| Egger's test for small-study effects: regress standard normal deviate of intervention effect estimate against its standard error | |||||
| • Number of studies = 15 | |||||
| • Root MSE = 2.029 | |||||
| Std_Eff | | Coefficient | Standard error | t | P>|t| | 95% CI |
| slope | | 0.259 | 0.271 | 0.950 | 0.357 | −0.327 to 0.844 |
| bias | | −1.549 | 1.277 | −1.210 | 0.247 | −4.309 to 1.211 |
| Begg's test for small-study effects: rank correlation between standardised intervention effect and its standard error | |||||
| • adjusted Kendall's Score (P − Q) = −17 | |||||
| • Standard deviation of score = 20.21 | |||||
| • Number of studies = 15 | |||||
| • z = 0.79 (continuity corrected) | |||||
| • Pr > |z| = 0.428 (continuity corrected) | |||||
| Peter's test for small-study effects: regress intervention effect estimate on 1/Ntot, with weights S × F/Ntot | |||||
| • Number of studies = 15 | |||||
| • Root MSE = 0.937 | |||||
| Std_Eff | | Coefficient | Standard error | t | P>|t| | 95% CI |
| bias | | 78.904 | 349.655 | 0.230 | 0.825 | −676.479 to 834.287 |
| constant | | −0.440 | 0.359 | −1.230 | 0.242 | −1.215 to 0.336 |
| Harbord's modified test for small-study effects: regress Z/√(V) on √(V) where Z is efficient score and V is score variance | |||||
| • Number of studies = 15 | |||||
| • Root MSE = 2.225 | |||||
| Z√(V) | Coefficient | Standard error | t | P>|t| | 95% CI |
| √(V) | 0.293 | 0.331 | 0.890 | 0.392 | −0.422 to 1.008 |
| bias | | −1.871 | 1.595 | −1.170 | 0.262 | −5.317 to 1.576 |
MSE = mean squared error.