PythonMeta 1.0

A Python module of Meta-Analysis, usually applied in systemtic reviews of Evidence-based Medicine.

##PythonMeta 1.0

##Info

Name = PythonMeta

Version = 1.0

Author = Deng Hongyong (dhy)

Email = [e-mail]

URL = www.pymeta.com

Date = 2019.3.21 (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 MA

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],str(studies[i][0]),str(studies[i][1]),str(studies[i][2]),str(studies[i][3]))
    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],
        rults[i][5],
        rults[i][1],
        rults[i][3],
        rults[i][4],
        100*(rults[i][2]/rults[0][2])
        )
    text += "%-10s %-6d  %-4.2f[%.2f %.2f]   %6d\n"%(
        rults[0][0],
        rults[0][5],
        rults[0][1],
        rults[0][3],
        rults[0][4],
        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],
        rults[0][8],
        str(round(rults[0][9],2))+"%")
    text += "Overall effect test: z=%.2f, p=%s\n"%(rults[0][10],rults[0][11])
    
    return text

def main():
    d = MA.Data()
    f = MA.Fig()
    m = MA.Meta()
    
    d.datatype = 'CATE'
    studies = d.getdata(d.readfile('studies.txt'))
    print(showstudies(studies))

    m.datatype=d.datatype
    m.models = 'Fixed'
    m.algorithm = 'MH'    
    m.effect = 'RR'
    results = m.meta(studies)
    print(m.models + " " + m.algorithm + " " + m.effect)
    print (showresults(results))

    f.forest(results).show()
    
if __name__ == '__main__':
    main()

Datafile: studies.txt

    A sample data file (e.g., studies.txt):

    #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: [e-mail]

Web: www.PyMeta.com