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descr_tableirr.R
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descr_tableirr.R
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# # # # # # # # # # # # # # # # # # # # #
# This script:
# takes a cohort name as defined in data_define_cohorts.R, and imported as an Arg
# creates descriptive outputs on patient characteristics by vaccination status at 0, 28, and 56 days.
#
# The script should be run via an action in the project.yaml
# The script must be accompanied by one argument,
# 1. the name of the cohort defined in data_define_cohorts.R
# # # # # # # # # # # # # # # # # # # # #
# Preliminaries ----
## Import libraries ----
library('tidyverse')
library('lubridate')
library('survival')
library('gt')
library('gtsummary')
## Import custom user functions from lib
source(here::here("lib", "utility_functions.R"))
source(here::here("lib", "redaction_functions.R"))
source(here::here("lib", "survival_functions.R"))
## import command-line arguments ----
args <- commandArgs(trailingOnly=TRUE)
if(length(args)==0){
# use for interactive testing
cohort <- "over80s"
removeobs <- FALSE
} else {
# use for actions
cohort <- args[[1]]
removeobs <- TRUE
}
## import global vars ----
gbl_vars <- jsonlite::fromJSON(
txt="./analysis/global-variables.json"
)
#list2env(gbl_vars, globalenv())
### import outcomes, exposures, and covariate formulae ----
## these are created in data_define_cohorts.R script
list_formula <- read_rds(here::here("output", "metadata", "list_formula.rds"))
list2env(list_formula, globalenv())
## create output directory ----
dir.create(here::here("output", cohort, "descriptive", "tables"), showWarnings = FALSE, recursive=TRUE)
## Import processed data ----
data_cohort <- read_rds(here::here("output", cohort, "data", "data_cohort.rds"))
characteristics <- read_rds(here::here("output", "metadata", "baseline_characteristics.rds"))
# create pt data ----
data_tte <- data_cohort %>%
transmute(
patient_id,
start_date,
end_date,
#composite of death, deregistration and end date
lastfup_date = pmin(death_date, end_date, dereg_date, na.rm=TRUE),
tte_enddate = tte(start_date, end_date, end_date),
# time to last follow up day
tte_lastfup = tte(start_date, lastfup_date, lastfup_date),
# time to deregistration
tte_dereg = tte(start_date, dereg_date, dereg_date),
# time to test
tte_covidtest = tte(start_date, covid_test_1_date, lastfup_date, na.censor=TRUE),
# time to positive test
tte_postest = tte(start_date, positive_test_1_date, lastfup_date, na.censor=TRUE),
# time to admission
tte_covidadmitted = tte(start_date, covidadmitted_1_date, lastfup_date, na.censor=TRUE),
#time to covid death
tte_coviddeath = tte(start_date, coviddeath_date, lastfup_date, na.censor=TRUE),
tte_noncoviddeath = tte(start_date, noncoviddeath_date, lastfup_date, na.censor=TRUE),
#time to death
tte_death = tte(start_date, death_date, lastfup_date, na.censor=TRUE),
tte_vaxany1 = tte(start_date, covid_vax_1_date, lastfup_date, na.censor=TRUE),
tte_vaxany2 = tte(start_date, covid_vax_2_date, lastfup_date, na.censor=TRUE),
ttecensored_vaxany1 = tte(start_date, covid_vax_1_date, lastfup_date, na.censor=FALSE),
ind_vaxany1 = censor_indicator(covid_vax_1_date, lastfup_date),
tte_vaxpfizer1 = tte(start_date, covid_vax_pfizer_1_date, lastfup_date, na.censor=TRUE),
tte_vaxpfizer2 = tte(start_date, covid_vax_pfizer_2_date, lastfup_date, na.censor=TRUE),
tte_vaxaz1 = tte(start_date, covid_vax_az_1_date, lastfup_date, na.censor=TRUE),
tte_vaxaz2 = tte(start_date, covid_vax_az_2_date, lastfup_date, na.censor=TRUE),
)
if(removeobs) rm(data_cohort)
data_tte_cp <- tmerge(
data1 = data_tte,
data2 = data_tte,
id = patient_id,
vaxany1_status = tdc(tte_vaxany1),
vaxany2_status = tdc(tte_vaxany2),
vaxpfizer1_status = tdc(tte_vaxpfizer1),
vaxpfizer2_status = tdc(tte_vaxpfizer2),
vaxaz1_status = tdc(tte_vaxaz1),
vaxaz2_status = tdc(tte_vaxaz2),
covidtest_status = tdc(tte_covidtest),
postest_status = tdc(tte_postest),
covidadmitted_status = tdc(tte_covidadmitted),
coviddeath_status = tdc(tte_coviddeath),
noncoviddeath_status = tdc(tte_noncoviddeath),
death_status = tdc(tte_death),
dereg_status= tdc(tte_dereg),
lastfup_status = tdc(tte_lastfup),
vaxany1 = event(tte_vaxany1),
vaxany2 = event(tte_vaxany2),
vaxpfizer1 = event(tte_vaxpfizer1),
vaxpfizer2 = event(tte_vaxpfizer2),
vaxaz1 = event(tte_vaxaz1),
vaxaz2 = event(tte_vaxaz2),
covidtest = event(tte_covidtest),
postest = event(tte_postest),
covidadmitted = event(tte_covidadmitted),
coviddeath = event(tte_coviddeath),
noncoviddeath = event(tte_noncoviddeath),
death = event(tte_death),
dereg = event(tte_dereg),
lastfup = event(tte_lastfup),
tstart = 0L,
tstop = tte_enddate # use enddate not lastfup because it's useful for status over time plots
)
alltimes <- expand(data_tte, patient_id, times=as.integer(full_seq(c(0, tte_enddate),1)))
data_pt <- tmerge(
data1 = data_tte_cp,
data2 = alltimes,
id = patient_id,
alltimes = event(times, times)
) %>%
arrange(patient_id, tstop) %>%
group_by(patient_id) %>%
mutate(
# define time since vaccination
timesincevaxany1 = cumsum(vaxany1_status),
timesincevaxany2 = cumsum(vaxany2_status),
timesincevaxpfizer1 = cumsum(vaxpfizer1_status),
timesincevaxpfizer2 = cumsum(vaxpfizer2_status),
timesincevaxaz1 = cumsum(vaxaz1_status),
timesincevaxaz2 = cumsum(vaxaz2_status),
twidth = tstop - tstart,
vaxany_status = vaxany1_status + vaxany2_status,
vaxpfizer_status = vaxpfizer1_status + vaxpfizer2_status,
vaxaz_status = vaxaz1_status + vaxaz2_status,
fup_any = (death_status==0 & dereg_status==0),
fup_pfizer = (death_status==0 & dereg_status==0 & vaxaz1_status==0),
fup_az = (death_status==0 & dereg_status==0 & vaxpfizer1_status==0 & tstart>=27),
all=0
) %>%
ungroup() %>%
# for some reason tmerge converts event indicators to numeric. So convert back to save space
mutate(across(
.cols = c("vaxany1",
"vaxany2",
"vaxpfizer1",
"vaxpfizer2",
"vaxaz1",
"vaxaz2",
"covidtest",
"postest",
"covidadmitted",
"coviddeath",
"noncoviddeath",
"death",
"dereg",
"lastfup",
"vaxany1_status",
"vaxany2_status",
"vaxpfizer1_status",
"vaxpfizer2_status",
"vaxaz1_status",
"vaxaz2_status",
"covidtest_status",
"postest_status",
"covidadmitted_status",
"coviddeath_status",
"noncoviddeath_status",
"death_status",
"dereg_status",
"lastfup_status",
),
.fns = as.integer
))
if(removeobs) rm(data_tte_cp)
## create person-time table ----
format_ratio = function(numer,denom, width=7){
paste0(
replace_na(scales::comma_format(accuracy=1)(numer), "--"),
" /",
str_pad(replace_na(scales::comma_format(accuracy=1)(denom),"--"), width=width, pad=" ")
)
}
rrCI_normal <- function(n, pt, ref_n, ref_pt, group, accuracy=0.001){
rate <- n/pt
ref_rate <- ref_n/ref_pt
rr <- rate/ref_rate
log_rr <- log(rr)
selog_rr <- sqrt((1/n)+(1/ref_n))
log_ll <- log_rr - qnorm(0.975)*selog_rr
log_ul <- log_rr + qnorm(0.975)*selog_rr
ll <- exp(log_ll)
ul <- exp(log_ul)
if_else(
group==levels(group)[1],
NA_character_,
paste0("(", scales::number_format(accuracy=accuracy)(ll), "-", scales::number_format(accuracy=accuracy)(ul), ")")
)
}
rrCI_exact <- function(n, pt, ref_n, ref_pt, group, accuracy=0.001){
# use exact methods if incidence is very low for immediate post-vaccine outcomes
rate <- n/pt
ref_rate <- ref_n/ref_pt
rr <- rate/ref_rate
ll = ref_pt/pt * (n/(ref_n+1)) * 1/qf(2*(ref_n+1), 2*n, p = 0.05/2, lower.tail = FALSE)
ul = ref_pt/pt * ((n+1)/ref_n) * qf(2*(n+1), 2*ref_n, p = 0.05/2, lower.tail = FALSE)
if_else(
group==levels(group)[1],
NA_character_,
paste0("(", scales::number_format(accuracy=accuracy)(ll), "-", scales::number_format(accuracy=accuracy)(ul), ")")
)
}
# get confidence intervals for rate ratio using unadjusted poisson GLM
# uses gtsummary not broom::tidy to make it easier to paste onto original data
rrCI_glm <- function(n, pt, x, accuracy=0.001){
dat<-tibble(n=n, pt=pt, x=x)
poismod <- glm(
formula = n ~ x + offset(log(pt*365.25)),
family=poisson,
data=dat
)
gtmodel <- tbl_regression(poismod, exponentiate=TRUE)$table_body %>%
filter(reference_row %in% FALSE) %>%
select(label, conf.low, conf.high)
dat2 <- left_join(dat, gtmodel, by=c("x"="label"))
if_else(
dat2$x==first(dat2$x),
NA_character_,
paste0("(", scales::number_format(accuracy=accuracy)(dat2$conf.low), "-", scales::number_format(accuracy=accuracy)(dat2$conf.high), ")")
)
}
pt_summary <- function(data, fup, timesince, postvaxcuts, baseline){
unredacted <- data %>%
mutate(
timesincevax = data[[timesince]],
fup = data[[fup]],
timesincevax_pw = timesince_cut(timesincevax, postvaxcuts, baseline),
) %>%
filter(fup==1) %>%
select(
timesincevax_pw,
postest_status,
covidadmitted_status,
coviddeath_status,
noncoviddeath_status,
death_status,
postest,
coviddeath,
covidadmitted,
noncoviddeath,
death
) %>%
group_by(timesincevax_pw) %>%
summarise(
postest_yearsatrisk=sum(postest_status==0)/365.25,
postest_n=sum(postest),
postest_rate=postest_n/postest_yearsatrisk,
covidadmitted_yearsatrisk=sum(covidadmitted_status==0)/365.25,
covidadmitted_n=sum(covidadmitted),
covidadmitted_rate=covidadmitted_n/covidadmitted_yearsatrisk,
coviddeath_yearsatrisk=sum(coviddeath_status==0)/365.25,
coviddeath_n=sum(coviddeath),
coviddeath_rate=coviddeath_n/coviddeath_yearsatrisk,
noncoviddeath_yearsatrisk=sum(noncoviddeath_status==0)/365.25,
noncoviddeath_n=sum(noncoviddeath),
noncoviddeath_rate=noncoviddeath_n/noncoviddeath_yearsatrisk,
death_yearsatrisk=sum(death_status==0)/365.25,
death_n=sum(death),
death_rate=death_n/death_yearsatrisk,
) %>%
ungroup() %>%
mutate(
postest_rr=postest_rate/first(postest_rate),
covidadmitted_rr=covidadmitted_rate/first(covidadmitted_rate),
coviddeath_rr=coviddeath_rate/first(coviddeath_rate),
noncoviddeath_rr=noncoviddeath_rate/first(noncoviddeath_rate),
death_rr=death_rate/first(death_rate),
postest_rrCI = rrCI_exact(postest_n, postest_yearsatrisk, first(postest_n), first(postest_yearsatrisk), timesincevax_pw, 0.01),
covidadmitted_rrCI = rrCI_exact(covidadmitted_n, covidadmitted_yearsatrisk, first(covidadmitted_n), first(covidadmitted_yearsatrisk), timesincevax_pw, 0.01),
coviddeath_rrCI = rrCI_exact(coviddeath_n, coviddeath_yearsatrisk, first(coviddeath_n), first(coviddeath_yearsatrisk), timesincevax_pw, 0.01),
noncoviddeath_rrCI = rrCI_exact(noncoviddeath_n, noncoviddeath_yearsatrisk, first(noncoviddeath_n), first(noncoviddeath_yearsatrisk), timesincevax_pw, 0.01),
death_rrCI = rrCI_exact(death_n, death_yearsatrisk, first(death_n), first(death_yearsatrisk), timesincevax_pw, 0.01),
)
redacted <- unredacted %>%
mutate(
postest_rate = redactor2(postest_n, 5, postest_rate),
covidadmitted_rate = redactor2(covidadmitted_n, 5, covidadmitted_rate),
coviddeath_rate = redactor2(coviddeath_n, 5, coviddeath_rate),
noncoviddeath_rate = redactor2(noncoviddeath_n, 5, noncoviddeath_rate),
death_rate = redactor2(death_n, 5, death_rate),
postest_rr = redactor2(postest_n, 5, postest_rr),
covidadmitted_rr = redactor2(covidadmitted_n, 5, covidadmitted_rr),
coviddeath_rr = redactor2(coviddeath_n, 5, coviddeath_rr),
noncoviddeath_rr = redactor2(noncoviddeath_n, 5, noncoviddeath_rr),
death_rr = redactor2(death_n, 5, death_rr),
postest_rrCI = redactor2(postest_n, 5, postest_rrCI),
covidadmitted_rrCI = redactor2(covidadmitted_n, 5, covidadmitted_rrCI),
coviddeath_rrCI = redactor2(coviddeath_n, 5, coviddeath_rrCI),
noncoviddeath_rrCI = redactor2(noncoviddeath_n, 5, noncoviddeath_rrCI),
death_rrCI = redactor2(death_n, 5, death_rrCI),
postest_n = redactor2(postest_n, 5),
covidadmitted_n = redactor2(covidadmitted_n, 5),
coviddeath_n = redactor2(coviddeath_n, 5),
noncoviddeath_n = redactor2(noncoviddeath_n, 5),
death_n = redactor2(death_n, 5)
)
redacted
}
data_summary_any <- local({
temp1 <- pt_summary(data_pt, "fup_any", "timesincevaxany1", postvaxcuts, "Unvaccinated")
temp2 <- pt_summary(data_pt, "fup_any", "all", postvaxcuts, "Total") %>% mutate(across(.cols=ends_with("_rr"), .fns = ~ NA_real_))
bind_rows(temp1, temp2) %>% mutate(brand ="Any vaccine")
})
data_summary_pfizer <- local({
temp1 <- pt_summary(data_pt, "fup_pfizer", "timesincevaxpfizer1", postvaxcuts, "Unvaccinated")
temp2 <- pt_summary(data_pt, "fup_pfizer", "all", postvaxcuts, "Total") %>% mutate(across(.cols=ends_with("_rr"), .fns = ~ NA_real_))
bind_rows(temp1, temp2) %>% mutate(brand ="BNT162b2")
})
data_summary_az <- local({
temp1 <- pt_summary(data_pt, "fup_az", "timesincevaxaz1", postvaxcuts, "Unvaccinated")
temp2 <- pt_summary(data_pt, "fup_az", "all", postvaxcuts, "Total") %>% mutate(across(.cols=ends_with("_rr"), .fns = ~ NA_real_))
bind_rows(temp1, temp2) %>% mutate(brand ="ChAdOx1")
})
data_summary <- bind_rows(
data_summary_any,
data_summary_pfizer,
data_summary_az
) %>%
mutate(
postest_q = format_ratio(postest_n,postest_yearsatrisk),
covidadmitted_q = format_ratio(covidadmitted_n,covidadmitted_yearsatrisk),
coviddeath_q = format_ratio(coviddeath_n,coviddeath_yearsatrisk),
noncoviddeath_q = format_ratio(noncoviddeath_n,noncoviddeath_yearsatrisk),
death_q = format_ratio(death_n,death_yearsatrisk),
) %>%
select(brand, starts_with("timesince"), ends_with(c("_q","_rr", "_rrCI")))
data_summary %>%
mutate(
postest_rr = scales::label_number(accuracy=0.01, trim=FALSE)(postest_rr),
covidadmitted_rr = scales::label_number(accuracy=0.01, trim=FALSE)(covidadmitted_rr),
coviddeath_rr = scales::label_number(accuracy=0.01, trim=FALSE)(coviddeath_rr),
noncoviddeath_rr = scales::label_number(accuracy=0.01, trim=FALSE)(noncoviddeath_rr),
death_rr = scales::label_number(accuracy=0.01, trim=FALSE)(death_rr),
) %>%
write_csv(here::here("output", cohort, "descriptive", "tables", "table_irr.csv"))
tab_summary <- data_summary %>%
gt(
groupname_col = "brand",
) %>%
cols_label(
brand = "Vaccine brand",
timesincevax_pw = "Time since first dose",
postest_q = "Events / person-years",
covidadmitted_q = "Events / person-years",
coviddeath_q = "Events / person-years",
noncoviddeath_q = "Events / person-years",
death_q = "Events / person-years",
# postest_yearsatrisk = "Person-years at risk",
# covidadmitted_yearsatrisk = "Person-years at risk",
# coviddeath_yearsatrisk = "Person-years at risk",
# noncoviddeath_yearsatrisk = "Person-years at risk",
# #death_yearsatrisk = "Person-years at risk",
#
# postest_n = "Events",
# covidadmitted_n = "Events",
# coviddeath_n = "Events",
# noncoviddeath_n = "Events",
# #death_n = "Events",
#
# postest_rate = "Rate/year",
# covidadmitted_rate = "Rate/year",
# coviddeath_rate = "Rate/year",
# noncoviddeath_rate = "Rate/year",
# #death_rate = "Rate/year"
postest_rr = "Rate ratio",
covidadmitted_rr = "Rate ratio",
coviddeath_rr = "Rate ratio",
noncoviddeath_rr = "Rate ratio",
death_rr = "Rate ratio",
postest_rrCI = "95% CI",
covidadmitted_rrCI = "95% CI",
coviddeath_rrCI = "95% CI",
noncoviddeath_rrCI = "95% CI",
death_rrCI = "95% CI"
) %>%
tab_spanner(
label = "Positive test",
columns = starts_with("postest")
) %>%
tab_spanner(
label = "COVID-19 hospitalisation",
columns = starts_with("covidadmitted")
) %>%
tab_spanner(
label = "COVID-19 death",
columns = starts_with("coviddeath")
) %>%
tab_spanner(
label = "Non-COVID-19 death",
columns = starts_with("noncoviddeath")
) %>%
tab_spanner(
label = "Any death",
columns = starts_with("death")
) %>%
# fmt_number(
# columns = ends_with(c("yearsatrisk")),
# decimals = 0
# ) %>%
fmt_number(
columns = ends_with(c("rr")),
decimals = 2
) %>%
fmt_missing(
everything(),
missing_text="--"
) %>%
cols_align(
align = "right",
columns = everything()
) %>%
cols_align(
align = "left",
columns = "timesincevax_pw"
)
gtsave(tab_summary, here::here("output", cohort, "descriptive", "tables", "table_irr.html"))
## note:
# the follow poisson model gives the same results eg for postest
# poismod <- glm(
# formula = postest_n ~ timesincevax_pw + offset(log(postest_yearsatrisk*365.25)),
# family=poisson,
# data=pt_summary(data_pt, "timesincevaxany1", postvaxcuts)
# )
# same but with person-time data
# poismod2 <- glm(
# formula = postest ~ timesincevax_pw ,
# family=poisson,
# data=data_pt %>% mutate(timesincevax_pw = timesince_cut(timesincevaxany1, postvaxcuts, "Unvaccinated")) %>% filter(postest_status==0, death_status==0, dereg_status==0)
# )
# and the following pyears call gives the same results
# pyears(
# Surv(time=tstart, time2=tstop, event=postest) ~ timesincevaxany1,
# data=data_pt %>% filter(postest_status==0),
# data.frame = TRUE
# )