Metadata-Version: 2.1
Name: PythonMeta
Version: 1.21
Summary: A Python module of Meta-Analysis, usually applied in systemtic reviews of Evidence-based Medicine.
Home-page: http://www.pymeta.com
Author: 邓宏勇Deng, Hongyong
Author-email: dephew@126.com
License: UNKNOWN
Description: PythonMeta
        ---
        Info
        ---
        Name = PythonMeta
        
        Version = 1.21 
        
        Author = 邓宏勇Deng Hongyong
        
        Email = dephew@126.com
        
        URL = www.pymeta.com
        
        Date = 2021.1.21
        
        History
        ---
        2019.7.23
        Ver 1.11 released.
        
        2021.1.20
        Ver 1.20 released.
        Fixed a bug while calculating random effect subgroup's weight.
        
        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
        ```
        
        Import
        ---
        Import the PythonMeta module in your code:
        ```
        import PythonMeta
        ```
        
        Functions and Classes
        ---
        There are four functions/classes in PythonMeta package:
        
        **Help()**(*function*): Show help information of PythonMeta.
        
        **Data()**(*class*): Set and Load data to analysis.
        * datatype (*attribute, string*): set the data type:'CATE' for CATEgorical/count/binary/dichotomous data, or 'CONT' for continuous data.
        * studies (*attribute, array*): Array to store the study data.
        * subgroup (*attribute, array*): Array to store the subgroup.
        * nototal (*attribute, binary*): Flag of do NOT calculate the total effect.
        * readfile(filename) (*method*): Read data file. 
            + Input: filename(string) (e.g. "c:\\1.txt"); 
            + Output: lines array (always as input of method getdata()). 
            (See Sample code and data files)
        * getdata(lines) (*method*): Load data into attribute array of 'studies'. 
            + Input: lines array (always from method readfile()); 
            + Output: attribute 'studies'. 
            (See Sample code and data files)
        
        **Meta()**(*class*): Set and perform the Meta-Analysis.
        * datatype (*attribute, string*): set the data type:'CATE' for CATEgorical/count/binary/dichotomous data, or 'CONT' for CONTinuous data. Attention: this attribute should same to Data().datatype.
        * studies (*attribute, array*): Array of study data to meta-analysis.
        * subgroup (*attribute, array*): Array to store the subgroup. Attention: this attribute should same to Data().subgroup.
        * nototal (*attribute, binary*): Flag of do NOT calculate the total effect. Attention: this attribute should same to Data().nototal.
        * models (*attribute, string*): set effect models as 'Fixed' or 'Random'.
        * effect (*attribute, string*): set effect size as 'OR':odds ratio; 'RR': risk ratio; 'RD':risk difference; 'MD':weighted mean diff; 'SMD':standard mean diff.
        * algorithm (*attribute, string*): set the algorithms of meta-analysis: 'MH':Mantel-Haenszel;'Peto';'IV':Inverse variance;'IV-Heg'(DEFAULT),'IV-Cnd','IV-Gls':for SMD algorithms
        * meta(studies, nosubgrp=False) (*method*): perform the meta-analysis. 
            + Input: 1, studies array (always from Data().getdata); 2, nosubgrp flag, False as default. 
            + Output: result array [[Total...],[study1...],[subgroup1,...],[studyn,...]...[subgroupk,...]]. 
            (See Sample code for more information)
        
        **Fig()**(*class*): Set and draw the result figures.
        * size (*attribute, integer array*): set the canvas size in inchs, default [6,6].
        * dpi (*attribute, integer*): set the resolution of figure (dot per inch), default 80pts.
        * title (*attribute, string*): set the title of figure.
        * nototal (*attribute, binary*): Flag of do NOT show the total effect, default False.
        * forest(results) (*method*): drawing the forest plot. 
            + Input: results array, always from Meta().meta.
            + Output: matplotlib.pyplot.figure object;
            (See Sample code for more information)
        * funnel(results) (*method*): drawing the funnel plot. 
            + Input: results array, always from Meta().meta;
            + Output: matplotlib.pyplot.figure object.
            (See Sample code for more information)
        
        Example
        ---
        
        Sample code: **sample.py**
        ```Python
        
        import PythonMeta as PMA
        
        def showstudies(studies,dtype):    
            #show continuous data
            if dtype.upper()=="CONT":
                text = "%-10s %-30s %-30s \n"%("Study ID","Experiment Group","Control Group")
                text += "%-10s %-10s %-10s %-10s %-10s %-10s %-10s \n"%(" ","m1","sd1","n1","m2","sd2","n2")
                for i in range(len(studies)):
                    text += "%-10s %-10s %-10s %-10s %-10s  %-10s %-10s \n"%(
                    studies[i][6],        #study ID
                    str(studies[i][0]),   #mean of group1
                    str(studies[i][1]),   #SD of group1
                    str(studies[i][2]),   #total num of group1
                    str(studies[i][3]),   #mean of group2
                    str(studies[i][4]),   #SD of group2
                    str(studies[i][5])    #total num of group2
                    )
                return text
                
            #show dichotomous data
            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"%(   # for each study
                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"%(         # for total effect
                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(stys,settings):
            d = PMA.Data()  #Load Data class
            m = PMA.Meta()  #Load Meta class
            f = PMA.Fig()   #Load Fig class
            
            #You should always tell the datatype first!!!
            d.datatype = settings["datatype"]                #set data type, 'CATE' for binary data or 'CONT' for continuous data
            studies = d.getdata(stys)                        #load data
            #studies = d.getdata(d.readfile("studies.txt"))  #get data from a data file, see examples of data files
            print(showstudies(studies,d.datatype))           #show studies
        
            m.datatype=d.datatype                            #set data type for meta-analysis calculating
            m.models = settings["models"]                    #set effect models: 'Fixed' or 'Random'
            m.algorithm = settings["algorithm"]              #set algorithm, based on datatype and effect size
            m.effect = settings["effect"]                    #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 forest plot
            f.funnel(results).show()                         #show funnel plot
            
        if __name__ == '__main__':
            samp_cate=[  #this array can be stored into a data file by lines, and loaded with d.readfile("filename")
            "Fang 2015,15,40,24,37",
            "Gong 2012,10,40,18,35",
            "Liu 2015,30,50,40,50",
            "Long 2012,19,40,26,40",
            "Wang 2003,7,86,15,86",
            "<subgroup>name=short term",
            "Chen 2008,20,60,28,60",
            "Guo 2014,31,51,41,51",
            "Li 2015,29,61,31,60",
            "Yang 2006,21,40,31,40",
            "Zhao 2012,27,40,30,40",
            "<subgroup>name=medium term",
            "#<nototal>",
            " ",
            "#This is a sample of binary data with subgroup.",
            "#Syntax: study name, e1, n1, e2, n2",
            "#e1,n1: events and number of experiment group;",
            "#e2,n2: events and number of control group.",
            "#And you can add a line of <nototal> to hide the Overall result."]
            
            samp_cont=[  #this array can be stored into a data file by lines, and loaded with d.readfile("filename")
            "Atmaca 2005, 20.9,  6.0,  15,  27.4,  8.5,  14",
            "Guo 2014,    12.8,  5.2,  51,  11.9,  5.3,  51",
            "Liu 2010,    23.38, 5.86, 35,  24.32, 5.43, 35",
            "Wang 2012,   15.67, 8.78, 43,  18.67, 9.87, 43",
            "Xu 2002,     15.49, 7.16, 50,  21.72, 8.07, 50",
            "Zhao 2012,   12.8,  5.7,  40,  13.0,  5.2,  40",
            " ",
            "#This is a sample of continuous data.",
            "#Input one study in a line;",
            "#Syntax: study name, m1, sd1, n1, m2, sd2, n2",
            "#m1, sd1, n1: mean, SD and number of experiment group;",
            "#m2, sd2, n2: mean, SD and number of control group."]
            
            #sample 1: dichotomous data
            settings={
            "datatype":"CATE",  #for CATEgorical/count/binary/dichotomous data
            "models":"Fixed",             #models: Fixed or Random
            "algorithm":"MH",             #algorithm: MH, Peto or IV
            "effect":"RR"}                #effect size: RR, OR, RD
            main(samp_cate,settings)
            
            #sample 2: continuous data
            settings={
            "datatype":"CONT",  #for CONTinuous data
            "models":"Fixed",             #models: Fixed or Random
            "algorithm":"IV",             #algorithm: IV
            "effect":"MD"}                #effect size: MD, SMD
            main(samp_cont,settings)
        ```
        
        Or you can load data from a file, like:
        ```
        studies = d.getdata(d.readfile("studies.txt")
        ```
        
        Here are some examples of data file:
        (Please remember all lines start with # are comment lines, which will be ignored while loading.)
        
        **Sample of continuous data**
        ```
        Atmaca 2005, 20.9,  6.0,  15,  27.4,  8.5,  14
        Guo 2014,    12.8,  5.2,  51,  11.9,  5.3,  51
        Liu 2010,    23.38, 5.86, 35,  24.32, 5.43, 35
        Wang 2012,   15.67, 8.78, 43,  18.67, 9.87, 43
        Xu 2002,     15.49, 7.16, 50,  21.72, 8.07, 50
        Zhao 2012,   12.8,  5.7,  40,  13.0,  5.2,  40
        
        #This is a sample of continuous data.
        #Input one study in a line;
        #Syntax: study name, m1, sd1, n1, m2, sd2, n2
        #m1, sd1, n1: mean, SD and number of experiment group;
        #m2, sd2, n2: mean, SD and number of control group.
        ```
        
        **Sample of dichotomous data**
        ```
        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.
        ```
        
        **Sample of data with subgroup**
        ```
        Fang 2015,15,40,24,37
        Gong 2012,10,40,18,35
        Liu 2015,30,50,40,50
        Long 2012,19,40,26,40
        Wang 2003,7,86,15,86
        <subgroup>name=short term
        Chen 2008,20,60,28,60
        Guo 2014,31,51,41,51
        Li 2015,29,61,31,60
        Yang 2006,21,40,31,40
        Zhao 2012,27,40,30,40
        <subgroup>name=medium term
        #<nototal>
        
        #This is a sample of subgroup.
        #Cumulative meta-analysis and Senstivity analysis will blind to all <subgroup> tags.
        #And you can add a line of <nototal> to hide the Overall result.
        ```
        Please download all above sample code and data files (maybe updated) at www.pymeta.com.
        
        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: 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
