如何创建森林图？

Question

我想将不同的风险比率合并到一个森林图中。我希望输出类似于 STATA 中的 metamiss 或 R 中的 metafor。如何在 Python 中执行此操作？

Answer 1

通过使用 zEPID 包，我创建了不同风险比率的森林图。

import matplotlib.image as mpimg
import numpy as np
import matplotlib.pyplot as plt
import zepid
from zepid.graphics import EffectMeasurePlot

labs = ["ACA(Isq=41.37% Tausq=0.146 pvalue=0.039 )",
        "ICA0(Isq=25.75% Tausq=0.092 pvalue=0.16 )",
        "ICA1(Isq=60.34% Tausq=0.121 pvalue=0.00 )",
        "ICAb(Isq=25.94% Tausq=0.083 pvalue=0.16 )",
        "ICAw(Isq=74.22% Tausq=0.465 pvalue=0.00 )"]
measure = [2.09,2.24,1.79,2.71,1.97]
lower = [1.49,1.63,1.33,2.00,1.25]
upper = [2.92,3.07,2.42,3.66,3.11]
p = EffectMeasurePlot(label=labs, effect_measure=measure, lcl=lower, ucl=upper)
p.labels(effectmeasure='RR')
p.colors(pointshape="D")
ax=p.plot(figsize=(7,3), t_adjuster=0.09, max_value=4, min_value=0.35 )
plt.title("Random Effect Model(Risk Ratio)",loc="right",x=1, y=1.045)
plt.suptitle("Missing Data Imputation Method",x=-0.1,y=0.98)
ax.set_xlabel("Favours Control      Favours Haloperidol       ", fontsize=10)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(True)
ax.spines['left'].set_visible(False)
plt.savefig("Missing Data Imputation Method",bbox_inches='tight')

Answer 2

statsmodels library has an API for doing simple meta-analysis and plotting forest plots. It supports DerSimonian-Laird (chi2) and Paule-Mandel (iterated). See the statsmodel docs 更多用例、选项和示例。

他们文档中的示例：

import numpy as np
from statsmodels.stats.meta_analysis import combine_effects

# dummy data
mean_effect = np.array([61.00,61.40,62.21,62.30,62.34,62.60,62.70,62.84,65.90])
var_effect = np.array([0.2025,1.2100,0.0900,0.2025,0.3844,0.5625,0.0676,0.0225,1.8225])
idx = ['lab1','lab2','lab3','lab4','lab5','lab6','lab7','lab8','lab9']

# meta-analysis and forest plot
results = combine_effects(mean_effect, var_effect, method_re="chi2", use_t=True, row_names=idx)
print(results.summary_frame())
fig = results.plot_forest()

输出：

                         eff    sd_eff     ci_low     ci_upp      w_fe      w_re
lab1               61.000000  0.450000  60.118016  61.881984  0.057436  0.123113
lab2               61.400000  1.100000  59.244040  63.555960  0.009612  0.040314
lab3               62.210000  0.300000  61.622011  62.797989  0.129230  0.159749
lab4               62.300000  0.450000  61.418016  63.181984  0.057436  0.123113
lab5               62.340000  0.620000  61.124822  63.555178  0.030257  0.089810
lab6               62.600000  0.750000  61.130027  64.069973  0.020677  0.071005
lab7               62.700000  0.260000  62.190409  63.209591  0.172052  0.169810
lab8               62.840000  0.150000  62.546005  63.133995  0.516920  0.194471
lab9               65.900000  1.350000  63.254049  68.545951  0.006382  0.028615
fixed effect       62.583397  0.107846  62.334704  62.832090  1.000000       NaN
random effect      62.390139  0.245750  61.823439  62.956838       NaN  1.000000
fixed effect wls   62.583397  0.189889  62.145512  63.021282  1.000000       NaN
random effect wls  62.390139  0.294776  61.710384  63.069893       NaN  1.000000

我还建议通读各个方法的文档，例如 combine_effects()，其中包含有关实施的其他注释和参考。

如何创建森林图？

How can I create a forest plot?

python-3.x

forestplot