Test-Treatment Threshold Simulator

Patient & Test

Pretest probability P 30%

Sensitivity Se 90%

Specificity Sp 85%

Benefit / Harm ratio B:H 3.0

Decision

TEST

Test threshold P_test

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Treat threshold P*

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Treat-w/o-test P_tx

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Pretest P

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If test +

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If test −

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How it works

Pauker & Kassirer's threshold model turns "should I test?" into three probability zones. The treatment threshold is the probability at which treating and not treating have equal expected utility:

P* = H / (H + B)
  B = net benefit of treating a diseased patient
  H = net harm of treating a healthy patient

A test with sensitivity Se and specificity Sp shifts probability via its likelihood ratios:

LR+ = Se / (1 − Sp)      LR− = (1 − Se) / Sp
posttest odds = pretest odds × LR
odds = P / (1 − P)        P = odds / (1 + odds)

The test is only worth ordering if its result could plausibly flip the decision. That defines two more thresholds, both derived from P* and the likelihood ratios:

P_test = odds(P*)/LR+  → below this, even a POSITIVE result can't reach P* — don't test, don't treat
P_tx   = odds(P*)/LR−  → above this, even a NEGATIVE result can't drop below P* — don't test, treat empirically

P below P_test — disease too unlikely to act on; skip both test and treatment.
P between P_test and P_tx — the test's result can genuinely change management; order it.
P above P_tx — disease likely enough that testing can't change the plan; treat empirically.

Real-world relevance: this exact framework is taught in clinical decision analysis to decide when imaging, biopsy or lab work-up is worth the cost, delay and risk versus acting directly on clinical probability.

Don't test / treat Order the test Treat empirically

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