Table 6.
Example | Meta‐analysis method | Statistical framework | Summary calibration, a (95% CI) |
|
95% prediction interval for O/E in a new population∗ | Probability 0.9 < O/E < 1.1 in a new population∗ | |
---|---|---|---|---|---|---|---|
Calibration for just NPV | |||||||
PTH data 1–2 h | Option B | Bayesian | 0.24 (−0.97 to 1.81) | 0.51 (0.03 to 0.98) | 0.86 to 1.05 | 0.95 | |
Option B | Frequentist | 0.093 (−1.06 to 1.25) | 0 | 0.87 to 1.03 | — | ||
PTH data 0–20 min | Option B | Bayesian | 0.021 (−0.82 to 1.02) | 0.51 (0.03 to 0.97) | 0.86 to 1.04 | 0.95 | |
Option B | Frequentist | −0.044 (−0.83 to 0.74) | 0.34 | 0.80 to 1.03 | — | ||
Calibration for just PPV | |||||||
Temperature data | Option A | Bayesian | −0.017 (−0.63 to 0.77) | 0.65 (0.11 to 0.99) | 0.90 to 1.03 | 0.98 | |
Option A | Frequentist | 0.015 (−0.75 to 0.78) | 0.70 | 0.87 to 1.03 | — |
All frequentist analyses used maximum likelihood estimation of model (17). All Bayesian analyses used a prior distribution of N(0, 1 000 000) for a, and a prior distribution of uniform(0, 1) for τ, with a 10 000 burn‐in followed by 100 000 samples for posterior inferences. Median values of the posterior distribution are shown for a and τ.
Based on a predicted positive predictive value (PPV) of 0.97 in the temperature analysis, and a negative predictive value (NPV) of 0.95 in the parathyroid (PTH) analysis.
O/E, observed/expected.