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sensitivity analysis intervals.R
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sensitivity analysis intervals.R
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library(readxl)
library(dplyr)
library(pammtools)
library(mgcv)
hbsdata=data.frame(coefs=NA,se=NA,pval=NA)
moidata=data.frame(coefs=NA,se=NA,pval=NA)
for(i in 1:100){
dt <- read_excel("Cleaned data.xlsx")
set.seed(i+1986)
dt$random = runif(nrow(dt))
for(j in 1:nrow(dt)){
dt$weeks[j]=ifelse(dt$start[j]==dt$date[j],dt$random[j]*4,(dt$date[j]-dt$date[j-1])*dt$random[j]+dt$weeks[j-1])
}
dt = dt %>%
group_by(infection_id) %>%
mutate(time=weeks,cumsum=cumsum(clinical),cumsum=ifelse(cumsum>1,1,cumsum),
cumcause =cumsum(cause))
#resolve malaria at the moment of symptomatic unless gametocytes are observed.
dt = dt %>% mutate(cause = ifelse(clinical==1 & cumcause==0,2,cause),
cause = ifelse(clinical==0 & cumsum==1,0,cause),
qpcr = ifelse(clinical==0 & cumsum==1,0,qpcr),
Tgam_uL_adj = ifelse(clinical==0 & cumsum==1,0,Tgam_uL_adj))
dtrm = dt %>% filter(!(clinical==0 & cumsum==1))
dtf <- dtrm %>% group_by(infection_id) %>% mutate(status=ifelse(cause==0,0,1),state=ifelse(sum(cause)==1,1,ifelse(sum(cause)==2,2,0)),symptomatic=ifelse(sum(clinical)>0,1,0),clinical=cumsum(clinical),clinical=ifelse(clinical>1,1,clinical),iniclinical=first(clinical)) %>% ungroup()
table(dtf$cause)
#asymptomatics only
#dtf = dtf %>% filter(iniclinical==0)
dtf2 = dtf %>% group_by(infection_id) %>% mutate(qpcr=first(qpcr),clinical=first(clinical))
faildt = unique(dtf2[dtf2$state==dtf2$cause | dtf2$state==0,] %>%
group_by(infection_id) %>%
mutate(time=ifelse(status==0,max(time),time),date=data.table::fifelse(status==0,last(date),date)) %>% dplyr::select("infection_id","cohortid","time","state","start","gender", "agecat", "HbS", "G6PD376", "G6PD202","moi","qpcr","clinical","iniclinical")) %>%
mutate(time=ifelse(time==0,1,time),id=infection_id) %>% select(-c("qpcr","clinical"))
cuts=sort(unique(c(faildt$time)))
ped <- as_ped(
data = faildt,
formula = Surv(time, state) ~ .,
cut=cuts,
id = "id", combine = TRUE) %>%
mutate(cause=as.factor(cause),
time=tend,
time=ifelse(tend==1,0,time))
ped = ped
ped = ped %>% select(-c("time")) %>% mutate(date=as.Date(start)+tstart)
ped = ped %>% select(c(colnames(ped)[-21]),"cause")
ped= ped %>% mutate(male= ifelse(gender=="Male",1,0),
hbs.homo = ifelse(HbS=="homo",1,0),
hbs.hetero = ifelse(HbS=="hetero",1,0),
g6pd376.homo = ifelse(G6PD376=="homo",1,0),
g6pd376.hetero = ifelse(G6PD376=="hetero",1,0),
g6pd202.homo = ifelse(G6PD202=="homo",1,0),
g6pd202.hetero = ifelse(G6PD202=="hetero",1,0),
age2 = ifelse(agecat==2,1,0),
age3 = ifelse(agecat==3,1,0),
moib = ifelse(moi>1,1,0))
# analysis ----------------------------------------------------------------
mod.c =gam(ped_status ~ cause+s(tend,by=cause,k=5,bs="bs") +
(hbs.homo+hbs.hetero):cause,
data = ped,
family = poisson(),
offset = offset)
sm.c=summary(mod.c)
hbsdata[i,]=c(mod.c$coefficients[5],sm.c$se[5],sm.c$p.pv[5])
mod.c =gam(ped_status ~ cause+s(tend,by=cause,k=5,bs="bs") +
(moib):cause,
data = ped,
family = poisson(),
offset = offset)
sm.c=summary(mod.c)
moidata[i,]=c(mod.c$coefficients[3],sm.c$se[3],sm.c$p.pv[3])
print(i)
}
sum(I(hbsdata$pval<0.05)) #81%
sum(I(moidata$pval<0.05)) #10%
mean1=mean(hbsdata$coefs)
mean2=mean(moidata$coefs)
se1 = (mean(sqrt(hbsdata$se))+(1+1/100)*sd(hbsdata$coefs))^2
se2 = (mean(sqrt(moidata$se))+(1+1/100)*sd(moidata$coefs))^2
zscore1=mean1/se1
zscore2=mean2/se2
2*pnorm(q=abs(zscore1), lower.tail=FALSE)
2*pnorm(q=abs(zscore2), lower.tail=FALSE)
hist(hbsdata$pval)
hist(moidata$pval)
# Longitudinal model ------------------------------------------------------
hbsdatapcr=data.frame(coefs=NA,se=NA,pval=NA)
moidatapcr=data.frame(coefs=NA,se=NA,pval=NA)
hbsdatagam=data.frame(coefs=NA,se=NA,pval=NA)
moidatagam=data.frame(coefs=NA,se=NA,pval=NA)
hbsdatagf=data.frame(coefs=NA,se=NA,pval=NA)
moidatagf=data.frame(coefs=NA,se=NA,pval=NA)
for(i in 1:100){
dt <- read_excel("Cleaned data.xlsx")
dtsub=unique(dt %>% select(infection_id))
set.seed(i+1986)
dtsub$random = runif(nrow(dtsub))
dt = dt %>% left_join(dtsub) %>%
group_by(infection_id) %>%
mutate(weeks=weeks+4*random,cumsum=cumsum(clinical),cumsum=ifelse(cumsum>1,1,cumsum),
cumcause =cumsum(cause))
dt = dt %>% mutate(cause = ifelse(clinical==1 & cumcause==0,2,cause),
cause = ifelse(clinical==0 & cumsum==1,0,cause),
qpcr = ifelse(clinical==0 & cumsum==1,0,qpcr),
Tgam_uL_adj = ifelse(clinical==0 & cumsum==1,0,Tgam_uL_adj))
dtrm = dt %>% filter(!(clinical==0 & cumsum==1))
dtrm= dtrm %>% mutate(male= ifelse(gender=="Male",1,0),
hbs.homo = ifelse(HbS=="homo",1,0),
hbs.hetero = ifelse(HbS=="hetero",1,0),
g6pd376.homo = ifelse(G6PD376=="homo",1,0),
g6pd376.hetero = ifelse(G6PD376=="hetero",1,0),
g6pd202.homo = ifelse(G6PD202=="homo",1,0),
g6pd202.hetero = ifelse(G6PD202=="hetero",1,0),
age2 = ifelse(agecat==2,1,0),
age3 = ifelse(agecat==3,1,0),
moib = ifelse(moi>1,1,0),
infection_id=as.factor(infection_id))
dtrm$logqpcr = log(dtrm$qpcr+0.1)
dtrm$loggam = log(dtrm$Tgam_uL_adj+0.001)
mod.c = bam(logqpcr ~ hbs.homo+hbs.hetero+s(weeks, bs="bs")+ s(infection_id, bs="re"),
family=gaussian(),
data=dtrm)
sm.c = summary(mod.c)
hbsdatapcr[i,]=c(mod.c$coefficients[3],sm.c$se[3],sm.c$p.pv[3])
mod.c = bam(logqpcr ~ moib+s(weeks, bs="bs")+ s(infection_id, bs="re"),
family=gaussian(),
data=dtrm)
sm.c = summary(mod.c)
moidatapcr[i,]=c(mod.c$coefficients[2],sm.c$se[2],sm.c$p.pv[2])
mod.c = bam(loggam ~ hbs.homo+hbs.hetero+
s(weeks, bs="bs")+ s(logqpcr,bs="bs")+ s(infection_id, bs="re"),
family=gaussian(),
data=dtrm)
sm.c = summary(mod.c)
hbsdatagam[i,]=c(mod.c$coefficients[3],sm.c$se[3],sm.c$p.pv[3])
mod.c = bam(loggam ~ moib+
s(weeks, bs="bs")+ s(logqpcr,bs="bs")+ s(infection_id, bs="re"),
family=gaussian(),
data=dtrm)
sm.c = summary(mod.c)
moidatagam[i,]=c(mod.c$coefficients[2],sm.c$se[2],sm.c$p.pv[2])
dtrm$logqpcr = log(dtrm$qpcr+0.1)
dtrm$loggamfrac = ifelse(dtrm$qpcr==0,NA,log((dtrm$Tgam_uL_adj/dtrm$qpcr)+0.001))
mod.c = bam(loggamfrac ~ hbs.homo+hbs.hetero+
s(weeks, bs="bs")+ s(logqpcr,bs="bs")+ s(infection_id, bs="re"),
family=gaussian(),
data=dtrm)
sm.c = summary(mod.c)
hbsdatagf[i,]=c(mod.c$coefficients[3],sm.c$se[3],sm.c$p.pv[3])
mod.c = bam(loggamfrac ~ moib+
s(weeks, bs="bs")+ s(logqpcr,bs="bs")+ s(infection_id, bs="re"),
family=gaussian(),
data=dtrm)
sm.c = summary(mod.c)
moidatagf[i,]=c(mod.c$coefficients[2],sm.c$se[2],sm.c$p.pv[2])
print(i)
}
sum(I(hbsdata$pval<0.05)) #81%
sum(I(moidata$pval<0.05)) #10%
sum(I(hbsdatapcr$pval<0.05)) #0%
sum(I(moidatapcr$pval<0.05)) #100%
sum(I(hbsdatagam$pval<0.05)) #100%
sum(I(moidatagam$pval<0.05)) #96%
sum(I(hbsdatagf$pval<0.05)) #99%
sum(I(moidatagf$pval<0.05)) #0%
library(ggplot2)
library(ggprism)
library(ggpubr)
ggarrange(
ggplot()+
geom_histogram(data=hbsdatapcr,aes(x=pval,fill="HbAS"),alpha=0.5)+
geom_histogram(data=moidatapcr,aes(x=pval,fill="MOI"),alpha=0.5)+
scale_fill_viridis_d(name="")+
xlab("P-values")+
ylab("Relative frequency (%)")+
theme_prism()+
theme(legend.position = "bottom")+
geom_vline(xintercept = 0.05,linetype="dashed")+
ggtitle("Parasite density trajectories over time"),
ggplot()+
geom_histogram(data=hbsdata,aes(x=pval,fill="HbAS"),alpha=0.5)+
geom_histogram(data=moidata,aes(x=pval,fill="MOI"),alpha=0.5)+
scale_fill_viridis_d(name="")+
xlab("P-values")+
ylab("Relative frequency (%)")+
theme_prism()+
theme(legend.position = "bottom")+
geom_vline(xintercept = 0.05,linetype="dashed")+
ggtitle("Time to starting gametocyte production"),
ggplot()+
geom_histogram(data=hbsdatagam,aes(x=pval,fill="HbAS"),alpha=0.5)+
geom_histogram(data=moidatagam,aes(x=pval,fill="MOI"),alpha=0.5)+
scale_fill_viridis_d(name="")+
xlab("P-values")+
ylab("Relative frequency (%)")+
theme_prism()+
theme(legend.position = "bottom")+
geom_vline(xintercept = 0.05,linetype="dashed")+
ggtitle("Gametocyte density trajectories over time"),
ggplot()+
geom_histogram(data=hbsdatagf,aes(x=pval,fill="HbAS"),alpha=0.5)+
geom_histogram(data=moidatagf,aes(x=pval,fill="MOI"),alpha=0.5)+
scale_fill_viridis_d(name="")+
xlab("P-values")+
ylab("Relative frequency (%)")+
theme_prism()+
theme(legend.position = "bottom")+
geom_vline(xintercept = 0.05,linetype="dashed")+
ggtitle("Gametocyte fraction trajectories over time"),
nrow=2, ncol=2, common.legend = TRUE)
ggsave("figures/sensitivity_pvals.pdf",device="pdf",dpi=300, width=12, height=9)
ggarrange(
ggplot()+
geom_histogram(data=hbsdatapcr,aes(x=exp(coefs),fill="HbAS"),alpha=0.5)+
geom_histogram(data=moidatapcr,aes(x=exp(coefs),fill="MOI"),alpha=0.5)+
scale_fill_viridis_d(name="")+
xlab("Effect ratio")+
ylab("Relative frequency (%)")+
theme_prism()+
scale_x_log10()+
theme(legend.position = "bottom")+
geom_vline(xintercept = 1,linetype="dashed")+
ggtitle("Parasite density trajectories over time"),
ggplot()+
geom_histogram(data=hbsdata,aes(x=exp(coefs),fill="HbAS"),alpha=0.5)+
geom_histogram(data=moidata,aes(x=exp(coefs),fill="MOI"),alpha=0.5)+
scale_fill_viridis_d(name="")+
xlab("Effect ratio")+
ylab("Relative frequency (%)")+
theme_prism()+
scale_x_log10()+
theme(legend.position = "bottom")+
geom_vline(xintercept = 1,linetype="dashed")+
ggtitle("Time to starting gametocyte production"),
ggplot()+
geom_histogram(data=hbsdatagam,aes(x=exp(coefs),fill="HbAS"),alpha=0.5)+
geom_histogram(data=moidatagam,aes(x=exp(coefs),fill="MOI"),alpha=0.5)+
scale_fill_viridis_d(name="")+
xlab("Effect ratio")+
ylab("Relative frequency (%)")+
theme_prism()+
scale_x_log10()+
theme(legend.position = "bottom")+
geom_vline(xintercept = 1,linetype="dashed")+
ggtitle("Gametocyte density trajectories over time"),
ggplot()+
geom_histogram(data=hbsdatagf,aes(x=exp(coefs),fill="HbAS"),alpha=0.5)+
geom_histogram(data=moidatagf,aes(x=exp(coefs),fill="MOI"),alpha=0.5)+
scale_fill_viridis_d(name="")+
xlab("Effect ratio")+
ylab("Relative frequency (%)")+
theme_prism()+
scale_x_log10()+
theme(legend.position = "bottom")+
geom_vline(xintercept = 1,linetype="dashed")+
ggtitle("Gametocyte fraction trajectories over time"),
nrow=2, ncol=2, common.legend = TRUE)
ggsave("figures/sensitivity_coefs.pdf",device="pdf",dpi=300, width=12, height=9)