2670:. Usually publications stating PPV of a throat swab are reporting on the probability that this bacterium is present in the throat, rather than that the patient is ill from the bacteria found. If presence of this bacterium always resulted in a sore throat, then the PPV would be very useful. However the bacteria may colonise individuals in a harmless way and never result in infection or disease. Sore throats occurring in these individuals are caused by other agents such as a virus. In this situation the gold standard used in the evaluation study represents only the presence of bacteria (that might be harmless) but not a causal bacterial sore throat illness. It can be proven that this problem will affect positive predictive value far more than negative predictive value. To evaluate diagnostic tests where the gold standard looks only at potential causes of disease, one may use an extension of the predictive value termed the
133:
141:
2654:(LR+). Also, note that a critical assumption is that the tests must be independent. As described Balayla et al., repeating the same test may violate the this independence assumption and in fact "A more natural and reliable method to enhance the positive predictive value would be, when available, to use a different test with different parameters altogether after an initial positive result is obtained.".
36:
898:
526:
601:
299:
745:
435:
2662:
PPV is used to indicate the probability that in case of a positive test, that the patient really has the specified disease. However, there may be more than one cause for a disease and any single potential cause may not always result in the overt disease seen in a patient. There is potential to mix up
829:
457:
2218:
Note that the PPV is not intrinsic to the test—it depends also on the prevalence. Due to the large effect of prevalence upon predictive values, a standardized approach has been proposed, where the PPV is normalized to a prevalence of 50%. PPV is directly proportional to the prevalence of the disease
2222:
To overcome this problem, NPV and PPV should only be used if the ratio of the number of patients in the disease group and the number of patients in the healthy control group used to establish the NPV and PPV is equivalent to the prevalence of the diseases in the studied population, or, in case two
2204:
The small positive predictive value (PPV = 10%) indicates that many of the positive results from this testing procedure are false positives. Thus it will be necessary to follow up any positive result with a more reliable test to obtain a more accurate assessment as to whether cancer is
616:" is the event that the test makes a negative prediction, and the subject has a positive result under the gold standard. With a perfect test, one which returns no false negatives, the value of the NPV is 1 (100%), and with a test which returns no true negatives the NPV value is zero.
546:
244:
2258:, below which the reliability of a positive screening test drops precipitously. That said, Balayla et al. showed that sequential testing overcomes the aforementioned Bayesian limitations and thus improves the reliability of screening tests. For a desired positive predictive value
2219:
or condition. In the above example, if the group of people tested had included a higher proportion of people with bowel cancer, then the PPV would probably come out higher and the NPV lower. If everybody in the group had bowel cancer, the PPV would be 100% and the NPV 0%.
2205:
present. Nevertheless, such a test may be useful if it is inexpensive and convenient. The strength of the FOB screen test is instead in its negative predictive value — which, if negative for an individual, gives us a high confidence that its negative result is true.
639:
2253:
confers inherent limitations on the accuracy of screening tests as a function of disease prevalence or pre-test probability. It has been shown that a testing system can tolerate significant drops in prevalence, up to a certain well-defined point known as the
341:
176:
results, respectively. The PPV and NPV describe the performance of a diagnostic test or other statistical measure. A high result can be interpreted as indicating the accuracy of such a statistic. The PPV and NPV are not intrinsic to the test (as
2223:
disease groups are compared, if the ratio of the number of patients in disease group 1 and the number of patients in disease group 2 is equivalent to the ratio of the prevalences of the two diseases studied. Otherwise, positive and negative
2504:
893:{\displaystyle {\text{FOR}}=1-{\text{NPV}}={\frac {\text{Number of false negatives}}{{\text{Number of true negatives}}+{\text{Number of false negatives}}}}={\frac {\text{Number of false negatives}}{\text{Number of negative calls}}}}
521:{\displaystyle {\text{FDR}}=1-{\text{PPV}}={\frac {\text{Number of false positives}}{{\text{Number of true positives}}+{\text{Number of false positives}}}}={\frac {\text{Number of false positives}}{\text{Number of positive calls}}}}
318:" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard. The ideal value of the PPV, with a perfect test, is 1 (100%), and the worst possible value would be zero.
596:{\displaystyle {\text{NPV}}={\frac {\text{Number of true negatives}}{{\text{Number of true negatives}}+{\text{Number of false negatives}}}}={\frac {\text{Number of true negatives}}{\text{Number of negative calls}}}}
294:{\displaystyle {\text{PPV}}={\frac {\text{Number of true positives}}{{\text{Number of true positives}}+{\text{Number of false positives}}}}={\frac {\text{Number of true positives}}{\text{Number of positive calls}}}}
2663:
related target conditions of PPV and NPV, such as interpreting the PPV or NPV of a test as having a disease, when that PPV or NPV value actually refers only to a predisposition of having that disease.
2242:). Preferably, in such cases, a large group of equivalent individuals should be studied, in order to establish separate positive and negative predictive values for use of the test in such individuals.
740:{\displaystyle {\text{NPV}}={\frac {{\text{specificity}}\times (1-{\text{prevalence}})}{{\text{specificity}}\times (1-{\text{prevalence}})+(1-{\text{sensitivity}})\times {\text{prevalence}}}}}
2238:, with the PPV and NPV referring to the ones established by the control groups, and the post-test probabilities referring to the ones for the tested individual (as estimated, for example, by
806:
430:{\displaystyle {\text{PPV}}={\frac {{\text{sensitivity}}\times {\text{prevalence}}}{{\text{sensitivity}}\times {\text{prevalence}}+(1-{\text{specificity}})\times (1-{\text{prevalence}})}}}
53:
3299:
2302:
2766:
3028:"The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation"
2357:
2644:
2579:
2559:
2530:
2349:
2276:
3174:"Etiologic predictive value of a rapid immunoassay for the detection of group A Streptococcus antigen from throat swabs in patients presenting with a sore throat"
2622:
2600:
2322:
917:
of the target condition is the same as the prevalence in the control group used to establish the negative predictive value, then the two are numerically equal.
2234:
of having a condition than the control groups used to establish the PPV and NPV, the PPV and NPV are generally distinguished from the positive and negative
1175:
208:
of the target condition is the same as the prevalence in the control group used to establish the positive predictive value, the two are numerically equal.
100:
72:
2651:
2239:
2224:
79:
2933:
86:
3216:
Gunnarsson, Ronny K.; Lanke, Jan (2002). "The predictive value of microbiologic diagnostic tests if asymptomatic carriers are present".
2949:
Brooks, Harold; Brown, Barb; Ebert, Beth; Ferro, Chris; Jolliffe, Ian; Koh, Tieh-Yong; Roebber, Paul; Stephenson, David (2015-01-26).
2750:
2698:
2039:
1281:
119:
68:
2703:
1597:
612:" is the event that the test makes a negative prediction, and the subject has a negative result under the gold standard, and a "
2977:"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation"
1884:
1061:
910:
201:
57:
3289:
2688:
1996:
1888:
934:
757:
624:
620:
326:
322:
93:
3155:
Jacques
Balayla. Bayesian Updating and Sequential Testing: Overcoming Inferential Limitations of Screening Tests.
1581:
46:
3294:
2156:
2103:
2070:
2035:
1442:
1362:
1337:
1275:
161:
132:
3173:
2235:
975:
140:
3284:
1776:
1757:
1389:
956:
310:" is the event that the test makes a positive prediction, and the subject has a positive result under the
2708:
2683:
2172:
1975:
1712:
1483:
212:
3172:
Orda, Ulrich; Gunnarsson, Ronny K; Orda, Sabine; Fitzgerald, Mark; Rofe, Geoffry; Dargan, Anna (2016).
1703:
Type I error: A test result which wrongly indicates that a particular condition or attribute is present
1676:
Type II error: A test result which wrongly indicates that a particular condition or attribute is absent
2895:"Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation"
2693:
2255:
2231:
2140:
1414:
988:
914:
446:
311:
233:
216:
205:
2054:
1971:
1903:
1309:
1217:
1168:
1099:
1065:
3241:
2856:
2760:
2000:
1880:
1744:
1732:
1720:
1211:
1057:
182:
178:
2281:
2876:"Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking"
2499:{\displaystyle n_{i}=\lim _{k\to \rho }\left\lceil {\frac {\ln \left}{\ln \left}}\right\rceil }
3233:
3198:
3133:
3059:
3008:
2929:
2814:
2746:
2742:
2713:
2250:
1719:
estimates may be obtained. In contrast, the sensitivity and specificity can be estimated from
1711:
Note that the positive and negative predictive values can only be estimated using data from a
1071:
190:
3225:
3188:
3123:
3090:
3049:
3039:
2998:
2988:
2921:
2848:
2804:
2796:
2734:
2629:
2564:
2537:
2515:
2327:
2261:
2227:
are more accurate than NPV and PPV, because likelihood ratios do not depend on prevalence.
165:
17:
2650:
Of note, the denominator of the above equation is the natural logarithm of the positive
3054:
3027:
3003:
2976:
2809:
2784:
2607:
2585:
2307:
1145:
1046:
613:
315:
3278:
2894:
2875:
2735:
1623:
1157:
1109:
1034:
609:
307:
173:
169:
3245:
1667:
A test result that correctly indicates the presence of a condition or characteristic
2860:
1829:
1694:
A test result that correctly indicates the absence of a condition or characteristic
1183:
2950:
2852:
2833:
2667:
35:
3193:
3044:
3095:
3078:
2993:
2925:
2020:
1716:
1470:
1250:
628:
330:
186:
3160:
2800:
913:
rather refers to a probability for an individual. Still, if the individual's
3260:
2741:(4th ed.). Baltimore, Md.: Lippincott Williams & Wilkins. pp.
1836:
909:
generally refers to what is established by control groups, while a negative
3237:
3202:
3137:
3063:
3012:
2818:
1792:
1535:
3128:
3111:
2666:
An example is the microbiological throat swab used in patients with a
204:
refers to a probability for an individual. Still, if the individual's
160:
respectively) are the proportions of positive and negative results in
3229:
1735:(FOB) screen test is used in 2030 people to look for bowel cancer:
200:
generally refers to what is established by control groups, while a
2671:
2951:"WWRP/WGNE Joint Working Group on Forecast Verification Research"
2916:
Ting, Kai Ming (2011). Sammut, Claude; Webb, Geoffrey I. (eds.).
3112:"Standardizing predictive values in diagnostic imaging research"
29:
27:
Statistical measures of whether a finding is likely to be true
2955:
Collaboration for
Australian Weather and Climate Research
2561:
is the number of testing iterations necessary to achieve
939:
2632:
2610:
2588:
2567:
2540:
2518:
2360:
2330:
2310:
2284:
2264:
832:
760:
642:
549:
460:
344:
247:
1070:probability of detection, hit rate,
60:. Unsourced material may be challenged and removed.
2638:
2616:
2594:
2573:
2553:
2524:
2498:
2343:
2316:
2296:
2270:
892:
800:
739:
595:
520:
429:
293:
2778:
2776:
2733:Fletcher, Robert H. Fletcher; Suzanne W. (2005).
1622:Threat score (TS), critical success index (CSI),
2375:
2230:When an individual being tested has a different
801:{\displaystyle {\text{NPV}}={\frac {TN}{TN+FN}}}
3026:Chicco D, Toetsch N, Jurman G (February 2021).
1715:or other population-based study in which valid
1685:the number of real negative cases in the data
1658:the number of real positive cases in the data
538:The negative predictive value is defined as:
8:
3181:International Journal of Infectious Diseases
2765:: CS1 maint: multiple names: authors list (
2874:Provost, Foster; Tom Fawcett (2013-08-01).
2737:Clinical epidemiology : the essentials
1174:probability of false alarm,
144:Positive and negative predictive values - 2
3161:https://doi.org/10.1186/s12911-021-01738-w
925:The following diagram illustrates how the
3300:Summary statistics for contingency tables
3192:
3127:
3094:
3053:
3043:
3002:
2992:
2808:
2631:
2609:
2587:
2566:
2545:
2539:
2517:
2467:
2407:
2394:
2378:
2365:
2359:
2335:
2329:
2324:, the number of positive test iterations
2309:
2283:
2263:
880:
869:
861:
855:
847:
833:
831:
769:
761:
759:
729:
718:
698:
681:
671:
654:
651:
643:
641:
583:
572:
564:
558:
550:
548:
508:
497:
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416:
396:
379:
371:
364:
356:
353:
345:
343:
281:
270:
262:
256:
248:
246:
120:Learn how and when to remove this message
69:"Positive and negative predictive values"
2899:Journal of Machine Learning Technologies
1737:
905:Although sometimes used synonymously, a
232:The positive predictive value (PPV), or
215:, the PPV statistic is often called the
196:Although sometimes used synonymously, a
189:. Both PPV and NPV can be derived using
139:
131:
2785:"Diagnostic tests 2: Predictive values"
2725:
1651:
150:positive and negative predictive values
136:Positive and negative predictive values
2758:
3151:
3149:
3147:
3116:Journal of Magnetic Resonance Imaging
7:
58:adding citations to reliable sources
3079:"Classification assessment methods"
2975:Chicco D, Jurman G (January 2020).
2957:. World Meteorological Organisation
619:The NPV can also be computed from
321:The PPV can also be computed from
25:
3083:Applied Computing and Informatics
2834:"An Introduction to ROC Analysis"
2699:Relevance (information retrieval)
813:The complement of the NPV is the
445:The complement of the PPV is the
2918:Encyclopedia of machine learning
2704:Receiver-operator characteristic
2304:, that approaches some constant
1598:Matthews correlation coefficient
34:
1861:(2030 × 1.48% × 67%)
534:Negative predictive value (NPV)
228:Positive predictive value (PPV)
45:needs additional citations for
3122:(2): 505, author reply 506–7.
2783:Altman, DG; Bland, JM (1994).
2445:
2433:
2425:
2413:
2382:
723:
709:
703:
689:
676:
662:
421:
407:
401:
387:
185:are); they depend also on the
1:
2893:Powers, David M. W. (2011).
2853:10.1016/j.patrec.2005.10.010
935:sensitivity, and specificity
2841:Pattern Recognition Letters
2689:Sensitivity and specificity
2658:Different target conditions
2127:= (10 / 30) / (1820 / 2000)
1152:false alarm, overestimation
980:bookmaker informedness (BM)
3316:
3194:10.1016/j.ijid.2016.02.002
3110:Heston, Thomas F. (2011).
3077:Tharwat A. (August 2018).
3045:10.1186/s13040-021-00244-z
2672:Etiologic Predictive Value
2297:{\displaystyle \rho <1}
2094:= (20 / 30) / (180 / 2000)
1982:probability of false alarm
1873:(2030 × 1.48% ×
3096:10.1016/j.aci.2018.08.003
2994:10.1186/s12864-019-6413-7
2926:10.1007/978-0-387-30164-8
2171:
2157:Negative predictive value
2104:Negative likelihood ratio
2071:Positive likelihood ratio
2036:Positive predictive value
2017:
1825:
1750:
1742:
1740:
1621:
1443:Negative predictive value
1363:Negative likelihood ratio
1338:Positive likelihood ratio
1276:Positive predictive value
1247:
1020:
951:
946:
942:
931:negative predictive value
927:positive predictive value
907:negative predictive value
883:Number of false negatives
871:Number of false negatives
858:Number of false negatives
574:Number of false negatives
511:Number of false positives
499:Number of false positives
486:Number of false positives
272:Number of false positives
198:positive predictive value
18:Negative predictive value
3157:BMC Med Inform Decis Mak
2801:10.1136/bmj.309.6947.102
2214:Other individual factors
886:Number of negative calls
863:Number of true negatives
589:Number of negative calls
586:Number of true negatives
566:Number of true negatives
561:Number of true negatives
514:Number of positive calls
491:Number of true positives
287:Number of positive calls
284:Number of true positives
264:Number of true positives
259:Number of true positives
2236:post-test probabilities
1805:precision × recall
1513:Balanced accuracy (BA)
1110:type II error
971:Predicted negative (PN)
966:Predicted positive (PP)
3218:Statistics in Medicine
2640:
2618:
2596:
2575:
2555:
2526:
2500:
2345:
2318:
2298:
2272:
1184:type I error
894:
802:
741:
597:
522:
431:
295:
145:
137:
3259:Gunnarsson, Ronny K.
2832:Fawcett, Tom (2006).
2709:Diagnostic odds ratio
2684:Binary classification
2646:is disease prevalence
2641:
2639:{\displaystyle \phi }
2619:
2597:
2576:
2574:{\displaystyle \rho }
2556:
2554:{\displaystyle n_{i}}
2527:
2525:{\displaystyle \rho }
2501:
2346:
2344:{\displaystyle n_{i}}
2319:
2299:
2273:
2271:{\displaystyle \rho }
2173:Diagnostic odds ratio
1906:(FNR), miss rate
1713:cross-sectional study
1616:FNR × FPR × FOR × FDR
1607:TPR × TNR × PPV × NPV
1582:Fowlkes–Mallows index
1053:miss, underestimation
911:post-test probability
895:
803:
742:
598:
523:
432:
296:
213:information retrieval
202:post-test probability
143:
135:
2694:False discovery rate
2630:
2608:
2586:
2565:
2538:
2516:
2358:
2328:
2308:
2282:
2262:
2256:prevalence threshold
2232:pre-test probability
2163:= 1820 / (10 + 1820)
2141:False discovery rate
1966:(100% − 1.48%)
1946:(100% − 1.48%)
1930:(100% − 1.48%)
1849:(2030 × 1.48%)
1783:= (20 + 1820) / 2030
1721:case-control studies
1415:False discovery rate
989:Prevalence threshold
915:pre-test probability
830:
758:
640:
547:
458:
447:false discovery rate
342:
245:
206:pre-test probability
54:improve this article
2880:O'Reilly Media, Inc
2055:False omission rate
1972:False positive rate
1904:False negative rate
1747:screen test outcome
1310:False omission rate
1169:False positive rate
1100:False negative rate
948:Predicted condition
817:false omission rate
442:cf. Bayes' theorem
3290:Statistical ratios
3265:Science Network TV
3129:10.1002/jmri.22466
2636:
2624:is the specificity
2614:
2602:is the sensitivity
2592:
2571:
2551:
2532:is the desired PPV
2522:
2496:
2389:
2341:
2314:
2294:
2268:
2147:= 180 / (20 + 180)
2061:= 10 / (10 + 1820)
2001:true negative rate
1950:(100% − 91%)
1881:True positive rate
1875:(100% − 67%)
1811:precision + recall
1781:= (TP + TN) / pop.
1745:Fecal occult blood
1733:fecal occult blood
1220:(SPC), selectivity
1212:True negative rate
1058:True positive rate
890:
798:
737:
593:
518:
427:
333:of the condition:
291:
183:true negative rate
179:true positive rate
146:
138:
2935:978-0-387-30164-8
2714:Sensitivity index
2617:{\displaystyle b}
2595:{\displaystyle a}
2490:
2483:
2449:
2374:
2317:{\displaystyle k}
2246:Bayesian updating
2240:likelihood ratios
2225:likelihood ratios
2202:
2201:
2045:= 20 / (20 + 180)
1648:
1647:
1164:correct rejection
888:
887:
884:
875:
872:
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122:
104:
16:(Redirected from
3307:
3295:Categorical data
3269:
3268:
3261:"EPV Calculator"
3256:
3250:
3249:
3230:10.1002/sim.1119
3213:
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2192:
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2183:
2161:= TN / (FN + TN)
2145:= FP / (TP + FP)
2125:
2123:
2122:
2119:
2116:
2108:
2092:
2090:
2089:
2086:
2083:
2075:
2059:= FN / (FN + TN)
2043:= TP / (TP + FP)
1983:
1978:
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1931:
1922:Actual condition
1876:
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1843:Actual condition
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1758:Total population
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1475:deltaP (Δp)
1467:
1464:
1463:
1461:
1460:
1457:
1454:
1439:
1436:
1435:
1433:
1432:
1429:
1426:
1411:
1410:
1408:
1407:
1404:
1401:
1384:
1383:
1381:
1380:
1377:
1374:
1359:
1358:
1356:
1355:
1352:
1349:
1334:
1331:
1330:
1328:
1327:
1324:
1321:
1306:
1303:
1302:
1300:
1299:
1296:
1293:
1284:
1279:
1271:
1270:
1268:
1267:
1264:
1261:
1243:
1240:
1239:
1237:
1236:
1233:
1230:
1221:
1208:
1205:
1204:
1202:
1201:
1198:
1195:
1186:
1180:
1178:
1165:
1153:
1134:
1131:
1130:
1128:
1127:
1124:
1121:
1112:
1106:
1096:
1093:
1092:
1090:
1089:
1086:
1083:
1074:
1054:
1042:
1023:Actual condition
1016:
1015:
1013:
1012:
1009:
1006:
1004:
1003:
985:
981:
962:
957:Total population
940:
899:
897:
896:
891:
889:
885:
882:
881:
876:
874:
873:
870:
865:
862:
857:
856:
851:
848:
837:
834:
819:
818:
807:
805:
804:
799:
797:
795:
778:
770:
765:
762:
746:
744:
743:
738:
736:
734:
733:
730:
722:
719:
702:
699:
685:
682:
679:
675:
672:
658:
655:
652:
647:
644:
602:
600:
599:
594:
592:
588:
585:
584:
579:
577:
576:
573:
568:
565:
560:
559:
554:
551:
527:
525:
524:
519:
517:
513:
510:
509:
504:
502:
501:
498:
493:
490:
485:
484:
479:
476:
465:
462:
436:
434:
433:
428:
426:
424:
420:
417:
400:
397:
383:
380:
375:
372:
369:
368:
365:
360:
357:
354:
349:
346:
300:
298:
297:
292:
290:
286:
283:
282:
277:
275:
274:
271:
266:
263:
258:
257:
252:
249:
236:, is defined as
166:diagnostic tests
125:
118:
114:
111:
105:
103:
62:
38:
30:
21:
3315:
3314:
3310:
3309:
3308:
3306:
3305:
3304:
3275:
3274:
3273:
3272:
3258:
3257:
3253:
3224:(12): 1773–85.
3215:
3214:
3210:
3187:(April): 32–5.
3176:
3171:
3170:
3166:
3154:
3145:
3109:
3108:
3104:
3076:
3075:
3071:
3025:
3024:
3020:
2987:(1): 6-1–6-13.
2974:
2973:
2969:
2960:
2958:
2948:
2947:
2943:
2936:
2915:
2914:
2910:
2892:
2891:
2887:
2873:
2872:
2868:
2836:
2831:
2830:
2826:
2782:
2781:
2774:
2757:
2753:
2732:
2731:
2727:
2722:
2680:
2660:
2628:
2627:
2606:
2605:
2584:
2583:
2563:
2562:
2541:
2536:
2535:
2514:
2513:
2472:
2463:
2456:
2429:
2409:
2403:
2396:
2390:
2361:
2356:
2355:
2331:
2326:
2325:
2306:
2305:
2280:
2279:
2260:
2259:
2248:
2216:
2211:
2198:
2193:
2187:
2184:
2181:
2180:
2178:
2169:
2164:
2162:
2153:
2148:
2146:
2133:
2128:
2126:
2120:
2117:
2114:
2113:
2111:
2106:
2100:
2095:
2093:
2087:
2084:
2081:
2080:
2078:
2073:
2067:
2062:
2060:
2051:
2046:
2044:
2032:
2027:
2025:
2013:
2008:
2006:
1999:, selectivity,
1993:
1988:
1986:
1981:
1976:
1965:
1963:
1961:
1955:
1949:
1945:
1943:
1941:
1935:
1929:
1927:
1925:
1923:
1917:
1912:
1910:
1907:
1900:
1895:
1893:
1874:
1872:
1870:
1864:
1860:
1858:
1852:
1848:
1846:
1844:
1834:
1832:
1828:
1821:
1816:
1810:
1807:
1804:
1803:
1801:
1796:
1789:
1784:
1782:
1760:
1729:
1710:
1708:
1707:
1702:
1698:
1693:
1689:
1684:
1680:
1675:
1671:
1666:
1662:
1657:
1653:
1638:
1635:
1632:
1631:
1629:
1627:
1626:
1615:
1613:
1611:
1606:
1604:
1602:
1601:
1590:
1588:
1586:
1585:
1572:
1569:
1566:
1565:
1563:
1561:
1554:
1551:
1548:
1547:
1545:
1543:
1542:
1539:
1526:
1523:
1520:
1519:
1517:
1515:
1514:
1502:
1499:
1496:
1495:
1493:
1491:
1490:
1485:
1479:= PPV + NPV − 1
1478:
1477:
1474:
1465:
1458:
1455:
1452:
1451:
1449:
1447:
1446:
1437:
1430:
1427:
1424:
1423:
1421:
1419:
1418:
1405:
1402:
1399:
1398:
1396:
1394:
1393:
1378:
1375:
1372:
1371:
1369:
1367:
1366:
1353:
1350:
1347:
1346:
1344:
1342:
1341:
1332:
1325:
1322:
1319:
1318:
1316:
1314:
1313:
1304:
1297:
1294:
1291:
1290:
1288:
1286:
1285:
1280:
1274:
1265:
1262:
1259:
1258:
1256:
1254:
1253:
1241:
1234:
1231:
1228:
1227:
1225:
1223:
1222:
1216:
1215:
1206:
1199:
1196:
1193:
1192:
1190:
1188:
1187:
1182:
1181:
1176:
1173:
1172:
1163:
1162:
1151:
1150:
1132:
1125:
1122:
1119:
1118:
1116:
1114:
1113:
1108:
1107:
1104:
1103:
1094:
1087:
1084:
1081:
1080:
1078:
1076:
1075:
1069:
1052:
1051:
1040:
1039:
1025:
1010:
1007:
1001:
999:
998:
997:
995:
993:
992:
984:= TPR + TNR − 1
983:
982:
979:
960:
959:
923:
860:
828:
827:
816:
815:
779:
771:
756:
755:
680:
653:
638:
637:
563:
545:
544:
536:
488:
456:
455:
370:
355:
340:
339:
261:
243:
242:
230:
225:
126:
115:
109:
106:
63:
61:
51:
39:
28:
23:
22:
15:
12:
11:
5:
3313:
3311:
3303:
3302:
3297:
3292:
3287:
3277:
3276:
3271:
3270:
3251:
3208:
3164:
3159:22, 6 (2022).
3143:
3102:
3069:
3032:BioData Mining
3018:
2967:
2941:
2934:
2908:
2885:
2866:
2847:(8): 861–874.
2824:
2772:
2751:
2724:
2723:
2721:
2718:
2717:
2716:
2711:
2706:
2701:
2696:
2691:
2686:
2679:
2676:
2659:
2656:
2648:
2647:
2635:
2625:
2613:
2603:
2591:
2581:
2570:
2548:
2544:
2533:
2521:
2507:
2506:
2494:
2487:
2481:
2478:
2475:
2471:
2466:
2462:
2459:
2453:
2447:
2444:
2441:
2438:
2435:
2432:
2427:
2424:
2421:
2418:
2415:
2412:
2406:
2402:
2399:
2393:
2387:
2384:
2381:
2377:
2373:
2368:
2364:
2338:
2334:
2313:
2293:
2290:
2287:
2267:
2251:Bayes' theorem
2247:
2244:
2215:
2212:
2210:
2207:
2200:
2199:
2176:
2170:
2160:
2154:
2144:
2138:
2135:
2134:
2109:
2101:
2076:
2068:
2058:
2052:
2042:
2033:
2023:
2018:
2015:
2014:
2004:
1994:
1984:
1969:
1953:
1937:False positive
1933:
1919:
1918:
1908:
1901:
1891:
1878:
1866:False negative
1862:
1850:
1841:
1823:
1822:
1799:
1794:
1790:
1780:
1774:
1768:
1762:
1761:(pop.) = 2030
1755:
1752:
1751:
1749:
1741:
1728:
1727:Worked example
1725:
1706:
1705:
1696:
1687:
1678:
1669:
1660:
1650:
1649:
1646:
1645:
1620:
1595:
1579:
1573:2 TP + FP + FN
1537:
1533:
1510:
1509:
1481:
1468:
1440:
1412:
1386:
1385:
1360:
1335:
1307:
1272:
1248:
1245:
1244:
1209:
1166:
1154:
1146:False positive
1142:
1136:
1135:
1105:miss rate
1097:
1055:
1047:False negative
1043:
1031:
1026:
1021:
1018:
1017:
986:
973:
968:
963:
953:
952:
950:
945:
943:
922:
919:
903:
902:
901:
900:
879:
868:
854:
846:
843:
840:
811:
810:
809:
808:
794:
791:
788:
785:
782:
777:
774:
768:
750:
749:
748:
747:
728:
725:
717:
714:
711:
708:
705:
697:
694:
691:
688:
678:
670:
667:
664:
661:
650:
614:false negative
606:
605:
604:
603:
582:
571:
557:
535:
532:
531:
530:
529:
528:
507:
496:
482:
474:
471:
468:
440:
439:
438:
437:
423:
415:
412:
409:
406:
403:
395:
392:
389:
386:
378:
363:
352:
316:false positive
304:
303:
302:
301:
280:
269:
255:
229:
226:
224:
221:
191:Bayes' theorem
128:
127:
42:
40:
33:
26:
24:
14:
13:
10:
9:
6:
4:
3:
2:
3312:
3301:
3298:
3296:
3293:
3291:
3288:
3286:
3285:Biostatistics
3283:
3282:
3280:
3266:
3262:
3255:
3252:
3247:
3243:
3239:
3235:
3231:
3227:
3223:
3219:
3212:
3209:
3204:
3200:
3195:
3190:
3186:
3182:
3175:
3168:
3165:
3162:
3158:
3152:
3150:
3148:
3144:
3139:
3135:
3130:
3125:
3121:
3117:
3113:
3106:
3103:
3097:
3092:
3088:
3084:
3080:
3073:
3070:
3065:
3061:
3056:
3051:
3046:
3041:
3037:
3033:
3029:
3022:
3019:
3014:
3010:
3005:
3000:
2995:
2990:
2986:
2982:
2978:
2971:
2968:
2956:
2952:
2945:
2942:
2937:
2931:
2927:
2923:
2919:
2912:
2909:
2904:
2900:
2896:
2889:
2886:
2881:
2877:
2870:
2867:
2862:
2858:
2854:
2850:
2846:
2842:
2835:
2828:
2825:
2820:
2816:
2811:
2806:
2802:
2798:
2795:(6947): 102.
2794:
2790:
2786:
2779:
2777:
2773:
2768:
2762:
2754:
2752:0-7817-5215-9
2748:
2744:
2739:
2738:
2729:
2726:
2719:
2715:
2712:
2710:
2707:
2705:
2702:
2700:
2697:
2695:
2692:
2690:
2687:
2685:
2682:
2681:
2677:
2675:
2673:
2669:
2664:
2657:
2655:
2653:
2633:
2626:
2611:
2604:
2589:
2582:
2568:
2546:
2542:
2534:
2519:
2512:
2511:
2510:
2492:
2485:
2479:
2476:
2473:
2469:
2464:
2460:
2457:
2451:
2442:
2439:
2436:
2430:
2422:
2419:
2416:
2410:
2404:
2400:
2397:
2391:
2385:
2379:
2371:
2366:
2362:
2354:
2353:
2352:
2336:
2332:
2311:
2291:
2288:
2285:
2265:
2257:
2252:
2245:
2243:
2241:
2237:
2233:
2228:
2226:
2220:
2213:
2208:
2206:
2197:
2174:
2168:
2158:
2155:
2152:
2142:
2139:
2137:
2136:
2132:
2105:
2102:
2099:
2072:
2069:
2066:
2056:
2053:
2050:
2041:
2037:
2034:
2031:
2022:
2019:
2016:
2012:
2007:= 1820 / 2000
2002:
1998:
1995:
1992:
1979:
1973:
1970:
1964:(2030 ×
1958:
1957:True negative
1954:
1944:(2030 ×
1938:
1934:
1928:(2030 ×
1924:negative (AN)
1921:
1920:
1916:
1905:
1902:
1899:
1890:
1886:
1882:
1879:
1867:
1863:
1855:
1854:True positive
1851:
1845:positive (AP)
1842:
1840:
1838:
1833:(as confirmed
1831:
1827:Patients with
1824:
1820:
1798:
1791:
1788:
1778:
1775:
1773:
1770:Test outcome
1769:
1767:
1764:Test outcome
1763:
1759:
1756:
1754:
1753:
1748:
1746:
1739:
1736:
1734:
1726:
1724:
1722:
1718:
1714:
1700:
1697:
1691:
1688:
1682:
1679:
1673:
1670:
1664:
1661:
1655:
1652:
1625:
1624:Jaccard index
1599:
1596:
1583:
1580:
1541:
1534:
1512:
1511:
1488:
1482:
1472:
1469:
1444:
1441:
1416:
1413:
1391:
1388:
1387:
1364:
1361:
1339:
1336:
1311:
1308:
1283:
1277:
1273:
1252:
1249:
1246:
1219:
1213:
1210:
1185:
1179:
1170:
1167:
1160:
1159:
1158:True negative
1155:
1148:
1147:
1143:
1141:
1138:
1137:
1111:
1101:
1098:
1073:
1067:
1063:
1059:
1056:
1049:
1048:
1044:
1037:
1036:
1035:True positive
1032:
1030:
1027:
1024:
1019:
990:
987:
977:
974:
972:
969:
967:
964:
958:
955:
954:
949:
944:
941:
938:
937:are related.
936:
932:
928:
920:
918:
916:
912:
908:
877:
866:
852:
844:
841:
838:
826:
825:
824:
823:
822:
820:
792:
789:
786:
783:
780:
775:
772:
766:
754:
753:
752:
751:
726:
715:
712:
706:
695:
692:
686:
668:
665:
659:
648:
636:
635:
634:
633:
632:
630:
626:
622:
617:
615:
611:
610:true negative
580:
569:
555:
543:
542:
541:
540:
539:
533:
505:
494:
480:
472:
469:
466:
454:
453:
452:
451:
450:
448:
443:
413:
410:
404:
393:
390:
384:
376:
361:
350:
338:
337:
336:
335:
334:
332:
328:
324:
319:
317:
313:
312:gold standard
309:
308:true positive
278:
267:
253:
241:
240:
239:
238:
237:
235:
227:
222:
220:
218:
214:
209:
207:
203:
199:
194:
192:
188:
184:
180:
175:
174:true negative
171:
170:true positive
167:
163:
159:
155:
151:
142:
134:
124:
121:
113:
102:
99:
95:
92:
88:
85:
81:
78:
74:
71: –
70:
66:
65:Find sources:
59:
55:
49:
48:
43:This article
41:
37:
32:
31:
19:
3264:
3254:
3221:
3217:
3211:
3184:
3180:
3167:
3156:
3119:
3115:
3105:
3086:
3082:
3072:
3035:
3031:
3021:
2984:
2981:BMC Genomics
2980:
2970:
2959:. Retrieved
2954:
2944:
2920:. Springer.
2917:
2911:
2902:
2898:
2888:
2879:
2869:
2844:
2840:
2827:
2792:
2788:
2736:
2728:
2665:
2661:
2649:
2508:
2249:
2229:
2221:
2217:
2203:
2195:
2166:
2150:
2130:
2097:
2064:
2048:
2029:
2010:
1990:
1987:= 180 / 2000
1968:× 91%)
1956:
1936:
1914:
1897:
1865:
1853:
1830:bowel cancer
1826:
1818:
1786:
1771:
1765:
1743:
1731:Suppose the
1730:
1709:
1699:
1690:
1681:
1672:
1663:
1654:
1639:TP + FN + FP
1156:
1144:
1140:Negative (N)
1139:
1045:
1033:
1029:Positive (P)
1028:
1022:
976:Informedness
970:
965:
947:
930:
926:
924:
921:Relationship
906:
904:
814:
812:
618:
607:
537:
444:
441:
320:
305:
231:
210:
197:
195:
157:
153:
149:
147:
116:
107:
97:
90:
83:
76:
64:
52:Please help
47:verification
44:
3089:: 168–192.
2905:(1): 37–63.
2668:sore throat
2351:needed is:
2107:(LR−)
2026:= 30 / 2030
2024:= AP / pop.
1997:Specificity
1889:sensitivity
1800:= 2 ×
1549:2 PPV × TPR
1484:Diagnostic
1218:specificity
1066:sensitivity
720:sensitivity
683:specificity
656:specificity
625:specificity
621:sensitivity
398:specificity
373:sensitivity
358:sensitivity
327:specificity
323:sensitivity
3279:Categories
3038:(13): 13.
2961:2019-07-17
2720:References
2021:Prevalence
1717:prevalence
1486:odds ratio
1471:Markedness
1251:Prevalence
731:prevalence
700:prevalence
673:prevalence
629:prevalence
418:prevalence
381:prevalence
366:prevalence
331:prevalence
329:, and the
223:Definition
187:prevalence
162:statistics
110:March 2012
80:newspapers
2761:cite book
2634:ϕ
2569:ρ
2520:ρ
2477:−
2461:
2440:−
2431:ϕ
2420:−
2417:ϕ
2401:
2386:ρ
2383:→
2286:ρ
2266:ρ
2188:LR−
2040:precision
2005:= TN / AN
1985:= FP / AN
1911:= 10 / 30
1909:= FN / AP
1894:= 20 / 30
1892:= TP / AP
1837:endoscopy
1591:PPV × TPR
1555:PPV + TPR
1521:TPR + TNR
1466:= 1 − FOR
1438:= 1 − PPV
1333:= 1 − NPV
1305:= 1 − FDR
1282:precision
1242:= 1 − FPR
1207:= 1 − TNR
1133:= 1 − TPR
1095:= 1 − FNR
1011:TPR - FPR
1002:TPR × FPR
845:−
727:×
716:−
696:−
687:×
669:−
660:×
608:where a "
473:−
414:−
405:×
394:−
377:×
362:×
314:, and a "
306:where a "
234:precision
217:precision
168:that are
3246:26163122
3238:12111911
3203:26873279
3138:21274995
3064:33541410
3013:31898477
2678:See also
2493:⌉
2392:⌈
2278:, where
2209:Problems
2194:≈
2165:≈
2129:≈
2096:≈
2063:≈
2028:≈
1977:fall-out
1913:≈
1896:≈
1785:≈
1777:Accuracy
1772:negative
1766:positive
1390:Accuracy
1177:fall-out
3055:7863449
3004:6941312
2861:2027090
2819:8038641
2810:2540558
2191:
2179:
2124:
2112:
2091:
2079:
2038:(PPV),
1974:(FPR),
1948:×
1883:(TPR),
1814:
1802:
1642:
1630:
1614:√
1605:√
1589:√
1576:
1564:
1558:
1546:
1530:
1518:
1506:
1494:
1462:
1450:
1434:
1422:
1409:
1400:TP + TN
1397:
1382:
1370:
1357:
1345:
1329:
1317:
1301:
1289:
1269:
1257:
1238:
1226:
1214:(TNR),
1203:
1191:
1171:(FPR),
1129:
1117:
1102:(FNR),
1091:
1079:
1068:(SEN),
1060:(TPR),
1014:
1000:√
996:
961:= P + N
821:(FOR):
449:(FDR):
94:scholar
3244:
3236:
3201:
3136:
3062:
3052:
3011:
3001:
2932:
2859:
2817:
2807:
2749:
2509:where
2175:(DOR)
2167:99.45%
1962:= 1820
1926:= 2000
1885:recall
1787:90.64%
1779:(ACC)
1600:(MCC)
1489:(DOR)
1473:(MK),
1445:(NPV)
1417:(FDR)
1392:(ACC)
1365:(LR−)
1340:(LR+)
1312:(FOR)
1278:(PPV),
1161:(TN),
1149:(FP),
1062:recall
1050:(FN),
1038:(TP),
627:, and
96:
89:
82:
75:
67:
3242:S2CID
3177:(PDF)
2857:S2CID
2837:(PDF)
2159:(NPV)
2151:90.0%
2143:(FDR)
2131:0.366
2074:(LR+)
2065:0.55%
2057:(FOR)
2030:1.48%
2003:(TNR)
1942:= 180
1915:33.3%
1898:66.7%
1819:0.174
1797:score
1584:(FM)
1540:score
1406:P + N
1266:P + N
1072:power
1005:- FPR
991:(PT)
101:JSTOR
87:books
3234:PMID
3199:PMID
3134:PMID
3060:PMID
3009:PMID
2930:ISBN
2815:PMID
2767:link
2747:ISBN
2289:<
2196:20.2
2098:7.41
1991:9.0%
1959:(TN)
1939:(FP)
1887:,
1871:= 10
1868:(FN)
1859:= 20
1856:(TP)
1847:= 30
1567:2 TP
181:and
172:and
164:and
156:and
148:The
73:news
3226:doi
3189:doi
3124:doi
3091:doi
3050:PMC
3040:doi
2999:PMC
2989:doi
2922:doi
2849:doi
2805:PMC
2797:doi
2793:309
2789:BMJ
2376:lim
2182:LR+
2121:TNR
2115:FNR
2088:FPR
2082:TPR
2049:10%
2011:91%
1835:on
1503:LR−
1497:LR+
1379:TNR
1373:FNR
1354:FPR
1348:TPR
1041:hit
849:NPV
835:FOR
763:NPV
645:NPV
552:NPV
477:PPV
463:FDR
347:PPV
250:PPV
211:In
158:NPV
154:PPV
56:by
3281::
3263:.
3240:.
3232:.
3222:21
3220:.
3197:.
3185:45
3183:.
3179:.
3146:^
3132:.
3120:33
3118:.
3114:.
3087:17
3085:.
3081:.
3058:.
3048:.
3036:14
3034:.
3030:.
3007:.
2997:.
2985:21
2983:.
2979:.
2953:.
2928:.
2901:.
2897:.
2878:.
2855:.
2845:27
2843:.
2839:.
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2791:.
2787:.
2775:^
2763:}}
2759:{{
2745:.
2743:45
2674:.
2458:ln
2398:ln
2177:=
2149:=
2110:=
2077:=
2047:=
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1989:=
1980:,
1952:)
1932:)
1877:)
1817:≈
1723:.
1633:TP
1628:=
1612:-
1603:=
1587:=
1562:=
1544:=
1516:=
1492:=
1459:PN
1453:TN
1448:=
1431:PP
1425:FP
1420:=
1395:=
1368:=
1343:=
1326:PN
1320:FN
1315:=
1298:PP
1292:TP
1287:=
1255:=
1229:TN
1224:=
1194:FP
1189:=
1120:FN
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1082:TP
1077:=
1064:,
994:=
978:,
933:,
929:,
631::
623:,
325:,
219:.
193:.
3267:.
3248:.
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3140:.
3126::
3099:.
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2882:.
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2821:.
2799::
2769:)
2755:.
2612:b
2590:a
2547:i
2543:n
2486:]
2480:b
2474:1
2470:a
2465:[
2452:]
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2437:k
2434:(
2426:)
2423:1
2414:(
2411:k
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2380:k
2372:=
2367:i
2363:n
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2333:n
2312:k
2292:1
2185:/
2118:/
2085:/
1839:)
1808:/
1795:1
1793:F
1636:/
1570:/
1552:/
1538:1
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1527:2
1524:/
1500:/
1456:/
1428:/
1403:/
1376:/
1351:/
1323:/
1295:/
1263:/
1260:P
1235:N
1232:/
1200:N
1197:/
1126:P
1123:/
1088:P
1085:/
1008:/
878:=
867:+
853:=
842:1
839:=
793:N
790:F
787:+
784:N
781:T
776:N
773:T
767:=
724:)
713:1
710:(
707:+
704:)
693:1
690:(
677:)
666:1
663:(
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581:=
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506:=
495:+
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388:(
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279:=
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254:=
152:(
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91:·
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