PyMeta

{"Python module": "Meta-analysis"}

About PyMeta.com | How to input data? | About the output plots | About the data type | About the author

PyMeta is an online Meta-analysis tool website. It was created and supported with Python, a strong and amazing computer language.

This web-based application 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;
- Cumulative meta-analysis;
- Sensitivity analysis (one or two factors);
- Plots drawing: forest plot, funnel plot, and bar-line, cross-block plot.

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.

Our web pages were well-compatible to handhold sets, e.g. android and iPhone, which means you can access this tool via mobile phone anywhere anytime, enjoy!

This is an ongoing project, so, any questions and suggestions from you are very welcome.

Please contact me with email, or say some words in Guestbook.

Please cite me in any publictions like:

Thank you.

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You can type or paste your studies into the data-input textarea.

Each study in one line, like:

**study name, e1, n1, e2, n2**

for binary data:

**e1,n1**: events and number of experiment group;

**e2,n2**: events and number of control group.

e.g.

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.

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.

or

for continuous data:

e.g.

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.

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.

Tags like '

And a tag of '

e.g.

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.

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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.

**Forest plot**

**Forest plot**

A, Title of the plot with some information:

- Effect measure: MD-Mean difference,SMD-Standard mean difference,RR-Risk ratio, OR-Odds ratio, RD-Ratio difference;

- Algorithm: IV-Inverse variance,MH-Mantel Haenszel,Peto;

- Effect models: Fixed or random models;

B, Included studies list;

C, Each study's effect, include CI line and central block(position for effect and size for weight);

D, Overall effect diamond, empty for high heterogeneity (I-square more than 50%) and filled for lower heterogeneity.

**Forest plot with subgroup**

**Forest plot with subgroup**

A, Subgroup effect;

B, Overall effect.

**Funnel plot**

**Funnel plot**

A, Scatter dots of studies;

B, Boundary lines of effect;

C, Overall effect line.

**Forest plot of cumulative meta-analysis**

**Forest plot of cumulative meta-analysis**

A, The cumulative studies list (downward);

B, Total effects while each study added in the pool.

**Polar_forest plot of sensitivity meta-analysis**

**Polar_forest plot of sensitivity meta-analysis**

This kind of figure is designed for reviews with large amounts of trials, and map the normal forest plot into a polar plot.

A, Effect while one or two trial(s) be removed, blue color means the I-square are still higher than 50%;

B, Red line for those I-square decreased to below 50% while one(or two) study removed;

C, Overall effect diamond (without any trial removed), again, empty for high heterogeneity and filled for lower heterogeneity.

**Bar_line of sensitivity meta-analysis**

**Bar_line of sensitivity meta-analysis**

A, I-square value of overall test;

B, 50% I-square line ('50%' always regarded as threshold value, lower means fewer heterogeneity and higher means high heterogeneity);

C, I-square bar, grey for overall, blue for those one(or two) study removed, but I-square still higher than 50%;

D, Red bar for those I-square decreased to below 50% while one(or two) study removed.

**Colored cross block of sensitivity meta-analysis**

**Colored cross block of sensitivity meta-analysis**

A, Each block shows the I-square value of which two crossed studies were removed, blue block for those I-square still higher than 50% after removing;

B, Red block for those I-square decreased to below 50% while two studies removed;

C, Grey for overall I-square.

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There are two options of study data type here: count data and continuous data.

Count data, also known as binary/categorical/dichotomous data, it is usually some non negative integers, used for event counting.

Continuous data, a class of real numbers(i.e. with a decimal point), usually used to record the range or level of the observed value.

Links: Statistical data type

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**Hongyong Deng**

Ph.D., Associate Professor

Academic Visitor of Nottingham Univ., UK

Editor of Cochrane Schizophrenia Group (Current Editors)

Science and Technology Information Center

Shanghai University of Traditional Chinese Medicine

1200 Cailun Road, Pudong New District

Shanghai, China 201203

Email: dephew@126.com

Tel:+86(21)51322251

Web: www.PyMeta.com

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