Metadata-Version: 2.1
Name: PythonMeta
Version: 1.0
Summary: A Python module of Meta-Analysis, usually applied in systemtic reviews of Evidence-based Medicine.
Home-page: http://www.pymeta.com
Author: Deng Hongyong
Author-email: dephew@126.com
License: UNKNOWN
Description: ##PythonMeta 1.0
        ---
        ##Info
        
        Name = PythonMeta
        
        Version = 1.0 
        
        Author = Deng Hongyong (dhy)
        
        Email = dephew@126.com
        
        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**
        ```Python
        
        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: dephew@126.com
        
        Web: www.PyMeta.com
Keywords: meta analysis,meta-analysis,meta_analysis,systematic review,EBM,Evidence-based Medicine
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Requires-Python: >=3.5
Description-Content-Type: text/markdown
