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Last updated: 2023-11-15

Checks: 5 2

Knit directory: ~/multistate2/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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File paths: absolute

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absolute	relative
~/multistate2//code/smoothtest.R	code/smoothtest.R
~/multistate2//code/newsmooth.R	code/newsmooth.R
~/multistate2/code/fitarray.R	code/fitarray.R
~/multistate2/code/arrayindicate.R	code/arrayindicate.R
~/multistate2//output/dfascvd_newbp.rds	output/dfascvd_newbp.rds
~/multistate2/code/frs30_URBUT/fun.frs_30y.R	code/frs30_URBUT/fun.frs_30y.R
~/multistate2/output/fosauc.pdf	output/fosauc.pdf

Repository version: 988af83

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    Ignored:    plots/.DS_Store

Unstaged changes:
    Modified:   analysis/NRI.Rmd
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    Modified:   analysis/index.Rmd
    Modified:   analysis/rocplots.Rmd

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/fos.Rmd) and HTML (docs/fos.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	ecd7828	Sarah Urbut	2023-10-13	Update
Rmd	273d024	Sarah Urbut	2023-09-22	Update
Rmd	4d7d65c	Sarah Urbut	2023-09-17	Update

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R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

#rm(list=ls())
source("multistate2/code/utils.R")
source("~/multistate2//code/smoothtest.R")
source("~/multistate2//code/newsmooth.R")
source("~/multistate2/code/fitarray.R")
library("reshape2")
source("~/multistate2/code/arrayindicate.R")
source("../code/frs30_URBUT/fun.frs_30ynew.R")
load("~/Library/CloudStorage/Dropbox-Personal///pheno_dir/output/merged_pheno_censor_final_withdrugs_smoke.rds")

fos=read.table("~/Desktop/sraempty/phs000007.v33.pht006027.v4.p14.c1.vr_wkthru_ex09_1_1001s.HMB-IRB-MDS.txt.gz",header = T,skip = 1,sep="\t")
fhs=read.table("~/Desktop/sraempty/phs000007.v33.pht007777.v3.p14.c1.vr_wkthru_ex32_0_0997s.HMB-IRB-MDS.txt.gz",header = T,skip = 1,sep="\t")
surv=read.table("/Users/sarahurbut/Desktop/sraempty/phs000007.v33.pht003316.v10.p14.c1.vr_survcvd_2019_a_1334s.HMB-IRB-MDS.txt.gz",skip=1,sep="\t",header=T)
dfh$cad.prs.lec=cut(dfh$cad.prs,breaks = c(-5,-0.84,0.84,5),labels = c("low","mid","high"))
dfh$int=interaction(dfh$f.31.0.0,dfh$cad.prs.lec)
# Relabel the levels of the interaction variable
levels(dfh$int) <- c(1,2,3,4,5,6)


train=dfh[1:(nrow(dfh)*0.80),]

dfascvd=readRDS("~/multistate2//output/dfascvd_newbp.rds")
test=dfh[!(dfh$identifier%in%train$identifier),]
test=merge(test,dfascvd[,-which(names(dfascvd)%in%c("age","anylipidmed0","bp_med2","smoke"))],by.x="identifier",by.y="sample_id")
test$ascvd_10y_accaha=test$as2
test$phenos.enrollment=test$f.21003.0.0
test=data.table(test)
source("~/multistate2/code/frs30_URBUT/fun.frs_30y.R")
library(CVrisk)
ages=c(40:80)
nstates=c("Health", "Ht","HyperLip","Dm","Cad","death","Ht&HyperLip","HyperLip&Dm","Ht&Dm","Ht&HyperLip&Dm")

library(CVrisk)
m=merge(fos[,c("shareid","AGE1","SBP1","HDL1","SEX","CURRSMK1","HRX1","LIPRX1","TC1","DMRX1")],surv,by = "shareid")
prs=fread("~/Library/CloudStorage/Dropbox-Personal/Fram_allchr_CAD_c1.profile")
prs$score=scale(prs$V2)

firstmeasure=na.omit(merge(m,prs[,c("V1","score")],by.x="shareid",by.y="V1"))
# in FOS, using the wkthru:
# SEX       1   Male
# SEX       2   Female
# SEX       .   Unknown

## for our model, female is 0, male is 1


firstmeasure$sex=ifelse(firstmeasure$SEX==2,"female","male")
firstmeasure$sexu=ifelse(firstmeasure$SEX==2,0,1)
firstmeasure$cad.prs.lec=cut(firstmeasure$score,breaks = c(-5.02,-0.84,0.84,5.02),labels = c("low","mid","high"))

firstmeasure$int=interaction(firstmeasure$sexu,firstmeasure$cad.prs.lec)
levels(firstmeasure$int) <- c(1,2,3,4,5,6)


firstmeasure$as2=compute_CVrisk2(firstmeasure,scores = "as2",age = "AGE1",gender = "sex",
                       race = "white",sbp = "SBP1",hdl = "HDL1",
                bp_med ="HRX1",totchol = "TC1",diabetes = "DMRX1",smoker = "CURRSMK1",lipid_med = "LIPRX1")$as2

    dat = data.frame(
      "id" = firstmeasure$shareid,
      "mysex" = as.factor(firstmeasure$sex),
      "myage" = firstmeasure$AGE1,
      "mysbp" = firstmeasure$SBP1,
      "mytreat" = firstmeasure$HRX1,
      "mysmoking" = firstmeasure$CURRSMK1,
      "mydiabetes" = firstmeasure$DMRX1,
      "mytotalchol" = firstmeasure$TC1,
      "myhdl" = firstmeasure$HDL1,
      "Race" = "white",
      "mystatnow" = firstmeasure$LIPRX1
    )
    #
    frs = fun.frs_30yn(
      dat,
      id = "id",
      sex = "mysex",
      age = "myage",
      sbp = "mysbp",
      treat = "mytreat",
      smoking = "mysmoking",
      diabetes = "mydiabetes",
      totalchol = "mytotalchol",
      hdl = "myhdl"
    )

firstmeasure$lt=frs$frs_orig
ages=c(20:80)
modelfit=fitfunc2(data.table(train),ages = ages,nstates = nstates,mode = "binomial",covariates ="cad.prs+f.31.0.0+smoke+antihtn_now+statin_now")
#                   

firstmeasure=firstmeasure[firstmeasure$LIPRX1==0&firstmeasure$HRX1==0&firstmeasure$TC1<240&firstmeasure$DMRX1==0,]

b=coefplotsmooth2(ages = ages,start = "Health",stop = "Cad",modelfit = modelfit,window_width = 20,span = 0.75,degree = 2)
coefs=b$custom_smooth

ten.year.new=lifetime.new=ascvd.30.year=ascvd.10.year=emp.ten.year=emp.lifetime=vector()
agesint=seq(40,55,by=5)
aucmat=semat=matrix(NA,nrow=length(agesint),ncol=4)
firstmeasure$round5=round_any(firstmeasure$AGE1,5,f = round)


for(i in 1:length(agesint)){
  age=agesint[i]
  

  atrisk=firstmeasure[which(firstmeasure$round5==age),]
  
   ar = data.frame(
      "intercept" = 1,
      "cad.prs" = atrisk$score,
      "sex" = atrisk$sexu,
      "smoke" = atrisk$CURRSMK1,
      "antihtn_now" = atrisk$HRX1,
      "statin_now" = atrisk$LIPRX1
      )
   
   ar=na.omit(ar)

    
    atrisk$msten= compute_prediction_product_matrix(
        atrisk = ar,
        agepredinterval = c(age:(age + 10)),
        coefmat = coefs
      )$PredictedIntervalrisk
    
     ten.year.new[i] = mean(atrisk$msten)

    
    atrisk$mslifetime=compute_prediction_product_matrix(
        atrisk = ar,
        agepredinterval = c(age:(80)),
        coefmat = coefs
      )$PredictedIntervalrisk
    lifetime.new[i] = mean(atrisk$mslifetime)
    emp.ten.year[i] = mean(atrisk$chd==1&atrisk$chddate<(365*10))
    emp.lifetime[i] = mean(atrisk$chd==1&atrisk$chddate<(365*(80-age)))
                                        
  
   ascvd.10.year[i]=mean(atrisk$as2)
   ascvd.30.year[i]=mean(atrisk$lt)
   
   
  atrisk$outcometen=ifelse(atrisk$chd==1&atrisk$chddate<(365*10),1,0)
  atrisk$outcomelife=ifelse(atrisk$chd==1&atrisk$chddate<(365*30),1,0)

     #d=d[!is.na(d$ascvd_10y_accaha),]
  aucmat[i, 1] = roc(atrisk$outcometen ~ atrisk$msten)$auc
  semat[i, 1] = sqrt(var(roc(atrisk$outcometen ~ atrisk$msten)))
  #
  aucmat[i, 2] = roc(atrisk$outcometen ~ atrisk$as2)$auc
  semat[i, 2] = sqrt(var(roc(atrisk$outcometen ~ atrisk$as2)))
  #
  aucmat[i, 3] = roc(atrisk$outcomelife ~ atrisk$mslifetime)$auc
  semat[i, 3] = sqrt(var(roc(atrisk$outcomelife ~ atrisk$mslifetime)))

  aucmat[i, 4] =  roc(atrisk$outcomelife ~ atrisk$lt)$auc
  semat[i, 4] = sqrt(var(roc(atrisk$outcomelife ~ atrisk$lt)))
  
}
  
  
  

library(ehaGoF)

pcten=gofRMSE(Obs = as.vector(emp.ten.year*100),Prd = as.vector(ascvd.10.year))
msten=gofRMSE(Obs = as.vector(emp.ten.year*100),Prd = as.vector(ten.year.new*100))
mslif=gofRMSE(Obs = as.vector(emp.lifetime*100),Prd = as.vector(lifetime.new*100))
pclif=gofRMSE(Obs = as.vector(emp.lifetime*100),Prd = as.vector(ascvd.30.year))


df <- data.frame(
  metric = c("PCEten", "MSTen", "MSLife", "FRS30y"),
  value = c(pcten,msten,mslif,pclif)
)

df$metric=factor(df$metric,levels = c("MSTen","PCEten","MSLife","FRS30y"))

# Create the barplot
rmse=ggplot(df, aes(x = metric, y = value,fill=metric)) +
  geom_bar(stat = "identity") +
  labs(y = "RMSE (%)", x = "Score",fill="score") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))



rownames(aucmat)=rownames(semat)=agesint
colnames(aucmat)=colnames(semat)=c("MSten","PCEten","MSLife","FRS30y")



# Convert matrices to data frames
df_auc <- as.data.frame(aucmat)
df_se <- as.data.frame(semat)

# Add age column
df_auc$age <- rownames(df_auc)
df_se$age <- rownames(df_se)

# Convert data frames to long format
df_auc_long <- df_auc %>%
  gather(key = "metric", value = "value", -age)

df_auc_long$metric=factor(df_auc_long$metric,levels = c("MSten","PCEten","MSLife","FRS30y"))

df_se_long <- df_se %>%
  gather(key = "metric", value = "se_value", -age)

df_se_long$metric=factor(df_se_long$metric,levels = c("MSten","PCEten","MSLife","FRS30y"))

# Join both data frames
df <- left_join(df_auc_long, df_se_long, by = c("age", "metric"))

# Plot using ggplot2
fos_auc=ggplot(df, aes(x = age, y = value, fill = metric, group = metric)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
  geom_errorbar(
    aes(ymin = value - se_value, ymax = value + se_value),
    position = position_dodge(width = 0.8),
    width = 0.25
  ) +
  labs(y = "AUC ROC", x = "Ages",fill="Score") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))


df <- data.frame(
  metric = c( "MSLife", "FRS30y"),
  value = c(pcten,msten,mslif,pclif)
)

df$metric=factor(df$metric,levels = c("MSLife","FRS30y"))

# Create the barplot
rmse=ggplot(df[c(3,4),], aes(x = metric, y = value,fill=metric)) +
  geom_bar(stat = "identity") +
  labs(y = "RMSE (%)", x = "Score",fill="score") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))



rownames(aucmat)=rownames(semat)=agesint
colnames(aucmat)=colnames(semat)=c("MSten","PCE","MSLife","FRS30y")



# Convert matrices to data frames
df_auc <- as.data.frame(aucmat[,c(3,4)])
df_se <- as.data.frame(semat[,c(3,4)])

# Add age column
df_auc$age <- rownames(df_auc)
df_se$age <- rownames(df_se)

# Convert data frames to long format
df_auc_long <- df_auc %>%
  gather(key = "metric", value = "value", -age)

df_auc_long$metric=factor(df_auc_long$metric,levels = c("MSLife","FRS30y"))

df_se_long <- df_se %>%
  gather(key = "metric", value = "se_value", -age)

df_se_long$metric=factor(df_se_long$metric,levels = c("MSLife","FRS30y"))

# Join both data frames
df <- left_join(df_auc_long, df_se_long, by = c("age", "metric"))

# Plot using ggplot2
fos_auc=ggplot(df, aes(x = age, y = value, fill = metric, group = metric)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
  geom_errorbar(
    aes(ymin = value - se_value, ymax = value + se_value),
    position = position_dodge(width = 0.8),
    width = 0.25
  ) +
  labs(y = "AUC ROC", x = "Ages",fill="Score") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ggsave(ggarrange(rmse,fos_auc,common.legend = T),file="~/multistate2/output/fosauc.pdf",dpi=600)

fos=read.table("~/Desktop/sraempty/phs000007.v33.pht006027.v4.p14.c1.vr_wkthru_ex09_1_1001s.HMB-IRB-MDS.txt.gz",header = T,skip = 1,sep="\t")
fhs=read.table("~/Desktop/sraempty/phs000007.v33.pht007777.v3.p14.c1.vr_wkthru_ex32_0_0997s.HMB-IRB-MDS.txt.gz",header = T,skip = 1,sep="\t")
surv=read.table("/Users/sarahurbut/Desktop/sraempty/phs000007.v33.pht003316.v10.p14.c1.vr_survcvd_2019_a_1334s.HMB-IRB-MDS.txt.gz",skip=1,sep="\t",header=T)
dfh$cad.prs.lec=cut(dfh$cad.prs,breaks = c(-5,-0.84,0.84,5),labels = c("low","mid","high"))
dfh$int=interaction(dfh$f.31.0.0,dfh$cad.prs.lec)
# Relabel the levels of the interaction variable
levels(dfh$int) <- c(1,2,3,4,5,6)


train=dfh[1:(nrow(dfh)*0.80),]

dfascvd=readRDS("~/multistate2//output/dfascvd_newbp.rds")
test=dfh[!(dfh$identifier%in%train$identifier),]
test=merge(test,dfascvd[,-which(names(dfascvd)%in%c("age","anylipidmed0","bp_med2","smoke"))],by.x="identifier",by.y="sample_id")
test$ascvd_10y_accaha=test$as2
test$phenos.enrollment=test$f.21003.0.0
test=data.table(test)
source("~/multistate2/code/frs30_URBUT/fun.frs_30y.R")
library(CVrisk)
ages=c(40:80)
nstates=c("Health", "Ht","HyperLip","Dm","Cad","death","Ht&HyperLip","HyperLip&Dm","Ht&Dm","Ht&HyperLip&Dm")

library(CVrisk)
m=merge(fos[,c("shareid","AGE1","SBP1","HDL1","SEX","CURRSMK1","HRX1","LIPRX1","TC1","DMRX1")],surv,by = "shareid")
prs=fread("~/Library/CloudStorage/Dropbox-Personal/Fram_allchr_CAD_c1.profile")
prs$score=scale(prs$V2)

firstmeasure=na.omit(merge(m,prs[,c("V1","score")],by.x="shareid",by.y="V1"))
# in FOS, using the wkthru:
# SEX       1   Male
# SEX       2   Female
# SEX       .   Unknown

## for our model, female is 0, male is 1


firstmeasure$sex=ifelse(firstmeasure$SEX==2,"female","male")
firstmeasure$sexu=ifelse(firstmeasure$SEX==2,0,1)
firstmeasure$cad.prs.lec=cut(firstmeasure$score,breaks = c(-5.02,-0.84,0.84,5.02),labels = c("low","mid","high"))

firstmeasure$int=interaction(firstmeasure$sexu,firstmeasure$cad.prs.lec)
levels(firstmeasure$int) <- c(1,2,3,4,5,6)


library(table1)
f2=setdiff(names(fos),names(firstmeasure))
fo2=data.table(fos)
fo2=fo2[,..f2]
fo2$id=fos$dbGaP_Subject_ID
dat=merge(firstmeasure,fo2,by.x = "dbGaP_Subject_ID",by.y="id")
dat[is.na(dat)]=0
dat=dat[-(which(dat$chddate<=0)),]
dat$HRever=apply(dat,1,function(x){
  ifelse(sum(x[grep(names(dat),pattern="HRX")]>0),1,0)})
dat$HT=apply(dat,1,function(x){
  ifelse(sum(x[grep(names(dat),pattern="SBP")]>150)>0,1,0)})
dat$HL=apply(dat,1,function(x){
  ifelse(sum(x[grep(names(dat),pattern="TC")]>200)>0,1,0)})
dat$DM=apply(dat,1,function(x){
  ifelse(sum(x[grep(names(dat),pattern="DMR")]>0),1,0)})

dat$agerec=as.numeric(dat$AGE1)
dat$agerec[dat$agerec<18]=18
dat$f.31.0.0=dat$sex
dat$Cad_0_Any=as.factor(ifelse(dat$chd==1,1,0))
dat$cad.prs.lev=factor(dat$cad.prs.lec,levels = c("low","mid","high"),labels=c("Low","Intermediate","High"))
dat$smoke=ifelse(dat$CURRSMK1==1,1,0)
dat$durationfollowed=apply(dat,1,function(x){
  round(max(as.numeric(x[grep(names(dat),pattern="DATE")]))/365.25,2)
})
dat$durationfollowed[dat$durationfollowed==0]=12
library(table1)
label(dat$f.31.0.0) <- "Sex"
label(dat$HRever) <- "Start an anti-Hypertensive"
label(dat$smoke) <- "Current Smoker"
label(dat$HT) <- "Develop Hypertension"
label(dat$DM) <- "Develop Diabetes"
label(dat$HL) <- "Develop Hyperlipidemia"
label(dat$Cad_0_Any) <- "Develop Coronary Disease"



library(table1)
label(dat$f.31.0.0) <- "Sex"
label(dat$durationfollowed)="Years Followed"
label(dat$SBP1) <- "SBP at Baseline"
label(dat$smoke) <- "Current Smoker"
label(dat$HT) <- "Develop Hypertension"
label(dat$DM) <- "Develop Diabetes"
label(dat$HL) <- "Develop Hyperlipidemia"
label(dat$Cad_0_Any) <- "Develop Coronary Disease"
label(dat$agerec) = "Age of First Measured"
f=table1(~ f.31.0.0 +agerec+HT+Cad_0_Any+HL+HRever+smoke+durationfollowed|cad.prs.lev, data=dat)

Session information

sessionInfo()