GRADE_table: GRADE table

In meta2020/net.meta: The Functions for Network Meta Analysis

Description

To calculate the GRADE table.

Usage

GRADE_table(
  study.CM,
  bmt,
  nmt,
  rob,
  ind,
  report.bias,
  effect.size = c("diff", "ratio"),
  clinical.effect.size,
  model = c("Bayes", "Freq"),
  imput.global.p = FALSE
)

Arguments

study.CM	contribution matrix from sutdyCM_matrix
bmt	result from bayesian net-meta model_gemtc
nmt	result from frequentist net-meta model_netmmeta
rob	risk of bias
ind	indirectness
report.bias	reported bias
effect.size	two types: "diff": difference; "ratio": ratio
clinical.effect.size	clinical effect size
model	"Bayes": Bayesian net-meta model or
imput.global.p	whether to impute p value of inconsistency

Value

GRADE table matrix

Examples

LDT1 <- read.csv(system.file("extdata", "HR_SH_D.csv", package = "net.meta"))
trt1 <- read.table(system.file("extdata", "HR_SH_D.txt", package = "net.meta"),
    header=TRUE,quote = '"', stringsAsFactors=FALSE)

trt1$description <- factor(trt1$description, trt1$description)
LDT1$study <- factor(LDT1$study, unique(LDT1$study))

set.seed(1)
bmt1 <- model_gemtc(
long.data=LDT1,
id.treatments=trt1,
reference="A",
outcome="HR",
mtc.n.adapt = 500, mtc.n.iter = 1000, mtc.thin = 20)

nmt1 <- model_netmeta(long.data = LDT1,
treatment=LDT1$treatment,
id.treatments = trt1,
reference = "A",
outcome = "HR")

name1 <- NULL
for(i in 1:(length(trt1$id)-1)){
  name1 <- c(name1, paste0(trt1$id[i], ":", trt1$id[-(1:i)]))
}

study.CM1 <- studyCM_matrix(name1, nmt1)

study.assess1 <- read.csv(system.file("extdata", "HR_SH_A.csv", package = "net.meta"))

RB.comp1 <- rep(0, nrow(study.CM1)) #1 Yes, 0 no


RESULT.F1 <- GRADE_table(
  study.CM1,
  bmt=bmt1,
  nmt=nmt1,
  rob=study.assess1$ROB,
  ind=study.assess1$IND,
  report.bias=RB.comp1,
  effect.size = "ratio",
  clinical.effect.size=1.25,
  model="Freq")

RESULT.B1 <- GRADE_table(
  study.CM1,
  bmt1,
  nmt1,
  rob=study.assess1$ROB,  ## define Rob per study
  ind=study.assess1$IND,  ## define Indirectness per study
  report.bias=RB.comp1,
  effect.size = "ratio",
  clinical.effect.size=1.25,
  model="Bayes")

meta2020/net.meta documentation built on March 30, 2021, 7:31 p.m.