Package 'jarbes'

Description

This function performers a Bayesian meta-analysis

Usage

b3lmeta(
  data,
  mean.mu.0 = 0,
  sd.mu.0 = 10,
  scale.sigma.between = 0.5,
  df.scale.between = 1,
  scale.sigma.within = 0.5,
  df.scale.within = 1,
  nr.chains = 2,
  nr.iterations = 10000,
  nr.adapt = 1000,
  nr.burnin = 1000,
  nr.thin = 1,
  be.quiet = FALSE,
  r2jags = TRUE
)

Arguments

Details

The results of the object of the class bcmeta can be extracted with R2jags or with rjags. In addition a summary, a print and a plot functions are implemented for this type of object.

Value

This function returns an object of the class "bmeta". This object contains the MCMC output of each parameter and hyper-parameter in the model and the data frame used for fitting the model.

References

Verde, P.E. (2021) A Bias-Corrected Meta-Analysis Model for Combining Studies of Different Types and Quality. Biometrical Journal; 1–17.

Examples

## Not run: 
library(jarbes)



## End(Not run)

Description

This function performers a Bayesian meta-analysis with DP as random effects

Usage

bcdpmeta(
  data,
  mean.mu.0 = 0,
  sd.mu.0 = 10,
  scale.sigma.between = 0.5,
  df.scale.between = 1,
  B.lower = 0,
  B.upper = 10,
  a.0 = 1,
  a.1 = 1,
  alpha.0 = 0.03,
  alpha.1 = 10,
  K = 30,
  nr.chains = 2,
  nr.iterations = 10000,
  nr.adapt = 1000,
  nr.burnin = 1000,
  nr.thin = 1,
  be.quiet = FALSE
)

Arguments

Details

The results of the object of the class bcdpmeta can be extracted with R2jags or with rjags. In addition a summary, a print and a plot functions are implemented for this type of object.

Value

This function returns an object of the class "bcdpmeta". This object contains the MCMC output of each parameter and hyper-parameter in the model and the data frame used for fitting the model.

References

Verde, P.E. (2021) A Bias-Corrected Meta-Analysis Model for Combining Studies of Different Types and Quality. Biometrical Journal; 1–17.

Examples

## Not run: 
library(jarbes)


# Example: Stemcells

data("stemcells")
stemcells$TE = stemcells$effect.size
stemcells$seTE = stemcells$se.effect

bm1 = bcdpmeta(stemcells)
summary(bm1)


## End(Not run)

Description

This function performers a Bayesian meta-analysis to jointly combine different types of studies. The random-effects follows a finite mixture of normal distributions.

Usage

bcmeta(
  data,
  mean.mu = 0,
  sd.mu = 10,
  scale.sigma.between = 0.5,
  df.scale.between = 1,
  B.lower = 0,
  B.upper = 10,
  a.0 = 1,
  a.1 = 1,
  nu = 0.5,
  nu.estimate = FALSE,
  b.0 = 1,
  b.1 = 2,
  nr.chains = 2,
  nr.iterations = 10000,
  nr.adapt = 1000,
  nr.burnin = 1000,
  nr.thin = 1,
  be.quiet = FALSE,
  r2jags = TRUE
)

Arguments

Details

The results of the object of the class bcmeta can be extracted with R2jags or with rjags. In addition a summary, a print and a plot functions are implemented for this type of object.

Value

This function returns an object of the class "bcmeta". This object contains the MCMC output of each parameter and hyper-parameter in the model and the data frame used for fitting the model.

References

Verde, P. E. (2017) Two Examples of Bayesian Evidence Synthesis with the Hierarchical Meta-Regression Approach. Chap.9, pag 189-206. Bayesian Inference, ed. Tejedor, Javier Prieto. InTech.

Verde, P.E. (2021) A Bias-Corrected Meta-Analysis Model for Combining Studies of Different Types and Quality. Biometrical Journal; 1–17.

Examples

## Not run: 
library(jarbes)

# Example ppvipd data

data(ppvipd)



## End(Not run)

Description

This function performers a Bayesian meta-analysis with DPM as random effects

Usage

bcmixmeta(
  data,
  x = NULL,
  mean.mu.0 = 0,
  sd.mu.0 = 10,
  scale.sigma.between = 0.5,
  df.scale.between = 1,
  scale.sigma.beta = 0.5,
  df.scale.beta = 1,
  B.lower = -15,
  B.upper = 15,
  a.0 = 0.5,
  a.1 = 1,
  alpha.0 = 0.03,
  alpha.1 = 2,
  K = 10,
  bilateral.bias = FALSE,
  nr.chains = 2,
  nr.iterations = 10000,
  nr.adapt = 1000,
  nr.burnin = 1000,
  nr.thin = 1,
  be.quiet = FALSE
)

Arguments

Details

The results of the object of the class bcmixmeta can be extracted with R2jags or with rjags. In addition a summary, a print and a plot functions are implemented for this type of object.

Value

This function returns an object of the class "bcmixmeta". This object contains the MCMC output of each parameter and hyper-parameter in the model and the data frame used for fitting the model.

References

Verde, P.E. and Rosner, G. L. (2024) A Bias-Corrected Bayesian Nonparamteric Model for Combining Studies with Varying Quality in Meta-Analysis. Biometrical Journal; (under revision).

Examples

## Not run: 
library(jarbes)


# Example: Stemcells

data("stemcells")
stemcells$TE = stemcells$effect.size
stemcells$seTE = stemcells$se.effect

# Beta(0.5, 1)
a.0 = 0.5
a.1 = 1

# alpha.max
 N = dim(stemcells)[1]
 alpha.max = 1/5 *((N-1)*a.0 - a.1)/(a.0 + a.1)

alpha.max

# K.max
K.max = 1 + 5*alpha.max
K.max = round(K.max)

K.max

set.seed(20233)

bcmix.2.stemcell = bcmixmeta(stemcells,
                            mean.mu.0=0, sd.mu.0=100,
                            B.lower = -15,
                            B.upper = 15,
                            alpha.0 = 0.5,
                            alpha.1 = alpha.max,
                            a.0 = a.0,
                            a.1 = a.1,
                            K = K.max,
                            sort.priors = FALSE,
                            df.scale.between = 1,
                            scale.sigma.between = 0.5,
                            nr.chains = 4,
                            nr.iterations = 50000,
                            nr.adapt = 1000,
                            nr.burnin = 10000,
                            nr.thin = 4)


 diagnostic(bcmix.2.stemcell, y.lim = c(-1, 15), title.plot = "Default priors")


 bcmix.2.stemcell.mcmc <- as.mcmc(bcmix.1.stemcell$BUGSoutput$sims.matrix)


theta.names <- paste(paste("theta[",1:31, sep=""),"]", sep="")
theta.b.names <- paste(paste("theta.bias[",1:31, sep=""),"]", sep="")

theta.b.greek.names <- paste(paste("theta[",1:31, sep=""),"]^B", sep="")
theta.greek.names <- paste(paste("theta[",1:31, sep=""),"]", sep="")


caterplot(bcmix.2.stemcell.mcmc,
         parms = theta.names,               # theta
         labels = theta.greek.names,
         greek = T,
         labels.loc="axis", cex =0.7,
         col = "black",
         style = "plain",
         reorder = F,
         val.lim =c(-6, 16),
         quantiles = list(outer=c(0.05,0.95),inner=c(0.16,0.84)),
         x.lab = "Effect: mean difference"
)
title( "95% posterior intervals of studies' effects")
caterplot(bcmix.2.stemcell.mcmc,
         parms = theta.b.names,             # theta.bias
         labels = theta.greek.names,
         greek = T,
         labels.loc="no",
         cex = 0.7,
         col = "grey",
         style = "plain", reorder = F,
         val.lim =c(-6, 16),
         quantiles = list(outer=c(0.025,0.975),inner=c(0.16,0.84)),
         add = TRUE,
         collapse=TRUE, cat.shift= -0.5,
)

attach.jags(bcmix.2.stemcell, overwrite = TRUE)
abline(v=mean(mu.0), lwd =2, lty =2)

legend(9, 20, legend = c("bias corrected", "biased"),
     lty = c(1,1), lwd = c(2,2), col = c("black", "grey"))



## End(Not run)

Description

This function performers a Bayesian meta-analysis

Usage

bmeta(
  data,
  mean.mu = 0,
  sd.mu = 10,
  scale.sigma.between = 0.5,
  df.scale.between = 1,
  nr.chains = 2,
  nr.iterations = 10000,
  nr.adapt = 1000,
  nr.burnin = 1000,
  nr.thin = 1,
  be.quiet = FALSE
)

Arguments

Details

The results of the object of the class bcmeta can be extracted with R2jags or with rjags. In addition a summary, a print and a plot functions are implemented for this type of object.

Value

This function returns an object of the class "bmeta". This object contains the MCMC output of each parameter and hyper-parameter in the model and the data frame used for fitting the model.

References

Verde, P.E. (2021) A Bias-Corrected Meta-Analysis Model for Combining Studies of Different Types and Quality. Biometrical Journal; 1–17.

Examples

## Not run: 
library(jarbes)

#Example: ppvipd

data(ppvipd)
bm1 = bmeta(ppvipd)

summary(bm1)
plot(bm1, x.lim = c(-3, 1), y.lim = c(0, 3))

diagnostic(bm1, study.names = ppvipd$name, post.p.value.cut = 0.1,
           lwd.forest = 1, shape.forest = 4)

# Example: Stemcells

data("stemcells")
stemcells$TE = stemcells$effect.size
stemcells$seTE = stemcells$se.effect

bm2 = bmeta(stemcells)
summary(bm2)
plot(bm2, x.lim = c(-1, 7), y.lim = c(0, 1))

diagnostic(bm2, study.names = stemcells$trial,
           post.p.value.cut = 0.05,
           lwd.forest = 0.5, shape.forest = 4)

diagnostic(bm2, post.p.value.cut = 0.05,
           lwd.forest = 0.5, shape.forest = 4)

## End(Not run)

Description

Meta-analysis of 40 Observational Studies from PubMed, Cocharane Library and SciELO databases that assessed the impact of diabetes, hypertension, cardiovascular disease, and the use of ACEI/ARB on severity and mortality of COVID-19 cases.

Format

A dataframe with 89 rows and 12 columns. Each row represents study results, the columns are:

author

Principal author and year of publication.

endpoint

Endoint: severity or mortality.

risk.factor

Possible risk factors: diabetes, hypertension, cardiovascular, ACE_ARB.

event.e

Number of events in the group with risk factor.

n.e

Number of patients in the group with risk factor.

event.c

Number of events in the group without risk factor.

n.c

Number of patients in the group with risk factor.

design

Study design: Case Series, Cross Sectional and Retrospective Cohort.

TE

Log Odds Ratio

seTE

Standard Error of the Log Odds Ratio

logitPc

Logit transformation of the proportion of events in the control group.

N

Total number of patients.

Source

de Almeida-Pititto, B., Dualib, P.M., Zajdenverg, L. et al. Severity and mortality of COVID 19 in patients with diabetes, hypertension and cardiovascular disease: a meta-analysis. Diabetol Metab Syndr 12, 75 (2020). https://doi.org/10.1186/s13098-020-00586-4

Description

Generic diagnostic function.

Usage

diagnostic(object, ...)

Arguments

Description

This function performers an approximated Bayesian cross-validation for a b3lmeta object

Usage

## S3 method for class 'b3lmeta'
diagnostic(
  object,
  post.p.value.cut = 0.05,
  study.names = NULL,
  size.forest = 0.4,
  lwd.forest = 0.2,
  shape.forest = 23,
  ...
)

Arguments

Description

This function performers an approximated Bayesian cross-validation for a bcmeta object and specially designed diagnostics to detect the existence of a biased component.

Usage

## S3 method for class 'bcdpmeta'
diagnostic(
  object,
  post.p.value.cut = 0.05,
  study.names = NULL,
  size.forest = 0.4,
  lwd.forest = 0.2,
  shape.forest = 23,
  bias.plot = TRUE,
  cross.val.plot = FALSE,
  level = c(0.5, 0.75, 0.95),
  x.lim = c(0, 1),
  y.lim = c(0, 10),
  x.lab = "P(Bias)",
  y.lab = "Mean Bias",
  title.plot = paste("Bias Diagnostics Contours (50%, 75% and 95%)"),
  kde2d.n = 25,
  marginals = TRUE,
  bin.hist = 30,
  color.line = "black",
  color.hist = "white",
  color.data.points = "black",
  alpha.data.points = 0.1,
  S = 5000,
  ...
)

Arguments

Description

This function performers an approximated Bayesian cross-validation for a bcmeta object and specially designed diagnostics to detect the existence of a biased component.

Usage

## S3 method for class 'bcmeta'
diagnostic(
  object,
  post.p.value.cut = 0.05,
  study.names = NULL,
  size.forest = 0.4,
  lwd.forest = 0.2,
  shape.forest = 23,
  bias.plot = TRUE,
  cross.val.plot = TRUE,
  level = c(0.5, 0.75, 0.95),
  x.lim = c(0, 1),
  y.lim = c(0, 10),
  x.lab = "P(Bias)",
  y.lab = "Mean Bias",
  title.plot = paste("Bias Diagnostics Contours (50%, 75% and 95%)"),
  kde2d.n = 25,
  marginals = TRUE,
  bin.hist = 30,
  color.line = "black",
  color.hist = "white",
  color.data.points = "black",
  alpha.data.points = 0.1,
  S = 5000,
  ...
)

Arguments

Description

This function performers an approximated Bayesian cross-validation for a bcmeta object and specially designed diagnostics to detect the existence of a biased component.

Usage

## S3 method for class 'bcmixmeta'
diagnostic(
  object,
  post.p.value.cut = 0.05,
  study.names = NULL,
  size.forest = 0.4,
  lwd.forest = 0.2,
  shape.forest = 23,
  bias.plot = TRUE,
  cross.val.plot = FALSE,
  level = c(0.5, 0.75, 0.95),
  x.lim = c(0, 1),
  y.lim = c(0, 10),
  x.lab = "P(Bias)",
  y.lab = "Mean Bias",
  title.plot = paste("Bias Diagnostics Contours (50%, 75% and 95%)"),
  kde2d.n = 25,
  marginals = TRUE,
  bin.hist = 30,
  color.line = "black",
  color.hist = "white",
  color.data.points = "black",
  alpha.data.points = 0.1,
  S = 5000,
  ...
)

Arguments

Description

This function performers an approximated Bayesian cross-validation for a b3lmeta object

Usage

## S3 method for class 'bmeta'
diagnostic(
  object,
  post.p.value.cut = 0.05,
  median.w = 1.5,
  study.names = NULL,
  size.forest = 0.4,
  lwd.forest = 0.2,
  shape.forest = 23,
  ...
)

Arguments

Description

This function performers a specially designed diagnostic for a hmr object

Usage

## S3 method for class 'hmr'
diagnostic(
  object,
  median.w = 1.5,
  study.names,
  size.forest = 0.4,
  lwd.forest = 0.2,
  shape.forest = 23,
  mu.phi = TRUE,
  mu.phi.x.lim.low = -10,
  mu.phi.x.lim.up = 10,
  colour.hist.mu.phi = "royalblue",
  colour.prior.mu.phi = "black",
  colour.posterior.mu.phi = "blue",
  title.plot.mu.phi = "Prior-to-Posterior Sensitivity",
  title.plot.weights = "Outlier Detection",
  ...
)

Arguments

Description

This function performers a specially designed diagnostic for a metarisk object

Usage

## S3 method for class 'metarisk'
diagnostic(
  object,
  median.w = 1.5,
  study.names,
  size.forest = 0.4,
  lwd.forest = 0.2,
  shape.forest = 23,
  ...
)

Arguments

Description

This function performers a Bayesian meta-analysis with DP as random effects

Usage

dpmeta(
  data,
  mean.mu.0 = 0,
  sd.mu.0 = 10,
  scale.sigma.between = 0.5,
  df.scale.between = 1,
  alpha.0 = 0.03,
  alpha.1 = 10,
  K = 30,
  nr.chains = 2,
  nr.iterations = 10000,
  nr.adapt = 1000,
  nr.burnin = 1000,
  nr.thin = 1,
  be.quiet = FALSE
)

Arguments

Details

The results of the object of the class bcmeta can be extracted with R2jags or with rjags. In addition a summary, a print and a plot functions are implemented for this type of object.

Value

This function returns an object of the class "dpmeta". This object contains the MCMC output of each parameter and hyper-parameter in the model and the data frame used for fitting the model.

References

Verde, P.E. (2021) A Bias-Corrected Meta-Analysis Model for Combining Studies of Different Types and Quality. Biometrical Journal; 1–17.

Examples

## Not run: 
library(jarbes)


# Example: Stemcells

data("stemcells")
stemcells$TE = stemcells$effect.size
stemcells$seTE = stemcells$se.effect

bm1 = dpmmeta(stemcells)
summary(bm1)
plot(bm1, x.lim = c(-1, 7), y.lim = c(0, 1))

diagnostic(bm1, study.names = stemcells$trial,
           post.p.value.cut = 0.05,
           lwd.forest = 0.5, shape.forest = 4)

diagnostic(bm1, post.p.value.cut = 0.05,
           lwd.forest = 0.5, shape.forest = 4)

## End(Not run)

Description

This function performers a Bayesian meta-analysis with DP as random effects

Usage

dpmetareg(
  data,
  x,
  mean.mu.0 = 0,
  sd.mu.0 = 10,
  scale.sigma.between = 0.5,
  df.scale.between = 1,
  alpha.0 = 0.03,
  alpha.1 = 10,
  K = 30,
  nr.chains = 2,
  nr.iterations = 10000,
  nr.adapt = 1000,
  nr.burnin = 1000,
  nr.thin = 1,
  be.quiet = FALSE
)

Arguments

Details

The results of the object of the class bcmeta can be extracted with R2jags or with rjags. In addition a summary, a print and a plot functions are implemented for this type of object.

Value

This function returns an object of the class "dpmeta". This object contains the MCMC output of each parameter and hyper-parameter in the model and the data frame used for fitting the model.

References

Verde, P.E. (2021) A Bias-Corrected Meta-Analysis Model for Combining Studies of Different Types and Quality. Biometrical Journal; 1–17.

Examples

## Not run: 
library(jarbes)


# Example: Stemcells

data("stemcells")
stemcells$TE = stemcells$effect.size
stemcells$seTE = stemcells$se.effect

bm1 = dpmmeta(stemcells)
summary(bm1)
plot(bm1, x.lim = c(-1, 7), y.lim = c(0, 1))

diagnostic(bm1, study.names = stemcells$trial,
           post.p.value.cut = 0.05,
           lwd.forest = 0.5, shape.forest = 4)

diagnostic(bm1, post.p.value.cut = 0.05,
           lwd.forest = 0.5, shape.forest = 4)

## End(Not run)

Description

This function performers a Bayesian meta-analysis with DPM as random effects

Usage

dpmmeta(
  data,
  mean.mu.0 = 0,
  sd.mu.0 = 10,
  scale.sigma.between = 0.5,
  df.scale.between = 1,
  alpha.0 = 0.03,
  alpha.1 = 10,
  K = 5,
  nr.chains = 2,
  nr.iterations = 10000,
  nr.adapt = 1000,
  nr.burnin = 1000,
  nr.thin = 1,
  be.quiet = FALSE
)

Arguments

Details

The results of the object of the class bcmeta can be extracted with R2jags or with rjags. In addition a summary, a print and a plot functions are implemented for this type of object.

Value

This function returns an object of the class "bmeta". This object contains the MCMC output of each parameter and hyper-parameter in the model and the data frame used for fitting the model.

References

Verde, P.E. (2021) A Bias-Corrected Meta-Analysis Model for Combining Studies of Different Types and Quality. Biometrical Journal; 1–17.

Examples

## Not run: 
library(jarbes)


# Example: Stemcells

data("stemcells")
stemcells$TE = stemcells$effect.size
stemcells$seTE = stemcells$se.effect

bm1 = dpmmeta(stemcells)
summary(bm1)
plot(bm1, x.lim = c(-1, 7), y.lim = c(0, 1))

diagnostic(bm1, study.names = stemcells$trial,
           post.p.value.cut = 0.05,
           lwd.forest = 0.5, shape.forest = 4)

diagnostic(bm1, post.p.value.cut = 0.05,
           lwd.forest = 0.5, shape.forest = 4)

## End(Not run)

Description

Generic effect function.

Usage

effect(object, ...)

Arguments

Description

This function estimates the posterior distribution for a subgroup of patients identified with the function hmr (Hierarchical Meta-Regression).

Usage

## S3 method for class 'hmr'
effect(
  object,
  B.lower = 0,
  B.upper = 3,
  k = 1,
  level = c(0.5, 0.75, 0.95),
  x.lim = c(-9, 5),
  y.lim = c(-1, 5),
  x.lab = "Baseline risk",
  y.lab = "Effectiveness",
  title.plot = paste("Posterior Effectiveness for a subgroup (50%, 75% and 95%)"),
  kde2d.n = 25,
  marginals = TRUE,
  bin.hist = 30,
  color.line = "black",
  color.hist = "white",
  color.data.points = "black",
  alpha.data.points = 0.1,
  S = 5000,
  display.probability = FALSE,
  line.no.effect = 0,
  font.size.title = 20,
  ...
)

Arguments

Description

Meta-analysis of 35 randomized controlled trials investigating the effectiveness in the application of adjuvant therapies for diabetic patients compared to medical routine care, where the endpoint was healing without amputations within a period less than or equal to one year.

Format

A matrix with 35 rows and 9 columns. Each row represents study results, the columns are:

Study

Name of the first author and year.

n_t

Number of patients in the treatment group.

n_c

Number of patients in the control group.

y_t

Number of heal patients in the treatment group.

y_c

Number of heal patients in the control group.

ndrop

Total number of drop out patients.

fup_weeks

Length of followup in weeks.

PAD

Inclusion of patients with peripheral arterial disease.

wagner_4

Inclusion of patients with Wagner score 3 and 4.

Source

The data were obtainded from: Centre for Clinical Practice at NICE (UK and others) (2011), Clinical guideline 119. Diabetic foot problems: Inpatient Management of Diabetic Foot Problems. Tech. rep., National Institute for Health and Clinical Excellence.

References

Verde, P.E. (2018) The Hierarchical Meta-Regression Approach and Learning from Clinical Evidence. Technical Report.

Description

Prospective cohort study.

Format

A dataframe with 260 rows and 18 columns. Each row represents a patient, the columns are:

healing.without.amp

Outcome variable: Healing without amputation with in one year.

duration_lesion_days

Duration of leasions in days at baseline.

PAD

Peripheral arterial disease yes/no.

neuropathy

Neuropathy yes/no.

first.ever.lesion

First ever lesion yes/no.

no.continuous.care

No continuous care yes/no.

male

yes/no.

diab.typ2

Diabetes type 2 yes/no.

insulin

Insulin dependent yes/no.

HOCHD

HOCHD yes/no.

HOS

HOCHD yes/no.

CRF

CRF yes/no.

dialysis

Dialysis yes/no.

DNOAP

DNOAP yes/no.

smoking.ever

Ever smoke yes/no.

age

Age at baseline in years.

diabdur

Diabetes duration at baseline.

wagner.class

Wagner score 1-2 vs. 3-4-5.

Source

Morbach, S, et al. (2012). Long-Term Prognosis of Diabetic Foot Patients and Their Limbs: Amputation and death over the course of a decade,Diabetes Care, 35, 10, 2012-2017.

References

Verde, P.E. (2018) The Hierarchical Meta-Regression Approach and Learning from Clinical Evidence. Technical Report.

Description

Meta-analysis of 15 studies investigating total hip replacement to compare the risk of revision of cemented and uncemented implantfixation modalities, by pooling treatment effectestimates from OS and RCTs.

Format

A dataframe with 15 rows and 12 columns. Each row represents study results, the columns are:

Study

Author and year.

Study_type

Study desing.

N_of_revisions

Number of revisions.

Total_cemented

Total number of cemmented cases.

N_of_revisions_uncemented

Number of uncemented revisions.

Total_uncemented

Total number of uncemmented cases.

Relative_risks_computed

RR calculated from the two by two table.

L95CI

Lower 95prc CI

U95CI

Upper 95prc CI

mean_age

Mean age of the study

proportion_of_women

Proportion of women in the study.

Follow_up

Time to follow-up in years.

Source

Schnell-Inderst P, Iglesias CP, Arvandi M, Ciani O, Matteucci Gothe R, Peters J, Blom AW, Taylor RS and Siebert U (2017). A bias-adjusted evidence synthesis of RCT and observational data: the case of total hip replacement. Health Econ. 26(Suppl. 1): 46–69.

Description

This function performers a Bayesian cross design synthesis. The function fits a hierarchical meta-regression model based on a bivariate random effects model.

Usage

hmr(
  data,
  two.by.two = TRUE,
  dataIPD,
  re = "normal",
  link = "logit",
  mean.mu.1 = 0,
  mean.mu.2 = 0,
  mean.mu.phi = 0,
  sd.mu.1 = 1,
  sd.mu.2 = 1,
  sd.mu.phi = 1,
  sigma.1.upper = 5,
  sigma.2.upper = 5,
  sigma.beta.upper = 5,
  mean.Fisher.rho = 0,
  sd.Fisher.rho = 1/sqrt(2),
  df = 4,
  df.estimate = FALSE,
  df.lower = 3,
  df.upper = 20,
  split.w = FALSE,
  nr.chains = 2,
  nr.iterations = 10000,
  nr.adapt = 1000,
  nr.burnin = 1000,
  nr.thin = 1,
  be.quiet = FALSE,
  r2jags = TRUE
)

Arguments

Details

The number of events in the control and treated group are modeled with two conditional Binomial distributions and the random-effects are based on a bivariate scale mixture of Normals.

The individual participant data is modeled as a Bayesian logistic regression for participants in the control group. Coefficients in the regression are modeled as exchangeables.

The function calculates the implicit hierarchical meta-regression, where the treatment effect is regressed to the baseline risk (rate of events in the control group). The scale mixture weights are used to adjust for internal validity and structural outliers identification.

The implicit hierarchical meta-regression is used to predict the treatment effect for subgroups of individual participant data.

Computations are done by calling JAGS (Just Another Gibbs Sampler) to perform MCMC (Markov Chain Monte Carlo) sampling and returning an object of the class mcmc.list.

Installation of JAGS: It is important to note that R 3.3.0 introduced a major change in the use of toolchain for Windows. This new toolchain is incompatible with older packages written in C++. As a consequence, if the installed version of JAGS does not match the R installation, then the rjags package will spontaneously crash. Therefore, if a user works with R version >= 3.3.0, then JAGS must be installed with the installation program JAGS-4.2.0-Rtools33.exe. For users who continue using R 3.2.4 or an earlier version, the installation program for JAGS is the default installer JAGS-4.2.0.exe.

Value

This function returns an object of the class "hmr". This object contains the MCMC output of each parameter and hyper-parameter in the model, the data frame used for fitting the model, the link function, type of random effects distribution and the splitting information for conflict of evidence analysis.

The results of the object of the class metadiag can be extracted with R2jags or with rjags. In addition a summary, a print and a plot function are implemented for this type of object.

References

Verde, P.E, Ohmann, C., Icks, A. and Morbach, S. (2016) Bayesian evidence synthesis and combining randomized and nonrandomized results: a case study in diabetes. Statistics in Medicine. Volume 35, Issue 10, 10 May 2016, Pages: 1654 to 1675.

Verde, P.E. (2017) The hierarchical meta-regression approach and learning from clinical evidence. Submited to the Biometrical Journal.

Verde, P.E. (2018) The Hierarchical Meta-Regression Approach and Learning from Clinical Evidence. Technical report.

Examples

## Not run: 
library(jarbes)

data("healing")
AD <- healing[, c("y_c", "n_c", "y_t", "n_t")]

data("healingipd")

IPD <- healingipd[, c("healing.without.amp", "PAD", "neuropathy",
"first.ever.lesion", "no.continuous.care", "male", "diab.typ2",
"insulin", "HOCHD", "HOS", "CRF", "dialysis", "DNOAP", "smoking.ever",
"diabdur", "wagner.class")]

mx2 <- hmr(AD, two.by.two = FALSE,
           dataIPD = IPD,
           re = "sm",
           link = "logit",
           sd.mu.1 = 2,
           sd.mu.2 = 2,
           sd.mu.phi = 2,
           sigma.1.upper = 5,
           sigma.2.upper = 5,
           sigma.beta.upper = 5,
           sd.Fisher.rho = 1.25,
           df.estimate = FALSE,
           df.lower = 3,
           df.upper = 10,
           nr.chains = 1,
           nr.iterations = 1500,
           nr.adapt = 100,
           nr.thin = 1)

print(mx2)




# This experiment corresponds to Section 4 in Verde (2018).
#
# Experiment: Combining aggretated data from RCTs and a single
# observational study with individual participant data.
#
# In this experiment we assess conflict of evidence between the RCTs
# and the observational study with a partially identified parameter
# mu.phi.
#
# We run two simulated data: 1) mu.phi = 0.5 which is diffucult to
# identify. 2) mu.phi = 2 which can be identify. The simulations are
# used to see if the hmr() function can recover mu.phi.
#


library(MASS)
library(ggplot2)
library(jarbes)
library(gridExtra)
library(mcmcplots)


# Simulation of the IPD data

invlogit <- function (x)
{
 1/(1 + exp(-x))
}

# Data set for mu.phi = 0.5 .........................................

# Parameters values
mu.phi.true <- 0.5
beta0 <- mu.1.true + mu.phi.true
beta1 <- 2.5
beta2 <- 2

# Regression variables

x1 <- rnorm(200)
x2 <- rbinom(200, 1, 0.5)

# Binary outcome as a function of "b0 + b1 * x1 + b2 * x2"

y <- rbinom(200, 1,
         invlogit(beta0 + beta1 * x1 + beta2 * x2))


# Preparing the plot to visualize the data
jitter.binary <- function(a, jitt = 0.05)

 ifelse(a==0, runif(length(a), 0, jitt),
        runif(length(a), 1-jitt, 1))



plot(x1, jitter.binary(y), xlab = "x1",
    ylab = "Success probability")

curve(invlogit(beta0 + beta1*x),
     from = -2.5, to = 2.5, add = TRUE, col ="blue", lwd = 2)
curve(invlogit(beta0 + beta1*x + beta2),
     from = -2.5, to = 2.5, add = TRUE, col ="red", lwd =2)
legend("bottomright", c("b2 = 0", "b2 = 2"),
      col = c("blue", "red"), lwd = 2, lty = 1)

noise <- rnorm(100*20)
dim(noise) <- c(100, 20)
n.names <- paste(rep("x", 20), seq(3, 22), sep="")
colnames(noise) <- n.names

data.IPD <- data.frame(y, x1, x2, noise)


# Application of HMR ...........................................

res.s2 <- hmr(AD.s1, two.by.two = FALSE,
             dataIPD = data.IPD,
             sd.mu.1 = 2,
             sd.mu.2 = 2,
             sd.mu.phi = 2,
             sigma.1.upper = 5,
             sigma.2.upper = 5,
             sd.Fisher.rho = 1.5)


print(res.s2)

# Data set for mu.phi = 2 ......................................
# Parameters values

mu.phi.true <- 2
beta0 <- mu.1.true + mu.phi.true
beta1 <- 2.5
beta2 <- 2

# Regression variables
x1 <- rnorm(200)
x2 <- rbinom(200, 1, 0.5)
# Binary outcome as a function of "b0 + b1 * x1 + b2 * x2"
y <- rbinom(200, 1,
           invlogit(beta0 + beta1 * x1 + beta2 * x2))

# Preparing the plot to visualize the data
jitter.binary <- function(a, jitt = 0.05)

 ifelse(a==0, runif(length(a), 0, jitt),
        runif(length(a), 1-jitt, 1))

plot(x1, jitter.binary(y), xlab = "x1",
    ylab = "Success probability")

curve(invlogit(beta0 + beta1*x),
     from = -2.5, to = 2.5, add = TRUE, col ="blue", lwd = 2)
curve(invlogit(beta0 + beta1*x + beta2),
     from = -2.5, to = 2.5, add = TRUE, col ="red", lwd =2)
legend("bottomright", c("b2 = 0", "b2 = 2"),
      col = c("blue", "red"), lwd = 2, lty = 1)

noise <- rnorm(100*20)
dim(noise) <- c(100, 20)
n.names <- paste(rep("x", 20), seq(3, 22), sep="")
colnames(noise) <- n.names

data.IPD <- data.frame(y, x1, x2, noise)

# Application of HMR ................................................


res.s3 <- hmr(AD.s1, two.by.two = FALSE,
             dataIPD = data.IPD,
             sd.mu.1 = 2,
             sd.mu.2 = 2,
             sd.mu.phi = 2,
             sigma.1.upper = 5,
             sigma.2.upper = 5,
             sd.Fisher.rho = 1.5
)

print(res.s3)

# Posteriors for mu.phi ............................
attach.jags(res.s2)
mu.phi.0.5 <- mu.phi
df.phi.05 <- data.frame(x = mu.phi.0.5)

attach.jags(res.s3)
mu.phi.1 <- mu.phi
df.phi.1 <- data.frame(x = mu.phi.1)


p1 <- ggplot(df.phi.05, aes(x=x))+
 xlab(expression(mu[phi])) +
 ylab("Posterior distribution")+
 xlim(c(-7,7))+
 geom_histogram(aes(y=..density..),fill = "royalblue",
              colour = "black", alpha= 0.4, bins=60) +
 geom_vline(xintercept = 0.64, colour = "black", size = 1.7, lty = 2)+
 geom_vline(xintercept = 0.5, colour = "black", size = 1.7, lty = 1)+
 stat_function(fun = dlogis,
               n = 101,
               args = list(location = 0, scale = 1), size = 1.5) + theme_bw()

p2 <- ggplot(df.phi.1, aes(x=x))+
 xlab(expression(mu[phi])) +
 ylab("Posterior distribution")+
 xlim(c(-7,7))+
 geom_histogram(aes(y=..density..),fill = "royalblue",
                colour = "black", alpha= 0.4, bins=60) +
 geom_vline(xintercept = 2.2, colour = "black", size = 1.7, lty = 2)+
 geom_vline(xintercept = 2, colour = "black", size = 1.7, lty = 1)+
 stat_function(fun = dlogis,
               n = 101,
               args = list(location = 0, scale = 1), size = 1.5) + theme_bw()

grid.arrange(p1, p2, ncol = 2, nrow = 1)


# Cater plots for regression coefficients ...........................

var.names <- names(data.IPD[-1])
v <- paste("beta", names(data.IPD[-1]), sep = ".")
mcmc.x <- as.rjags.mcmc(res.s2$BUGSoutput$sims.matrix)
mcmc.x.2 <- as.mcmc.rjags(res.s2)
mcmc.x.3 <- as.mcmc.rjags(res.s3)

greek.names <- paste(paste("beta[",1:22, sep=""),"]", sep="")
par.names <- paste(paste("beta.IPD[",1:22, sep=""),"]", sep="")

caterplot(mcmc.x.2,
         parms = par.names,
         col = "black", lty = 1,
         labels = greek.names,
         greek = T,
         labels.loc="axis", cex =0.7,
         style = "plain",reorder = F, denstrip = F)

caterplot(mcmc.x.3,
         parms = par.names,
         col = "grey", lty = 2,
         labels = greek.names,
         greek = T,
         labels.loc="axis", cex =0.7,
         style = "plain",reorder = F, denstrip = F,
         add = TRUE,
         collapse=TRUE, cat.shift=-0.5)



abline(v=0, lty = 2, lwd = 2)
abline(v =2, lty = 2, lwd = 2)
abline(v =2.5, lty = 2, lwd = 2)

# End of the examples.


## End(Not run)

Description

This function performers a Bayesian meta-analysis to analyse heterogeneity of the treatment effect as a function of the baseline risk. The function fits a hierarchical meta-regression model based on a bivariate random effects model.

Usage

metarisk(
  data,
  two.by.two = TRUE,
  re = "normal",
  link = "logit",
  mean.mu.1 = 0,
  mean.mu.2 = 0,
  sd.mu.1 = 1,
  sd.mu.2 = 1,
  sigma.1.upper = 5,
  sigma.2.upper = 5,
  mean.Fisher.rho = 0,
  sd.Fisher.rho = 1/sqrt(2),
  df = 4,
  df.estimate = FALSE,
  df.lower = 3,
  df.upper = 20,
  split.w = FALSE,
  nr.chains = 2,
  nr.iterations = 10000,
  nr.adapt = 1000,
  nr.burnin = 1000,
  nr.thin = 1,
  be.quiet = FALSE,
  r2jags = TRUE
)

Arguments

Details

The number of events in the control and treated group are modeled with two conditional Binomial distributions and the random-effects are based on a bivariate scale mixture of Normals.

The function calculates the implicit hierarchical meta-regression, where the treatment effect is regressed to the baseline risk (rate of events in the control group). The scale mixture weights are used to adjust for internal validity and structural outliers identification.

Computations are done by calling JAGS (Just Another Gibbs Sampler) to perform MCMC (Markov Chain Monte Carlo) sampling and returning an object of the class mcmc.list.

Value

This function returns an object of the class "metarisk". This object contains the MCMC output of each parameter and hyper-parameter in the model, the data frame used for fitting the model, the link function, type of random effects distribution and the splitting information for conflict of evidence analysis.

The results of the object of the class metadiag can be extracted with R2jags or with rjags. In addition a summary, a print and a plot functions are implemented for this type of object.

References

Verde, P.E. and Curcio, D. (2019) Hierarchical Meta-Regression Modelling: The Case of The Pneumococcal Polysaccharide Vaccine. Technical Report.

Verde, P.E. (2019) The hierarchical meta-regression approach and learning from clinical evidence. Biometrical Journal. 1 - 23.

Verde, P. E. (2017) Two Examples of Bayesian Evidence Synthesis with the Hierarchical Meta-Regression Approach. Chap.9, pag 189-206. Bayesian Inference, ed. Tejedor, Javier Prieto. InTech.

Examples

## Not run: 
library(jarbes)

# This example is used to test the function and it runs in about 5 seconds.
# In a real application you must increase the number of MCMC interations.
# For example use: nr.burnin = 5000 and nr.iterations = 20000





# The following examples corresponds to Section 4 in Verde (2019).
# These are simulated examples to investigate internal and
# external validity bias in meta-analysis.


library(MASS)
library(ggplot2)
library(jarbes)
library(gridExtra)
library(mcmcplots)

#Experiment 1: External validity bias

set.seed(2018)
# mean control
pc <- 0.7
# mean treatment
pt <- 0.4
# reduction of "amputations" odds ratio
OR <- (pt/(1-pt)) /(pc/(1-pc))
OR

# mu_2
log(OR)
mu.2.true <- log(OR)
#sigma_2
sigma.2.true <- 0.5
# mu_1
mu.1.true <- log(pc/(1-pc))
mu.1.true
#sigma_1
sigma.1.true <- 1
# rho
rho.true <- -0.5
Sigma <- matrix(c(sigma.1.true^2, sigma.1.true*sigma.2.true*rho.true,
                 sigma.1.true*sigma.2.true*rho.true, sigma.2.true^2),
                 byrow = TRUE, ncol = 2)
Sigma

theta <- mvrnorm(35, mu = c(mu.1.true, mu.2.true),
                Sigma = Sigma )


plot(theta, xlim = c(-2,3))
abline(v=mu.1.true, lty = 2)
abline(h=mu.2.true, lty = 2)
abline(a = mu.2.true, b=sigma.2.true/sigma.1.true * rho.true, col = "red")
abline(lm(theta[,2]~theta[,1]), col = "blue")

# Target group
mu.T <- mu.1.true + 2 * sigma.1.true
abline(v=mu.T, lwd = 3, col = "blue")
eta.true <- mu.2.true + sigma.2.true/sigma.1.true*rho.true* mu.T
eta.true
exp(eta.true)
abline(h = eta.true, lty =3, col = "blue")
# Simulation of each primary study:
n.c <- round(runif(35, min = 30, max = 60),0)
n.t <- round(runif(35, min = 30, max = 60),0)
y.c <- y.t <- rep(0, 35)
p.c <- exp(theta[,1])/(1+exp(theta[,1]))
p.t <- exp(theta[,2]+theta[,1])/(1+exp(theta[,2]+theta[,1]))
for(i in 1:35)
{
 y.c[i] <- rbinom(1, n.c[i], prob = p.c[i])
 y.t[i] <- rbinom(1, n.t[i], prob = p.t[i])
}

AD.s1 <- data.frame(yc=y.c, nc=n.c, yt=y.t, nt=n.t)

##########################################################
incr.e <- 0.05
incr.c <- 0.05
n11 <- AD.s1$yt
n12 <- AD.s1$yc
n21 <- AD.s1$nt - AD.s1$yt
n22 <- AD.s1$nc - AD.s1$yc
AD.s1$TE <- log(((n11 + incr.e)*(n22 + incr.c))/((n12 + incr.e) * (n21 + incr.c)))
AD.s1$seTE <- sqrt((1/(n11 + incr.e) + 1/(n12 + incr.e) +
                     1/(n21 + incr.c) + 1/(n22 + incr.c)))

Pc <- ((n12 + incr.c)/(AD.s1$nc + 2*incr.c))

AD.s1$logitPc <- log(Pc/(1-Pc))

AD.s1$Ntotal <- AD.s1$nc + AD.s1$nt
rm(list=c("Pc", "n11","n12","n21","n22","incr.c", "incr.e"))


dat.points <- data.frame(TE = AD.s1$TE, logitPc = AD.s1$logitPc, N.total = AD.s1$Ntotal)
###################################################################

res.s1 <- metarisk(AD.s1, two.by.two = FALSE, sigma.1.upper = 5,
                  sigma.2.upper = 5,
                  sd.Fisher.rho = 1.5)

print(res.s1, digits = 4)


library(R2jags)
attach.jags(res.s1)
eta.hat <- mu.2 + rho*sigma.2/sigma.1*(mu.T - mu.1)
mean(eta.hat)
sd(eta.hat)

OR.eta.hat <- exp(eta.hat)
mean(OR.eta.hat)
sd(OR.eta.hat)
quantile(OR.eta.hat, probs = c(0.025, 0.5, 0.975))

ind.random <- sample(1:18000, size = 80, replace = FALSE)

##############################################################
p1 <- ggplot(dat.points, aes(x = logitPc, y = TE, size = N.total)) +
      xlab("logit Baseline Risk")+
      ylab("log(Odds Ratio)")+
      geom_point(shape = 21, colour = "blue") + scale_size_area(max_size=10)+
      scale_x_continuous(name= "Rate of The Control Group (logit scale)",
                       limits=c(-2, 5)) +
     geom_vline(xintercept = mu.T, colour = "blue", size = 1, lty = 1) +
       geom_hline(yintercept = eta.true, colour = "blue", size = 1, lty = 1)+
         geom_abline(intercept=beta.0[ind.random],
                   slope=beta.1[ind.random],alpha=0.3,
                   colour = "grey", size = 1.3, lty = 2)+
         geom_abline(intercept = mean(beta.0[ind.random]),
         slope = mean(beta.1[ind.random]),
         colour = "black", size = 1.3, lty = 2)+
     geom_abline(intercept = mu.2.true, slope = sigma.2.true/sigma.1.true * rho.true,
     colour = "blue", size = 1.2)+ theme_bw()


# Posterior of eta.hat

eta.df <- data.frame(x = OR.eta.hat)


p2 <- ggplot(eta.df, aes(x = x)) +
 xlab("Odds Ratio") +
 ylab("Posterior distribution")+
 geom_histogram(fill = "royalblue", colour = "black", alpha= 0.4, bins=80) +
 geom_vline(xintercept = exp(eta.true), colour = "black", size = 1.7, lty = 1)+
 geom_vline(xintercept = c(0.28, 0.22, 0.35), colour = "black", size = 1, lty = 2)+
 theme_bw()


grid.arrange(p1, p2, ncol = 2, nrow = 1)

#Experiment 2: Internal validity bias and assesing conflict of evidence between the RCTs.


set.seed(2018)
ind <- sample(1:35, size=5, replace = FALSE)
ind
AD.s4.contaminated <- AD.s1[ind,1:4]
AD.s4.contaminated$yc <- AD.s1$yt[ind]
AD.s4.contaminated$yt <- AD.s1$yc[ind]
AD.s4.contaminated$nc <- AD.s1$nt[ind]
AD.s4.contaminated$nt <- AD.s1$nc[ind]
AD.s4.contaminated <- rbind(AD.s4.contaminated,
                         AD.s1[-ind,1:4])

res.s4 <- metarisk(AD.s4.contaminated,
                   two.by.two = FALSE,
                   re = "sm",
                   sigma.1.upper = 3,
                   sigma.2.upper = 3,
                   sd.Fisher.rho = 1.5,
                   df.estimate = TRUE)

print(res.s4, digits = 4)

attach.jags(res.s4)

w.s <- apply(w, 2, median)
w.u <- apply(w, 2, quantile, prob = 0.75)
w.l <- apply(w, 2, quantile, prob = 0.25)

studies <- c(ind,c(1,3,4,5,6,8,9,10,11,13,14,17,18,19,20:35))


dat.weights <- data.frame(x = studies,
                         y = w.s,
                        ylo  = w.l,
                        yhi  = w.u)

# Outliers:
w.col <- studies %in% ind
w.col.plot <- ifelse(w.col, "black", "grey")
w.col.plot[c(9,17, 19,27,34,35)] <- "black"

w.plot <- function(d){
  # d is a data frame with 4 columns
  # d$x gives variable names
  # d$y gives center point
  # d$ylo gives lower limits
  # d$yhi gives upper limits

  p <- ggplot(d, aes(x=x, y=y, ymin=ylo, ymax=yhi) )+
       geom_pointrange( colour=w.col.plot, lwd =0.8)+
       coord_flip() + geom_hline(yintercept = 1, lty=2)+
       xlab("Study ID") +
       ylab("Scale mixture weights") + theme_bw()
       return(p)}

w.plot(dat.weights)

#List of other possible statistical models:
#    1) Different link functions: logit, cloglog and probit

#    2) Different random effects distributions:
#       "normal" or "sm = scale mixtures".

#    3) For the scale mixture random effects:
#       split.w = TRUE => "split the weights".

#    4) For the scale mixture random effects:
#       df.estimate = TRUE => "estimate the degrees of freedom".

#    5) For the scale mixture random effects:
#       df.estimate = TRUE => "estimate the degrees of freedom".

#    6) For the scale mixture random effects:
#       df = 4 => "fix the degrees of freedom to a particual value".
#       Note that df = 1 fits a Cauchy bivariate distribution to
#       the random effects.
#End of the examples


## End(Not run)

Description

Generic plot function for bcmeta object in jarbes.

Generic plot function for b3lmeta object in jarbes.

Usage

## S3 method for class 'b3lmeta'
plot(
  x,
  x.lim = c(-3, 3),
  y.lim = c(0, 2.7),
  x.lab = "Treatment Effect: log(OR)",
  y.lab = "Posterior",
  title.plot.1 = "Mean Design Components",
  title.plot.2 = "Three Levels Bayesian Meta-Analysis",
  ...
)

## S3 method for class 'b3lmeta'
plot(
  x,
  x.lim = c(-3, 3),
  y.lim = c(0, 2.7),
  x.lab = "Treatment Effect: log(OR)",
  y.lab = "Posterior",
  title.plot.1 = "Mean Design Components",
  title.plot.2 = "Three Levels Bayesian Meta-Analysis",
  ...
)

Arguments

Description

Generic plot function for bcdpmeta object in jarbes.

Usage

## S3 method for class 'bcdpmeta'
plot(
  x,
  x.lim = c(-3, 3),
  y.lim = c(0, 2),
  x.lab = "Treatment Effect: log(OR)",
  y.lab = "Posterior",
  title.plot.1 = "Model Components",
  title.plot.2 = "Bias Corrected Meta-Analysis",
  ...
)

Arguments

Description

Generic plot function for bcmeta object in jarbes.

Usage

## S3 method for class 'bcmeta'
plot(
  x,
  x.lim = c(-3, 3),
  y.lim = c(0, 2),
  x.lab = "Treatment Effect: log(OR)",
  y.lab = "Posterior",
  title.plot.1 = "Model Components",
  title.plot.2 = "Bias Corrected Meta-Analysis",
  ...
)

Arguments

Description

Generic plot function for bmeta object in jarbes.

Usage

## S3 method for class 'bmeta'
plot(
  x,
  x.lim = c(-3, 3),
  y.lim = c(0, 2),
  x.lab = "Treatment Effect: log(OR)",
  y.lab = "Posterior",
  title.plot = "Simple Bayesian Meta-Analysis",
  ...
)

Arguments

Description

Generic plot function for bmeta object in jarbes.

Usage

## S3 method for class 'dpmeta'
plot(
  x,
  x.lim = c(-3, 3),
  y.lim = c(0, 2),
  x.lab = "Treatment Effect: log(OR)",
  y.lab = "Posterior",
  title.plot = "Simple Bayesian Meta-Analysis",
  ...
)

Arguments

Description

Generic plot function for dpmeta object in jarbes.

Usage

## S3 method for class 'dpmmeta'
plot(
  x,
  x.lim = c(-3, 3),
  y.lim = c(0, 2),
  x.lab = "Treatment Effect: log(OR)",
  y.lab = "Posterior",
  title.plot = "Simple Bayesian Meta-Analysis",
  ...
)

Arguments

Description

Generic plot function for hmr object in jarbes.

Usage

## S3 method for class 'hmr'
plot(
  x,
  x.lim = c(-5, 2.8),
  y.lim = c(-2, 1),
  x.lab = "Event rate of The Control Group (logit scale)",
  y.lab = "No improvement <- Effectiveness -> Improvement",
  title.plot = "HMR: Effectiveness Against Baseline Risk",
  AD.colour = "red",
  IPD.colour = "blue",
  Study.Types = c("AD-RCTs", "IPD-RWD"),
  ...
)

Arguments

Description

Generic plot function for metarisk object in jarbes.

Usage

## S3 method for class 'metarisk'
plot(
  x,
  x.lim = c(-5, 2.8),
  y.lim = c(-2, 1),
  x.lab = "Rate of The Control Group (logit scale)",
  y.lab = "No improvement <- Treatment effect -> Improvement",
  title.plot = "Treatment Effect Against Baseline Risk",
  ...
)

Arguments

Description

PPV23 (23-valent pneumococcal polysaccharide vaccine) with 16 Randomized Clinical Trials (RCTs); outcome variable CAP (community-acquired pneumonia).

This data frame corresponds to 16 randomized control trials (RCTs) reporting efficacy of the PPV (Pneumococcal Polysaccharide) vaccine in preventing CAP (community acquired pneumonia). The data frame contains the evaluation of Risk of Bias (RoB) of the trials and some study population characteristics.

Format

A matrix with 16 rows and 18 columns. Each row represents study results, the columns are:

Name_Year

Name of the first author and year.

Year

Year of publication.

yt

Number of infections in the intervention group.

nt

Number of patients in the intervention group.

yc

Number of infections in the control group.

nc

Number of patients in the control group.

TE

Treatment Effect as Log Odds Ratio.

seTE

Standard Error of the TE.

logitPc

Observed baseline rate in logit scale.

N

Total sample size.

Study_Design

Description of the study design.

Intervention

Type of vaccine used for itervention.

Valency

0 = PPV23; 1 = PPV-Other.

low_income

Indicates low income patients population with 0 = no; 1 = yes.

R1

Random sequence generation (selection bias: low;high;unclear.

R2

Allocation concealment (selection bias): low;high;unclear.

R3

Confounding: low;high;unclear.

R4

Blinding of participants and personnel (performace bias): low;high;unclear.

R5

Blinding of outcome assessment (detection bias): low;high;unclear.

R6

Incomplete outcome data (attrition bias): low;high;unclear.

R7

Selective reporting (reporting bias): low;high;unclear.

Participants

Comments on patients characteristics.

Source

The data were obtainded from: Moberley et al. (2013).

References

Moberley, S., Holden, J., Tatham, D., and Andrews, R. (2013), Vaccines for preventing pneumococcal infection in adults., Cochrane Database of Systematic Reviews, Issue 1. Art. No.: CD000422. DOI:10.1002/14651858.CD000422.pub3.

Verde, P.E. and Curcio, D. (2017) Hierarchical Meta-Regression Modelling: The Case of The Pneumococcal Polysaccharide Vaccine. Technical Report.

Description

PPV23 (23-valent pneumococcal polysaccharide vaccine) with 3 Randomized Clinical Trials; 5 Cohort Studies and 3 Case-Control Studies.

The outcome variable IPD (Invasive Pneumococcal Disease).

Format

A matrix with 11 rows and 6 columns. Each row represents study results, the columns are:

name

Name of the first author and year.

TE

Treatment Effect as Log Odds Ratio.

seTE

Standard Error of the TE.

n.v

Number of patients in the vaccination group.

n.c

Number of patients in the control group.

design

Description of the study design.

Source

The data were obtainded from: Falkenhorst et al. (2017).

References

Falkenhorst, G., Remschmidt, C., Harder, T., Hummers-Pradier, E., Wichmann, O., and Bogdan, C. (2017) Effectiveness of the 23-Valent Pneumococcal Polysaccharide Vaccine(PPV23) against Pneumococcal Disease in the Elderly: Systematic Review and Meta-Analysis. PLoS ONE 12(1): e0169368. doi:10.1371/journal.pone.0169368.

Verde, P.E. and Curcio, D. (2017) Hierarchical Meta-Regression Modelling: The Case of The Pneumococcal Polysaccharide Vaccine. Technical Report.

Description

Generic print function for bcmeta object in jarbes.

Usage

## S3 method for class 'b3lmeta'
print(x, digits, ...)

Arguments

Description

Generic print function for bcdpmeta object in jarbes.

Usage

## S3 method for class 'bcdpmeta'
print(x, digits, ...)

Arguments

Description

Generic print function for bcmeta object in jarbes.

Usage

## S3 method for class 'bcmeta'
print(x, digits, ...)

Arguments

Description

Generic print function for bcdpmeta object in jarbes.

Usage

## S3 method for class 'bcmixmeta'
print(x, digits, ...)

Arguments

Description

Generic print function for bcmeta object in jarbes.

Usage

## S3 method for class 'bmeta'
print(x, digits, ...)

Arguments

Description

Generic print function for dpmeta object in jarbes.

Usage

## S3 method for class 'dpmeta'
print(x, digits, ...)

Arguments

Description

Generic print function for dpmeta object in jarbes.

Usage

## S3 method for class 'dpmetareg'
print(x, digits, ...)

Arguments

Description

Generic print function for dpmmeta object in jarbes.

Usage

## S3 method for class 'dpmmeta'
print(x, digits, ...)

Arguments

Description

Generic print function for hmr object in jarbes.

Usage

## S3 method for class 'hmr'
print(x, digits = 3, intervals = c(0.025, 0.25, 0.5, 0.75, 0.975), ...)

Arguments

Description

Generic print function for metarisk object in jarbes.

Usage

## S3 method for class 'metarisk'
print(x, digits, ...)

Arguments

Description

Meta-analysis of 31 randomized controled 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.

Format

A matrix with 31 rows and 5 columns. Each row represents study results, the columns are:

trial

ID name of the trial.

effect.size

treatment effect is measured as the difference of the ejection fraction between groups, which measures the improvement of left ventricular function in the heart.

se.effect

Standard Error of the effect.size.

sample.size

Total number of patients in the trial.

n.discrep

Number of detected discrepancies in the published trial. Discrepancies are defined as two or more reported facts that cannot both be true because they are logically or mathematically incompatible.

Source

Nowbar, A N, et al. (2014) Discrepancies in autologous bone marrow stem cell trials and enhancemen of ejection fraction (DAMASCENE): weighted regression and meta-analysis. BMJ, 348,1-9.

References

Verde, P. E. (2017) Two Examples of Bayesian Evidence Synthesis with the Hierarchical Meta-Regression Approach. Chap.9, pag 189-206. Bayesian Inference, ed. Tejedor, Javier Prieto. InTech.

Description

Generic summary function for bmeta object in jarbes

Usage

## S3 method for class 'b3lmeta'
summary(object, digits = 3, ...)

Arguments

Description

Generic summary function for bcdpmeta object in jarbes

Usage

## S3 method for class 'bcdpmeta'
summary(object, digits = 3, ...)

Arguments

Description

Generic summary function for bcmeta object in jarbes

Usage

## S3 method for class 'bcmeta'
summary(object, digits = 3, ...)

Arguments

Description

Generic summary function for bmeta object in jarbes

Usage

## S3 method for class 'bmeta'
summary(object, digits = 3, ...)

Arguments

Description

Generic summary function for dpmmeta object in jarbes

Usage

## S3 method for class 'dpmeta'
summary(object, digits = 3, ...)

Arguments

Description

Generic summary function for dpmmeta object in jarbes

Usage

## S3 method for class 'dpmmeta'
summary(object, digits = 3, ...)

Arguments

Description

Generic summary function for hmr object in jarbes

Usage

## S3 method for class 'hmr'
summary(object, digits = 3, ...)

Arguments

Description

Generic summary function for metarisk object in jarbes

Usage

## S3 method for class 'metarisk'
summary(object, digits = 3, ...)

Arguments

Description

Meta-analysis of 22 Observational Studies from PubMed, Cochrane Library and SciELO databases that assessed the relationship of a positive ICPC (Isolated Choroid Plexus Cyst) on Trisomy 21

Format

A dataframe with 22 rows and 6 columns. Each row represents study results, the columns are:

year

Year of publication.

author

Principal author of the publication.

y

Number of cases of ICPC with Trisomy 21.

n

Total number o cases with ICPC.

mean.GA

Mean gestational time in weeks.

study.design

Study design: prospective or retrospective cohort.

Source

Kürten C, Knippel A, Verde P, Kozlowski P. A Bayesian risk analysis for Trisomy 21 in isolated choroid plexus cyst: combining a prenatal database with a meta-analysis. J Matern Fetal Neonatal Med. 2019 Jun 11:1-9. doi: 10.1080/14767058.2019.1622666. Epub ahead of print. PMID: 31113245.