A Python module of Meta-Analysis, usually applied in systemtic reviews of Evidence-based Medicine.
Name = PythonMeta
Version = 1.1
Author = Deng Hongyong (dhy)
Email = email@example.com
URL = www.pymeta.com
Date = 2019.7.21 (First developed in 2017)
This is a Meta-Analysis package.
This module was designed to perform some Evidence-based medicine (EBM) tasks, such as:
- Combining effect measures (OR, RR, RD for count data and MD, SMD for continuous data);
- Heterogeneity test(Q/Chi-square test);
- Subgroup analysis;
- Plots drawing: forest plot, funnel plot, etc.
Statistical algorithms in this software cited from: Jonathan J Deeks and Julian PT Higgins, on behalf of the Statistical Methods Group of The Cochrane Collaboration. Statistical algorithms in Review Manager 5, August 2010.
Please cite me in any publictions like: Deng Hongyong. PyMeta, Python module of Meta-analysis, cited 20xx-xx-xx (or your time); 1 screen(s). Available from URL: http://www.pymeta.com
This is an ongoing project, so, any questions and suggestions from you are very welcome.
Install and update using
pip install PythonMeta
Sample code: sample.py
import PythonMeta as PMA def showstudies(studies): text = "%-10s %-20s %-20s \n"%("Study ID","Experiment Group","Control Group") text += "%-10s %-10s %-10s %-10s %-10s \n"%(" ","e1","n1","e2","n2") for i in range(len(studies)): text += "%-10s %-10s %-10s %-10s %-10s \n"%( studies[i], #study ID str(studies[i]), #event num of group1 str(studies[i]), #total num of group1 str(studies[i]), #event num of group2 str(studies[i]) #total num of group2 ) return text def showresults(rults): text = "%-10s %-6s %-18s %-10s"%("Study ID","n","ES[95% CI]","Weight(%)\n") for i in range(1,len(rults)): text += "%-10s %-6d %-4.2f[%.2f %.2f] %6.2f\n"%( rults[i], #study ID rults[i], #total num rults[i], #effect size rults[i], #lower of CI rults[i], #higher of CI 100*(rults[i]/rults) #weight ) text += "%-10s %-6d %-4.2f[%.2f %.2f] %6d\n"%( rults, #total effect size name rults, #total N (all studies) rults, #total effect size rults, #total lower CI rults, #total higher CI 100 ) text += "%d studies included (N=%d)\n"%(len(rults)-1,rults) text += "Heterogeneity: Tau\u00b2=%.3f "%(rults) if not rults==None else "Heterogeneity: " text += "Q(Chisquare)=%.2f(p=%s); I\u00b2=%s\n"%( rults, #Q test value rults, #p value for Q test str(round(rults,2))+"%") #I-square value text += "Overall effect test: z=%.2f, p=%s\n"%(rults,rults) #z-test value and p-value return text def main(): d = PMA.Data() #Load Data class m = PMA.Meta() #Load Meta class f = PMA.Fig() #Load Fig class d.datatype = 'CATE' #set data type, 'CATE' for binary data or 'CONT' for continuous data studies = d.getdata(d.readfile('studies.txt')) #get data from 'studies.txt' print(showstudies(studies)) #show studies m.datatype=d.datatype #set data type for meta-analysis calculating m.models = 'Fixed' #set effect models: 'Fixed' or 'Random' m.algorithm = 'MH' #set algorithm, based on datatype and effect size m.effect = 'RR' #set effect size:RR/OR/RD for binary data; SMD/MD for continuous data results = m.meta(studies) #performing the analysis print(m.models + " " + m.algorithm + " " + m.effect) print (showresults(results)) #show results table f.forest(results).show() #show results figure f.funnel(results).show() #show results figure if __name__ == '__main__': main()
Fang 2015, 15, 40, 24, 37 Gong 2012, 10, 40, 18, 35 Liu 2015, 30, 50, 40, 50 Long 2012, 19, 40, 26, 40 Pan 2015a, 57, 100, 68, 100 Wang 2001, 13, 18, 17, 18 Wang 2003, 7, 86, 15, 86 #This is a sample of binary data. #Input one study in a line; #Syntax: study name, e1, n1, e2, n2 #e1,n1: events and number of experiment group; #e2,n2: events and number of control group.
Deng Hongyong Ph.D
Shanghai University of Traditional Chinese Medicine
Shanghai, China 201203
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