The "Bayesian_Bivariate_Model.RMD" file is a R Markdown script that conducts Bivariate Meta-Analysis to evaluate the accuracy of an index test
(test which is under evaluation) under the assumption that the reference test is perfect.  The script requires certain input from
the user.  Those information needed are regrouped in the chunk bloc that covers rows 23-30 of the script.  More advanced user can certainly edit 
other sections of the script to suit their need, but aside from the rows 23-30, the script is built to be fully automated.

Below are the information the script expects from the user

-> The name of the file containing the dataset (command row 25).  The data should be provided as a .txt file in the form  of a matrix with as many 
rows as there are studies and 4 columns labeled as tp, fp, fn, tn, where 
***** tp=number of subjects who are True Positives
***** fp=number of subjects who are False Positives 
***** fn=number of subjects who are False Negatives
***** tn=number of subjects who are True Negatives

-> The number of precision digits to be displayed in the output (command row 26)

-> The number of iterations to drop during the "burn-in" phase (command row 27)

-> The number of iterations to keep to form the posterior samples (command row 28)

-> The number of independent chains to run (command row 29)


*A toy example dataset is provided to allow the user to run and familiarize itself with the script.  It is a systematic review by Butler-Laporte et al (2021)à
comparing the Saliva and Nasopharyngeal swab NAAT test for detection of COVID-19

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The "Bayesian_Latent_Class_Model.RMD" file is a R Markdown script that conducts Bivariate Meta-Analysis to evaluate the accuracy of an index test
(test which is under evaluation) against a reference test in the contexte where the latter is not perfect.  The script requires certain input from
the user.  Those information needed are regrouped in the chunk bloc that covers rows 23-61 of the script.  More advanced user can certainly edit 
other sections of the script to suit their need, but aside from the rows 23-61, the script is built to be fully automated.

Below are the information the script expects from the user

-> The name of the file containing the dataset (command row 24).  The data should be provided as a .txt file in the form  of a matrix with as many 
rows as there are studies and 4 columns labeled as n11, n10, n01, n00, where 
***** n11=number of subjects positive on both tests
***** n10=number of subjects with index test positive and reference test negative 
***** n01=number of subjects with index test negative and reference test positive
***** n00=number of subjects negative on both tests

-> The number of precision digits to be displayed in the output (command row 25)

-> The number of iterations to drop during the "burn-in" phase (command row 26)

-> The number of iterations to keep to form the posterior samples (command row 27)

-> The number of independent chains to run (command row 28)

-> Whether to model conditional dependence or not (command row 29)

-> Initial values for key parameters (command rows 42, 47-50)


After running the script, the output will include the following

-> The complete data from the .txt file

-> A list of the prior distributions used on the parameters of the model

-> A summary of the posterior estimates of the parameters of the model (including convergence diagnostic statistics)

-> Summary plot displaying the posterior credible region and the posterior prediction region as well as a summary of the posterior parameter estimates
needed to implement in RevMan software

-> Convergence diagnostic plots for key parameters of the model.

 
The output produced by the markdown script shows subtle differences depending on the model assumption (
conditional dependence or conditional independence model).  A dataset (Saliv_COVID.txt)* is provided as an example if the user would like to familiarize
theirself with the script.

*A toy example dataset is provided to allow the user to run and familiarize itself with the script.  It is a systematic review by Butler-Laporte et al (2021)à
comparing the Saliva and Nasopharyngeal swab NAAT test for detection of COVID-19


*Butler-Laporte, G, Lawandi, A, Schiller, I, Yao, M, Dendukuri, N, McDOnald, E. G. and Lee, T. C. 2021. 
“Comparison of Saliva and Nasopharyngeal Swab Nucleic Acid Amplification Testing for Detection of SARS-CoV-2; A Systematic Review and Meta-analysis.” 
JAMA Intern Med.