jarbes ChangeLog

Version 2.0.0 -- January-Febrary-March 2022

Commitments for the next version

* bmeta: scale mixture random effects
* diagnostic bmeta: compare Bayesian cross-validation and scale mixture weights

* Vignettes:
  * bmeta
  * bcmeta
  * b3lmeta
  * metarisk
  * hmr

* bforest: a forest plot for bmeta; bcmeta; b3lmeta.

* hmr: Posterior prediction of the treatment effect to an new group of patients

* Effective number of studies in bcmeta and bmeta and b3lmeta

* Function: betaplot for the regression coefficients of hmr

* Binomial and Poisson likelihoods in bmeta, bcmeta and b3lmeta using
the arguments "family=...", "link="..."

* Tweedy's formula for bias: this is to understand the different bias
correction approaches (e.g. bmeta vs. bcmeta vs. b3lmeta vs. bmetareg)

* Planned:  Meta-regression function........................
* Function: bmetareg
* Function: summary.bmetareg
* Function: plot for bmetareg
* Function: diagnostic for bmetareg
* Function: betaplot for the regression coefficients of bmetareg

* Publication bias modeling.

Done in version 2.0.0 ......................................

* Approximated Bayesian Cross-Validation for bmeta, b3lmeta, bcmeta.
* Function: diagnostic for bcmeta (specific model and Bayesian cross-validation)
* Function: diagnoistic for bmeta (Bayesian cross-validation)
* Function: diagnoistic for b3lmeta (Bayesian cross-validation)

* Include as argument the labels and axis information for plot functions:
    hmr      (done)
    metarisk (done)
    bcmeta   (done)
    bmeta    (done)
    b3lmeta  (done)

* plot.bcmeta: add the arrows and the text for the distributions...

* summary.b3lmeta: add the means by groups! (done)

* Function: plot for bcmeta (done)
* Function: plot for bmeta  (done)
* Function: plot for b3lmeta (done)

* Function: bl3meta for three levels hierarchical meta-analysis (done)
* Function: summary for bl3meta (done)
* The function b3lmeta replaced the function ges

* Labels (posterior-prior) in the diagnostic function of hmr (done)
* Function bmeta for simple bayesian metaanalysis (done)
* Function summary for bmeta (done)
* Summary functions:
* Include the prior parameters in the object type "hmr" (done)
* Include the prior parameters in the object type "bcmeta" (done)
* Include the prior parameters in the object type "metarisk" (done)
* Include the prior parameters in the object type "bmeta" (done)
* Include the prior parameters in the object type "b3lmeta" (done)


Version 1.9.6 -- December 2021

  * Function: bcmeta implements the "Bias-Corrected" Meta-analysis model
  * Function: summary for bcmeta
  * Function: plot for metarisk
  * Function: summary for metarisk
  * Function: diagnostic for metarisk
  * Function: plot for hmr
  * Function: summary for hmr
  * Function: diagnostic for hmr
  * New data frame covid19: meta-analysis of risk factors for complications
    and death in COVID-19 patients.
  * The function bcmeta replaced the function gesmix


Version 1.8.0 -- Summer 2020

  * The function "metamix" is not part of jarbes anymore. It will be
  replaced by the function "bcmeta"


Version 1.7.4 -- November - December 2019

  * Preparation for the paper on mixture models

Version 1.7.3 -- April - May 2019

  * Correction in the example of data ppvcap,
  metariks(..., two.by.two = TRUE,...)

  * Typo correction in the example of ges() "ppvipv" must be "ppvipd"


Version 1.7.2 -- February-March 2019
  * New function: "gesmix"" performs the finite mixture random effects bias
  analysis of Verde 2017 and Verde and Curcio 2019.

  * New arguments for the function "ges":
      EmBi = "Empirical Bias"standing for "penalization of observational
      studies"
      ExPe = "Explicit Penalization" for observational studies.
  * All if()s 've been checked. The reason was:
    Issue from CRAN compilation:
    --- failure: length > 1 in coercion to logical ---

Version 1.7.1 -- December 2018
  * A bug in function hmr() is fixed.

Version 1.7.0 -- June 2018
  * Implementation of the function "hmr" for combining aggregated data and
  individual participant data.
  * New dataset "healing": this dataset corresponds to a systematic review of
  aggregated data. The primary endpoint is healing without amputation in one
  year follow up.
  * New dataset "healingipd": This dataset corresponds to individual
  participant data for diabetic patients.

Version 1.1.0		-- December 2017
  * Implementation of the "ges" function for Generalized Evidence Synthesis.
  * Implementation of Half Cauchy priors for components of variances.
  * Implementation of Empirical Bias adjustement for Observational Studies.
  * Implementation of Penalization methods for Observationa Studies.
  * First prototype of the "hmr" function for combining IPD and AD data.

Version 1.0.0		-- September 2017
  * Implementation of the "metarisk"" function for bivariate hierarchical
    meta-regression of aggregated data.
  * Documentation improved.

Version 0.5.0 -- January 2017
  * Creation of data examples:
  * ppv.cap: PPV23 (23-valent pneumococcal polysaccharide vaccine) with 16 Randomized
    Clinical Trials (RCTs); outcome variable CAP (community-acquired pneumonia).

  * ppv.ipde: PPV23 with 3 RCTs and observational studies (5 cohorts and 3 case
    controls); all data types are aggregated results; outcome variable IPD
    (invasive pneumococcal disease).

  * stem.cells: 31 randomized controlled trials (RCTs) of two treatment groups of
    heart disease patients, where the treatment group received bone marrow stem
    cells and the control group a placebo treatment.

  * opti: one pragmatic trial, the OPTIMIZE trial, which evaluates the clinical
    effectiveness of a perioperative, cardiac output–guided hemodynamic therapy
    algorithm.
    And 21 small RCTs with evaluation of Risk of Bias. All are studies have
    aggregated data.

  * foot.ad: 36 RCTs which investigate adjunctive therapies vs. routine medical care in
    diabetic patients. The primary outcome is healing without foot amputation. There
    are aggregated covariates describing studies and patients characteristics.

  * foot.ipd: A cohort study with 260 diabetic patients, the outcome variable is
    healing without amputation in a followup of one year. In addition we have 14
    potential risk factors.






