assessing information from multilevel and continuous tests likelihood ratios for results other than...

Assessing Information from Multilevel and Continuous

Tests

Likelihood Ratios for results other than “+” or “-”

Michael A. Kohn, MD, MPP10/2/2008

Four Main Points

1) Dichotomizing a multi-level test by choosing a fixed cutpoint reduces the value of the test.

2) The ROC curve summarizes the ability of the test to differentiate between D+ and D- individuals.

3) LR(result) = P(result|D+)/P(result|D-) = slope of ROC curve.

(NOTE: Do not calculate an LR(+) or LR(-) for a multilevel test.)

4) Pre-Test Odds x LR(result) = Post-Test Odds

NOTE

Do not calculate an LR(+) or LR(-) for a test with more than two possible results.

Additional Topics

Optimal Cutoffs Walking Man C Statistic

Example from Chapter 3

65-year-old woman with mammogram suspicious for malignancy

Pre-test probability ≈ 0.015LR(“suspicious for malignancy”) ≈

100Post-test probability = ?

Update Pre-Test Probability Using LR(test result)

1) Convert pre-test probability (P) to pre-test odds. Pre-Test Odds = P/(1-P)

2) Calculate LR. P(result|D+)/P(result|D-). 3) Post-Test Odds = Pre-Test Odds × LR4) Convert post-test odds to post-test

probability. Prob = Odds/(1+Odds)

Update Pre-Test Probability Using LR(test result)

1) Pre-test probability P = 0.015Pre-test odds = P/(1-P) ≈ 0.015

2) LR(“Suspicious for Malignancy”) = 100

3) Post-Test Odds = 0.015 × 100 = 1.54) Post-test probability =

Odds/(1+Odds) = 1.5/2.5 = 0.60

Can Use Slide Rule

Can Use Excel

Pre-Test Probability 0

LR(test result) 0Post-Test Probability 0.00

Can Use Web-Based Calculator

We will come back to this

(This ends the example for Chapter 3.)

Evaluating the Test--Test Characteristics

For dichotomous tests, we discussed sensitivity P(+|D+) and specificity P(-|D-)

For multi-level and continuous tests, we will discuss the Receiver Operating Characteristic (ROC) curve

Using the Test Result to Make Decisions about a Patient

For dichotomous tests, we use the LR(+) if the test is positive and the LR(-) if the test is negative

For multilevel and continuous tests, we use the LR(r), where r is the result of the test

Septic ArthritisBacterial infection in a joint.

Clinical ScenarioDoes this Adult Patient Have Septic Arthritis?


A 48-year-old woman with a history of rheumatoid arthritis who has been treated with long-term, low-dose prednisone presents to the emergency department with a 2-day history of a red, swollen right knee that is painful to touch. She reports no prior knee swelling and no recent trauma or knee surgery, illegal drug use, rash, uveitis, or risky sexual behavior. On examination, she is afebrile and has a right knee effusion. Her peripheral white blood cell (WBC) count is 11 000/µL and her erythrocyte sedimentation rate (ESR) is 55 mm/h. An arthrocentesis is performed, and the initial Gram stain is negative.

Margaretten, M. E., J. Kohlwes, et al. (2007). Jama 297(13): 1478-88.

You have the synovial white blood cell (WBC) count.


Assume pre-test probability of septic arthritis is 0.38.

How do you use the synovial WBC result to determine the likelihood of septic arthritis?


Why Not Make It a Dichotomous Test?

Synovial Septic ArthritisWBC Count Yes No

>25,000 77% 27%

≤ 25,000 23% 73%

TOTAL* 100% 100%*Note that these could have come from a study where the patients with septic arthritis (D+ patients) were sampled separately from those without (D- patients).



Sensitivity = 77%Specificity = 73%

LR(+) = 0.77/(1 - 0.73) = 2.9LR(-) = (1 - 0.77)/0.73 = 0.32

“+” = > 25,000/uL “-” = ≤ 25,000/uL

Clinical ScenarioSynovial WBC = 48,000/mL

Pre-test prob: 0.38LR(+) = 2.9Post-Test prob = ?


Pre-test prob: 0.38Pre-test odds: 0.38/0.62 = 0.61LR(+) = 2.9Post-Test Odds = Pre-Test Odds x

LR(+)= 0.61 x 2.9 = 1.75

Post-Test prob = 1.75/(1.75+1) = 0.64

Clinical ScenarioSynovial WBC = 48,000/mLSlide Rule

Pre-test prob: 0.38LR(+) = 2.9Post-Test prob =

(Demonstrate Slide Rule)

Can Use Excel

Pre-Test Probability 0.38 0.612903

LR(test result) 2.9 1.777419Post-Test Probability 0.64

Pre-test prob: 0.38LR(+) = 2.9Post-Test prob =

Can Use Web-Based Calculator

http://www.quesgen.com/Calculators/PostProdOfDisease/PostProdOfDisease.html

P(+|D+) = Sensitivity = 77%P(+|D-) = 1 - Specificity = 1 - 73% = 27%


Pre-test prob: 0.38

LR = ?

Post-Test prob =?

Clinical Scenario Synovial WBC = 128,000/mL

Pre-test prob: 0.38 Pre-test odds: 0.38/0.62 = 0.61LR = 2.9 (same as for WBC=48,000!)Post-Test Odds = Pre-Test Odds x

LR(+)= 0.61 x 2.9 = 1.75

Post-Test prob = 1.75/(1.75+1) = .64


Because you lose information. The risk associated with a synovial WBC=48,000 is equated with the risk associated with WBC=128,000.

Choosing a fixed cutpoint to dichotomize a multi-level or continuous test throws away information and reduces the value of the test.

Main Point 1: Avoid Making Multilevel Tests Dichotomous

Dichotomizing a multi-level or continuous test by choosing a fixed cutpoint reduces the value of the test

WBC (/uL) Interval

% of Septic Arthritis

% of No Septic Arthritis

>100,000 29% 1%>50,000-100,000 33% 7%

>25,000-50,000 15% 19%

0 - 25,000 23% 73%

TOTAL 100% 100%

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0 - 25,000 >25,000-50,000

>50,000-100,000

>100,000

No Septic Arthritis

Septic Arthritis

Synovial Fluid WBC Count

Histogram Does not reflect prevalence of D+ (Dark D+

columns add to 100%, Open D- columns add to 100%)

Sensitivity and specificity depend on the cutpoint chosen to separate “positives” from “negatives”

The ROC curve is drawn by serially lowering the cutpoint from highest (most abnormal) to lowest (least abnormal).*

* Just said that choosing a fixed cutpoint reduces the value of the test. The key issues are 1) the ROC curve is for evaluating the test, not the patient, and 2) drawing the ROC curve requires varying the cutpoint, not choosing a fixed cutpoint.

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0 - 25,000 >25,000-50,000

>50,000-100,000

>100,000

Negative Positive

Cutoff = ∞Sensitivity = 0%1 - Specificity = 0%

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Negative Positive

Cutoff = 100,000Sensitivity = 29%1 - Specificity = 1%

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Negative Positive


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>50,000-100,000

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Negative Positive

Cutoff = 0Sensitivity = 100%1 - Specificity = 100%

WBC Count (x1000/uL)

Sensitivity

1 - Specificity

> ∞ 0% 0%

> 100 29% 1%

> 50 62% 8%

> 25 77% 27%

≥ 0 100% 100%


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Cutoff > ∞

Cutoff > 100k

Cutoff > 50k

Cutoff > 25k

Cutoff ≥ 0

0 5 10 15 20 25 30

WBC Count (1000/uL)

D-: No Septic ArthritisD+: Septic Arthritis

30 2400

0

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0 0.2 0.4 0.6 0.8 1

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WBC Count (1000/uL)

D-: No Septic ArthritisD+: Septic Arthritis

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WBC Count (1000/uL)

D-: No Septic ArthritisD+:Septic Arthritis

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0.8

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0 0.2 0.4 0.6 0.8 1

Test Discriminates Fairly Well Between D+ and D-

-40 -20 0 20 40 60Test Result

D-D+

Test Discriminates Well Between D+ and D-

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Test Discriminates Poorly Between D+ and D-

-40 -20 0 20 40 60Test Result

D-D+

Test Discriminates Poorly Between D+ and D-

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Cutoff > ∞

Cutoff > 100k

Cutoff > 50k

Cutoff > 25k

Cutoff ≥ 0

Area Under Curve = 0.8114

Area Under ROC Curve


Summary measure of test’s discriminatory ability

Probability that a randomly chosen D+ individual will have a more positive test result than a randomly chosen D- individual


Corresponds to the Mann-Whitney U Test Statistic (= Wilcoxon Rank Sum), which is the non-parametric equivalent of Student’s t test.

Also corresponds to the “c statistic” reported in logistic regression models

Main Point 2ROC Curve Describes the Test,

Not the Patient

Describes the test’s ability to discriminate between D+ and D- individuals

Not particularly useful in interpreting a test result for a given patient

ROC Curve Describes the Test, Not the Patient

Clinical Scenario

Synovial WBC count = 48,000


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Sen

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Cutoff > ∞

Cutoff > 100k

Cutoff > 50k

Cutoff > 25k

Cutoff ≥ 0

Sensitivity, Specificity, LR(+), and LR(-) of the Synovial Fluid WBC Count for Septic

Arthritis at 3 Different Cutoffs

WBC (/uL)

Sensitivity

Specificity LR+ LR-

>100,000 29% 99% 29.0 0.7

>50,000 62% 92% 7.8 0.4

>25,000 77% 73% 2.9 0.3

Synovial WBC Count = 48,000/uL

Which LR should we use?

Sensitivity, Specificity, LR(+), and LR(-) of the Synovial Fluid WBC Count for Septic

Arthritis at 3 Different Cutoffs

WBC (/uL)

Sensitivity

Specificity LR+ LR-

>100,000 29% 99% 29.0 0.7

>50,000 62% 92% 7.8 0.4

>25,000 77% 73% 2.9 0.3

Synovial WBC Count = 48,000/uL

Which LR should we use? NONE of THESE!

Likelihood Ratios

LR(+) = Sensitivity/(1 – Specificity) = P(+|D+)/P(+|D-)

LR(-) = (1 – Sensitivity)/Specificity

= P(-|D+)/P(-|D-)

Likelihood Ratios

LR(result) = P(result|D+)/P(result|D-)

P(Result) in patient WITH disease----------------------------------------------------P(Result) in patients WITHOUT disease

WOWO

With Over WithOut

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0 - 25,000 >25,000-50,000

>50,000-100,000

>100,000

No Septic Arthritis

Septic Arthritis

Likelihood RatiosThe ratio of the height of the D+ distribution to the height of the D- distribution

15%19%

LR = 15%/19% = 0.8

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> 25k

15%

19%Slope = 15%/19% =0.8

Likelihood RatioWBC (/uL) Interval

% of D+ % of D-

Interval LR

>100,000 29% 1% 29.0

>50,000-100,000 33% 7% 4.7

>25,000-50,000 15% 19% 0.8

0 - 25,000 23% 73% 0.3

Common Mistake

When given an “ROC Table,” it is tempting to calculate an LR(+) or LR(-) as if the test were “dichotomized” at a particular cutoff.

Example: LR(+,25,000) = 77%/27% = 2.9This is NOT the LR of a particular result

(e.g. WBC >25,000 and ≤ 50,000); it is the LR(+) if you divide “+” from “-” at 25,000.

WBC (/uL)

Sensitivity

Specificity LR+ LR-

>100,000 29% 99% 29.0 0.7

>50,000 62% 92% 7.8 0.4

>25,000 77% 73% 2.9 0.3

Common Mistake

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Slope = 77%/27% = 2.9

Slope = 0.8

27%

77%

> 25,000

Common Mistake

Common Mistake

From JAMA paper:“Her synovial WBC count of 48,000/µL

increases the probability from 38% to 64%.” (Used LR = 2.9)

Correct calculation:Her synovial WBC count of 48,000/µL

decreases the probability from 38% to 33%.” (Used LR = 0.8)

Main Point 3 Likelihood Ratio

P(Result) in patients WITH disease------------------------------------------------------P(Result) in patients WITHOUT disease

Slope of ROC Curve

Do not calculate an LR(+) or LR(-) for a multilevel test.

NOTE

Do not calculate an LR(+) or LR(-) for a test with more than two possible results.

Clinical ScenarioSynovial WBC = 48,000/uL*

Pre-test prob: 0.38Pre-test odds: 0.38/0.62 = 0.61LR(WBC btw 25,000 and 50,000) = 0.8Post-Test Odds = Pre-Test Odds x LR(48)

= 0.61 x 0.8 = 0.49Post-Test prob = 0.49/(0.49+1) = 0.33

*Can use slide rule, Excel, or web page

Clinical ScenarioSynovial WBC = 128,000/uL*

Pre-test prob: 0.38Pre-test odds: 0.38/0.62 = 0.61LR(128,000/uL) = 29Post-Test Odds = Pre-Test Odds x

LR(128)= 0.61 x 29 = 17.8

Post-Test prob = 17.8/(17.8+1) = 0.95

*Can use slide rule, Excel, or web page

Clinical Scenario

WBC = 48,000/uL Post-Test Prob = 0.33WBC = 128,000/uL Post-Test Prob = 0.95

(Recall that dichotomizing the WBC with a fixed cutpoint of 25,000/uL meant that WBC = 48,000/uL would be treated the same as WBC = 128,000/uL and post-test prob = 0.64)

Main Point 4Bayes’s Rule

Pre-Test Odds x LR(result) = Post-Test Odds

What you knew before + What you learned = What you know now

Summary

1) Dichotomizing a multi-level test by choosing a fixed cutpoint reduces the value of the test.

2) The ROC curve summarizes the discriminatory ability of the test.

3) LR(result) = P(result|D+)/P(result|D-) = Slope of ROC Curve (NOTE: Do not calculate an LR(+) or LR(-) for a multilevel test.)

4) Pre-Test Odds x LR(result) = Post-Test Odds

Conforms to Clinical Intuition

Synovial WBC for Septic Arthritis

WBC < 2000 very reassuring WBC 2000 – 25,000 somewhat

reassuringWBC 25,000 – 50,000 indeterminateWBC 50,000 – 100,000 worrisomeWBC > 100,000 very worrisome

Peripheral WBC Count for Bacteremia in Febrile Infant

Bacteremia No Bacteremia

WBC Number % Number % LR

<5 8 21% 201 5% 3.95

5-15 13 34% 2727 72% 0.47

≥15 17 45% 844 22% 2.00

≥20 9 24% 255 7%3.50 [& 0.82]

Total 38 3772

“Interval” LRs as reported in the paper (Ann Emerg Med 42:216-225)

What if WBC count is 18? Which LR should you use? LR = 2.0 because 18 ≥ 15, or LR = 0.82 because 18 < 20?

Peripheral WBC Count for Bacteremia in Febrile Infant

Actual Interval LRs

What if WBC count is 18? Which LR should you use? LR = 1.35.

Bacteremia No Bacteremia

WBC Number % Number % LR

<5 8 21% 201 5% 3.95

5-15 13 34% 2727 72% 0.47

15-20 8 21% 589 16% 1.35

> 20 9 24% 255 7% 3.50

38 3772

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>20

> 15

> 5

LR does not decrease steadily as WBC count decreases. Interval LRs still useful, but AUROC not a good measure of test’s discrimination.

Peripheral WBC Count for Bacteremia in Febrile Infant < 3 Months Old

< 5 Very concerning5 – 15 Slightly reassuring16 – 20 Slightly concerning> 20 Concerning

Additional Topics

Optimal Cutoffs Walking Man C Statistic

#Wang, C. S., J. M. FitzGerald, et al. (2005). "Does this dyspneic patient in the emergency department have congestive heart failure?" JAMA 294(15): 1944-56.

Refers to:Maisel, A. S., P. Krishnaswamy, et al. (2002). "Rapid

measurement of B-type natriuretic peptide in the emergency diagnosis of heart failure." N Engl J Med 347(3): 161-7.

Optimal Cutoffs

BNP to distinguish between COPD exacerbation and CHF in the ED patient with dyspnea

Optimal Cutoffs

What is the single best cutoff to define a BNP as “positive” for CHF?

BNP, 500 pg/ml?

BNP, 1000 pg/ml ?

Optimal Cutpoints

Dichotomizing a continuous test by choosing a fixed cutoff reduces the value of the test.

And do NOT choose the point where the ROC curve is closest to the upper left hand corner.

Optimal Cutoffs

But, for a continuous variable, you do have to define intervals.

How do you choose your cutpoints to define the intervals?

BNP, 500 pg/ml?

BNP, 1000 pg/ml ?

BNP for CHF

BNP < 100 Not CHFBNP 100 – 500 doesn’t change

likelihood muchBNP 500 – 1000 increases likelihood

of CHFBNP > 1000 really increases

likelihood of CHF.

THE WALKING MAN OR …

… WHAT CAN YOU LEARN FROM ROC CURVES LIKE THESE?

Bonsu, B. K. and M. B. Harper (2003). "Utility of the peripheral blood white blood cell count for identifying sick young infants who need lumbar puncture." Ann Emerg Med 41(2): 206-14.

“Walking Man” Approach to ROC Curves

Divide vertical axis into d steps, where d is the number of D+ individuals

Divide horizontal axis into n steps, where n is the number of D- individuals

Sort individuals from most to least abnormal test result

Moving from the first individual (with the most abnormal test result) to the last (with the least abnormal test result)…

“Walking Man” (continued) …call out “D” if the individual is D+

and “N” if the individual is D- Let the walking man know when you

reach a new value of the test The walking man takes a step up

every time he hears “D” and a step to the right every time he hears “N”

When you reach a new value of the test, he drops a stone.

Synovial WBC Count in 5 Patients with Septic Arthritis

PatientWBC Count(x 1000/uL)

D1 128

D2 92

D3 64

D4 37

D5 24

Synovial WBC Count in 10 Patients Without Septic Arthritis

Patient WBC Count (x 1000)

N1 71

N2 48

N3 37

N4 23

N5 12

N6 12

N7 8

N8 7

N9 6

N10 0

Septic Arthritis No Septic Arthritis

128

92

71

64

48

37 37

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23

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12

8

7

6

0

DDNDN(DN)DN(NN)NNNN

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D-

D+

… WHAT CAN YOU LEARN FROM ROC CURVES LIKE THESE?

Calculating the c Statistic

The c statistic for the area under an ROC curve is calculated using the same information as the Wilcoxon Rank Sum statistic (or Mann-Whitney U, which is equivalent) and gives identical P values.

Non-parametric equivalent of the t test statistic comparing two means.

In the “walking man” approach to tracing out the ROC curve, the actual values of the test are not important for the shape of the ROC curve or the area under it--only the ranking of the values.

Septic Arthritis No Septic Arthritis

128

92

71

64

48

37 37

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8

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6

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0 1 2 3 4 5 6 7 8 9 10

D-

D+

Boxes under Curve = 43.5

Total Boxes = 50

Area Under Curve = 43.5/50 = 0.87

BACTEREMIA NO BACTEREMIA

1

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6.5 6.5

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9

10.5

10.5

12

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15

S = 21.5

Replace Test Results with Ranks

S = 21.5Smin = d(d+1)/2 = 5(6)/2 = 15Smax = dn + Smin = 5(10) + 15 = 65

C = (Smax – S) / (Smax – Smin)* = (65 – 21.5) / (65 – 15) = 43.5/50 = 0.87

* Smax – Smin = dn

Calculating the C Statistic

assessing information from multilevel and continuous tests likelihood ratios for results other than...

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