Meta-analysis · effect + CI

Forest plot example: hazard ratios with subgroups

Forest plot built from published hazard ratios and 95% confidence intervals, grouped into subgroups with subtotals and a test for subgroup differences.

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StudyWeightHR [95% CI]Short follow-upStudy A 20157.9%0.82 [0.61, 1.10]Study B 20168.0%0.74 [0.55, 0.99]Study C 20189.8%0.91 [0.70, 1.18]Study D 20195.3%0.68 [0.47, 0.98]Subtotal (95% CI)31.0%0.80 [0.69, 0.93]Heterogeneity: Tau² = 0.00;Chi² = 1.98, df = 3 (P = 0.576); I² = 0%Test for overall effect: Z = 2.92 (P = 0.004)Long follow-upStudy E 201224.2%0.88 [0.76, 1.02]Study F 201417.8%0.79 [0.66, 0.95]Study G 201712.0%1.02 [0.81, 1.29]Study H 202015.0%0.71 [0.58, 0.87]Subtotal (95% CI)69.0%0.84 [0.73, 0.96]Heterogeneity: Tau² = 0.01;Chi² = 6.14, df = 3 (P = 0.105); I² = 51%Test for overall effect: Z = 2.57 (P = 0.010)Total (95% CI)100.0%0.83 [0.76, 0.90]0.20.5125Favours treatmentFavours controlHeterogeneity: Tau² = 0.00; Chi² = 8.38, df = 7 (P = 0.300); I² = 16%Test for overall effect: Z = 4.28 (P < 0.001)Test for subgroup differences: Chi² = 0.20, df = 1 (P = 0.657); I² = 0%
Hazard ratios from eight trials, grouped by length of follow-up. Illustrative data.

About this example

Survival outcomes are usually reported as hazard ratios with 95% confidence intervals. Paste the estimate and its limits for each study; the standard error is recovered from the interval on the log scale.

Adding a subgroup column gives each subgroup its own diamond and heterogeneity statistics, plus a test for subgroup differences under the plot.

The data

StudyHRLowerUpperFollow-up
Study A 20150.820.611.1Short follow-up
Study B 20160.740.550.99Short follow-up
Study C 20180.910.71.18Short follow-up
Study D 20190.680.470.98Short follow-up
Study E 20120.880.761.02Long follow-up
Study F 20140.790.660.95Long follow-up
Study G 20171.020.811.29Long follow-up
Study H 20200.710.580.87Long follow-up

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