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metals_asd_models.qmd
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---
title: "metals_asd_models"
format: html
editor: source
---
# Packages
```{r load}
#| message: false
library(sas7bdat)
library(openxlsx)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(corrplot)
library(logbin)
library(epitools)
library(meta)
library(gridExtra)
library(here)
load(file.path(here(),'../Data/dat_for_models_20Sept2023.RDA'))
```
# Strict cutoffs for timepoints (T1/T2 and T3)
```{r}
# before and after EARLI
metals.earli.T2$child_id %>% unique() %>% length()
#173
metals.earli.T3$child_id %>% unique() %>% length()
#172
metals.earli.T2.strict <- metals.earli.T2 %>% filter(GA_collect < 28)
metals.earli.T3.strict <- metals.earli.T3 %>% filter(GA_collect >= 28)
metals.earli.T2.strict$child_id %>% unique() %>% length()
#161
metals.earli.T3.strict$child_id %>% unique() %>% length()
#162
# before and after MARBLES
metals.marbles.T2$child_id %>% unique() %>% length()
#167
metals.marbles.T3$child_id %>% unique() %>% length()
#238
metals.marbles.T2.strict <- metals.marbles.T2 %>% filter(GA_collect < 28)
metals.marbles.T3.strict <- metals.marbles.T3 %>% filter(GA_collect >= 28)
metals.marbles.T2.strict$child_id %>% unique() %>% length()
#167
metals.marbles.T3.strict$child_id %>% unique() %>% length()
#238
# rename time variable
metals.earli.T2.strict$Time <- "T1/T2"
metals.earli.T3.strict$Time <- "T3"
metals.marbles.T2.strict$Time <- "T1/T2"
metals.marbles.T3.strict$Time <- "T3"
earli.full <- rbind(metals.earli.T2.strict, metals.earli.T3.strict)
marbles.full <- rbind(metals.marbles.T2.strict, metals.marbles.T3.strict)
earli.full <- earli.full %>% filter(Analyte.Code=="Lead")
marbles.full <- marbles.full %>% filter(Analyte.Code=="Lead")
earli.full$Time <- factor(earli.full$Time)
marbles.full$Time <- factor(marbles.full$Time)
```
# Plot gestational ages
```{r}
# EARLI
ggplot(earli.full, aes(x=GA_collect, fill=Time)) +
geom_histogram(data = earli.full %>% filter(Time=='T1/T2'),
alpha=0.9, boundary=0, binwidth=1, closed='left', position="identity") +
geom_histogram(data = earli.full %>% filter(Time=='T3'),
alpha=0.9, boundary=0, binwidth=1, closed='left', position="identity") +
theme_classic() + xlim(0,40) + geom_vline(xintercept = c(28)) +
ylab("") + xlab("Gestational Age at Urine Collection") +
guides(fill=guide_legend(title="Trimester"))
# MARBLES
ggplot(marbles.full, aes(x=GA_collect, fill=Time)) +
geom_histogram(data = marbles.full %>% filter(Time=='T1/T2'),
alpha=0.9, boundary=0, binwidth=1, closed='left', position="identity") +
geom_histogram(data = marbles.full %>% filter(Time=='T3'),
alpha=0.9, boundary=0, binwidth=1, closed='left', position="identity") +
theme_classic() + xlim(0,40) + geom_vline(xintercept = c(28)) +
ylab("") + xlab("Gestational Age at Urine Collection") +
guides(fill=guide_legend(title="Trimester"))
```
# Additional Variables
```{r}
library(sas7bdat)
E_augment <- read.csv("G:/My Drive/EARLI MARBLES Cross Compare/EARLI_mdsAncestry.csv")
covars <- read.sas7bdat("G:/My Drive/EARLI MARBLES Cross Compare/e_m_covars_v9_15aug23.sas7bdat")
# table(earli.full$coi_id %in% covars$COI_ID)
# table(marbles.full$child_id %in% covars$COI_ID)
#
# table(covars %>% filter(COI_ID %in% earli.full$coi_id) %>% select(MomRaceEth), exclude=NULL)
#
# table(covars %>% filter(COI_ID %in% marbles.full$child_id) %>% select(MomRaceEth), exclude=NULL)
E_augment_select <- E_augment %>% filter(IID %in% earli.full$child_id)
# table(E_augment_select$classification.PCA, E_augment_select$race.questionnaire, exclude=NULL)
# merge in race eth
metals.earli.T2.strict$MomRaceEth <- covars[match(metals.earli.T2.strict$coi_id, covars$COI_ID), "MomRaceEth"]
metals.earli.T3.strict$MomRaceEth <- covars[match(metals.earli.T3.strict$coi_id, covars$COI_ID), "MomRaceEth"]
metals.marbles.T2.strict$MomRaceEth <- covars[match(metals.marbles.T2.strict$child_id, covars$COI_ID), "MomRaceEth"]
metals.marbles.T3.strict$MomRaceEth <- covars[match(metals.marbles.T3.strict$child_id, covars$COI_ID), "MomRaceEth"]
re_categorize <- function(dat){
dat$MomRaceEth <- case_when(
dat$MomRaceEth == 1 ~ 'White',
dat$MomRaceEth == 2 ~ 'Black',
dat$MomRaceEth %in% c(4,5) ~ 'Asian or Pacific Islander',
dat$MomRaceEth %in% c(6,7) ~ 'Hispanic',
.default = 'Other/Multiracial'
)
}
metals.earli.T2.strict$MomRaceEth <- re_categorize(metals.earli.T2.strict)
metals.earli.T3.strict$MomRaceEth <- re_categorize(metals.earli.T3.strict)
metals.marbles.T2.strict$MomRaceEth <- re_categorize(metals.marbles.T2.strict)
metals.marbles.T3.strict$MomRaceEth <- re_categorize(metals.marbles.T3.strict)
# metals.earli.T2.strict %>% filter(Analyte.Code=='Lead') %>% select(MomRaceEth) %>% table
# metals.earli.T3.strict %>% filter(Analyte.Code=='Lead') %>% select(MomRaceEth) %>% table
# metals.marbles.T2.strict %>% filter(Analyte.Code=='Lead') %>% select(MomRaceEth) %>% table
# metals.marbles.T3.strict %>% filter(Analyte.Code=='Lead') %>% select(MomRaceEth) %>% table
#smoking exclude
# table(earli.full$Time, earli.full$preg_smoke, exclude=NULL)
# 0 1 <NA>
# T1/T2 117 7 37
# T3 126 5 31
# table(marbles.full$Time, marbles.full$preg_smoke, exclude=NULL)
# 0 1 NaN
# T1/T2 151 7 9
# T3 213 9 16
metals.earli.T2.strict <- metals.earli.T2.strict %>% filter(is.na(preg_smoke) | preg_smoke!=1)
metals.earli.T3.strict <- metals.earli.T3.strict %>% filter(is.na(preg_smoke) | preg_smoke!=1)
metals.marbles.T2.strict <- metals.marbles.T2.strict %>% filter(is.na(preg_smoke) | preg_smoke!=1)
metals.marbles.T3.strict <- metals.marbles.T3.strict %>% filter(is.na(preg_smoke) | preg_smoke!=1)
```
# Revised Tables
### Table 1
```{r}
library(gt)
library(gtsummary)
fisher_sim_p <- function(data, variable, by, ...) {
result <- list()
result$p <- stats::fisher.test(x = data %>% pull({{variable}}), y = data %>% pull({{by}}), simulate.p.value = T)$p.value
result$test <- "Fisher's test with simulated p-value"
result
}
earli.T2 <- metals.earli.T2.strict %>% filter(Analyte.Code=="Lead")
earli.T3 <- metals.earli.T3.strict %>% filter(Analyte.Code=="Lead")
marbles.T2 <- metals.marbles.T2.strict %>% filter(Analyte.Code=="Lead")
marbles.T3 <- metals.marbles.T3.strict %>% filter(Analyte.Code=="Lead")
rownames(earli.T2) <- earli.T2$child_id
rownames(earli.T3) <- earli.T3$child_id
rownames(marbles.T2) <- marbles.T2$child_id
rownames(marbles.T3) <- marbles.T3$child_id
### individual cohort/time
earli.T2 <- earli.T2 %>% select(child_id, asd, MomEdu , maternal_age, MomRaceEth,
coi_gender, GA_collect)
earli.T2 <- na.omit(earli.T2)
earli.T2$MomEdu <- ifelse(earli.T2$MomEdu, "College Degree", "No Degree")
restab.eT2 <- tbl_summary(earli.T2 %>% select(asd, MomEdu , maternal_age, MomRaceEth,
coi_gender, GA_collect),
by=asd,
digits=list(
all_continuous() ~ c(2,1)
),
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
label=list(MomEdu~"Education",
MomRaceEth~"Self Report Race/Ethnicity",
maternal_age~"Age",
coi_gender~"Infant Sex",
GA_collect~"Gestational Age at Sample Collection")
) %>% add_p(test=list(MomRaceEth ~ "fisher_sim_p"))
earli.T3 <- earli.T3 %>% select(child_id, asd, MomEdu , maternal_age, MomRaceEth,
coi_gender, GA_collect)
earli.T3 <- na.omit(earli.T3)
earli.T3$MomEdu <- ifelse(earli.T3$MomEdu, "College Degree", "No Degree")
restab.eT3 <- tbl_summary(earli.T3 %>% select(asd, MomEdu , maternal_age, MomRaceEth,
coi_gender, GA_collect),
by=asd,
digits=list(
all_continuous() ~ c(2,1)
),
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
label=list(MomEdu~"Education",
MomRaceEth~"Self Report Race/Ethnicity",
maternal_age~"Age",
coi_gender~"Infant Sex",
GA_collect~"Gestational Age at Sample Collection")
) %>% add_p(test=list(MomRaceEth ~ "fisher_sim_p"))
marbles.T2 <- marbles.T2 %>% select(child_id, asd, MomEdu , maternal_age, MomRaceEth,
coi_gender, GA_collect)
marbles.T2 <- na.omit(marbles.T2)
marbles.T2$MomEdu <- ifelse(marbles.T2$MomEdu, "College Degree", "No Degree")
restab.mT2 <- tbl_summary(marbles.T2 %>% select(asd, MomEdu , maternal_age, MomRaceEth,
coi_gender, GA_collect),
by=asd,
digits=list(
all_continuous() ~ c(2,1)
),
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
label=list(MomEdu~"Education",
MomRaceEth~"Self Report Race/Ethnicity",
maternal_age~"Age",
coi_gender~"Infant Sex",
GA_collect~"Gestational Age at Sample Collection")
) %>% add_p(test=list(MomRaceEth ~ "fisher_sim_p"))
marbles.T3 <- marbles.T3 %>% select(child_id, asd, MomEdu , maternal_age, MomRaceEth,
coi_gender, GA_collect)
marbles.T3 <- na.omit(marbles.T3)
marbles.T3$MomEdu <- ifelse(marbles.T3$MomEdu, "College Degree", "No Degree")
restab.mT3 <- tbl_summary(marbles.T3 %>% select(asd, MomEdu , maternal_age, MomRaceEth,
coi_gender, GA_collect),
by=asd,
digits=list(
all_continuous() ~ c(2,1)
),
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
label=list(MomEdu~"Education",
MomRaceEth~"Self Report Race/Ethnicity",
maternal_age~"Age",
coi_gender~"Infant Sex",
GA_collect~"Gestational Age at Sample Collection")
) %>% add_p(test=list(MomRaceEth ~ "fisher_sim_p"))
restab.eT2 %>% as_flex_table() %>%
flextable::save_as_docx(path=file.path(here(),'../Tables/Revised/EARLI_T2.docx'))
restab.eT3 %>% as_flex_table() %>%
flextable::save_as_docx(path=file.path(here(),'../Tables/Revised/EARLI_T3.docx'))
restab.mT2 %>% as_flex_table() %>%
flextable::save_as_docx(path=file.path(here(),'../Tables/Revised/MARBLES_T2.docx'))
restab.mT3 %>% as_flex_table() %>%
flextable::save_as_docx(path=file.path(here(),'../Tables/Revised/MARBLES_T3.docx'))
### combined
earli.T2$samp <- rownames(earli.T2)
earli.T3$samp <- rownames(earli.T3)
earli.T2$cohort <- "EARLI"
earli.T3$cohort <- "EARLI"
marbles.T2$samp <- rownames(marbles.T2)
marbles.T3$samp <- rownames(marbles.T3)
marbles.T2$cohort <- "MARBLES"
marbles.T3$cohort <- "MARBLES"
earli.ind <- rbind(earli.T2, earli.T3)
earli.ind <- earli.ind %>% filter(!duplicated(samp))
marbles.ind <- rbind(marbles.T2, marbles.T3)
marbles.ind <- marbles.ind %>% filter(!duplicated(samp))
earli.ind$GA_T2 <- earli.T2[match(earli.ind$child_id, earli.T2$child_id),"GA_collect"]
earli.ind$GA_T3 <- earli.T3[match(earli.ind$child_id, earli.T3$child_id),"GA_collect"]
marbles.ind$GA_T2 <- marbles.T2[match(marbles.ind$child_id, marbles.T2$child_id),"GA_collect"]
marbles.ind$GA_T3 <- marbles.T3[match(marbles.ind$child_id, marbles.T3$child_id),"GA_collect"]
restab.E <- tbl_summary(earli.ind %>% select(asd, MomEdu , maternal_age, MomRaceEth,
coi_gender, GA_T2, GA_T3),
by=asd,
digits=list(
all_continuous() ~ c(2,1)
),
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
label=list(MomEdu~"Education",
MomRaceEth~"Self Report Race/Ethnicity",
maternal_age~"Age",
coi_gender~"Infant Sex")
) %>% add_p(test=list(MomRaceEth ~ "fisher_sim_p"))
restab.E %>% as_flex_table() %>%
flextable::save_as_docx(path=file.path(here(),'../Tables/Revised/EARLI.docx'))
restab.M <- tbl_summary(marbles.ind %>% select(asd, MomEdu , maternal_age, MomRaceEth,
coi_gender, GA_T2, GA_T3),
by=asd,
digits=list(
all_continuous() ~ c(2,1)
),
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} ({p}%)"),
label=list(MomEdu~"Education",
MomRaceEth~"Self Report Race/Ethnicity",
maternal_age~"Age",
coi_gender~"Infant Sex")
) %>% add_p(test=list(MomRaceEth ~ "fisher_sim_p"))
restab.M %>% as_flex_table() %>%
flextable::save_as_docx(path=file.path(here(),'../Tables/Revised/MARBLES.docx'))
### accounting
nrow(earli.T2)
nrow(earli.T3)
nrow(marbles.T2)
nrow(marbles.T3)
table(c(rownames(earli.T2), rownames(earli.T3))) %>% table()
table(c(rownames(marbles.T2), rownames(marbles.T3))) %>% table()
```
### Supp Table 1
```{r}
urine.all <- rbind(metals.earli.T2.strict %>% select(Analyte.Code, Concentration, cohort, LOD, Time),
metals.earli.T3.strict %>% select(Analyte.Code, Concentration, cohort, LOD, Time),
metals.marbles.T2.strict %>% select(Analyte.Code, Concentration, cohort, LOD, Time),
metals.marbles.T3.strict %>% select(Analyte.Code, Concentration, cohort, LOD, Time))
LOD.descriptives <- urine.all %>% group_by(Analyte.Code, cohort, LOD, Time) %>%
summarize(n=n(), above_lod=sum(Concentration>LOD),
pct_over_LOD=round(above_lod/n*100,1)) %>%
data.frame()
head(LOD.descriptives)
write.csv(LOD.descriptives, file=file.path(here(),'../Tables/Revised/LOD_descriptives.csv'), row.names = F)
```
### Supp Table 2
```{r}
out.et2 <- metals.earli.T2.strict %>% group_by(Analyte.Code) %>% summarize(sum(is.na(Concentration_imp_sg_log2)))
out.et3 <- metals.earli.T3.strict %>% group_by(Analyte.Code) %>% summarize(sum(is.na(Concentration_imp_sg_log2)))
out.mt2 <- metals.marbles.T2.strict %>% group_by(Analyte.Code) %>% summarize(sum(is.na(Concentration_imp_sg_log2)))
out.mt3 <- metals.marbles.T3.strict %>% group_by(Analyte.Code) %>% summarize(sum(is.na(Concentration_imp_sg_log2)))
outliers <- cbind(out.et2, out.et3[,2], out.mt2[,2], out.mt3[,2])
colnames(outliers) <- c('Metal', 'EARLI_TE', 'EARLI_TL', 'MARBLES_TE', 'MARBLES_TL')
write.csv(outliers, file=file.path(here(),'../Tables/Revised/outliers_excluded_count.csv'), row.names=F, quote=F)
```
### Supp Table 3 and 4
```{r}
urine.metals <- read.csv(file.path(here(), "../Data/urine_metals.csv"), header=T)
rounding <- function(x){
x$mean <- signif(x$mean, 3)
x$sd <- signif(x$sd, 3)
x$median <- signif(x$median, 3)
x$IQR <- signif(x$IQR, 3)
x
}
earli.T2.summ <- urine.metals %>% filter(SID %in% metals.earli.T2.strict$SID) %>% group_by(Analyte.Code) %>%
summarize(mean=mean(Concentration_sg, na.rm=T), sd=sd(Concentration_sg, na.rm=T), median=median(Concentration_sg, na.rm=T),
IQR=IQR(Concentration_sg, na.rm=T), n=n(), above_lod=sum(Concentration>LOD, na.rm=T), pct_over_LOD=round(above_lod/n*100,1)) %>% data.frame()
earli.T3.summ <- urine.metals %>% filter(SID %in% metals.earli.T3.strict$SID) %>% group_by(Analyte.Code) %>%
summarize(mean=mean(Concentration_sg, na.rm=T), sd=sd(Concentration_sg, na.rm=T), median=median(Concentration_sg, na.rm=T),
IQR=IQR(Concentration_sg, na.rm=T), n=n(), above_lod=sum(Concentration>LOD, na.rm=T), pct_over_LOD=round(above_lod/n*100,1)) %>% data.frame()
marbles.T2.summ <- urine.metals %>% filter(SID %in% metals.marbles.T2.strict$SID) %>% group_by(Analyte.Code) %>%
summarize(mean=mean(Concentration_sg, na.rm=T), sd=sd(Concentration_sg, na.rm=T), median=median(Concentration_sg, na.rm=T),
IQR=IQR(Concentration_sg, na.rm=T), n=n(), above_lod=sum(Concentration>LOD, na.rm=T), pct_over_LOD=round(above_lod/n*100,1)) %>% data.frame()
marbles.T3.summ <- urine.metals %>% filter(SID %in% metals.marbles.T3.strict$SID) %>% group_by(Analyte.Code) %>%
summarize(mean=mean(Concentration_sg, na.rm=T), sd=sd(Concentration_sg, na.rm=T), median=median(Concentration_sg, na.rm=T),
IQR=IQR(Concentration_sg, na.rm=T), n=n(), above_lod=sum(Concentration>LOD, na.rm=T), pct_over_LOD=round(above_lod/n*100,1)) %>% data.frame()
earli.T2.summ <- rounding(earli.T2.summ)
earli.T3.summ <- rounding(earli.T3.summ)
marbles.T2.summ <- rounding(marbles.T2.summ)
marbles.T3.summ <- rounding(marbles.T3.summ)
earli.T2.summ$cohort <- 'EARLI'
earli.T3.summ$cohort <- 'EARLI'
marbles.T2.summ$cohort <- 'MARBLES'
marbles.T3.summ$cohort <- 'MARBLES'
earli.T2.summ$Time <- 'T1/T2'
earli.T3.summ$Time <- 'T3'
marbles.T2.summ$Time <- 'T1/T2'
marbles.T3.summ$Time <- 'T3'
T2.summ <- rbind(earli.T2.summ[,c(1,9,2:8)], marbles.T2.summ[,c(1,9,2:8)])
T3.summ <- rbind(earli.T3.summ[,c(1,9,2:8)], marbles.T3.summ[,c(1,9,2:8)])
T2.summ <- T2.summ[order(T2.summ$Analyte.Code, T2.summ$cohort), ]
T3.summ <- T3.summ[order(T3.summ$Analyte.Code, T3.summ$cohort), ]
T2.summ
T3.summ
write.csv(T2.summ, file=file.path(here(),"../Tables/Revised/metal_descriptives_T2.csv"), row.names=F, quote=F)
write.csv(T3.summ, file=file.path(here(),"../Tables/Revised/metal_descriptives_T3.csv"), row.names=F, quote=F)
```
### Supp Table 5
```{r}
time.cors <- sapply(metals, FUN=function(x){
ET2 = metals.earli.T2.strict %>% filter(child_id %in% earli.T2$child_id & Analyte.Code==x)
ET3 = metals.earli.T3.strict %>% filter(child_id %in% earli.T3$child_id & Analyte.Code==x)
MT2 = metals.marbles.T2.strict %>% filter(child_id %in% marbles.T2$child_id & Analyte.Code==x)
MT3 = metals.marbles.T3.strict %>% filter(child_id %in% marbles.T3$child_id & Analyte.Code==x)
Eboth = intersect(ET2$child_id, ET3$child_id)
Mboth = intersect(MT2$child_id, MT3$child_id)
ET2 = ET2[match(Eboth, ET2$child_id),]
ET3 = ET3[match(Eboth, ET3$child_id),]
MT2 = MT2[match(Mboth, MT2$child_id),]
MT3 = MT3[match(Mboth, MT3$child_id),]
E.corr = round(cor(ET2$Concentration, ET3$Concentration,
use="complete.obs", method='spearman'),2)
M.corr = round(cor(MT2$Concentration, MT3$Concentration,
use="complete.obs", method='spearman'),2)
data.frame(cbind(E.corr, M.corr))
})
write.csv(t(time.cors), file.path(here(),'../Tables/Revised/time_cor.csv'))
```
# Model Fitting and Meta Analysis Functions
```{r}
# fit models for ASD and NTD vs TD
# pd = dataframe with data
# logistic = F to get risk ratio estimates
# logistic = T to get odds ratio estimates
model_fit_logbin <- function(pd, logistic=F){
asd.res <- list()
ntd.res <- list()
for(met in metals){
sub = pd %>% filter(Analyte.Code==met)
asd = sub %>% filter(asd %in% c('TD','ASD'))
asd$asd = ifelse(asd$asd=='TD',0,1)
ntd = sub %>% filter(asd %in% c('TD','Non-TD'))
ntd$asd = ifelse(ntd$asd=='TD',0,1)
print(met)
if(mod.type[met] == 'continuous'){
if(!logistic){
fit.asdglm = glm(asd ~ Concentration_imp_sg_log2 + MomEdu + coi_gender + maternal_age + MomRaceEth + GA_collect,
data=asd, family=binomial)
fit.asd = probratio(fit.asdglm, scale='log', method='delta')
fit.ntdglm = glm(asd ~ Concentration_imp_sg_log2 + MomEdu + coi_gender + maternal_age + MomRaceEth + GA_collect,
data=ntd, family=binomial)
fit.ntd = probratio(fit.ntdglm, scale='log', method='delta')
}else{
fit.asd = glm(asd ~ Concentration_imp_sg_log2 + MomEdu + coi_gender + maternal_age + MomRaceEth + GA_collect,
data=asd, family=binomial)
fit.ntd = glm(asd ~ Concentration_imp_sg_log2 + MomEdu + coi_gender + maternal_age + MomRaceEth + GA_collect,
data=ntd, family=binomial)
}
}else{
asd$overLOD <- asd$Concentration > asd$LOD
ntd$overLOD <- ntd$Concentration > ntd$LOD
if(!logistic){
fit.asdglm = glm(asd ~ overLOD + MomEdu + coi_gender + maternal_age + MomRaceEth + GA_collect,
data=asd, family=binomial)
fit.asd = probratio(fit.asdglm, scale='log', method='delta')
fit.ntdglm = glm(asd ~ overLOD + MomEdu + coi_gender + maternal_age + MomRaceEth + GA_collect,
data=ntd, family=binomial)
fit.ntd = probratio(fit.ntdglm, scale='log', method='delta')
}else{
fit.asd = glm(asd ~ overLOD + MomEdu + coi_gender + maternal_age + MomRaceEth + GA_collect,
data=asd, family=binomial)
fit.ntd = glm(asd ~ overLOD + MomEdu + coi_gender + maternal_age + MomRaceEth + GA_collect,
data=ntd, family=binomial)
}
}
asd.res[[met]] <- fit.asd
ntd.res[[met]] <- fit.ntd
}
return(list(asd=asd.res, ntd=ntd.res))
}
# fit models for ASD and NTD vs TD no covars
# pd = dataframe with data
# logistic = F to get risk ratio estimates
# logistic = T to get odds ratio estimates
model_fit_logbin_crude <- function(pd, logistic=F){
asd.res <- list()
ntd.res <- list()
for(met in metals){
sub = pd %>% filter(Analyte.Code==met)
asd = sub %>% filter(asd %in% c('TD','ASD'))
asd$asd = ifelse(asd$asd=='TD',0,1)
ntd = sub %>% filter(asd %in% c('TD','Non-TD'))
ntd$asd = ifelse(ntd$asd=='TD',0,1)
print(met)
if(mod.type[met] == 'continuous'){
if(!logistic){
fit.asdglm = glm(asd ~ Concentration_imp_sg_log2,
data=asd, family=binomial)
fit.asd = probratio(fit.asdglm, scale='log', method='delta')
fit.ntdglm = glm(asd ~ Concentration_imp_sg_log2,
data=ntd, family=binomial)
fit.ntd = probratio(fit.ntdglm, scale='log', method='delta')
}else{
fit.asd = glm(asd ~ Concentration_imp_sg_log2,
data=asd, family=binomial)
fit.ntd = glm(asd ~ Concentration_imp_sg_log2,
data=ntd, family=binomial)
}
}else{
asd$overLOD <- asd$Concentration > asd$LOD
ntd$overLOD <- ntd$Concentration > ntd$LOD
if(!logistic){
fit.asdglm = glm(asd ~ overLOD,
data=asd, family=binomial)
fit.asd = probratio(fit.asdglm, scale='log', method='delta')
fit.ntdglm = glm(asd ~ overLOD,
data=ntd, family=binomial)
fit.ntd = probratio(fit.ntdglm, scale='log', method='delta')
}else{
fit.asd = glm(asd ~ overLOD,
data=asd, family=binomial)
fit.ntd = glm(asd ~ overLOD,
data=ntd, family=binomial)
}
}
asd.res[[met]] <- fit.asd
ntd.res[[met]] <- fit.ntd
}
return(list(asd=asd.res, ntd=ntd.res))
}
# get effect estimates, standard deviations, p-values from results
# res = results from model_fit_logbin function
# pd = dataframe used in above function
extract_res <- function(res, pd, logistic=F){
summ <- pd %>% group_by(Analyte.Code, asd) %>%
filter(!is.na(Concentration) & asd %in% c('TD','ASD')) %>%
summarize(mean=mean(Concentration),
sd=sd(Concentration),
above_lod=sum(Concentration>LOD),
n=n(),
pct_over_LOD=round(above_lod/n*100,1)) %>%
data.frame()
if(logistic){
estimates <- sapply(metals, FUN=function(x){
summary(res[[x]])$coefficients[2,1]
})
sds <- sapply(metals, FUN=function(x){
summary(res[[x]])$coefficients[2,2]
})
pvals <- sapply(metals, FUN=function(x){
summary(res[[x]])$coefficients[2,4]
})
}else{
estimates <- sapply(metals, FUN=function(x){
res[[x]][1,1]
})
sds <- sapply(metals, FUN=function(x){
res[[x]][1,2]
})
pvals <- sapply(metals, FUN=function(x){
res[[x]][1,4]
})
}
fits <- summ %>% group_by(Analyte.Code) %>%
mutate(model=mod.type[Analyte.Code],
estimate=estimates[Analyte.Code],
se=sds[Analyte.Code],
pval=pvals[Analyte.Code]) %>% data.frame
# fits$lower <- ifelse(fits$model=='binary',
# round(exp(fits$estimate-1.96*fits$se),2), round(exp(fits$estimate*IQRs[fits$Analyte.Code]-1.96*fits$se*IQRs[fits$Analyte.Code]),4))
# fits$OR <- ifelse(fits$model=='binary',
# round(exp(fits$estimate),2),
# round(exp(fits$estimate*IQRs[fits$Analyte.Code]),4))
# fits$upper <- ifelse(fits$model=='binary',
# round(exp(fits$estimate+1.96*fits$se),2), round(exp(fits$estimate*IQRs[fits$Analyte.Code]+1.96*fits$se*IQRs[fits$Analyte.Code]),4))
fits$lower <- signif(exp(fits$estimate-1.96*fits$se),4)
fits$OR <- signif(exp(fits$estimate),4)
fits$upper <- signif(exp(fits$estimate+1.96*fits$se),4)
fits$CI <- paste0(fits$OR, '(', fits$lower, ',', fits$upper,')')
fits$Analyte.Code[seq(2,nrow(fits),2)] <- NA
fits$model[seq(2,nrow(fits),2)] <- NA
fits$estimate[seq(2,nrow(fits),2)] <- NA
fits$se[seq(2,nrow(fits),2)] <- NA
fits$pval[seq(2,nrow(fits),2)] <- NA
fits$CI[seq(2,nrow(fits),2)] <- NA
fits
}
# formatting results
# res = results from above fnction
# cohort = EARLI or MARBLES
# IQR = vector of metals IQR values for corresponding time/cohort
process_res <- function(res, cohort, IQR){
pro <- res[!is.na(res$Analyte.Code), c('Analyte.Code','model','lower','OR','upper','CI','pval', 'estimate', 'se')]
pro <- pro %>% filter(!Analyte.Code %in% c('Beryllium','Platinum','Tungsten','Uranium','Vanadium'))
pro$cohort <- cohort
IQR <- IQR[pro$Analyte.Code]
# pro$estimate <- ifelse(pro$Analyte.Code %in% c('Antimony', 'Cadmium', 'Chromium', 'Lead'),
# pro$estimate,
# pro$estimate * IQR[pro$Analyte.Code])
# pro$se <- ifelse(pro$Analyte.Code %in% c('Antimony', 'Cadmium', 'Chromium', 'Lead'),
# pro$se,
# pro$se * IQR[pro$Analyte.Code])
pro$estimate <- pro$estimate
pro$se <- pro$se
pro
}
# group together results
# res1 and res2 = results files from process_res
binding <- function(res1, res2){
res.bind <- rbind(res1, res2)
res.bind$Analyte.Code <- factor(res.bind$Analyte.Code)
res.bind$Analyte.Code <- factor(res.bind$Analyte.Code, levels=rev(levels(res.bind$Analyte.Code)))
res.bind
}
# meta analyze results
# res = output from binding function
meta <- function(res){
met.res = lapply(metals, FUN=function(x){
results = res[res$Analyte.Code==x, ]
metagen(estimate,
se,
#n.e=N,
data=results,
studlab=results$cohort,
comb.fixed = TRUE,
comb.random = FALSE,
prediction=TRUE,
sm="RR")
})
names(met.res) <- metals
met.res
}
# create table of meta analysis results
meta.res.tab <- function(res){
metals <- names(res)
EARLI = lapply(res, FUN=function(x){
tab = data.frame(OR = exp(x$TE[1]) %>% round(2))
tab$CI = paste0('(',exp(x$TE[1]-1.96*x$seTE[1]) %>% round(2),',',exp(x$TE[1]+1.96*x$seTE[1]) %>% round(2),')')
tab$p = x$pval[1] %>% signif(2)
tab
})
EARLI = bind_rows(EARLI)
EARLI = cbind(metals, EARLI)
MARBLES = lapply(res, FUN=function(x){
tab = data.frame(OR = exp(x$TE[2]) %>% round(2))
tab$CI = paste0('(',exp(x$TE[2]-1.96*x$seTE[2]) %>% round(2),',',exp(x$TE[2]+1.96*x$seTE[2]) %>% round(2),')')
tab$p = x$pval[2] %>% signif(2)
tab
})
MARBLES = bind_rows(MARBLES)
MARBLES = cbind(metals, MARBLES)
meta = lapply(res, FUN=function(x){
tab = data.frame(OR = exp(x$TE.fixed) %>% round(2))
tab$CI = paste0('(',exp(x$TE.fixed-1.96*x$seTE.fixed) %>% round(2),',',exp(x$TE.fixed+1.96*x$seTE.fixed) %>% round(2),')')
tab$p = x$pval.fixed %>% signif(2)
tab
})
meta = bind_rows(meta)
meta = cbind(metals, meta)
tabs = list(EARLI=EARLI, MARBLES=MARBLES, meta=meta)
combi = bind_cols(tabs)
combi = combi[,-c(5,9)]
combi[match(c(metals.b, metals.c), combi[,1]),]
}
metals.c <- metals[!metals %in% c('Antimony', "Cadmium", "Chromium", "Lead")]
metals.c1 <- metals.c[1:7]
metals.c2 <- metals.c[8:13]
metals.b <- c('Antimony', "Cadmium", "Chromium", "Lead")
```
# Model fitting
```{r}
metals.earli.T2 <- metals.earli.T2.strict
metals.earli.T3 <- metals.earli.T3.strict
metals.marbles.T2 <- metals.marbles.T2.strict
metals.marbles.T3 <- metals.marbles.T3.strict
metals.earli.T2$MomRaceEth <- ifelse(metals.earli.T2$MomRaceEth == "White", "White", "Non-White")
metals.earli.T3$MomRaceEth <- ifelse(metals.earli.T3$MomRaceEth == "White", "White", "Non-White")
metals.marbles.T2$MomRaceEth <- ifelse(metals.marbles.T2$MomRaceEth == "White", "White", "Non-White")
metals.marbles.T3$MomRaceEth <- ifelse(metals.marbles.T3$MomRaceEth == "White", "White", "Non-White")
```
```{r models}
#| message: false
earli.resT2.crude <- model_fit_logbin_crude(metals.earli.T2)
earli.resT3.crude <- model_fit_logbin_crude(metals.earli.T3)
marbles.resT2.crude <- model_fit_logbin_crude(metals.marbles.T2)
marbles.resT3.crude <- model_fit_logbin_crude(metals.marbles.T3)
earli.resT2.logbin <- model_fit_logbin(metals.earli.T2)
earli.resT3.logbin <- model_fit_logbin(metals.earli.T3)
marbles.resT2.logbin <- model_fit_logbin(metals.marbles.T2)
marbles.resT3.logbin <- model_fit_logbin(metals.marbles.T3)
earli.resT2.logistic <- model_fit_logbin(metals.earli.T2, logistic=T)
earli.resT3.logistic <- model_fit_logbin(metals.earli.T3, logistic=T)
marbles.resT2.logistic <- model_fit_logbin(metals.marbles.T2, logistic=T)
marbles.resT3.logistic <- model_fit_logbin(metals.marbles.T3, logistic=T)
```
```{r extract}
#| message: false
# crude models
earli.asd.T2.crude <- extract_res(earli.resT2.crude$asd, metals.earli.T2)
earli.ntd.T2.crude <- extract_res(earli.resT2.crude$ntd, metals.earli.T2)
earli.asd.T3.crude <- extract_res(earli.resT3.crude$asd, metals.earli.T3)
earli.ntd.T3.crude <- extract_res(earli.resT3.crude$ntd, metals.earli.T3)
marbles.asd.T2.crude <- extract_res(marbles.resT2.crude$asd, metals.marbles.T2)
marbles.ntd.T2.crude <- extract_res(marbles.resT2.crude$ntd, metals.marbles.T2)
marbles.asd.T3.crude <- extract_res(marbles.resT3.crude$asd, metals.marbles.T3)
marbles.ntd.T3.crude <- extract_res(marbles.resT3.crude$ntd, metals.marbles.T3)
# main models
earli.asd.T2.poi <- extract_res(earli.resT2.logbin$asd, metals.earli.T2)
earli.ntd.T2.poi <- extract_res(earli.resT2.logbin$ntd, metals.earli.T2)
earli.asd.T3.poi <- extract_res(earli.resT3.logbin$asd, metals.earli.T3)
earli.ntd.T3.poi <- extract_res(earli.resT3.logbin$ntd, metals.earli.T3)
marbles.asd.T2.poi <- extract_res(marbles.resT2.logbin$asd, metals.marbles.T2)
marbles.ntd.T2.poi <- extract_res(marbles.resT2.logbin$ntd, metals.marbles.T2)
marbles.asd.T3.poi <- extract_res(marbles.resT3.logbin$asd, metals.marbles.T3)
marbles.ntd.T3.poi <- extract_res(marbles.resT3.logbin$ntd, metals.marbles.T3)
# logistic sensitivity
earli.asd.T2.logistic <- extract_res(earli.resT2.logistic$asd, metals.earli.T2, logistic=T)
earli.ntd.T2.logistic <- extract_res(earli.resT2.logistic$ntd, metals.earli.T2, logistic=T)
earli.asd.T3.logistic <- extract_res(earli.resT3.logistic$asd, metals.earli.T3, logistic=T)
earli.ntd.T3.logistic <- extract_res(earli.resT3.logistic$ntd, metals.earli.T3, logistic=T)
marbles.asd.T2.logistic <- extract_res(marbles.resT2.logistic$asd, metals.marbles.T2, logistic=T)
marbles.ntd.T2.logistic <- extract_res(marbles.resT2.logistic$ntd, metals.marbles.T2, logistic=T)
marbles.asd.T3.logistic <- extract_res(marbles.resT3.logistic$asd, metals.marbles.T3, logistic=T)
marbles.ntd.T3.logistic <- extract_res(marbles.resT3.logistic$ntd, metals.marbles.T3, logistic=T)
```
# forest plot
```{r forest}
#crude
res.earli.T2.asd.crude <- process_res(earli.asd.T2.crude, "EARLI", IQRs.T2)
res.earli.T2.ntd.crude <- process_res(earli.ntd.T2.crude, "EARLI", IQRs.T2)
res.earli.T3.asd.crude <- process_res(earli.asd.T3.crude, "EARLI", IQRs.T3)
res.earli.T3.ntd.crude <- process_res(earli.ntd.T3.crude, "EARLI", IQRs.T3)
res.marbles.T2.asd.crude <- process_res(marbles.asd.T2.crude, "MARBLES", IQRs.mT2)
res.marbles.T2.ntd.crude <- process_res(marbles.ntd.T2.crude, "MARBLES", IQRs.mT2)
res.marbles.T3.asd.crude <- process_res(marbles.asd.T3.crude, "MARBLES", IQRs.mT3)
res.marbles.T3.ntd.crude <- process_res(marbles.ntd.T3.crude, "MARBLES", IQRs.mT3)
#log reg
res.earli.T2.asd.poi <- process_res(earli.asd.T2.poi, "EARLI", IQRs.T2)
res.earli.T2.ntd.poi <- process_res(earli.ntd.T2.poi, "EARLI", IQRs.T2)
res.earli.T3.asd.poi <- process_res(earli.asd.T3.poi, "EARLI", IQRs.T3)
res.earli.T3.ntd.poi <- process_res(earli.ntd.T3.poi, "EARLI", IQRs.T3)
res.marbles.T2.asd.poi <- process_res(marbles.asd.T2.poi, "MARBLES", IQRs.mT2)
res.marbles.T2.ntd.poi <- process_res(marbles.ntd.T2.poi, "MARBLES", IQRs.mT2)
res.marbles.T3.asd.poi <- process_res(marbles.asd.T3.poi, "MARBLES", IQRs.mT3)
res.marbles.T3.ntd.poi <- process_res(marbles.ntd.T3.poi, "MARBLES", IQRs.mT3)
# #logistic reg
res.earli.T2.asd.logistic <- process_res(earli.asd.T2.logistic, "EARLI", IQRs.T2)
res.earli.T2.ntd.logistic <- process_res(earli.ntd.T2.logistic, "EARLI", IQRs.T2)
res.earli.T3.asd.logistic <- process_res(earli.asd.T3.logistic, "EARLI", IQRs.T3)
res.earli.T3.ntd.logistic <- process_res(earli.ntd.T3.logistic, "EARLI", IQRs.T3)
res.marbles.T2.asd.logistic <- process_res(marbles.asd.T2.logistic, "MARBLES", IQRs.mT2)
res.marbles.T2.ntd.logistic <- process_res(marbles.ntd.T2.logistic, "MARBLES", IQRs.mT2)
res.marbles.T3.asd.logistic <- process_res(marbles.asd.T3.logistic, "MARBLES", IQRs.mT3)
res.marbles.T3.ntd.logistic <- process_res(marbles.ntd.T3.logistic, "MARBLES", IQRs.mT3)
### group time/outcome
res.asdT2.crude <- binding(res.earli.T2.asd.crude, res.marbles.T2.asd.crude)
res.asdT3.crude <- binding(res.earli.T3.asd.crude, res.marbles.T3.asd.crude)
res.ntdT2.crude <- binding(res.earli.T2.ntd.crude, res.marbles.T2.ntd.crude)
res.ntdT3.crude <- binding(res.earli.T3.ntd.crude, res.marbles.T3.ntd.crude)
save(res.asdT2.crude, res.asdT3.crude, res.ntdT2.crude, res.ntdT3.crude,
file=file.path(here(),'/../Data/crude_results_Apr2024.RDA'))
res.asdT2.poi <- binding(res.earli.T2.asd.poi, res.marbles.T2.asd.poi)
res.asdT3.poi <- binding(res.earli.T3.asd.poi, res.marbles.T3.asd.poi)
res.ntdT2.poi <- binding(res.earli.T2.ntd.poi, res.marbles.T2.ntd.poi)
res.ntdT3.poi <- binding(res.earli.T3.ntd.poi, res.marbles.T3.ntd.poi)
save(res.asdT2.poi, res.asdT3.poi, res.ntdT2.poi, res.ntdT3.poi,
file=file.path(here(),'/../Data/logbin_results_Apr2024.RDA'))
res.asdT2.logit <- binding(res.earli.T2.asd.logistic, res.marbles.T2.asd.logistic)
res.asdT3.logit <- binding(res.earli.T3.asd.logistic, res.marbles.T3.asd.logistic)
res.ntdT2.logit <- binding(res.earli.T2.ntd.logistic, res.marbles.T2.ntd.logistic)
res.ntdT3.logit <- binding(res.earli.T3.ntd.logistic, res.marbles.T3.ntd.logistic)
save(res.asdT2.logit, res.asdT3.logit, res.ntdT2.logit, res.ntdT3.logit,
file=paste0(here(),'/../Data/logistic_results_Apr2024.RDA'))
```
# Meta Analysis
## crude binomial
```{r}
load(file=paste0(file.path(here(),'../Data/crude_results_20Sept2023.RDA')))
#load(file=paste0(path,'Data/poisson_results.RDA'))
res.asdT2 <- res.asdT2.crude
res.asdT3 <- res.asdT3.crude
res.ntdT2 <- res.ntdT2.crude
res.ntdT3 <- res.ntdT3.crude
metals <- unique(res.asdT2$Analyte.Code) %>% as.character
meta.asdT2 <- meta(res.asdT2)
meta.asdT3 <- meta(res.asdT3)
meta.ntdT2 <- meta(res.ntdT2)
meta.ntdT3 <- meta(res.ntdT3)
### format for forest plot
meta.forest <- function(meta.res){
lapply(metals, FUN=function(x){
res = meta.res[[x]]
coefs = data.frame(metal=x,
lab=c('Meta','EARLI','MARBLES'),
effect=c(res$TE.fixed, res$TE[1], res$TE[2]),
lower=c(res$lower.fixed, res$lower[1], res$lower[2]),
upper=c(res$upper.fixed, res$upper[1], res$upper[2]))
coefs$lab = factor(coefs$lab, levels=c('Meta','EARLI','MARBLES'))
coefs$OR = exp(coefs$effect)
coefs$OR.lower = exp(coefs$lower)
coefs$OR.upper = exp(coefs$upper)
coefs$alpha = I(ifelse(coefs$lab=='Meta', 1.0, ifelse(coefs$lab=='EARLI',0.4, 0.2)))
coefs$shape = I(ifelse(coefs$lab=='Meta', 18, 16))
coefs$metal = ifelse(res$pval.fixed < 0.05, paste0(coefs$metal,'*'), coefs$metal)
coefs$p = res$pval.fixed
coefs
})
}
coefs.asdT2 <- meta.forest(meta.asdT2) %>% bind_rows()
coefs.asdT3 <- meta.forest(meta.asdT3) %>% bind_rows()
coefs.ntdT2 <- meta.forest(meta.ntdT2) %>% bind_rows()
coefs.ntdT3 <- meta.forest(meta.ntdT3) %>% bind_rows()
metals.c <- metals[!metals %in% c('Antimony', "Cadmium", "Chromium", "Lead")]
metals.c1 <- metals.c[1:7]
metals.c2 <- metals.c[8:13]
metals.b <- c('Antimony', "Cadmium", "Chromium", "Lead")
### combined timepoint figures
meta_plots_2T <-function(coefs, name){
coefs$metal <- gsub("\\*","",coefs$metal)
coefs$col <- ifelse(coefs$lab=='EARLI', "gray50",
ifelse(coefs$lab=='MARBLES', "gray45",
ifelse(coefs$lab=='Meta' & coefs$p < 0.05, "red2",
ifelse(coefs$lab=='Meta' & coefs$p < 0.1, "orange2", "gray40"))))
forest.bin <- ggplot(data=coefs[grepl(paste(metals.b, collapse='|'), coefs.asdT2$metal),],
aes(x=lab, y=OR, ymin=OR.lower, ymax=OR.upper, alpha=alpha, shape=shape, col=I(col))) +
geom_pointrange(position=position_dodge2(width=1), size=0.75) +
geom_hline(yintercept=1, linetype=2) +
theme_bw() + facet_grid(metal ~ time) +
theme(text = element_text(size=10), strip.text.y = element_text(angle=0)) +
xlab('') + ylab('Risk Ratio') +
coord_flip(ylim=c(0,4))