Pregnancy All

#### IMPORT LIBRARIES ####
rm(list = ls())
library(brms)
library(dplyr) 
library(ggplot2)
library(rethinking)
library(dmetar)
library(meta)
library(tidybayes)
library(ggridges)
library(grid)
library(gridExtra)
library(glue)
library(stringr)
library(forcats)
library(readxl)
library(tidyr)
library(dmetar)
theme_set(theme_bw())

#### LOAD DATA ####
df <- read_excel("PregnantVaccineData.xlsx")

#### Meta-analysis of proportions - no sub-group analysis, no cleaning, just proportions ####
def<-df%>%
  select(Study, Propmeta, eventexp, nexp)%>%
  drop_na()%>%
  filter(Propmeta=="1")%>%
  metaprop(event=eventexp,
           n=nexp, 
           data=.,
           studlab = Study,
           sm="Plogit",
           level = 0.95,
           fixed=TRUE, 
           random=TRUE,
           hakn=F,
           method.tau="DL")
forest(def)

#### Meta-analysis of odds ratios - including all studies included by the paper itself - no further selection ####
def<-df%>%
  select(Study, ORmeta, eventexp, nexp, eventcontrol, ncontrol)%>%
  filter(ORmeta=="1")%>%
  drop_na()%>%
  metabin(eventexp, 
          nexp, 
          eventcontrol, 
          ncontrol,
          studlab=Study,
          data = ., 
          method = "Inverse",
          sm="OR",
          MH.exact = TRUE,
          fixed = FALSE,
          random = TRUE,
          method.tau = "REML",
          hakn = TRUE,
          prediction = TRUE)
summary(def)
exp(def$TE.random)
def$tau2
find.outliers(def)
def.inf <- InfluenceAnalysis(def, random = TRUE)
plot(def.inf, "baujat")
plot(def.inf, "influence")
plot(def.inf, "es")
plot(def.inf, "i2")
forest.meta(def, layout = "RevMan5")
drapery(def, 
        labels ="studlab",
        type = "pval", 
        legend = FALSE)

funnel(def)
metabias(def, method.bias = 'linreg', k.min = 10, plotit = T)

#### Meta-analysis of odds ratio Stillbirth - replicating Nature analysis ####
df2<-df%>%
  select(Study, 
         eventexp, nexp, 
         eventcontrol, ncontrol, 
         Outcome)%>%
  filter(Outcome=="Stillbirth")
def2<-metabin(eventexp, 
          nexp, 
          eventcontrol, 
          ncontrol,
          studlab=Study,
          tau.common=FALSE,
          data = df2, 
          method = "GLMM",
          sm="OR",
          MH.exact = TRUE,
          fixed = FALSE,
          random = TRUE,
          method.tau = "ML",
          hakn = TRUE,
          prediction = TRUE)
summary(def2)
forest.meta(def2, layout = "RevMan5")
exp(def2$TE.random)
def2$tau2
find.outliers(def2)
def.inf <- InfluenceAnalysis(def2, random = TRUE)
plot(def.inf, "baujat")
plot(def.inf, "influence")
plot(def.inf, "es")
plot(def.inf, "i2")
drapery(def2, 
        labels ="studlab",
        type = "pval", 
        legend = FALSE)
funnel(def2)
metabias(def2, method.bias = 'linreg', k.min = 5, plotit = T)

#### Meta-analysis of odds ratio Stillbirth - replicating Nature analysis - sub-group analysis by design ####
df3<-df%>%
  select(Study,
         eventexp, nexp, 
         eventcontrol, ncontrol, 
         Outcome,Design)%>%
  filter(Outcome=="Stillbirth")%>%
  drop_na()
def3<-metabin(eventexp, 
          nexp, 
          eventcontrol, 
          ncontrol,
          studlab=Study,
          subgroup=Design,
          tau.common=FALSE,
          data = df3, 
          method = "GLMM",
          sm="OR",
          MH.exact = TRUE,
          fixed = FALSE,
          random = TRUE,
          method.tau = "ML",
          hakn = TRUE,
          prediction = TRUE)
summary(def3)
forest.meta(def3, layout = "RevMan5")
exp(def3$TE.random)
def3$tau2
find.outliers(def3)
def.inf <- InfluenceAnalysis(def3, random = TRUE)
plot(def.inf, "baujat")
plot(def.inf, "influence")
plot(def.inf, "es")
plot(def.inf, "i2")
drapery(def3, 
        labels ="studlab",
        type = "pval", 
        legend = FALSE)
funnel(def3)
metabias(def3, method.bias = 'linreg', k.min = 5, plotit = T)


### Reply from Dr. Asma Khalil
df1 <- read_excel("PregnantVaccineData.xlsx")
df11<-df1%>%
  select(Study,
         eventexp, 
         nexp, 
         eventcontrol, ncontrol, 
         Outcome)%>%
  filter(Outcome=="Stillbirth")%>%
  drop_na()
m1<-metabin(eventexp, 
              nexp, 
              eventcontrol, 
              ncontrol,
              studlab=Study,
              tau.common=FALSE,
              data = df11, 
              method = "GLMM",
              sm="OR",
              method.tau = "ML",
              prediction = TRUE)
m1
forest.meta(m1, layout = "RevMan5")

m2<-metabin(eventexp, 
            nexp, 
            eventcontrol, 
            ncontrol,
            studlab=Study,
            tau.common=FALSE,
            data = df11,
            method ="GLMM",
            model.glmm = "CM.AL",
            sm="OR",
            method.tau = "ML",
            prediction = TRUE,
            hakn = TRUE)
m2
forest.meta(m2, layout = "RevMan5")


m3<-metabin(eventexp, 
            nexp, 
            eventcontrol, 
            ncontrol,
            studlab=Study,
            data = df11, 
            method = "GLMM",
            model.glmm = "CM.EL",
            sm="OR",
            tau.common=FALSE,
            method.tau = "ML",
            prediction = TRUE)
m3
forest.meta(m3, layout = "RevMan5")

m4<-metabin(eventexp, 
            nexp, 
            eventcontrol, 
            ncontrol,
            studlab=Study,
            data = df11, 
            method = "MH",
            sm="OR",
            tau.common=FALSE,
            method.tau = "ML",
            prediction = TRUE,
            hakn = TRUE)
m4
summary(m4)


m5<-metabin(eventexp, 
            nexp, 
            eventcontrol, 
            ncontrol,
            studlab=Study,
            data = df11, 
            method = "Inverse",
            sm="OR",
            tau.common=FALSE,
            method.tau = "ML",
            prediction = TRUE,
            hakn = TRUE)
m5


m6<-metabin(eventexp, 
            nexp, 
            eventcontrol, 
            ncontrol,
            studlab=Study,
            data = df11, 
            method = "Peto",
            sm="OR",
            tau.common=FALSE,
            method.tau = "ML",
            prediction = TRUE,
            hakn = TRUE)
m6

## Applying GLMM model of Nature authors after correspondence: no sub-group
def2<-metabin(eventexp, 
              nexp, 
              eventcontrol, 
              ncontrol,
              studlab=Study,
              tau.common=FALSE,
              data = df11, 
              method = "GLMM",
              model.glmm = "CM.AL",
              sm="OR",
              MH.exact = TRUE,
              fixed = FALSE,
              random = TRUE,
              method.tau = "ML",
              prediction = TRUE)
def2


## Applying GLMM model of Nature authors after correspondence: sub-group
df3<-df1%>%
  select(Study,
         eventexp, 
         nexp, 
         eventcontrol, 
         ncontrol, 
         Outcome,
         Design)%>%
  filter(Outcome=="Stillbirth")%>%
  drop_na()
def3<-metabin(eventexp, 
              nexp, 
              eventcontrol, 
              ncontrol,
              studlab=Study,
              subgroup=Design,
              tau.common=FALSE,
              data = df3, 
              method = "GLMM",
              sm="OR",
              model.glmm = "CM.AL",
              method.tau = "ML",
              prediction = TRUE)
def3
def4<-metabin(eventexp, 
              nexp, 
              eventcontrol, 
              ncontrol,
              studlab=Study,
              subgroup=Design,
              tau.common=FALSE,
              data = df3, 
              method = "GLMM",
              sm="OR",
              model.glmm = "CM.AL",
              method.tau = "ML",
              hakn = TRUE,
              prediction = TRUE)
def4


## Applying GLMM model of Nature authors on Canada, Uk and Israel
df4<-df1%>%
  select(Study,
         eventexp, 
         nexp, 
         eventcontrol, 
         ncontrol, 
         Outcome,
         Country)%>%
  filter(Outcome=="Stillbirth" & 
           Study %in% c("BORNOntario, 2021","UKHSA, 2021","Goldshtein, 2021"))%>%
  drop_na()
def5<-metabin(eventexp, 
              nexp, 
              eventcontrol, 
              ncontrol,
              studlab=Study,
              tau.common=FALSE,
              data = df4, 
              method = "GLMM",
              model.glmm = "CM.AL",
              sm="OR",
              method.tau = "ML",
              hakn = FALSE,
              prediction = TRUE)
def5
def6<-metabin(eventexp, 
              nexp, 
              eventcontrol, 
              ncontrol,
              studlab=Study,
              tau.common=FALSE,
              data = df4, 
              method = "GLMM",
              model.glmm = "CM.AL",
              sm="OR",
              method.tau = "ML",
              hakn = TRUE,
              prediction = TRUE)
def6
forest.meta(def6, layout = "RevMan5")