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aspirin.mortality.R
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# source('code.R')
# source('code.R')
library(survival)
library(knitr)
library(haven)
library(cmprsk)
## data import ##
SASData <- read_sas("aspirin_prostatecan_aug2020.sas7bdat")
#write.csv(SASData,"aspirin_prostatecan.csv")
d=SASData
#d=read.csv("aspirin_prostatecan.csv",header=T)
dat=d
dat[dat == "9999"]=NA
#dat[dat == "7777"]=0
dat[dat == "8888"]=NA
###################################################################################################
data=data.frame(dat)
# bmi convert #
bmi=cut(data$BMI, breaks=c(0,18.5,25,30,35,40,100),right=F)
data$BMI=as.numeric(bmi)
# combine fa.x and br.y prostate history, with Fa_Cancer.z #
history_combine=function(x,y,z){
x=ifelse(z==0,0,x)
#y=ifelse(z==0,0,y)
x[x=='7777'] = 0
y[y=='7777'] = 0
e=ifelse(x==1|y==1,1,NA)
e=ifelse(is.na(x) & is.na(y) ,-9,e)
e[is.na(e)] = 0
e[e == -9] = NA
return(e)
}
data$family=history_combine(data$Fa_ProstateCancer,data$Br_ProstateCancer,data$Fa_Cancer)
# combine OTCNSAIDS.x and RxNSAIDS.y as nsaids #
nsaid_combine=function(x,y){
z=ifelse(is.na(x),y,x)
z[z=='7777']=NA
return(z)
}
data$nsaids=nsaid_combine(data$OTCNSAIDS,data$RxNSAIDS)
# re-define Asprin use #
# combine 2 variables #
variable_combine=function(x,y){
x[is.na(x)]= -9
y[is.na(y)]= -9
z=ifelse( (x+y) %in% c(-8,1,2), 1,ifelse( (x+y) == 0,0,NA))
return(z)
}
## any aspirin ##
data$Aspirin_any = variable_combine(data$Aspirin, data$LowAspirin )
##regular aspirin ##
data$Aspirin_reg = ifelse(!data$Aspirin == 1 & data$Aspirin_any ==1, NA, data$Aspirin_any )
## total 22426 enrollment, Prostate_Cancer AgeDX , Cancer Registry 1058 case ##
## vital month 156 median, 143 mean, 6627 death ##
data$pr=ifelse(data$Prostate_Cancer==1,"PrCa",NA)
data$pr[is.na(data$pr)]="NoPrCa"
data$pr=as.numeric(as.factor(data$pr))-1
####
data$t1=ifelse(is.na(data$Prostate_Cancer), as.numeric(data$VitalStatus_Months)-24,as.numeric(data$VitalStatus_Months))
data$time=ifelse(is.na(data$Incidence_Months),data$t1,data$Incidence_Months)
data$time=ifelse(data$time > 0, data$time, NA)
###############################################################################################################
###
### overall survival ###
#switch to higher version R for plotting#
library(survival)
library("survminer")
############################# function #######################
### variable with OS ###
cox.OS.var=function(data,var){
survival_data=data
survival_data=subset(data,VitalStatus_Months > 0 & time > 0 )
#survival_data$vitalstatus_new=ifelse(survival_data$Year_Death > 2015 | is.na(survival_data$Year_Death),"A",survival_data$VitalStatus)
survival_data$death=as.numeric(as.factor(survival_data$VitalStatus))-1
survival_data[,var] = ifelse(survival_data[,var] == '7777',NA,survival_data[,var])
if(length(levels(factor(data[,var]))) ==2) {
cox.sum=summary(coxph(Surv(VitalStatus_Months, death)~ factor(get(var)),data=survival_data))
} else{cox.sum=summary(coxph(Surv(VitalStatus_Months, death)~ get(var),data=survival_data))}
p=round(cox.sum$coefficients[1,"Pr(>|z|)"],digits=3)
hr=round(cox.sum$coefficients[1,"exp(coef)"],digits=2)
ci=round(cox.sum$conf.int[1,c("lower .95", "upper .95")],digits=2)
hr.ci.p=paste0(hr,"(",ci[1],"-",ci[2],")"," p=",p)
print(paste(var, hr.ci.p))
#return(hr.ci.p)
#return(cox.sum)
}
### event time ####
event.person.mo=function(data,var) {
survival_data=subset(data, time > 0 & VitalStatus_Months > 0 & !(is.na(data[,var])))
survival_data$VitalStatus=ifelse(survival_data$Year_Death > yrs | is.na(survival_data$Year_Death),"A",survival_data$VitalStatus)
survival_data$death=as.numeric(as.factor(survival_data$VitalStatus))-1
tl=table(survival_data$death,survival_data[,var])
set0=sum(subset(survival_data, get(var) == 0)$VitalStatus_Months )
set1=sum(subset(survival_data, get(var) == 1)$VitalStatus_Months )
#sum.tl= qpcR:::rbind.na(c(tl[2,1],set0),c(tl[2,2],set1))
sum.tl= rbind(c(tl[2,1],set0),c(tl[2,2],set1))
colnames(sum.tl)=c("events","person-month")
rownames(sum.tl)=c("No","Yes")
return(sum.tl)
}
### OS use Aspirin_re ###
cox.OS=function(data){
survival_data=subset(data,VitalStatus_Months > 0 & time > 0 & !is.na(Aspirin_re) )
survival_data$VitalStatus=ifelse(survival_data$Year_Death > yrs | is.na(survival_data$Year_Death),"A",survival_data$VitalStatus)
survival_data$death=as.numeric(as.factor(survival_data$VitalStatus))-1
cox.sum=summary(coxph(Surv(VitalStatus_Months, death)~Aspirin_re + Enrollment_Age ,data=survival_data))
p=round(cox.sum$coefficients[1,"Pr(>|z|)"],digits=3)
hr=round(cox.sum$coefficients[1,"exp(coef)"],digits=2)
ci=round(cox.sum$conf.int[1,c("lower .95", "upper .95")],digits=2)
hr.ci.p=paste0(hr,"(",ci[1],"-",ci[2],")"," p=",p)
return(hr.ci.p)
#return(cox.sum)
}
cox.OS.age=function(data){
survival_data=subset(data,VitalStatus_Months > 0 & time > 0 & !is.na(Aspirin_re) )
survival_data$VitalStatus=ifelse(survival_data$Year_Death > yrs | is.na(survival_data$Year_Death),"A",survival_data$VitalStatus)
survival_data$death=as.numeric(as.factor(survival_data$VitalStatus))-1
cox.sum=summary(coxph(Surv(Enrollment_Age, VitalStatus_Age, death) ~ Aspirin_re,data=survival_data))
p=round(cox.sum$coefficients[1,"Pr(>|z|)"],digits=3)
hr=round(cox.sum$coefficients[1,"exp(coef)"],digits=2)
ci=round(cox.sum$conf.int[1,c("lower .95", "upper .95")],digits=2)
hr.ci.p=paste0(hr,"(",ci[1],"-",ci[2],")"," p=",p)
return(hr.ci.p)
#return(cox.sum)
}
cox.OS.adj=function(data){
survival_data=subset(data,VitalStatus_Months > 0 & time > 0 &!is.na(Aspirin_re) )
survival_data$VitalStatus=ifelse(survival_data$Year_Death > yrs | is.na(survival_data$Year_Death),"A",survival_data$VitalStatus)
survival_data$death=as.numeric(as.factor(survival_data$VitalStatus))-1
#stage=ifelse(survival_data$SCCS_SummAJCCStage %in% c("III","IV"),"High",ifelse(survival_data$SCCS_SummAJCCStage %in% c("I","II"),"Low",NA))
#gl=ifelse(survival_data$Earliest_Gleason_Score %in% c("002","003","004","005","006","007"),"Low",ifelse(survival_data$Earliest_Gleason_Score %in% c("008","009","010"),"High",NA))
survival_data$SmokingStatus = ifelse(survival_data$SmokingStatus == '7777',NA,survival_data$SmokingStatus)
smk <- model.matrix.lm(~factor(SmokingStatus,levels=c(3,2,1)), data = survival_data,na.action="na.pass")
aggressive = ifelse(survival_data$Earliest_Gleason_Score %in% c("008","009","010") | survival_data$SCCS_SummAJCCStage == 'IV' |
survival_data$TNM_Clin_M %in% c('1','1B','1C') | survival_data$TNM_Path_M %in% c('1A','1B') |
survival_data$TNM_Clin_N == '1' | survival_data$TNM_Path_N == '1' |
survival_data$TNM_Clin_T == '4' | survival_data$TNM_Path_T == '4',1,0)
cox.sum=summary(coxph(Surv(VitalStatus_Months, death)~ Aspirin_re
+ Enrollment_Age
+ BMI + Education
#+ HHIncome
+ family
+ smk[,2]+smk[,3]
+ Diabetes + BPH
#+ StatinUse
#+ Stroke_TIA
+ factor(Enrollment_Year)
+ aggressive
+ PSA_Ever
+ DRE_Ever + Acetaminophen + nsaids
, data=survival_data))
p=round(cox.sum$coefficients[1,"Pr(>|z|)"],digits=3)
hr=round(cox.sum$coefficients[1,"exp(coef)"],digits=2)
ci=round(cox.sum$conf.int[1,c("lower .95", "upper .95")],digits=2)
hr.ci.p=paste0(hr,"(",ci[1],"-",ci[2],")"," p=",p)
return(hr.ci.p)
#return(cox.sum)
}
#data$Aspirin_re=data$Aspirin_any
#data$Aspirin_re=data$Aspirin_reg
#cox.OS.adj(data)
cox.OS.adj.age=function(data){
survival_data=subset(data,VitalStatus_Months > 0 & time > 0 &!is.na(Aspirin_re) )
survival_data$VitalStatus=ifelse(survival_data$Year_Death > yrs | is.na(survival_data$Year_Death),"A",survival_data$VitalStatus)
survival_data$death=as.numeric(as.factor(survival_data$VitalStatus))-1
#stage=ifelse(survival_data$SCCS_SummAJCCStage %in% c("III","IV"),"High",ifelse(survival_data$SCCS_SummAJCCStage %in% c("I","II"),"Low",NA))
#gl=ifelse(survival_data$Earliest_Gleason_Score %in% c("002","003","004","005","006","007"),"Low",ifelse(survival_data$Earliest_Gleason_Score %in% c("008","009","010"),"High",NA))
survival_data$SmokingStatus = ifelse(survival_data$SmokingStatus == '7777',NA,survival_data$SmokingStatus)
smk <- model.matrix.lm(~factor(SmokingStatus,levels=c(3,2,1)), data = survival_data,na.action="na.pass")
aggressive = ifelse(survival_data$Earliest_Gleason_Score %in% c("008","009","010") | survival_data$SCCS_SummAJCCStage == 'IV' |
survival_data$TNM_Clin_M %in% c('1','1B','1C') | survival_data$TNM_Path_M %in% c('1A','1B') |
survival_data$TNM_Clin_N == '1' | survival_data$TNM_Path_N == '1' |
survival_data$TNM_Clin_T == '4' | survival_data$TNM_Path_T == '4',1,0)
cox.sum=summary(coxph(Surv(Enrollment_Age,VitalStatus_Age, death)~ Aspirin_re
+ BMI + Education
#+ HHIncome
+ family
+ smk[,2]+smk[,3]
+ Diabetes + BPH
#+ StatinUse
#+ Stroke_TIA
+ factor(Enrollment_Year)
+ aggressive
+ PSA_Ever
+ DRE_Ever + Acetaminophen + nsaids
, data=survival_data))
p=round(cox.sum$coefficients[1,"Pr(>|z|)"],digits=3)
hr=round(cox.sum$coefficients[1,"exp(coef)"],digits=2)
ci=round(cox.sum$conf.int[1,c("lower .95", "upper .95")],digits=2)
hr.ci.p=paste0(hr,"(",ci[1],"-",ci[2],")"," p=",p)
return(hr.ci.p)
#return(cox.sum)
}
cmp=function(cif){
cif=subset(cif,VitalStatus_Months > 0 & time > 0 & !is.na(Aspirin_re) )
cif$VitalStatus=ifelse(cif$Year_Death > yrs | is.na(cif$Year_Death),"A",cif$VitalStatus)
cif$status= ifelse( cif$VitalStatus == 'A', 0, ifelse(cif$VitalStatus == 'D',1,NA))
cif$status= ifelse(cif$VitalStatus == 'D' & cif$CauseOfDeath == '', 2, cif$status)
### Fine-Gray model ###
cov1=model.matrix.lm(~Aspirin_re + Enrollment_Age, data = cif, na.action = 'na.pass')[,-1]
#cov1= model.matrix.lm(~Aspirin_re + Education + HHIncome+ Enrollment_Age+BMI+ PSA_Ever + Fa_ProstateCancer, data= cif, na.action = 'na.pass')[,-1]
fg_model= crr(cif$VitalStatus_Months,cif$status,cov1=cov1)
fg_model.sum = summary(fg_model)
p=round(fg_model.sum$coef[1,"p-value"],digits=3)
hr=round(fg_model.sum$coef[1,"exp(coef)"],digits=2)
ci=round(fg_model.sum$conf.int[1,c("2.5%", "97.5%")],digits=2)
hr.ci.p=paste0(hr,"(",ci[1],"-",ci[2],")"," p=",p)
return(hr.ci.p)
}
cmp.adj=function(cif){
cif=subset(cif,VitalStatus_Months > 0 & time > 0 & !is.na(Aspirin_re) )
cif$VitalStatus=ifelse(cif$Year_Death > yrs | is.na(cif$Year_Death),"A",cif$VitalStatus)
cif$status= ifelse( cif$VitalStatus == 'A', 0, ifelse(cif$VitalStatus == 'D',1,NA))
cif$status= ifelse(cif$VitalStatus == 'D' & cif$CauseOfDeath == '', 2, cif$status)
### Fine-Gray model ###
#cov1=model.matrix.lm(~Aspirin_re + Enrollment_Age, data = cif, na.action = 'na.pass')[,-1]
smk <- model.matrix.lm(~factor(SmokingStatus,levels=c(3,2,1)), data = cif,na.action="na.pass")
aggressive = ifelse(cif$Earliest_Gleason_Score %in% c("008","009","010") | cif$SCCS_SummAJCCStage == 'IV' |
cif$TNM_Clin_M %in% c('1','1B','1C') | cif$TNM_Path_M %in% c('1A','1B') |
cif$TNM_Clin_N == '1' | cif$TNM_Path_N == '1' |
cif$TNM_Clin_T == '4' | cif$TNM_Path_T == '4',1,0)
cov1= model.matrix.lm(~Aspirin_re
+ Enrollment_Age + BMI + Education
#+ HHIncome
+ family + smk[,2]+smk[,3] + Diabetes + BPH
#+ StatinUse
#+ Stroke_TIA
+ factor(Enrollment_Year)
+ aggressive
+ PSA_Ever
+ DRE_Ever
+ Acetaminophen + nsaids
, data=cif,na.action='na.pass')[,-1]
fg_model= crr(cif$VitalStatus_Months,cif$status,cov1=cov1)
fg_model.sum = summary(fg_model)
p=round(fg_model.sum$coef[1,"p-value"],digits=3)
hr=round(fg_model.sum$coef[1,"exp(coef)"],digits=2)
ci=round(fg_model.sum$conf.int[1,c("2.5%", "97.5%")],digits=2)
hr.ci.p=paste0(hr,"(",ci[1],"-",ci[2],")"," p=",p)
return(hr.ci.p)
}
######################## function end ############################
# prostate cancer specific #
temp=data
temp$VitalStatus = ifelse(temp$VitalStatus == "D" & temp$CauseOfDeath=="", "A",temp$VitalStatus )
# variables with mortality #
var.table=rbind(
cox.OS.var(temp,"Aspirin_any"),
cox.OS.var(temp,"Aspirin_reg"),
cox.OS.var(temp,"family"),
cox.OS.var(temp,"Diabetes"),
cox.OS.var(temp,"BPH"),
cox.OS.var(temp,"StatinUse"),
cox.OS.var(temp,"Stroke_TIA"),
cox.OS.var(temp,"PSA_Ever"),
cox.OS.var(temp,"DRE_Ever"),
cox.OS.var(temp,"Acetaminophen"),
cox.OS.var(temp,"nsaids"),
cox.OS.var(temp,"Enrollment_Age"),
cox.OS.var(temp,"SmokingStatus"),
cox.OS.var(temp,"BMI"),
cox.OS.var(temp,"Education"),
cox.OS.var(temp,"HHIncome"),
cox.OS.var(temp, "Enrollment_Year")
)
colnames(var.table) = c('var HR p')
#write.csv(var.table,"temp.var.OS.csv",quote = F)
## table of mortality ##
pr.os.table= function(data){
temp$Aspirin_re=temp$Aspirin_any
any.t = event.person.mo(temp,"Aspirin_re")
any.os= cox.OS(temp)
any.os.adj= cox.OS.adj(temp)
any.cmp = cmp(temp)
any.cmp.adj=cmp.adj(temp)
temp$Aspirin_re=temp$Aspirin_reg
reg.t = event.person.mo(temp,"Aspirin_re")
reg.os= cox.OS(temp)
reg.os.adj= cox.OS.adj(temp)
reg.cmp = cmp(temp)
reg.cmp.adj=cmp.adj(temp)
os.table=qpcR:::rbind.na(cbind(any.t,c(NA,any.os),c(NA,any.os.adj),c(NA,any.cmp),c(NA,any.cmp.adj)),
cbind(reg.t,c(NA,reg.os),c(NA,reg.os.adj),c(NA,reg.cmp),c(NA,reg.cmp.adj)))
colnames(os.table)=c('Events', 'PY', 'HR (95% CI) P value', 'adj HR (95% CI) P value','CMP HR (95% CI) P value', 'CMP adj HR (95% CI) P value')
return(os.table)
}
pr.os.table.age = function(data){
temp$Aspirin_re=temp$Aspirin_any
any.t = event.person.mo(temp,"Aspirin_re")
any.os= cox.OS.age(temp)
any.os.adj= cox.OS.adj.age(temp)
any.cmp = cmp(temp)
any.cmp.adj=cmp.adj(temp)
temp$Aspirin_re=temp$Aspirin_reg
reg.t = event.person.mo(temp,"Aspirin_re")
reg.os= cox.OS.age(temp)
reg.os.adj= cox.OS.adj.age(temp)
reg.cmp = cmp(temp)
reg.cmp.adj=cmp.adj(temp)
os.table=qpcR:::rbind.na(cbind(any.t,c(NA,any.os),c(NA,any.os.adj),c(NA,any.cmp),c(NA,any.cmp.adj)),
cbind(reg.t,c(NA,reg.os),c(NA,reg.os.adj),c(NA,reg.cmp),c(NA,reg.cmp.adj)))
colnames(os.table)=c('Events', 'PY', 'HR (95% CI) P value', 'adj HR (95% CI) P value','CMP HR (95% CI) P value', 'CMP adj HR (95% CI) P value')
return(os.table)
}
yrs=2015
table2015=pr.os.table(temp)
table2015.age= pr.os.table.age(temp)
yrs=2016
table2016=pr.os.table(temp)
table2016.age= pr.os.table.age(temp)
yrs=2017
table2017=pr.os.table(temp)
table2017.age= pr.os.table.age(temp)
yrs=2018
table2018=pr.os.table(temp)
table2018.age= pr.os.table.age(temp)
all.table=rbind(table2015,table2016,table2017,table2018,
NA,
table2015.age,table2016.age,table2017.age,table2018.age)
write.csv(all.table,'table3.csv')
# high stage #
temp=subset(data,pr==0 | SCCS_SummAJCCStage %in% c("III","IV"))
km.OS(temp,"High stage")
# high Gleason #
temp=subset(data,pr==0 | Earliest_Gleason_Score %in% c("008","009","010"))
km.OS(temp,"High Gleason")
km.OS=function(survival_data,title){
survival_data=subset(survival_data,time > 0 )
survival_data$vitalstatus_new=ifelse(survival_data$Year_Death > 2015 | is.na(survival_data$Year_Death),"A",survival_data$VitalStatus)
survival_data$death=as.numeric(as.factor(survival_data$vitalstatus_new))-1
fit<- survfit(Surv(VitalStatus_Months, death) ~ Aspirin_re, data = survival_data)
# Drawing survival curves
ggsurv=ggsurvplot(fit, data=survival_data,pval = F, conf.int = T,risk.table = T,title = title,ylim=c(0,0.01),xlab="Time(months)",fun="event")
ggsurv$plot <- ggsurv$plot +
ggplot2::annotate("text",
x = 50, y = 0.98, # x and y coordinates of the text
label = paste0("HR=",cox.OS(survival_data)), size = 5)
ggsurv
return(ggsurv)
}
temp=data
km.OS(temp,"")
Fig1=km.OS(temp,"Prostate Specfic Death")
pdf("Fig1.pdf")
print(Fig1, newpage = FALSE)
dev.off()
################################################################################################################
vars=c("Aspirin_re","Enrollment_Age","Education","HHIncome","BMI","SmokingStatus",
"family","Diabetes","BPH","StatinUse","Stroke_TIA","Enrollment_Year","PSA_Ever","DRE_Ever","Acetaminophen","nsaids")
+ Enrollment_Age
+ BMI + Education
#+ HHIncome
+ family
+ smk[,2]+smk[,3]
+ Diabetes + BPH
#+ StatinUse
#+ Stroke_TIA
+ factor(Enrollment_Year)
+ aggressive
+ PSA_Ever
+ DRE_Ever + Acetaminophen + nsaids
for (i in vars) {
vars.sum = cox.OS.var(temp,i)
print(c(vars.sum,i))
}
for (i in vars) {
vars.sum = cox.OS.var(data,i)
print(c(vars.sum,i))
}
event.person.mo(temp,"Aspirin_re")
cox.OS(temp)
cox.OS.adj(temp)
non_scr=subset(temp, PSA_Ever ==0)
event.person.mo(non_scr,"Aspirin_re")
cox.OS(non_scr)
cox.OS.adj(non_scr)
scr=subset(temp, PSA_Ever == 1)
event.person.mo(scr,"Aspirin_re")
cox.OS(scr)
cox.OS.adj(scr)
### competing risk ####
### CIF ###
cmp.adj(data)