misc_code/main.R
In audrey-b/BUGSnet: Bayesian network meta-analyses in compliance with best practice and reporting guidelines
#source("misc_code/load.packages.R")
#source("R/functions.R")
pathA <-"C:/Users/audre/Documents/Compile BUGSnet/BUGSnet/"
sepA <- "/"
pathJ <- "C:\\Users\\justi\\Desktop\\Lighthouse\\nmapackage\\BUGSnet\\"
sepJ <- "\\"
path <- pathA
sep <- sepA
source(paste0(path,"misc_code",sep,"load.packages.R"))
library(devtools)
library(roxygen2)
library(gemtc)
library(knitr)
library(readxl)
load_all(path = paste0(path,"R"))
rawdata <- read_excel(paste0(path,"data","\\","rate_example.xlsx"))
rawdata <- read_excel("data/continuous_example.xlsx",
col_types = c("text", "numeric", "numeric",
"text", "text", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric"))
dich.slr <- data.prep(arm.data = rawdata,
varname.t = "trt_name",
varname.s = "trial")
# Import data --------------------------------------------------------
age <- rnorm(nrow(thrombolytic$data.ab), 60, 10)
dich.slr <- data.prep(arm.data = cbind(tbl_df(thrombolytic$data.ab),age),
varname.t = "treatment",
varname.s = "study")
#ensure that treatment and study variables are both of class character
dich.slr$arm.data$treatment <- as.character(dich.slr$arm.data$treatment)
dich.slr$arm.data$study <- as.character(dich.slr$arm.data$study)
pma(data = dich.slr,
name.trt1 = "SK",
name.trt2 = "Ret",
outcome = "responders",
N = "sampleSize")
# Network Characteristics -------------------------------------------------
net.plot(dich.slr, flag="PLCB", node.scale = 1.5, edge.scale=0.5, label.offset1 = 4, label.offset2 = 4)
network.char <- net.tab(data = dich.slr,
outcome = "responders",
N = "sampleSize",
type.outcome = "continuous",
time = NULL)
network.char$network
network.char$intervention
network.char$comparison
#NMA
fixed_effects_model <- nma.model(data=dich.slr,
outcome="responders",
N="sampleSize",
reference="SK",
type="consistency",
family="binomial",
link="logit",
effects="fixed")
random_effects_model <- nma.model(data=dich.slr,
outcome="responders",
N="sampleSize",
type="consistency",
reference="SK",
family="binomial",
link="logit",
effects="random")
sink("Z:/ResearchDocuments/Research/BUGSnet/code.bug")
cat(random_effects_model$bugs)
sink()
random_effects_results <- nma.run(random_effects_model,
monitor = c("d", "dev", "r", "n","totresdev","rhat","sigma","beta"),
n.adapt=1000,
n.burnin=1000,
n.iter=10000)
random_effects_fit <- nma.fit(random_effects_results, main = "Random Effects Model" )
random_effects_fit$DIC
random_effects_fit$pD
random_effects_fit$pmdev
sucra.out <- nma.rank(random_effects_results, largerbetter=FALSE, cov.value=NULL)
sucra.out$sucraplot
sucra.out$rankogram
nma.forest(random_effects_results,
comparator="SK",
central.tdcy = "median")
nma.league(random_effects_results,
central.tdcy = "median",
log.scale = TRUE)
#nma.regplot(random_effects_results, x.range=c(38,84))
# Network Plots -----------------------------------------------------------
#
# # Patient Characteristics plots--------------------------------------------
# source("baselines.plot.R")
#
# #png(filename="data/test_pdfs", res=1000, height=7500, width=10000)
# comp_graph_comparison(x=slr$baseline.data,
# trial.id = "trial",
# treat.id = "trt",
# measure.val = "age_estimate",
# measure.sd = "age_SD",
# x.lim = c(25,65),
# x.label = "Average Age (Years)" )
# #graphics.off()
#
# #png(filename="data/test_pdfs", res=1000, height=7500, width=10000)
# comp_graph_trial(data=slr$baseline.data,
# trial.id="trial",
# treat.id="trt",
# measure.val = "age_estimate",
# x.lim = c(25,65),
# x.label = "Average Age (Years)" )
# #graphics.off()
# Pairwise Comparisons ----------------------------------------------------
# NOTE: specify correct treatment: slr$treatments
# NOTE: specify correct oucome: colnames(slr$arm.data)
#
# source("pairwise.R")
# png("output/pairwise.plot.png", height=720, width=1920, res=200)
# pairwise.output <- pairwise(slr,
# name.trt1 = "Placebo",
# name.trt2 = "Vernakalant IV",
# outcome="r1",
# N="N",
# method = "MH",
# method.tau="DL",
# sm = "RR")
# graphics.off()
#
# tmp1 <- pairwise.output$summary
#
# pairwise.output2 <- pairwise(slr,
# name.trt1 = "Placebo",
# name.trt2 = "Vitamin D",
# outcome="o2",
# N="n2",
# method = "MH",
# method.tau="DL",
# sm = "OR")
#
# tmp2 <- pairwise.output2$summary
#
# # NOTE: okay to ignore warning message here
# pairwise.results <- bind_rows(tmp1, tmp2)
# rm(tmp1, tmp2)
#
# source("pairwise.all.R")
#
# pairwise.output.all <- pairwise.all(slr,
# outcome="r1",
# N="N",
# sm="RR")
#
# pairwise.output.all$se.effect %>% max
#
#
# # Nma with JAGS -----------------------------------------------------------
#
# # NOTE: turned off: print("The baseline treatment was ...")
#
# source("nma.bugs.R")
#
# makebugs <- nma.bugs(slr,
# outcome="r1",
# N="N",
# baseline.name="Placebo",
# family="binomial",
# link="logit",
# effects="random")
#
# makebugs
#
# source("nma.analysis.R")
#
# jagsoutput <- nma.analysis(makebugs,
# monitor = c("d"),
# n.adapt=10000,
# n.burnin=10000,
# n.iter=100000)
#
# jagsmodel <- jagsoutput$model
# jagssamples <- jagsoutput$samples
# jagstrtkey <- jagsoutput$trt.key
#
# # # trace plots
# #png("output/traceplot%02d.png")
# #plot(jagssamples)
# #graphics.off()
# #
# # summary(jagssamples)
#
# # Assess Model fit --------------------------------------------------------
# source("assess.model.fit.R")
#
#
#
# # League Table ------------------------------------------------------------
#
# source("league.table.R")
#
# league.out <- leaguetable(jagsoutput)
#
# leaguetable(jagsoutput, central.tdcy="mean")
#
# leaguetable(jagsoutput, central.tdcy="median", layout="long")
#
# write.csv(league.out, "output/leaguetable.csv")
#
#
#
#
# # SUCRA -------------------------------------------------------------------
#
# source("sucra.table.R")
#
# sucra.out <- sucra(jagsoutput, largerbetter=FALSE)
#
# sucra.out.table <- sucra.out$s.table
#
# sucra.out.plot <- sucra.out$s.plot
#
# write.csv(sucra.out.table, "output/sucra.csv")
#
# png("output/sucra.png", height=1080, width=1920, res=250)
# plot(sucra.out.plot)
# graphics.off()
#
#
#
# # NMA Forest Plot --------------------------------------------------------
#
# # NOTE: make sure you use the correct base.trt val
#
# source("nma.forestplot.R")
#
forest.out <- nma.forest(fixed_effects_results, comparator="SK")
#
# nma.forestplot(jagsoutput, central.tdcy="median", base.trt="Placebo")
#
# #nma.forestplot(jagsoutput, base.trt="2", line.size=1.5)
#
# #png("output/forest.png", width=900, height=500)
# #plot(forest.out)
# #graphics.off()3
#
# # Inconsistency pairwise vs nma ------------------------------------------
#
# source("inconsistency.PMA.R")
#
# inconsistency1 <- inconsistency.PMA(slr=slr,
# jagsoutput=jagsoutput,
# base.trt="treatment 1",
# model="random",
# central.tdcy="median",
# outcome="r1",
# N="N")
#
# inconsistency1$table
# incons.plot.pma <- inconsistency1$plot
#
# write.csv(inconsistency1$table, file = "output/inconsistencyPMA.csv")
#
#
# png("output/inconsistencyPMA.png", height=1080, width=1920, res=200)
# plot(incons.plot.pma)
# graphics.off()
#
#
# # Inconsistency Model TSD4-------------------------------------------------
#
# source("nma.bugs.R")
# source("nma.analysis.R")
#
# makebugsincons <- nma.bugs(slr,
# outcome="r1",
# N="N",
# baseline.name="treatment 1",
# family="binomial",
# link="logit",
# effects="random",
# type="inconsistency")
#
# inconsistency <- nma.analysis(makebugsincons,
# n.adapt=10000,
# n.burnin=10000,
# n.iter=100000)
#
# jags.incons.model <- inconsistency$model
# jags.incons.samples <- inconsistency$samples
# jags.incons.trtkey <- inconsistency$trt.key
#
# png("output/traceplot-incons.png")
# plot(jags.incons.samples)
# graphics.off()
# #
# # summary.incons <- summary(jags.incons.samples)
#
# source("inconsistency.model.R")
#
# incons.results <- inconsistency.model(inconsistency, jagsoutput)
#
# png("output/inconsistencymodel.png", height=1080, width=1920, res=250)
# plot(incons.results)
# graphics.off()
audrey-b/BUGSnet documentation built on Feb. 2, 2025, 5:10 p.m.
R Package Documentation
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#source("misc_code/load.packages.R")
#source("R/functions.R")
pathA <-"C:/Users/audre/Documents/Compile BUGSnet/BUGSnet/"
sepA <- "/"
pathJ <- "C:\\Users\\justi\\Desktop\\Lighthouse\\nmapackage\\BUGSnet\\"
sepJ <- "\\"
path <- pathA
sep <- sepA
source(paste0(path,"misc_code",sep,"load.packages.R"))
library(devtools)
library(roxygen2)
library(gemtc)
library(knitr)
library(readxl)
load_all(path = paste0(path,"R"))
rawdata <- read_excel(paste0(path,"data","\\","rate_example.xlsx"))
rawdata <- read_excel("data/continuous_example.xlsx",
col_types = c("text", "numeric", "numeric",
"text", "text", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric"))
dich.slr <- data.prep(arm.data = rawdata,
varname.t = "trt_name",
varname.s = "trial")
# Import data --------------------------------------------------------
age <- rnorm(nrow(thrombolytic$data.ab), 60, 10)
dich.slr <- data.prep(arm.data = cbind(tbl_df(thrombolytic$data.ab),age),
varname.t = "treatment",
varname.s = "study")
#ensure that treatment and study variables are both of class character
dich.slr$arm.data$treatment <- as.character(dich.slr$arm.data$treatment)
dich.slr$arm.data$study <- as.character(dich.slr$arm.data$study)
pma(data = dich.slr,
name.trt1 = "SK",
name.trt2 = "Ret",
outcome = "responders",
N = "sampleSize")
# Network Characteristics -------------------------------------------------
net.plot(dich.slr, flag="PLCB", node.scale = 1.5, edge.scale=0.5, label.offset1 = 4, label.offset2 = 4)
network.char <- net.tab(data = dich.slr,
outcome = "responders",
N = "sampleSize",
type.outcome = "continuous",
time = NULL)
network.char$network
network.char$intervention
network.char$comparison
#NMA
fixed_effects_model <- nma.model(data=dich.slr,
outcome="responders",
N="sampleSize",
reference="SK",
type="consistency",
family="binomial",
link="logit",
effects="fixed")
random_effects_model <- nma.model(data=dich.slr,
outcome="responders",
N="sampleSize",
type="consistency",
reference="SK",
family="binomial",
link="logit",
effects="random")
sink("Z:/ResearchDocuments/Research/BUGSnet/code.bug")
cat(random_effects_model$bugs)
sink()
random_effects_results <- nma.run(random_effects_model,
monitor = c("d", "dev", "r", "n","totresdev","rhat","sigma","beta"),
n.adapt=1000,
n.burnin=1000,
n.iter=10000)
random_effects_fit <- nma.fit(random_effects_results, main = "Random Effects Model" )
random_effects_fit$DIC
random_effects_fit$pD
random_effects_fit$pmdev
sucra.out <- nma.rank(random_effects_results, largerbetter=FALSE, cov.value=NULL)
sucra.out$sucraplot
sucra.out$rankogram
nma.forest(random_effects_results,
comparator="SK",
central.tdcy = "median")
nma.league(random_effects_results,
central.tdcy = "median",
log.scale = TRUE)
#nma.regplot(random_effects_results, x.range=c(38,84))
# Network Plots -----------------------------------------------------------
#
# # Patient Characteristics plots--------------------------------------------
# source("baselines.plot.R")
#
# #png(filename="data/test_pdfs", res=1000, height=7500, width=10000)
# comp_graph_comparison(x=slr$baseline.data,
# trial.id = "trial",
# treat.id = "trt",
# measure.val = "age_estimate",
# measure.sd = "age_SD",
# x.lim = c(25,65),
# x.label = "Average Age (Years)" )
# #graphics.off()
#
# #png(filename="data/test_pdfs", res=1000, height=7500, width=10000)
# comp_graph_trial(data=slr$baseline.data,
# trial.id="trial",
# treat.id="trt",
# measure.val = "age_estimate",
# x.lim = c(25,65),
# x.label = "Average Age (Years)" )
# #graphics.off()
# Pairwise Comparisons ----------------------------------------------------
# NOTE: specify correct treatment: slr$treatments
# NOTE: specify correct oucome: colnames(slr$arm.data)
#
# source("pairwise.R")
# png("output/pairwise.plot.png", height=720, width=1920, res=200)
# pairwise.output <- pairwise(slr,
# name.trt1 = "Placebo",
# name.trt2 = "Vernakalant IV",
# outcome="r1",
# N="N",
# method = "MH",
# method.tau="DL",
# sm = "RR")
# graphics.off()
#
# tmp1 <- pairwise.output$summary
#
# pairwise.output2 <- pairwise(slr,
# name.trt1 = "Placebo",
# name.trt2 = "Vitamin D",
# outcome="o2",
# N="n2",
# method = "MH",
# method.tau="DL",
# sm = "OR")
#
# tmp2 <- pairwise.output2$summary
#
# # NOTE: okay to ignore warning message here
# pairwise.results <- bind_rows(tmp1, tmp2)
# rm(tmp1, tmp2)
#
# source("pairwise.all.R")
#
# pairwise.output.all <- pairwise.all(slr,
# outcome="r1",
# N="N",
# sm="RR")
#
# pairwise.output.all$se.effect %>% max
#
#
# # Nma with JAGS -----------------------------------------------------------
#
# # NOTE: turned off: print("The baseline treatment was ...")
#
# source("nma.bugs.R")
#
# makebugs <- nma.bugs(slr,
# outcome="r1",
# N="N",
# baseline.name="Placebo",
# family="binomial",
# link="logit",
# effects="random")
#
# makebugs
#
# source("nma.analysis.R")
#
# jagsoutput <- nma.analysis(makebugs,
# monitor = c("d"),
# n.adapt=10000,
# n.burnin=10000,
# n.iter=100000)
#
# jagsmodel <- jagsoutput$model
# jagssamples <- jagsoutput$samples
# jagstrtkey <- jagsoutput$trt.key
#
# # # trace plots
# #png("output/traceplot%02d.png")
# #plot(jagssamples)
# #graphics.off()
# #
# # summary(jagssamples)
#
# # Assess Model fit --------------------------------------------------------
# source("assess.model.fit.R")
#
#
#
# # League Table ------------------------------------------------------------
#
# source("league.table.R")
#
# league.out <- leaguetable(jagsoutput)
#
# leaguetable(jagsoutput, central.tdcy="mean")
#
# leaguetable(jagsoutput, central.tdcy="median", layout="long")
#
# write.csv(league.out, "output/leaguetable.csv")
#
#
#
#
# # SUCRA -------------------------------------------------------------------
#
# source("sucra.table.R")
#
# sucra.out <- sucra(jagsoutput, largerbetter=FALSE)
#
# sucra.out.table <- sucra.out$s.table
#
# sucra.out.plot <- sucra.out$s.plot
#
# write.csv(sucra.out.table, "output/sucra.csv")
#
# png("output/sucra.png", height=1080, width=1920, res=250)
# plot(sucra.out.plot)
# graphics.off()
#
#
#
# # NMA Forest Plot --------------------------------------------------------
#
# # NOTE: make sure you use the correct base.trt val
#
# source("nma.forestplot.R")
#
forest.out <- nma.forest(fixed_effects_results, comparator="SK")
#
# nma.forestplot(jagsoutput, central.tdcy="median", base.trt="Placebo")
#
# #nma.forestplot(jagsoutput, base.trt="2", line.size=1.5)
#
# #png("output/forest.png", width=900, height=500)
# #plot(forest.out)
# #graphics.off()3
#
# # Inconsistency pairwise vs nma ------------------------------------------
#
# source("inconsistency.PMA.R")
#
# inconsistency1 <- inconsistency.PMA(slr=slr,
# jagsoutput=jagsoutput,
# base.trt="treatment 1",
# model="random",
# central.tdcy="median",
# outcome="r1",
# N="N")
#
# inconsistency1$table
# incons.plot.pma <- inconsistency1$plot
#
# write.csv(inconsistency1$table, file = "output/inconsistencyPMA.csv")
#
#
# png("output/inconsistencyPMA.png", height=1080, width=1920, res=200)
# plot(incons.plot.pma)
# graphics.off()
#
#
# # Inconsistency Model TSD4-------------------------------------------------
#
# source("nma.bugs.R")
# source("nma.analysis.R")
#
# makebugsincons <- nma.bugs(slr,
# outcome="r1",
# N="N",
# baseline.name="treatment 1",
# family="binomial",
# link="logit",
# effects="random",
# type="inconsistency")
#
# inconsistency <- nma.analysis(makebugsincons,
# n.adapt=10000,
# n.burnin=10000,
# n.iter=100000)
#
# jags.incons.model <- inconsistency$model
# jags.incons.samples <- inconsistency$samples
# jags.incons.trtkey <- inconsistency$trt.key
#
# png("output/traceplot-incons.png")
# plot(jags.incons.samples)
# graphics.off()
# #
# # summary.incons <- summary(jags.incons.samples)
#
# source("inconsistency.model.R")
#
# incons.results <- inconsistency.model(inconsistency, jagsoutput)
#
# png("output/inconsistencymodel.png", height=1080, width=1920, res=250)
# plot(incons.results)
# graphics.off()
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