funnel-meta-analysis · python · pygal · anyplot.ai000.050.050.10.10.150.150.20.20.250.250.30.3-1.00-1.00-0.90-0.90-0.80-0.80-0.70-0.70-0.60-0.60-0.50-0.50-0.40-0.40-0.30-0.30-0.20-0.20-0.10-0.100.000.000.100.10funnel-meta-analysis · python · pygal · anyplot.aiLog Odds Ratio (Effect Size)Standard Error(precision ↑)Adams 2018: LOR=-0.50, SE=0.081197.4891025641025296.5897435897436Baker 2019: LOR=-0.42, SE=0.111364.3865384615387400.05128205128204Davis 2020: LOR=-0.46, SE=0.091280.9378205128207331.0769230769231Garcia 2021: LOR=-0.48, SE=0.131239.2134615384616469.02564102564105Harris 2022: LOR=-0.44, SE=0.071322.6621794871796262.10256410256414Lee 2023: LOR=-0.43, SE=0.101343.524358974359365.5641025641026Nelson 2024: LOR=-0.51, SE=0.121176.6269230769233434.5384615384615Chen 2019: LOR=-0.68, SE=0.17821.9698717948719606.974358974359Evans 2020: LOR=-0.25, SE=0.161719.0435897435896572.4871794871796Foster 2021: LOR=-0.55, SE=0.211093.178205128205744.9230769230769Ibrahim 2022: LOR=-0.38, SE=0.221447.8352564102565779.4102564102565Jones 2022: LOR=-0.20, SE=0.181823.3544871794873641.4615384615385Kim 2023: LOR=-0.72, SE=0.26738.521153846154917.3589743589744Martinez 2023: LOR=-0.08, SE=0.232073.700641025641813.8974358974359O'Brien 2024: LOR=-0.58, SE=0.241030.591666666667848.384615384615295% Pseudo CIPooled Effect (LOR = -0.47)Null Effect (LOR = 0)High-precision studiesLow-precision studies (bias region)