INPUT Y$470 IF Y$="YES" THEN 130480 IF Y$="NO" THEN 490 ELSE 450490 END

760 CAN MED ASSOC J, VOL. 132, APRIL 1, 1985information se. page 875

Disease label: The identity of the condition from which a patient suffers. Itmay be the name of a precisely defined disorder identified by a battery oftests, a probability statement based on consideration of what is most likelyamong several possibilities, or an opinion based on pattern recognition.

-J.M. Last (editor): "A Dictionary ofEpidemiology", Oxford University Press,

New York, 1983: 29

760 CAN MED ASSOC J,-VOL. 132, APRIL 1, 1985 For prescribing information s" .875 -