PythonMeta 1.26
A Python module of Meta-Analysis, usually applied in systemtic reviews of Evidence-based Medicine.
Requires Python: >=3.5
Keywords: meta analysis,meta-analysis,meta_analysis,systematic review,EBM,Evidence-based Medicine

PythonMeta
---
Info
---
Name = PythonMeta

Version = 1.26 

Author = 邓宏勇Deng Hongyong

Email = dephew@126.com

URL = www.pymeta.com

Date = 2021.11.17

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.

2021.1.22
Ver 1.23 released.
Fixed a bug of Funnel plot.

2021.10.16
Ver 1.25 released.
1, Changes for funnel plot;
2, Egger's test for publication bias.

2021.11.17
Ver 1.26 released.
Some minor changes.

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. PythonMeta, 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)
* Eggers_test (meta-rults) (*method*): Egger's test for publication bias.
    + Input: result from self.meta();
    + Output: (Intercept(far away from 0 means bias), t-value, p-value(<0.05 means bias), std. dev., CI).

**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.subgroup=d.subgroup                            #set the subgroup
    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
    print(m.Eggers_test(results))                    #Eggers_test: (Intercept, t-value, p-value, SD  and CI)
    
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