Heller title: “Phar7380 PD Project” Dec 8, 2024 output: html_document: default pdf_document: default word_document: default — #libraries
library(ggplot2)
library(dplyr)
library(knitr)#theme
my_theme<-function(x){theme_bw()+
theme(text = element_text(size=20))+
theme(axis.line.y = element_line(size = 2.0))+
theme(axis.line.x = element_line(size = 2.0))+
theme(axis.ticks = element_line(size = 1.5,colour="black"))+
theme(axis.ticks.length= unit(0.45, "cm"))+
theme(axis.title.y =element_text(vjust=1.2))+
theme(axis.title.x =element_text(vjust=-0.2))+
theme(axis.text=element_text(colour="black"))+
theme(panel.background = element_rect(fill ="white"))}#40 mg dose 10 subjects IDs 1 through 10
Read res file
sim40_10n<-read.table("C:\\Heller\\PHAR7380\\sandbox\\q2w40mg_10n.res",skip=1,header=T)
#PK Profile
ggplot(data=sim40_10n%>%filter(CMT==2),aes(TIME/7,IPRED,group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## i Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## i Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
#PD Profile
ggplot(data=sim40_10n%>%filter(CMT==3),aes(TIME/7,IPRED))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CRP (mg/L)")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#Create summary files for PK and PD 10 subjects IDs 1 through 10
sim40_10nsum2<-sim40_10n%>%filter(CMT==2)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))
sim40_10nsum3<-sim40_10n%>%filter(CMT==3)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#plots for 40mg dose PK using sim40_10n
ggplot(data=sim40_10n%>%filter(CMT==2),aes(TIME/7,PRED))+
geom_line(size=0.5)+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#median and 95% CI using sim40_10nsum2
ggplot(data=sim40_10nsum2,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim40_10nsum2,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim40_10nsum2,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Conc(mg/L)ah")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#plots for 40mg dose CRP using sim40_10n
ggplot(data=sim40_10n%>%filter(CMT==3),aes(TIME/7,PRED))+
geom_line(size=0.5)+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CD4 count/mL")
#labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)a")+
#scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
labs(x="Time after dose (week)",y="CRP (mg/L)")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#median and 95% CI using sim40_10nsum3
ggplot(data=sim40_10nsum3,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim40_10nsum3,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim40_10nsum3,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CRP (mg/L)")
labs(x="Time after dose (week)",y="CRP (mg/L)")+geom_hline(yintercept=8, linetype="dashed", color = "red")+scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#8040 dose n=10
sim8040_10n<-read.table("C:\\Heller\\PHAR7380\\sandbox\\ld80mgq2w40_n10.res",skip=1,header=T)
#PK Profile
ggplot(data=sim8040_10n%>%filter(CMT==2),aes(TIME/7,IPRED))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#PD Profile
ggplot(data=sim8040_10n%>%filter(CMT==3),aes(TIME/7,IPRED))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CRP (mg/L)")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
Plots with CI for 8040 Dose
#PK Profile
ggplot(data=sim8040_10n%>%filter(CMT==2),aes(TIME/7,IPRED))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#PD Profile
ggplot(data=sim8040_10n%>%filter(CMT==3),aes(TIME/7,IPRED))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CRP (mg/L)")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
sim8040_10nsum2<-sim8040_10n%>%filter(CMT==2)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))
sim8040_10nsum3<-sim8040_10n%>%filter(CMT==3)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#plots 8040 dose n=10
ggplot(data=sim8040_10n%>%filter(CMT==3),aes(TIME/7,PRED))+
geom_line(size=0.5)+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CD4 count/mL")
#labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)a")+
#scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
labs(x="Time after dose (week)",y="CRP (mg/L)")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#median and 95% CI using sim40_10nsum3
ggplot(data=sim8040_10nsum3,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim40_10nsum3,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim40_10nsum3,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CRP (mg/L)")
labs(x="Time after dose (week)",y="CRP (mg/L)")+geom_hline(yintercept=8, linetype="dashed", color = "red")+scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#40 mg dose n=100 subjects IDs 1 through 100, Populatio
sim40_100n<-read.table("C:\\Heller\\PHAR7380\\sandbox\\q2w40mg_100n.res",skip=1,header=T)
#PK Profile
ggplot(data=sim40_100n%>%filter(CMT==2),aes(TIME/7,PRED))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Conc (mg/L) 40q2w")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#PD Profile
ggplot(data=sim40_100n%>%filter(CMT==3),aes(TIME/7,PRED))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CRP (mg/L) 40q2w")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
Read res file 40mg q2w individual
sim40_100n<-read.table("C:\\Heller\\PHAR7380\\sandbox\\q2w40mg_100n.res",skip=1,header=T)
#PK Profile
ggplot(data=sim40_100n%>%filter(CMT==2),aes(TIME/7,IPRED,group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Conc (mg/L) 40q2w")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#PD Profile
ggplot(data=sim40_100n%>%filter(CMT==3),aes(TIME/7,IPRED,group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CRP (mg/L) 40q2w")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#Create summary files for PK and PD 100 subjects IDs 1 through 100
sim40_100nsum2<-sim40_100n%>%filter(CMT==2)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))
sim40_100nsum3<-sim40_10n%>%filter(CMT==3)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#plots for 40mg dose PK using sim40_100n
ggplot(data=sim40_100n%>%filter(CMT==2),aes(TIME/7,PRED))+
geom_line(size=0.5)+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#median and 95% CI using sim40_10nsum2
ggplot(data=sim40_100nsum2,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim40_100nsum2,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim40_100nsum2,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Conc(mg/L)ah")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#plots for 40mg dose CRP using sim40_100n
ggplot(data=sim40_100n%>%filter(CMT==3),aes(TIME/7,PRED))+
geom_line(size=0.5)+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CD4 count/mL")
#labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)a")+
#scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
labs(x="Time after dose (week)",y="CRP (mg/L)")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#median and 95% CI using sim40_100nsum3
ggplot(data=sim40_100nsum3,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim40_100nsum3,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim40_100nsum3,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CRP (mg/L)")
labs(x="Time after dose (week)",y="CRP (mg/L)")+geom_hline(yintercept=8, linetype="dashed", color = "red")+scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#8040 mg dose n=100 subjects IDs 1 through 100
Read res file
sim8040_100n<-read.table("C:\\Heller\\PHAR7380\\sandbox\\ld80mgq2w40_n100.res",skip=1,header=T)
#PK Profile
ggplot(data=sim8040_100n%>%filter(CMT==2),aes(TIME/7,IPRED,group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNTConc(mg/L)8040")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#PD Profile
ggplot(data=sim8040_100n%>%filter(CMT==3),aes(TIME/7,IPRED, group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CRP(mg/L) 8040")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#Create summary files for 8040 PK and PD 100 subjects IDs 1 through 100
sim8040_100nsum2<-sim8040_100n%>%filter(CMT==2)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))
sim8040_100nsum3<-sim8040_10n%>%filter(CMT==3)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#plots for 8040mg dose PK using sim8040_100n
ggplot(data=sim8040_100n%>%filter(CMT==2),aes(TIME/7,PRED))+
geom_line(size=0.5)+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#median and 95% CI using sim40_10nsum2
ggplot(data=sim8040_100nsum2,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim8040_100nsum2,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim8040_100nsum2,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Conc(mg/L)ah")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#plots for 8040mg dose CRP using sim8040_100n
ggplot(data=sim8040_100n%>%filter(CMT==3),aes(TIME/7,PRED))+
geom_line(size=0.5)+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CD4 count/mL")
#labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)a")+
#scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
labs(x="Time after dose (week)",y="CRP (mg/L)")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#median and 95% CI using sim40_100nsum3
ggplot(data=sim8040_100nsum3,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim8040_100nsum3,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim8040_100nsum3,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CRP (mg/L)")
labs(x="Time after dose (week)",y="CRP (mg/L)")+geom_hline(yintercept=8, linetype="dashed", color = "red")+scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#16040 mg dose n=100 subjects IDs 1 through 100
Read res file
sim16040_100n<-read.table("C:\\Heller\\PHAR7380\\sandbox\\ld160mgq2w40_n100.res",skip=1,header=T)
#PK Profile
ggplot(data=sim16040_100n%>%filter(CMT==2),aes(TIME/7,IPRED,group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNTConc(mg/L) 16040")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#PD Profile
ggplot(data=sim16040_100n%>%filter(CMT==3),aes(TIME/7,IPRED, group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CRP(mg/L) 16040")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#Create summary files for 16040 PK and PD 100 subjects IDs 1 through 100
sim16040_100nsum2<-sim16040_100n%>%filter(CMT==2)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))
sim16040_100nsum3<-sim16040_100n%>%filter(CMT==3)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#plots for 16040mg dose PK using sim16040_100n
ggplot(data=sim16040_100n%>%filter(CMT==2),aes(TIME/7,PRED))+
geom_line(size=0.5)+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#median and 95% CI using sim40_10nsum2
ggplot(data=sim16040_100nsum2,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim16040_100nsum2,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim16040_100nsum2,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Conc(mg/L)ah")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#plots for 8040mg dose CRP using sim8040_100n
ggplot(data=sim16040_100n%>%filter(CMT==3),aes(TIME/7,PRED))+
geom_line(size=0.5)+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CD4 count/mL")
#labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)a")+
#scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
labs(x="Time after dose (week)",y="CRP (mg/L)")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#median and 95% CI using sim40_100nsum3
ggplot(data=sim16040_100nsum3,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim16040_100nsum3,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim16040_100nsum3,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CRP (mg/L)")
labs(x="Time after dose (week)",y="CRP (mg/L)")+geom_hline(yintercept=8, linetype="dashed", color = "red")+scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#32040 mg dose n=100 subjects IDs 1 through 100
Read res file 32040
sim32040_100n<-read.table("C:\\Heller\\PHAR7380\\sandbox\\ld320mgq2w40_n100.res",skip=1,header=T)
#PK Profile
ggplot(data=sim32040_100n%>%filter(CMT==2),aes(TIME/7,IPRED,group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNTConc(mg/L)32040")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#PD Profile
ggplot(data=sim32040_100n%>%filter(CMT==3),aes(TIME/7,IPRED, group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CRP(mg/L) 32040")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#Create summary files for 32040 PK and PD 100 subjects IDs 1 through 100
sim32040_100nsum2<-sim32040_100n%>%filter(CMT==2)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))
sim32040_100nsum3<-sim32040_100n%>%filter(CMT==3)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#plots for 32040mg dose PK using sim32040_100n
ggplot(data=sim32040_100n%>%filter(CMT==2),aes(TIME/7,PRED))+
geom_line(size=0.5)+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#median and 95% CI using sim40_10nsum2
ggplot(data=sim32040_100nsum2,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim32040_100nsum2,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim32040_100nsum2,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Conc(mg/L)")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#plots for 8040mg dose CRP using sim8040_100n
ggplot(data=sim32040_100n%>%filter(CMT==3),aes(TIME/7,PRED))+
geom_line(size=0.5)+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CD4 count/mL")
#labs(x="Time after dose (week)",y="UNT763 Concentration (mg/L)a")+
#scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
labs(x="Time after dose (week)",y="CRP (mg/L)")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#median and 95% CI using sim40_100nsum3
ggplot(data=sim32040_100nsum3,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim32040_100nsum3,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim32040_100nsum3,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CRP (mg/L)")
labs(x="Time after dose (week)",y="CRP (mg/L)")+geom_hline(yintercept=8, linetype="dashed", color = "red")+scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
Read res file 32080 and pop plots
sim32080_100n<-read.table("C:\\Heller\\PHAR7380\\sandbox\\ld320mgq2w80_n100.res",skip=1,header=T)
#PK Profile
ggplot(data=sim32080_100n%>%filter(CMT==2),aes(TIME/7,PRED))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Conc(mg/L) 32080")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#PD Profile
ggplot(data=sim32080_100n%>%filter(CMT==3),aes(TIME/7,PRED))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CRP(mg/L) 32080")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#PK Profile
ggplot(data=sim32080_100n%>%filter(CMT==2),aes(TIME/7,IPRED, group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Conc(mg/L) 32080")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#PD Profile
ggplot(data=sim32080_100n%>%filter(CMT==3),aes(TIME/7,IPRED, group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CRP(mg/L) 32080")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#create 95 CI summary tables for 32080
sim32080_100nsum2<-sim32080_100n%>%filter(CMT==2)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))
sim32080_100nsum3<-sim32080_100n%>%filter(CMT==3)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#32080 plots median 95 CI
#PK median and 95% CI
ggplot(data=sim32080_100nsum2,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim32080_100nsum2,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim32080_100nsum2,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNTConc(mg/L) 32080")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#32080 plots median 95 CI
ggplot(data=sim32080_100nsum3,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim32080_100nsum3,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim32080_100nsum3,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CRP (mg/L)")
labs(x="Time after dose (week)",y="CRP(mg/L) 32080")+geom_hline(yintercept=8, linetype="dashed", color = "red")+scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#read file 320160 and population plots
sim320160_100n<-read.table("C:\\Heller\\PHAR7380\\sandbox\\ld320mgq2w160_n100.res",skip=1,header=T)
#PK Profile
ggplot(data=sim320160_100n%>%filter(CMT==2),aes(TIME/7,PRED))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Conc(mg/L) 320160")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#PD Profile
ggplot(data=sim32080_100n%>%filter(CMT==3),aes(TIME/7,PRED))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CRP(mg/L) 320160")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#individual predictions 320160
#PK Profile
ggplot(data=sim320160_100n%>%filter(CMT==2),aes(TIME/7,IPRED,group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNT763 Conc(mg/L) 320160")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#PD Profile
ggplot(data=sim32080_100n%>%filter(CMT==3),aes(TIME/7,IPRED,group=ID))+
geom_line(size=0.5)+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="CRP(mg/L) 320160")+
geom_hline(yintercept=8, linetype="dashed", color = "red")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#create 95ci summary files for 320160
sim320160_100nsum2<-sim320160_100n%>%filter(CMT==2)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))
sim320160_100nsum3<-sim320160_100n%>%filter(CMT==3)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu975=quantile(IPRED,0.975),cu025=quantile(IPRED,0.025))#95 ci for PK and CRP for 320160
ggplot(data=sim320160_100nsum2,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim320160_100nsum2,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim320160_100nsum2,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
labs(x="Time after dose (week)",y="UNTConc(mg/L)320160")+
scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
ggplot(data=sim320160_100nsum3,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim320160_100nsum3,aes((TIME/7),cu975),size=0.5,linetype="dashed")+
geom_line(data=sim320160_100nsum3,aes((TIME/7),cu025),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CRP (mg/L)")
labs(x="Time after dose (week)",y="CRP (mg/L)320160")+geom_hline(yintercept=8, linetype="dashed", color = "red")+scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
#table for 320160
sim320160_100n%>%
select(ID,TIME,CMT,IPRED)%>%
group_by(TIME)%>%
filter(CMT==3)%>%
#mutate(suppression=(IPRED)%>%
filter(IPRED>=8)%>%
summarise(number=n_distinct(ID))%>%kable| TIME | number |
|---|---|
| 1 | 99 |
| 2 | 83 |
| 3 | 46 |
| 4 | 23 |
| 5 | 15 |
| 6 | 9 |
| 7 | 7 |
| 8 | 6 |
| 9 | 4 |
| 10 | 4 |
| 11 | 4 |
| 12 | 4 |
| 13 | 4 |
| 14 | 4 |
| 15 | 3 |
| 16 | 3 |
| 17 | 3 |
| 18 | 3 |
| 19 | 3 |
| 20 | 3 |
| 21 | 2 |
| 22 | 2 |
| 23 | 2 |
| 24 | 2 |
| 25 | 2 |
| 26 | 2 |
| 27 | 2 |
| 28 | 2 |
| 29 | 2 |
| 30 | 2 |
| 31 | 2 |
| 32 | 2 |
| 33 | 2 |
| 34 | 2 |
| 35 | 2 |
| 36 | 2 |
| 37 | 2 |
| 38 | 2 |
| 39 | 2 |
| 40 | 2 |
| 41 | 2 |
| 42 | 2 |
| 43 | 2 |
| 44 | 2 |
| 45 | 2 |
| 46 | 2 |
| 47 | 2 |
| 48 | 2 |
| 49 | 2 |
| 50 | 2 |
| 51 | 2 |
| 52 | 2 |
| 53 | 2 |
| 54 | 2 |
| 55 | 2 |
| 56 | 2 |
| 57 | 2 |
| 58 | 2 |
| 59 | 2 |
| 60 | 2 |
| 61 | 2 |
| 62 | 2 |
| 63 | 2 |
| 64 | 2 |
| 65 | 2 |
| 66 | 2 |
| 67 | 2 |
| 68 | 2 |
| 69 | 2 |
| 70 | 2 |
| 71 | 2 |
| 72 | 2 |
| 73 | 2 |
| 74 | 2 |
| 75 | 2 |
| 76 | 2 |
| 77 | 2 |
| 78 | 2 |
| 79 | 2 |
| 80 | 2 |
| 81 | 2 |
| 82 | 2 |
| 83 | 2 |
| 84 | 2 |
| 85 | 2 |
| 86 | 2 |
| 87 | 2 |
| 88 | 2 |
| 89 | 2 |
| 90 | 2 |
| 91 | 2 |
| 92 | 2 |
| 93 | 2 |
| 94 | 2 |
| 95 | 2 |
| 96 | 2 |
| 97 | 2 |
| 98 | 2 |
| 99 | 2 |
| 100 | 2 |
| 101 | 2 |
| 102 | 2 |
| 103 | 2 |
| 104 | 1 |
| 105 | 2 |
#table for 32080
sim32080_100n%>%
select(ID,TIME,CMT,IPRED)%>%
group_by(TIME)%>%
filter(CMT==3)%>%
#mutate(suppression=(IPRED)%>%
filter(IPRED>=8)%>%
summarise(number=n_distinct(ID))%>%kable| TIME | number |
|---|---|
| 1 | 99 |
| 2 | 83 |
| 3 | 46 |
| 4 | 23 |
| 5 | 15 |
| 6 | 9 |
| 7 | 7 |
| 8 | 6 |
| 9 | 4 |
| 10 | 4 |
| 11 | 4 |
| 12 | 4 |
| 13 | 4 |
| 14 | 4 |
| 15 | 3 |
| 16 | 3 |
| 17 | 3 |
| 18 | 3 |
| 19 | 3 |
| 20 | 3 |
| 21 | 3 |
| 22 | 3 |
| 23 | 3 |
| 24 | 3 |
| 25 | 3 |
| 26 | 3 |
| 27 | 3 |
| 28 | 5 |
| 29 | 5 |
| 30 | 3 |
| 31 | 3 |
| 32 | 3 |
| 33 | 3 |
| 34 | 3 |
| 35 | 3 |
| 36 | 3 |
| 37 | 3 |
| 38 | 4 |
| 39 | 5 |
| 40 | 6 |
| 41 | 7 |
| 42 | 8 |
| 43 | 8 |
| 44 | 6 |
| 45 | 6 |
| 46 | 4 |
| 47 | 4 |
| 48 | 4 |
| 49 | 4 |
| 50 | 5 |
| 51 | 5 |
| 52 | 6 |
| 53 | 8 |
| 54 | 8 |
| 55 | 8 |
| 56 | 8 |
| 57 | 8 |
| 58 | 8 |
| 59 | 7 |
| 60 | 6 |
| 61 | 6 |
| 62 | 5 |
| 63 | 5 |
| 64 | 6 |
| 65 | 7 |
| 66 | 8 |
| 67 | 8 |
| 68 | 8 |
| 69 | 8 |
| 70 | 9 |
| 71 | 9 |
| 72 | 8 |
| 73 | 8 |
| 74 | 7 |
| 75 | 7 |
| 76 | 7 |
| 77 | 7 |
| 78 | 7 |
| 79 | 8 |
| 80 | 8 |
| 81 | 8 |
| 82 | 8 |
| 83 | 9 |
| 84 | 9 |
| 85 | 9 |
| 86 | 8 |
| 87 | 8 |
| 88 | 8 |
| 89 | 7 |
| 90 | 7 |
| 91 | 7 |
| 92 | 8 |
| 93 | 8 |
| 94 | 8 |
| 95 | 8 |
| 96 | 8 |
| 97 | 9 |
| 98 | 9 |
| 99 | 9 |
| 100 | 8 |
| 101 | 8 |
| 102 | 8 |
| 103 | 8 |
| 104 | 8 |
| 105 | 8 |
#16040
sim16040_100n%>%
select(ID,TIME,CMT,IPRED)%>%
group_by(TIME)%>%
filter(CMT==3)%>%
#mutate(suppression=(IPRED)%>%
filter(IPRED>=8)%>%
summarise(number=n_distinct(ID))%>%kable| TIME | number |
|---|---|
| 1 | 99 |
| 2 | 92 |
| 3 | 79 |
| 4 | 62 |
| 5 | 44 |
| 6 | 36 |
| 7 | 31 |
| 8 | 29 |
| 9 | 27 |
| 10 | 25 |
| 11 | 26 |
| 12 | 27 |
| 13 | 27 |
| 14 | 28 |
| 15 | 27 |
| 16 | 26 |
| 17 | 24 |
| 18 | 23 |
| 19 | 23 |
| 20 | 23 |
| 21 | 23 |
| 22 | 23 |
| 23 | 25 |
| 24 | 25 |
| 25 | 26 |
| 26 | 27 |
| 27 | 28 |
| 28 | 29 |
| 29 | 29 |
| 30 | 28 |
| 31 | 27 |
| 32 | 27 |
| 33 | 26 |
| 34 | 26 |
| 35 | 26 |
| 36 | 28 |
| 37 | 28 |
| 38 | 28 |
| 39 | 28 |
| 40 | 31 |
| 41 | 33 |
| 42 | 35 |
| 43 | 35 |
| 44 | 32 |
| 45 | 28 |
| 46 | 28 |
| 47 | 28 |
| 48 | 28 |
| 49 | 28 |
| 50 | 28 |
| 51 | 29 |
| 52 | 31 |
| 53 | 33 |
| 54 | 37 |
| 55 | 37 |
| 56 | 38 |
| 57 | 37 |
| 58 | 36 |
| 59 | 35 |
| 60 | 31 |
| 61 | 29 |
| 62 | 29 |
| 63 | 30 |
| 64 | 31 |
| 65 | 33 |
| 66 | 35 |
| 67 | 37 |
| 68 | 37 |
| 69 | 37 |
| 70 | 43 |
| 71 | 42 |
| 72 | 37 |
| 73 | 36 |
| 74 | 35 |
| 75 | 31 |
| 76 | 31 |
| 77 | 31 |
| 78 | 35 |
| 79 | 35 |
| 80 | 36 |
| 81 | 37 |
| 82 | 37 |
| 83 | 43 |
| 84 | 44 |
| 85 | 44 |
| 86 | 39 |
| 87 | 36 |
| 88 | 35 |
| 89 | 34 |
| 90 | 34 |
| 91 | 34 |
| 92 | 35 |
| 93 | 35 |
| 94 | 37 |
| 95 | 37 |
| 96 | 40 |
| 97 | 44 |
| 98 | 45 |
| 99 | 44 |
| 100 | 40 |
| 101 | 36 |
| 102 | 36 |
| 103 | 35 |
| 104 | 34 |
| 105 | 35 |
sim32040_100n%>%
select(ID,TIME,CMT,IPRED)%>%
group_by(TIME)%>%
filter(CMT==3)%>%
#mutate(suppression=(IPRED)%>%
filter(IPRED>=8)%>%
summarise(number=n_distinct(ID))%>%kable| TIME | number |
|---|---|
| 1 | 99 |
| 2 | 84 |
| 3 | 46 |
| 4 | 23 |
| 5 | 15 |
| 6 | 9 |
| 7 | 7 |
| 8 | 6 |
| 9 | 4 |
| 10 | 4 |
| 11 | 4 |
| 12 | 4 |
| 13 | 4 |
| 14 | 4 |
| 15 | 4 |
| 16 | 3 |
| 17 | 3 |
| 18 | 3 |
| 19 | 3 |
| 20 | 3 |
| 21 | 4 |
| 22 | 4 |
| 23 | 5 |
| 24 | 5 |
| 25 | 8 |
| 26 | 8 |
| 27 | 9 |
| 28 | 9 |
| 29 | 9 |
| 30 | 9 |
| 31 | 9 |
| 32 | 9 |
| 33 | 9 |
| 34 | 9 |
| 35 | 9 |
| 36 | 9 |
| 37 | 9 |
| 38 | 10 |
| 39 | 10 |
| 40 | 10 |
| 41 | 12 |
| 42 | 14 |
| 43 | 14 |
| 44 | 13 |
| 45 | 11 |
| 46 | 11 |
| 47 | 11 |
| 48 | 12 |
| 49 | 12 |
| 50 | 13 |
| 51 | 16 |
| 52 | 17 |
| 53 | 19 |
| 54 | 20 |
| 55 | 22 |
| 56 | 25 |
| 57 | 25 |
| 58 | 23 |
| 59 | 21 |
| 60 | 21 |
| 61 | 20 |
| 62 | 21 |
| 63 | 21 |
| 64 | 22 |
| 65 | 23 |
| 66 | 24 |
| 67 | 26 |
| 68 | 28 |
| 69 | 28 |
| 70 | 31 |
| 71 | 30 |
| 72 | 28 |
| 73 | 26 |
| 74 | 25 |
| 75 | 25 |
| 76 | 24 |
| 77 | 25 |
| 78 | 25 |
| 79 | 27 |
| 80 | 29 |
| 81 | 30 |
| 82 | 31 |
| 83 | 31 |
| 84 | 33 |
| 85 | 33 |
| 86 | 31 |
| 87 | 30 |
| 88 | 29 |
| 89 | 27 |
| 90 | 28 |
| 91 | 28 |
| 92 | 29 |
| 93 | 30 |
| 94 | 31 |
| 95 | 33 |
| 96 | 33 |
| 97 | 33 |
| 98 | 35 |
| 99 | 35 |
| 100 | 33 |
| 101 | 32 |
| 102 | 31 |
| 103 | 30 |
| 104 | 29 |
| 105 | 30 |
#table for 16040
sim16040_100n%>%
select(ID,TIME,CMT,IPRED)%>%
group_by(TIME)%>%
filter(CMT==3)%>%
#mutate(suppression=(IPRED)%>%
filter(IPRED>=8)%>%
summarise(number=n_distinct(ID))%>%kable| TIME | number |
|---|---|
| 1 | 99 |
| 2 | 92 |
| 3 | 79 |
| 4 | 62 |
| 5 | 44 |
| 6 | 36 |
| 7 | 31 |
| 8 | 29 |
| 9 | 27 |
| 10 | 25 |
| 11 | 26 |
| 12 | 27 |
| 13 | 27 |
| 14 | 28 |
| 15 | 27 |
| 16 | 26 |
| 17 | 24 |
| 18 | 23 |
| 19 | 23 |
| 20 | 23 |
| 21 | 23 |
| 22 | 23 |
| 23 | 25 |
| 24 | 25 |
| 25 | 26 |
| 26 | 27 |
| 27 | 28 |
| 28 | 29 |
| 29 | 29 |
| 30 | 28 |
| 31 | 27 |
| 32 | 27 |
| 33 | 26 |
| 34 | 26 |
| 35 | 26 |
| 36 | 28 |
| 37 | 28 |
| 38 | 28 |
| 39 | 28 |
| 40 | 31 |
| 41 | 33 |
| 42 | 35 |
| 43 | 35 |
| 44 | 32 |
| 45 | 28 |
| 46 | 28 |
| 47 | 28 |
| 48 | 28 |
| 49 | 28 |
| 50 | 28 |
| 51 | 29 |
| 52 | 31 |
| 53 | 33 |
| 54 | 37 |
| 55 | 37 |
| 56 | 38 |
| 57 | 37 |
| 58 | 36 |
| 59 | 35 |
| 60 | 31 |
| 61 | 29 |
| 62 | 29 |
| 63 | 30 |
| 64 | 31 |
| 65 | 33 |
| 66 | 35 |
| 67 | 37 |
| 68 | 37 |
| 69 | 37 |
| 70 | 43 |
| 71 | 42 |
| 72 | 37 |
| 73 | 36 |
| 74 | 35 |
| 75 | 31 |
| 76 | 31 |
| 77 | 31 |
| 78 | 35 |
| 79 | 35 |
| 80 | 36 |
| 81 | 37 |
| 82 | 37 |
| 83 | 43 |
| 84 | 44 |
| 85 | 44 |
| 86 | 39 |
| 87 | 36 |
| 88 | 35 |
| 89 | 34 |
| 90 | 34 |
| 91 | 34 |
| 92 | 35 |
| 93 | 35 |
| 94 | 37 |
| 95 | 37 |
| 96 | 40 |
| 97 | 44 |
| 98 | 45 |
| 99 | 44 |
| 100 | 40 |
| 101 | 36 |
| 102 | 36 |
| 103 | 35 |
| 104 | 34 |
| 105 | 35 |
#Create 90CI summary files for 32040 PK and PD 100 subjects IDs 1 through 100 - named 2A, 3A
sim32040_100nsum2A<-sim32040_100n%>%filter(CMT==2)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu95=quantile(IPRED,0.95),cu05=quantile(IPRED,0.05))
sim32040_100nsum3A<-sim32040_100n%>%filter(CMT==3)%>%group_by(TIME)%>%summarise(cmed=median(IPRED),cu95=quantile(IPRED,0.95),cu05=quantile(IPRED,0.05))#Try using a wider confidence interval for 320, ie 90% (950 and 050)
#median and 90% CI using sim16040_100nsum3A
ggplot(data=sim32040_100nsum3A,aes((TIME/7),cmed*1))+
geom_line(size=0.5)+
geom_line(data=sim32040_100nsum3A,aes((TIME/7),cu95),size=0.5,linetype="dashed")+
geom_line(data=sim32040_100nsum3A,aes((TIME/7),cu05),size=0.5,linetype="dashed")+
#scale_x_continuous(limits = c(0,6))+
theme_bw()+
my_theme()+
#labs(x="Time after dose (week)",y="CRP (mg/L)")
labs(x="Time after dose (week)",y="CRP (mg/L),90CI")+geom_hline(yintercept=8, linetype="dashed", color = "red")+scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16),limits = c(0,16))
table for 32040
sim32040_100n%>%
select(ID,TIME,CMT,IPRED)%>%
group_by(TIME)%>%
filter(CMT==3)%>%
#mutate(suppression=(IPRED)%>%
filter(IPRED>=8)%>%
summarise(number=n_distinct(ID))%>%kable| TIME | number |
|---|---|
| 1 | 99 |
| 2 | 84 |
| 3 | 46 |
| 4 | 23 |
| 5 | 15 |
| 6 | 9 |
| 7 | 7 |
| 8 | 6 |
| 9 | 4 |
| 10 | 4 |
| 11 | 4 |
| 12 | 4 |
| 13 | 4 |
| 14 | 4 |
| 15 | 4 |
| 16 | 3 |
| 17 | 3 |
| 18 | 3 |
| 19 | 3 |
| 20 | 3 |
| 21 | 4 |
| 22 | 4 |
| 23 | 5 |
| 24 | 5 |
| 25 | 8 |
| 26 | 8 |
| 27 | 9 |
| 28 | 9 |
| 29 | 9 |
| 30 | 9 |
| 31 | 9 |
| 32 | 9 |
| 33 | 9 |
| 34 | 9 |
| 35 | 9 |
| 36 | 9 |
| 37 | 9 |
| 38 | 10 |
| 39 | 10 |
| 40 | 10 |
| 41 | 12 |
| 42 | 14 |
| 43 | 14 |
| 44 | 13 |
| 45 | 11 |
| 46 | 11 |
| 47 | 11 |
| 48 | 12 |
| 49 | 12 |
| 50 | 13 |
| 51 | 16 |
| 52 | 17 |
| 53 | 19 |
| 54 | 20 |
| 55 | 22 |
| 56 | 25 |
| 57 | 25 |
| 58 | 23 |
| 59 | 21 |
| 60 | 21 |
| 61 | 20 |
| 62 | 21 |
| 63 | 21 |
| 64 | 22 |
| 65 | 23 |
| 66 | 24 |
| 67 | 26 |
| 68 | 28 |
| 69 | 28 |
| 70 | 31 |
| 71 | 30 |
| 72 | 28 |
| 73 | 26 |
| 74 | 25 |
| 75 | 25 |
| 76 | 24 |
| 77 | 25 |
| 78 | 25 |
| 79 | 27 |
| 80 | 29 |
| 81 | 30 |
| 82 | 31 |
| 83 | 31 |
| 84 | 33 |
| 85 | 33 |
| 86 | 31 |
| 87 | 30 |
| 88 | 29 |
| 89 | 27 |
| 90 | 28 |
| 91 | 28 |
| 92 | 29 |
| 93 | 30 |
| 94 | 31 |
| 95 | 33 |
| 96 | 33 |
| 97 | 33 |
| 98 | 35 |
| 99 | 35 |
| 100 | 33 |
| 101 | 32 |
| 102 | 31 |
| 103 | 30 |
| 104 | 29 |
| 105 | 30 |
```