PythonMeta 1.1

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

PythonMeta

Info

Name = PythonMeta

Version = 1.1

Author = Deng Hongyong (dhy)

Email = [e-mail]

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: [e-mail]

Web: www.PyMeta.com