A Python module of Meta-Analysis, usually applied in systemtic reviews of Evidence-based Medicine.
Project description
PythonMeta
Info
Name = PythonMeta
Version = 1.1
Author = Deng Hongyong (dhy)
Email = dephew@126.com
URL = www.pymeta.com
Date = 2019.7.21 (First developed in 2017)
About
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.
Installing
Install and update using pip
:
pip install PythonMeta
Example
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][4], #study ID
str(studies[i][0]), #event num of group1
str(studies[i][1]), #total num of group1
str(studies[i][2]), #event num of group2
str(studies[i][3]) #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][0], #study ID
rults[i][5], #total num
rults[i][1], #effect size
rults[i][3], #lower of CI
rults[i][4], #higher of CI
100*(rults[i][2]/rults[0][2]) #weight
)
text += "%-10s %-6d %-4.2f[%.2f %.2f] %6d\n"%(
rults[0][0], #total effect size name
rults[0][5], #total N (all studies)
rults[0][1], #total effect size
rults[0][3], #total lower CI
rults[0][4], #total higher CI
100
)
text += "%d studies included (N=%d)\n"%(len(rults)-1,rults[0][5])
text += "Heterogeneity: Tau\u00b2=%.3f "%(rults[0][12]) if not rults[0][12]==None else "Heterogeneity: "
text += "Q(Chisquare)=%.2f(p=%s); I\u00b2=%s\n"%(
rults[0][7], #Q test value
rults[0][8], #p value for Q test
str(round(rults[0][9],2))+"%") #I-square value
text += "Overall effect test: z=%.2f, p=%s\n"%(rults[0][10],rults[0][11]) #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()
Datafile: studies.txt
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.
Contact
Deng Hongyong Ph.D
Shanghai University of Traditional Chinese Medicine
Shanghai, China 201203
Email: dephew@126.com
Web: www.PyMeta.com
Project details
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