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table1.Rmd
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table1.Rmd
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---
title: "Personalized Monitoring of HIV Viral Load Through Prognostic Healthcare (#179)"
author: "IeDEA | FIMP"
output:
rmdformats::material:
self_contained: true
thumbnails: true
lightbox: true
gallery: false
highlight: tango
---
```{r setup, include=FALSE}
options(java.parameters = "-Xmx15g")
knitr::opts_chunk$set(warning=FALSE,
message=FALSE,
echo=FALSE,
#dpi=96,
# fig.width=7,# fig.height=4, # Default figure widths
# dev="png", #dev.args=list(type="cairo"), # The png device
# Change to dev="postscript" if you want the EPS-files
# for submitting. Also remove the dev.args() as the postscript
# doesn't accept the type="cairo" argument.
error=FALSE)
# Evaluate the figure caption after the plot
#knitr::opts_knit$set(eval.after='fig.cap')
# Use the table counter that the htmlTable() provides
options(table_counter = TRUE)
# Use the figCapNo() with roman letters
#options(fig_caption_no_roman = TRUE)
#options(kableExtra.latex.load_packages = F)
# Then install the Grmd-package by running below code:
#devtools::install_github("gforge/Grmd")
# function to install missing packages
ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg, dependencies = TRUE, repos='http://cran.rstudio.com/')
sapply(pkg, require, character.only = TRUE)
}
#install.packages('package_name', dependencies=TRUE, repos='http://cran.rstudio.com/')
packages =c( "dplyr", "readxl","Hmisc","Gmisc", "magrittr", "flextable", "MASS", "tidyverse", "caret", "knitr", "kableExtra","xtable", "stargazer", "ggpubr", "haven", "PerformanceAnalytics", "naniar", "gridExtra", "ggthemes", "TSstudio")
ipak(packages)
# packages not pubished
#devtools::install_github("kassambara/easyGgplot2")
library(easyGgplot2)
# packages not pubished
#devtools::install_github('cttobin/ggthemr')
#library(ggthemr)
select = dplyr::select; summarize = dplyr::summarize; rename = dplyr::rename; mutate = dplyr::mutate;
#ggthemr("flat")
#source("unbalanced_functions.R")
source("table1_utils.R")
```
# Explaratory Data Analysis
The goal of the exploratory analysis is to identify the key predictors, generate insights and dicover trends and patters. Throughout this process we are mainly interested in detecting:
* Correlation and assoicitations
* Trends in time e.g viral load
* Outliers
* Any other key information that might be useful in ML modeling
```{r warning=FALSE}
rural.cb <- tibble::tribble(
~code, ~label,
1,'Urban', 2,'Mostly urban', 3,'Mostly rural', 4,'Rural', 9,'Unknown'
)
level.cb <- tibble::tribble(
~code, ~label,
1,'Health centre',2,'District hospital',3,'Regional, provincial or university hospital',9,'Unknown'
)
civstfmt.cb <- tibble::tribble(
~code, ~label,
1,'Never Married and Not Living w/Partner',2,'Legally Married',3,'Living w/Partner',4,'Separated',5,'Divorced',6,'Widowed'
)
entptfmt.cb <- tibble::tribble(
~code, ~label,
1,'outpatient care',2,'inpatient care',3,'pmtct care',4,'TB ward care',
5,'community-based org',6,'private care',7,'self-referral',8,'other',
9,'RTC (routine testing and counseling)',10,'VCT',11,'HCT(home-based)',
12,'MCH (mother child health)',13,'TB clinic',14,'Research Institute',15,'Family Member',
16,'Transfer-In',17,'STI Clinic',18,'Unknown',19,'Exposed infant',20,'Under 5 yrs',
21,'IDU outreach',22,'YYC(Young Child Clinic)'
)
pillfmt.cb <- tibble::tribble(
~code, ~label,
1,'none',2,'very few',3,'about half',4,'most',5,'all'
)
adhfmt.cb <- tibble::tribble(
~code, ~label,
1,'Good: 3 or fewer missed doses since last visit',
2,'Poor: 4 or more missed doses since last visit',3,'Good: fewer than 2 missed days',
4,'Poor: 2 or more missed days'
)
arvadfmt.cb <- tibble::tribble(
~code, ~label,
1,'GOOD (>, 95%)',2,'FAIR (85-94%)',3,'POOR (< 85%)'
)
ynfmt.cb <- tibble::tribble(
~code, ~label,
1,'Yes',2,'No'
)
zeroonefmt.cb <- tibble::tribble(
~code, ~label,
1,'Yes',0,'No'
)
```
## Assumptions
```{r warning=F}
getFirstXnonNA=function(data,n=3,var){
#first = dplyr::first(na.omit(value))
return(data %>%
arrange(ptidno, `apptdate`) %>%
group_by(ptidno) %>%
filter(visit_num <= n) %>%
mutate(
var_new=dplyr::first(na.omit(!!as.symbol(var)))
)%>%
ungroup())%>%
filter(visit_num == 1)
#filter(apptdate == min(apptdate)) %>%
}
getFirstNonNAInXMonths=function(data,m=3,var){
#first = dplyr::first(na.omit(value))
days=m*30
return(data %>%
arrange(ptidno, `apptdate`) %>%
group_by(ptidno) %>%
filter(apptdate <= min(apptdate)+days) %>%
mutate(
var_new=dplyr::first(na.omit(!!as.symbol(var)))
)%>%
ungroup())%>%
filter(visit_num == 1)
#filter(apptdate == min(apptdate)) %>%
}
getBaselineOnArvStartWithinXMonths=function(data,m=3,var, refdate){
#first = dplyr::first(na.omit(value))
days=m*30
return(data %>%
arrange(ptidno, `apptdate`) %>%
group_by(ptidno) %>%
filter(apptdate <= !!as.symbol(refdate)) %>%
mutate(
var_new=dplyr::first(na.omit(!!as.symbol(var)))
)%>%
ungroup())%>%
filter(visit_num == 1)
#filter(apptdate == min(apptdate)) %>%
}
binarize <- function(x) {
case_when(
x=='1'~ "Yes",
x=='0'~ "No",
is.na(x) ~ "No",
TRUE ~ x)
}
```
```{r warning=F,cache=T}
# Import Dataset
patient.cross.init =haven::read_sas("ak_vlmonitord.sas7bdat")
patient.long.init =haven::read_sas("ak_vlmonitorl.sas7bdat")
```
```{r warning=F}
# codes
patient.long.init$PillsTakenLast7Days <- factor(pillfmt.cb[match(patient.long.init$PillsTakenLast7Days, pillfmt.cb$code), ]$label)
patient.long.init$art_adherence2 <- factor(adhfmt.cb[match(patient.long.init$art_adherence2, adhfmt.cb$code), ]$label)
patient.long.init$arvadherence <- factor(arvadfmt.cb[match(patient.long.init$arvadherence, arvadfmt.cb$code), ]$label)
# merge dataset
patient.long = patient.long.init %>%
arrange(ptidno, `apptdate`) %>%
group_by(ptidno) %>%
mutate(visit_num = row_number())%>%
mutate(
# vl date
vl1_date = as.Date(dplyr::nth(na.omit(ifelse(is.na(viralload), NA , apptdate)),1), origin="1970-01-01"),
vl2_date = as.Date(dplyr::nth(na.omit(ifelse(is.na(viralload), NA , apptdate)),2), origin="1970-01-01"),
vl3_date = as.Date(dplyr::nth(na.omit(ifelse(is.na(viralload), NA , apptdate)),3), origin="1970-01-01"),
# num days to vl
num_days_to_vl1 = dplyr::nth(na.omit(ifelse(is.na(viralload), NA , apptdate-min(apptdate))),1),
num_days_to_vl2 = dplyr::nth(na.omit(ifelse(is.na(viralload), NA , apptdate-min(apptdate))),2),
num_days_to_vl3 = dplyr::nth(na.omit(ifelse(is.na(viralload), NA , apptdate-min(apptdate))),3),
# difference in days between vl2 and vl2
days_from_vl1_vl2 = num_days_to_vl2- num_days_to_vl1,
days_from_vl2_vl3 = num_days_to_vl3- num_days_to_vl2,
# vl count
vl_count1 = dplyr::nth(na.omit(viralload),1),
vl_count2 = dplyr::nth(na.omit(viralload),2),
vl_count3 = dplyr::nth(na.omit(viralload),3),
vl_failure1 = factor(ifelse(vl_count1<=1000, "No","Yes")),
vl_failure2 = factor(ifelse(vl_count2<=1000, "No","Yes")),
vl_failure3 = factor(ifelse(vl_count3<=1000, "No","Yes")),
# num encunters to vl
num_encs_to_vl1 = dplyr::nth(na.omit(ifelse(is.na(viralload), NA , visit_num-1)),1),
num_encs_to_vl2 = dplyr::nth(na.omit(ifelse(is.na(viralload), NA , visit_num-1)),2),
num_encs_to_vl3 = dplyr::nth(na.omit(ifelse(is.na(viralload), NA , visit_num-1)),3),
# factor
WHOstage= factor(WHOstage)
)%>%
ungroup() %>% left_join(patient.cross.init%>%select(ptidno, arvstart), c("ptidno"="ptidno"))
# handle AMPATH's data missing xrossection civilstatus
civilstatus.cx=getFirstNonNAInXMonths(patient.long,3,'civilstatus')%>%select(ptidno, civilstatus=var_new)
# handle all sites missing baseline features
Height.cx=getFirstNonNAInXMonths(patient.long,3,'Height')%>%select(ptidno, Height=var_new)
Weight.cx=getFirstNonNAInXMonths(patient.long,3,'Weight')%>%select(ptidno, Weight=var_new)
bmi.cx=getFirstNonNAInXMonths(patient.long,3,'bmi')%>%select(ptidno, bmi=var_new)
nhif.cx=getFirstNonNAInXMonths(patient.long,3,'nhif')%>%select(ptidno, nhif=var_new)
# perform joining on the missing varibles
patient.cross = patient.cross.init%>%
mutate(transfer=ifelse(is.na(transfer)==T,0,1))%>%
left_join(civilstatus.cx, c("ptidno"="ptidno"))%>%
left_join(Height.cx, c("ptidno"="ptidno"))%>%
left_join(Weight.cx, c("ptidno"="ptidno"))%>%
left_join(bmi.cx, c("ptidno"="ptidno"))%>%
left_join(nhif.cx, c("ptidno"="ptidno"))%>%
mutate(
civilstatusENROL=ifelse(program=="AMPATH",civilstatus,civilstatusENROL)
)
#merge master dataset
dataset=patient.cross %>% left_join(select(filter(patient.long, visit_num == 1),
num_days_to_vl1,num_days_to_vl2,num_days_to_vl3,vl_failure1,
vl_failure2,vl_failure3,
days_from_vl1_vl2,days_from_vl2_vl3,
vl_count1,vl_count2,vl_count3,ptidno,vl1_date,vl2_date,
vl3_date,
num_encs_to_vl1,num_encs_to_vl2,num_encs_to_vl3
), c("ptidno"="ptidno"))
#Replace numerical codes with strings from codebook
dataset$civilstatusENROL <- factor(civstfmt.cb[match(dataset$civilstatusENROL, civstfmt.cb$code), ]$label)
dataset$male <- factor(zeroonefmt.cb[match(dataset$male, zeroonefmt.cb$code), ]$label)
dataset$transfer <- factor(zeroonefmt.cb[match(dataset$transfer, zeroonefmt.cb$code), ]$label)
dataset$Death <- factor(zeroonefmt.cb[match(dataset$Death, zeroonefmt.cb$code), ]$label)
dataset$nhif <- factor(zeroonefmt.cb[match(dataset$nhif, zeroonefmt.cb$code), ]$label)
dataset$entrypoint = factor(entptfmt.cb[match(dataset$entrypoint, entptfmt.cb$code), ]$label)
dataset= dataset %>%
mutate(across(c('Death','male', 'entrypoint', "civilstatusENROL", "transfer", "nhif"), as.factor))
# assuming if not 1 is 0
# Relevel
varList= list(
"Yes"=c('male','transfer','Death')
)
#dataset= relevelBy(varList,dataset)
patient.long.cross = patient.long %>% left_join(patient.cross, c("ptidno"="ptidno"))
#str(dataset)
```
* AMPATH civil status is collected longitudinal; as such missing data on the crossectional dataset has to be replaced with longitudinal civil status recorded not > 3 months after the first encounter
* We have assumed if is.na(transfer) then No else Yes
* Unrecorded data (occurring at the 1st encounter) on Height, Weight, BMI, NHIF have to be replaced with non-null next encounter recorded < 3 months from the first encounter
* Viral Load index - It is not possible to know when the next VL is drawn longitudinally, as shown in the table below. As such, we are treating each VL result as individual tests ordered independently.
```{r warning=F, eval=T}
patient.long %>% select(ptidno,visit_num,apptdate,VLundetectable,viralload,viralloadc,Height)%>%
filter(ptidno==52764) %>% arrange(ptidno, `apptdate`) %>% kable () %>%
kable_styling(bootstrap_options = "striped", full_width = F, position = "left")
```
# Viral Load Counts
We will start by looking at the overall distribution of VL
## Distribution
```{r fig.align='center',warning=FALSE, cache=T}
hiv_dfl=dataset%>%filter(!is.na(vl_count1))
a =hiv_dfl%>% mutate(VL_Failure=ifelse(vl_count1<=1000, "No","Yes")) %>%
ggboxplot( palette = "jco", ylab = "VL Count",
y = c("vl_count1"),
x = c("VL_Failure"),
shape = "VL_Failure",
yscale = c("log2"),
add = "jitter",
color = "VL_Failure", add.params = list(size = 0.2, alpha=0.3, jitter = 0.2) )+
scale_color_manual(values= c("#00AFBB", "#E7B800"))+ scale_fill_manual(values= c("#00AFBB", "#E7B800"))
b =hiv_dfl%>% mutate(VL_Failure=ifelse(vl_count2<=1000, "No","Yes")) %>%
ggboxplot( palette = "jco", ylab = "VL Count 2",
y = c("vl_count2"),
x = c("VL_Failure"),
shape = "VL_Failure",
yscale = c("log2"),
add = "jitter",
color = "VL_Failure", add.params = list(size = 0.2, alpha=0.3, jitter = 0.2) ) +
scale_color_manual(values= c("#00AFBB", "#E7B800"))+ scale_fill_manual(values= c("#00AFBB", "#E7B800"))
c = hiv_dfl%>% mutate(VL_Failure=ifelse(vl_count1<=1000, "No","Yes")) %>%
ggplot2.histogram( xName="vl_count1", backgroundColor="white", bins=80,
brewerPalette="Paired",
groupName="VL_Failure", xScale="log2",rug = TRUE ,
alpha=0.5, addDensity=TRUE, removePanelGrid=TRUE ,removePanelBorder=TRUE,
addMeanLine=TRUE, meanLineSize=1) + xlab("VL Count 1")+
scale_color_manual(values= c("#00AFBB", "#E7B800")) + scale_fill_manual(values= c("#00AFBB", "#E7B800"))
d = hiv_dfl%>% mutate(VL_Failure=ifelse(vl_count2<=1000, "No","Yes")) %>%
ggplot2.histogram( xName="vl_count2", backgroundColor="white", bins=80,
brewerPalette="Paired",
groupName="VL_Failure", xScale="log2", xlab = "VL Count 2",rug = TRUE ,
alpha=0.5, addDensity=TRUE, removePanelGrid=TRUE ,removePanelBorder=TRUE,
addMeanLine=TRUE, meanLineSize=1) + xlab("VL Count 2")+
scale_color_manual(values= c("#00AFBB", "#E7B800"))+ scale_fill_manual(values= c("#00AFBB", "#E7B800"))
ggarrange(a,b,c,d,
common.legend = TRUE, legend="bottom",
ncol = 2, nrow=2)
```
Is a cutoff of 1000 valid with relation to VL detectability limit? To understll start by decomposing the viral load counts into its three components that is inline with the goal of our analyis- Viral load 1, viral load 2 and viral load 3.
## Overall Viral Load Counts Detection Limit
```{r fig.align='center',warning=FALSE, cache=T}
patient.long.cross %>%
filter(!is.na(viralload))%>%
mutate(year = as.numeric(format(apptdate,'%Y')), onARV=factor(ifelse(onARV==1, "Yes", "No")))%>%
filter(!is.na(viralload))%>%
filter( viralload <= 500 ) %>%
ggplot(aes(x=apptdate, y=viralload)) +
geom_jitter(alpha=0.3, aes(color=onARV)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
```
## MBARARA Viral Load Detection Limit
```{r fig.align='center',warning=FALSE, cache=T}
patient.long.cross %>%
filter(program=="MBARARA")%>%
filter(!is.na(viralload))%>%
mutate(year = as.numeric(format(apptdate,'%Y')), onARV=factor(ifelse(onARV==1, "Yes", "No")))%>%
filter(!is.na(viralload))%>%
filter( viralload <= 600 ) %>%
ggplot(aes(x=apptdate, y=viralload)) +
geom_jitter(alpha=0.3, aes(color=onARV)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
```
## MASAKA Viral Load Detection Limit
```{r fig.align='center',warning=FALSE,cache=T}
patient.long.cross %>%
filter(program=="MASAKA")%>%
filter(!is.na(viralload))%>%
mutate(year = as.numeric(format(apptdate,'%Y')), onARV=factor(ifelse(onARV==1, "Yes", "No")))%>%
filter(!is.na(viralload))%>%
filter( viralload <= 70 ) %>%
ggplot(aes(x=apptdate, y=viralload)) +
geom_jitter(alpha=0.3, aes(color=onARV)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
```
## RAKAI Viral Load Detection Limit
```{r fig.align='center',warning=FALSE,cache=T}
a = patient.long.cross %>%
filter(program=="RAKAI")%>%
filter(!is.na(viralload))%>%
mutate(year = as.numeric(format(apptdate,'%Y')), onARV=factor(ifelse(onARV==1, "Yes", "No")))%>%
filter(!is.na(viralload))%>%
filter( viralload <= 1200 ) %>%
ggplot(aes(x=apptdate, y=viralload)) +
geom_jitter(alpha=0.3, aes(color=onARV)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
b = patient.long.cross %>%
filter(program=="RAKAI")%>%
filter(!is.na(viralload))%>%
mutate(year = as.numeric(format(apptdate,'%Y')), onARV=factor(ifelse(onARV==1, "Yes", "No")))%>%
filter(!is.na(viralload))%>%
filter( viralload <= 80 ) %>%
ggplot(aes(x=apptdate, y=viralload)) +
geom_jitter(alpha=0.3, aes(color=onARV)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
ggarrange(a,b,
common.legend = TRUE,
ncol = 2, nrow=1)
```
## IDI Viral Load Detection Limit
```{r fig.align='center',warning=FALSE,cache=T}
patient.long.cross %>%
filter(program=="IDI")%>%
filter(!is.na(viralload))%>%
mutate(year = as.numeric(format(apptdate,'%Y')), onARV=factor(ifelse(onARV==1, "Yes", "No")))%>%
filter(!is.na(viralload))%>%
filter( viralload <= 100 ) %>%
ggplot(aes(x=apptdate, y=viralload)) +
geom_jitter(alpha=0.3, aes(color=onARV)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
```
## AMPATH Viral Load Detection Limit
```{r fig.align='center',warning=FALSE,cache=T}
a = patient.long.cross %>%
filter(program=="AMPATH")%>%
filter(!is.na(viralload))%>%
mutate(year = as.numeric(format(apptdate,'%Y')), onARV=factor(ifelse(onARV==1, "Yes", "No")))%>%
filter(!is.na(viralload))%>%
filter( viralload <= 20 ) %>%
ggplot(aes(x=apptdate, y=viralload)) +
geom_jitter(alpha=0.3, aes(color=onARV)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
b = patient.long.cross %>%
filter(program=="AMPATH")%>%
filter(!is.na(viralload))%>%
mutate(year = as.numeric(format(apptdate,'%Y')), onARV=factor(ifelse(onARV==1, "Yes", "No")))%>%
filter(!is.na(viralload))%>%
filter( viralload <= 50 ) %>%
ggplot(aes(x=apptdate, y=viralload)) +
geom_jitter(alpha=0.3, aes(color=onARV)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
ggarrange(a,b,
common.legend = TRUE,
ncol = 2, nrow=1)
```
## FACES Viral Load Detection Limit
```{r fig.align='center',warning=FALSE,cache=T}
patient.long.cross %>%
filter(program=="FACES")%>%
filter(!is.na(viralload))%>%
mutate(year = as.numeric(format(apptdate,'%Y')), onARV=factor(ifelse(onARV==1, "Yes", "No")))%>%
filter(!is.na(viralload))%>%
filter( viralload <= 70 ) %>%
ggplot(aes(x=apptdate, y=viralload)) +
geom_jitter(alpha=0.3, aes(color=onARV)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
```
## KISESA Viral Load Detection Limit
```{r fig.align='center',warning=FALSE,cache=T}
patient.long.cross %>%
filter(program=="KISESA")%>%
filter(!is.na(viralload))%>%
mutate(year = as.numeric(format(apptdate,'%Y')), onARV=factor(ifelse(onARV==1, "Yes", "No")))%>%
filter(!is.na(viralload))%>%
filter( viralload <= 60 ) %>%
ggplot(aes(x=apptdate, y=viralload)) +
geom_jitter(alpha=0.3, aes(color=onARV)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
```
## MOROGORO Viral Load Detection Limit
```{r fig.align='center',warning=FALSE,cache=T}
patient.long.cross %>%
filter(program=="MOROGORO")%>%
filter(!is.na(viralload))%>%
mutate(year = as.numeric(format(apptdate,'%Y')), onARV=factor(ifelse(onARV==1, "Yes", "No")))%>%
filter(!is.na(viralload))%>%
filter( viralload <= 60 ) %>%
ggplot(aes(x=apptdate, y=viralload)) +
geom_jitter(alpha=0.3, aes(color=onARV)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
```
## TUMBI Viral Load Detection Limit
```{r fig.align='center',warning=FALSE,cache=T}
patient.long.cross %>%
filter(program=="TUMBI")%>%
filter(!is.na(viralload))%>%
mutate(year = as.numeric(format(apptdate,'%Y')), onARV=factor(ifelse(onARV==1, "Yes", "No")))%>%
filter(!is.na(viralload))%>%
filter( viralload <= 60 ) %>%
ggplot(aes(x=apptdate, y=viralload)) +
geom_jitter(alpha=0.3, aes(color=onARV)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
```
We also need to look if this is consisten with VL index
## Viral Load 1 Detection Limit
```{r fig.align='center',warning=FALSE, cache=T}
dataset %>%
mutate(year = as.numeric(format(vl1_date,'%Y')), on_ART=factor(ifelse(arvstartTHERAP<=vl1_date, "Yes", "No")))%>%
filter(!is.na(vl_count1))%>%
filter( vl_count1 <= 500 ) %>%
ggplot(aes(x=vl1_date, y=vl_count1)) +
geom_jitter(alpha=0.3, aes(color=on_ART)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
```
## Viral Load 2 Detection Limit
```{r fig.align='center',warning=FALSE, cache=T}
dataset %>%
mutate(year = as.numeric(format(vl2_date,'%Y')), on_ART=factor(ifelse(arvstartTHERAP<=vl2_date, "Yes", "No")))%>%
filter(!is.na(vl_count2))%>%
filter( vl_count2 <= 500 ) %>%
ggplot(aes(x=vl2_date, y=vl_count2)) +
geom_jitter(alpha=0.3, aes(color=on_ART)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
```
## Viral Load 3 Detection Limit
```{r fig.align='center',warning=FALSE, cache=T}
dataset %>%
mutate(year = as.numeric(format(vl3_date,'%Y')), on_ART=factor(ifelse(arvstartTHERAP<=vl3_date, "Yes", "No")))%>%
filter(!is.na(vl_count3))%>%
filter( vl_count3 <= 500 ) %>%
ggplot(aes(x=vl3_date, y=vl_count3)) +
geom_jitter(alpha=0.3, aes(color=on_ART)) +
geom_smooth() +
facet_wrap( ~ program, scales = "free_x")+
theme_clean()
```
## Viral Load Count | Programs
```{r fig.align='center',warning=FALSE}
na.rm.var=F;stats=F
# Getting descriptive statistics
mergeDesc(
"First VL count" = getTable1Stats("vl_count1","program",desc_both,statistics=stats,na.rm=na.rm.var),
"Second VL count" = getTable1Stats("vl_count2","program",desc_both,statistics=stats,na.rm=na.rm.var),
"Third VL count" = getTable1Stats("vl_count3","program",desc_both,statistics=stats,na.rm=na.rm.var),
"Days (from enrollment) to first VL" = getTable1Stats("num_days_to_vl1","program",desc_both,statistics=stats,na.rm=na.rm.var),
"Days (from enrollment) to second VL" = getTable1Stats("num_days_to_vl2","program",desc_both,statistics=stats,na.rm=na.rm.var),
"Days (from enrollment) to third VL" = getTable1Stats("num_days_to_vl3","program",desc_both,statistics=stats,na.rm=na.rm.var),
"Days (from 1st VL) to second VL" = getTable1Stats("days_from_vl1_vl2","program",desc_both,statistics=stats,na.rm=na.rm.var),
"Days (from 2nd VL) to thrid VL" = getTable1Stats("days_from_vl2_vl3","program",desc_both,statistics=stats,na.rm=na.rm.var),
"# of encounter B4 first VL" = getTable1Stats("num_encs_to_vl1","program",desc_both,statistics=stats,na.rm=na.rm.var),
"# of encounter B4 second VL" = getTable1Stats("num_encs_to_vl2","program",desc_both,statistics=stats,na.rm=na.rm.var),
"# of encounter B4 third VL" = getTable1Stats("num_encs_to_vl3","program",desc_both,statistics=stats,na.rm=na.rm.var)
#"NHIF at enrollment" = getTable1Stats("nhif","program",desc_both,statistics=stats,na.rm=na.rm.var)
)%>%
htmlTable( caption = "<b> Socio-demographics </b>",useViewer=T,ctable = TRUE, align = 'lcccc',
n.cgroup = c(1,9), cgroup = c('', 'Programs') ,tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
## First VL Seasonality Trends
Plot the explanatory variable distribution for both the variables to understand the variability uniquely explained (The non-intersecting part of the blue and the red is the variation explained by the variable)
```{r results='asis', fig.keep='all', echo=FALSE, results='asis', fig.keep='all', eval=T }
idata=dataset %>%
#filter(program=="AMPATH")%>%
mutate(year = as.numeric(format(vl1_date,'%Y')), on_ART=factor(ifelse(arvstartTHERAP<=vl1_date, "Yes", "No")))%>%
filter(!is.na(vl_count1))
xts( idata$vl_count1, order.by=idata$vl1_date ) %>%
.['2014/2020-12-31'] %>%
apply.monthly(. , mean) %>%
ts( ., start = c(2014,1,1), frequency = 12) %>%
# ts_decompose(., type = "all")
ts_heatmap(.)
```
```{r results='asis', fig.keep='all', echo=FALSE, results='asis', fig.keep='all', eval=T }
idata=dataset %>%
#filter(program=="AMPATH")%>%
mutate(year = as.numeric(format(vl1_date,'%Y')), on_ART=factor(ifelse(arvstartTHERAP<=vl1_date, "Yes", "No")))%>%
filter(!is.na(vl_count1))
xts( idata$vl_count1, order.by=idata$vl1_date ) %>%
.['2014/2020-12-31'] %>%
apply.monthly(. , mean) %>%
ts( ., start = c(2014,1,1), frequency = 12) %>%
ts_decompose(., type = "all")
# ts_heatmap(.)
```
```{r fig.align='center',warning=FALSE, cache=T, eval=T}
idata=dataset %>%
#filter(program=="MBARARA")%>%
mutate(year = as.numeric(format(vl1_date,'%Y')), on_ART=factor(ifelse(arvstartTHERAP<=vl1_date, "Yes", "No")))%>%
filter(!is.na(num_days_to_vl1))
xts( idata$num_days_to_vl1, order.by=idata$vl1_date ) %>%
.['2014/2020-12-31'] %>%
apply.monthly(. , mean) %>%
ts( ., start = c(2014,1,1), frequency = 12) %>%
ts_seasonal(., type = 'all', title = "Trends")
# ts_heatmap(.)
```
# Missing Data
## Socio-demographics Missing Data Summary
```{r fig.align='center',warning=FALSE, cache=T}
x=c('male','Death','civilstatusENROL', 'transfer','Height','Weight', 'entrypoint', 'nhif', 'bmi','program', 'ageatarvstart' )
y=c('vl_count1','vl_count2','vl_count3')
kable( dataset%>%select(c(x,y)) %>%naniar::miss_var_summary()%>%top_n(20,n_miss))
naniar::gg_miss_var(dataset%>%select(c(x,y)) )+ labs(title = "Frequency of Missingness")
naniar::gg_miss_fct(x = dataset%>%select(c(x,y, "program")), fct = program)
p1 = naniar::gg_miss_fct(x = dataset%>%select(c(x,y, "vl_failure1")), fct = vl_failure1)
p2 =naniar::gg_miss_fct(x = dataset%>%select(c(x,y, "vl_failure2")), fct = vl_failure2)
p3 =naniar::gg_miss_fct(x = dataset%>%select(c(x,y, "vl_failure3")), fct = vl_failure3)
p4 =naniar::gg_miss_fct(x = dataset%>%select(c(x,y, "Death")), fct = Death)
gridExtra::grid.arrange(p1,p2,p3,
p4 ,
ncol = 2, nrow=2)
```
# Bivariate Analysis
To understand the relationship of each variable with the outcome variable:
## Socio-demographics
### Overall Socio-demographics Correlation
```{r fig.align='center',warning=FALSE, cache=T}
PerformanceAnalytics::chart.Correlation(dataset%>%
mutate(
male=ifelse(male=="Yes", 1,0),
Death=ifelse(Death=="Yes", 1,0),
transfer=ifelse(transfer=="Yes", 1,0),
married=ifelse(civilstatusENROL=="Legally Married", 1,0)
)%>%
select(Weight,bmi,Height,male,Death,married,"ageatarvstart", vl_count2, vl_count1), histogram=TRUE, pch=22)
```
### Socio-demographics | Viral Load Failure 1
```{r fig.align='center',warning=FALSE, cache=T}
na.rm.var=F;stats=T
# Getting descriptive statistics
mergeDesc(
"age at first exposure to ARV" = getTable1Stats("ageatarvstart","vl_failure1",desc_both,statistics=stats,na.rm=na.rm.var),
"male gender" = getTable1Stats("male","vl_failure1",desc_both,statistics=stats,na.rm=na.rm.var),
"death" = getTable1Stats("Death","vl_failure1",desc_both,statistics=stats,na.rm=na.rm.var),
"civil status at enrollment" = getTable1Stats("civilstatusENROL","vl_failure1",desc_both,statistics=stats,na.rm=na.rm.var),
"patient transferred out" = getTable1Stats("transfer","vl_failure1",desc_both,statistics=stats,na.rm=na.rm.var),
"Height (cm) at enrollment" = getTable1Stats("Height","vl_failure1",desc_both,statistics=stats,na.rm=na.rm.var),
"Weight (kg) at enrollment" = getTable1Stats("Weight","vl_failure1",desc_both,statistics=stats,na.rm=na.rm.var),
"BMI at enrollment" = getTable1Stats("bmi","vl_failure1",desc_both,statistics=stats,na.rm=na.rm.var),
#"First VL count" = getTable1Stats("vl_count1","program",desc_both,statistics=stats,na.rm=na.rm.var),
"Second VL count" = getTable1Stats("vl_count2","vl_failure1",desc_both,statistics=stats,na.rm=na.rm.var),
"Third VL count" = getTable1Stats("vl_count3","vl_failure1",desc_both,statistics=stats,na.rm=na.rm.var)
#"entry point to care" = getTable1Stats("entrypoint","program",desc_both,statistics=stats,na.rm=na.rm.var)
#"NHIF at enrollment" = getTable1Stats("nhif","program",desc_both,statistics=stats,na.rm=na.rm.var)
)%>%
htmlTable( caption = "<b> Socio-demographics </b>",useViewer=T,ctable = TRUE, align = 'lcccc',
n.cgroup = c(1,2,1 ), cgroup = c('', 'First Virologic Failure', '') ,tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
```{r fig.align='center',warning=FALSE}
naniar::gg_miss_upset(dataset%>%select(x, 'vl_failure1'), nsets = 5,nintersects = NA)
```
### Socio-demographics | Viral Load Failure 2
```{r fig.align='center',warning=FALSE}
na.rm.var=F;stats=T
# Getting descriptive statistics
mergeDesc(
"age at first exposure to ARV" = getTable1Stats("ageatarvstart","vl_failure2",desc_both,statistics=stats,na.rm=na.rm.var),
"male gender" = getTable1Stats("male","vl_failure2",desc_both,statistics=stats,na.rm=na.rm.var),
"death" = getTable1Stats("Death","vl_failure2",desc_both,statistics=stats,na.rm=na.rm.var),
"civil status at enrollment" = getTable1Stats("civilstatusENROL","vl_failure2",desc_both,statistics=stats,na.rm=na.rm.var),
"patient transferred out" = getTable1Stats("transfer","vl_failure2",desc_both,statistics=stats,na.rm=na.rm.var),
"Height (cm) at enrollment" = getTable1Stats("Height","vl_failure2",desc_both,statistics=stats,na.rm=na.rm.var),
"Weight (kg) at enrollment" = getTable1Stats("Weight","vl_failure2",desc_both,statistics=stats,na.rm=na.rm.var),
"BMI at enrollment" = getTable1Stats("bmi","vl_failure2",desc_both,statistics=stats,na.rm=na.rm.var),
"First VL count" = getTable1Stats("vl_count1","vl_failure2",desc_both,statistics=stats,na.rm=na.rm.var),
#"Second VL count" = getTable1Stats("vl_count2","program",desc_both,statistics=stats,na.rm=na.rm.var),
"Third VL count" = getTable1Stats("vl_count3","vl_failure2",desc_both,statistics=stats,na.rm=na.rm.var)
#"entry point to care" = getTable1Stats("entrypoint","program",desc_both,statistics=stats,na.rm=na.rm.var)
#"NHIF at enrollment" = getTable1Stats("nhif","program",desc_both,statistics=stats,na.rm=na.rm.var)
)%>%
htmlTable( caption = "<b> Socio-demographics </b>",useViewer=T,ctable = TRUE, align = 'lcccc',
n.cgroup = c(1,2,1 ), cgroup = c('', 'Second Virologic Failure', '') ,tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
```{r fig.align='center',warning=FALSE}
naniar::gg_miss_upset(dataset%>%select(x, 'vl_failure2'), nsets = 5,nintersects = NA)
```
### Socio-demographics | Viral Load Failure 3
```{r fig.align='center',warning=FALSE}
na.rm.var=F;stats=T
# Getting descriptive statistics
mergeDesc(
"age at first exposure to ARV" = getTable1Stats("ageatarvstart","vl_failure3",desc_both,statistics=stats,na.rm=na.rm.var),
"male gender" = getTable1Stats("male","vl_failure3",desc_both,statistics=stats,na.rm=na.rm.var),
"death" = getTable1Stats("Death","vl_failure3",desc_both,statistics=stats,na.rm=na.rm.var),
"civil status at enrollment" = getTable1Stats("civilstatusENROL","vl_failure3",desc_both,statistics=stats,na.rm=na.rm.var),
"patient transferred out" = getTable1Stats("transfer","vl_failure3",desc_both,statistics=stats,na.rm=na.rm.var),
"Height (cm) at enrollment" = getTable1Stats("Height","vl_failure3",desc_both,statistics=stats,na.rm=na.rm.var),
"Weight (kg) at enrollment" = getTable1Stats("Weight","vl_failure3",desc_both,statistics=stats,na.rm=na.rm.var),
"BMI at enrollment" = getTable1Stats("bmi","vl_failure3",desc_both,statistics=stats,na.rm=na.rm.var),
"First VL count" = getTable1Stats("vl_count1","vl_failure3",desc_both,statistics=stats,na.rm=na.rm.var),
"Second VL count" = getTable1Stats("vl_count2","vl_failure3",desc_both,statistics=stats,na.rm=na.rm.var)
#"Third VL count" = getTable1Stats("vl_count3","program",desc_both,statistics=stats,na.rm=na.rm.var)
#"entry point to care" = getTable1Stats("entrypoint","program",desc_both,statistics=stats,na.rm=na.rm.var)
#"NHIF at enrollment" = getTable1Stats("nhif","program",desc_both,statistics=stats,na.rm=na.rm.var)
)%>%
htmlTable( caption = "<b> Socio-demographics </b>",useViewer=T,ctable = TRUE, align = 'lcccc',
n.cgroup = c(1,2,1 ), cgroup = c('', 'Third Virologic Failure', '') ,tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
```{r fig.align='center',warning=FALSE}
naniar::gg_miss_upset(dataset%>%select(x, 'vl_failure3'), nsets = 5,nintersects = NA)
```
### Socio-demographics | Program
```{r fig.align='center',warning=FALSE}
na.rm.var=F;stats=F
# Getting descriptive statistics
mergeDesc(
"age at first exposure to ARV" = getTable1Stats("ageatarvstart","program",desc_both,statistics=stats,na.rm=na.rm.var),
"male gender" = getTable1Stats("male","program",desc_both,statistics=stats,na.rm=na.rm.var),
"death" = getTable1Stats("Death","program",desc_both,statistics=stats,na.rm=na.rm.var),
"civil status at enrollment" = getTable1Stats("civilstatusENROL","program",desc_both,statistics=stats,na.rm=na.rm.var),
"patient transferred out" = getTable1Stats("transfer","program",desc_both,statistics=stats,na.rm=na.rm.var),
#"Height (cm) at enrollment" = getTable1Stats("'Height","program",desc_both,statistics=stats,na.rm=na.rm.var),
"Weight (kg) at enrollment" = getTable1Stats("Weight","program",desc_both,statistics=stats,na.rm=na.rm.var),
"BMI at enrollment" = getTable1Stats("bmi","program",desc_both,statistics=stats,na.rm=na.rm.var)
#"entry point to care" = getTable1Stats("entrypoint","program",desc_both,statistics=stats,na.rm=na.rm.var)
#"NHIF at enrollment" = getTable1Stats("nhif","program",desc_both,statistics=stats,na.rm=na.rm.var)
)%>%
htmlTable( caption = "<b> Socio-demographics </b>",useViewer=T,ctable = TRUE, align = 'lcccc',
n.cgroup = c(1,9 ), cgroup = c('', 'Programs') ,tfoot="<sup>‡</sup>"
)%>% htmlTable::addHtmlTableStyle(col.rgroup = c("#FFF", "#EEF"))
```
```{r fig.align='center',warning=FALSE}
naniar::gg_miss_upset(dataset%>%select(x, 'program'), nsets = 5,nintersects = NA)
```
## Clinical Profile
```{r fig.align='center',warning=FALSE, cache=F}
# binarize response
binary.var =c(
'Cough','KSdiag','NephroCardiomyopathy','TBsignNone','cervicalcadiag','chronicisosporiasisdiag',
'coughTB','cryptococcdiag','cryptospordiag','cytomegdiag','encephaldiag','esocandiddiag','ethambutol',
'ethambutolinh','herpessvdiag','WeightLoss','inh','inhtbtx','lymphomadiag',
'mdrTbTx','mycobacdiag','mycosisdiag','nightsweats','nightsweatsTB','lymphomadiag','oiProph','on_ARV','otherTbTx',
'pcpdiag','pmldiag','pyrazinamide','rifabutin','rifafour','rifampicin','rifater','rifinah','rsbPneumoniaDiag','salmonelladiag',
'streptomycin','tbextrapulmdiag','tbpulmdiag','tbtx','three_FDC','wastingsyndiag','toxoplasdiag','weightlossTB'
)
```
```{r fig.align='center',warning=FALSE, cache=T}
refdate='arvstart'