Evidence is mixed on whether likelihood ratio formats yield more accurate post-test probability estimates than sensitivity and specificity¶
Claim · All claims
Evidence · 1 study · reviewr3·q2
Currency · newest study 2015 · not yet checked against its sources · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Three studies found LR-based approaches gave more accurate post-test probability estimates than sensitivity and specificity alone, while three found less accurate estimates. → Whiting Penny F 2015
Evidence¶
Whiting Penny F 2015¶
Whiting Penny F, Davenport Clare, Jameson Catherine, Burke Margaret, Sterne Jonathan A C, Hyde Chris, Ben-Shlomo Yoav. (2015). How well do health professionals interpret diagnostic information? A systematic review. BMJ Open. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4521525/
q2 · i? · review · r3
Narrative synthesis of six studies assessing LR formats for post-test probability estimation. The review reports two studies found less correct responses with LRs compared with sensitivity and specificity presented numerically, two found more correct responses with LRs, and one found similarly poor estimation for both formats.
"Two studies demonstrated less correct responses for post-test probability estimation with LRs (described in words in one and numerical in the other) compared with sensitivity and specificity presented numerically."
Discussion¶
Related Claims¶
- Health professionals are less likely to identify the correct definition of likelihood ratios than of sensitivity and specificity — related
- Only a minority of health professionals can define likelihood ratios, predictive values and specificity when asked to verbalise definitions — related
- Presenting accuracy data as natural frequencies rather than probabilities improves post-test probability estimation and speed of calculations — a broader claim this one bears on
- Post-test probability is more poorly estimated for negative than positive test results in some studies — related
- Health professionals' estimation of post-test probability is generally poor with a tendency to overestimation — related
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