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itt_analysis.R
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itt_analysis.R
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################################################################
# This script:
# - Conducts regression model and plots predicted values
################################################################
# For running locally only #
# setwd("C:/Users/aschaffer/OneDrive - Nexus365/Documents/GitHub/vax-fourth-dose-RD")
# getwd()
# Import libraries #
library('tidyverse')
library('lubridate')
library('arrow')
library('here')
library('reshape2')
library('dplyr')
library('fs')
library('ggplot2')
library('RColorBrewer')
library('data.table')
dir_create(here::here("output", "covid_outcomes"), showWarnings = FALSE, recurse = TRUE)
dir_create(here::here("output", "covid_outcomes", "by_start_date"), showWarnings = FALSE, recurse = TRUE)
dir_create(here::here("output", "covid_outcomes", "figures"), showWarnings = FALSE, recurse = TRUE)
data_nov <- read.csv(here::here("output", "covid_outcomes", "by_start_date", "outcomes_byage_3mon_2022-11-26.csv")) %>%
subset(!is.na(age_mos3) & age_mos3 >= 540 & age_mos3 < 660)
data_sep <- read.csv(here::here("output", "covid_outcomes", "by_start_date", "outcomes_byage_3mon_2022-09-03.csv")) %>%
subset(!is.na(age_mos3) & age_mos3 >= 540 & age_mos3 < 660)
mod_pred <- function(data, out, start, name){
len <- nrow(data)
df <- data %>%
mutate(over50 = if_else(age_mos3 >= 600, 1, 0, 0),
age_mos3_c = as.numeric((age_mos3 - 600) / 3),
quarter = rep(1:4, length.out = len)) %>%
rename(outcome = {{out}})
mod <- glm(outcome / 100000 ~ age_mos3_c*over50, data = df,
family = binomial("logit"), weights = total)
newdata <- df %>% mutate(over50 = 0)
pred.df1 <- predict(mod, se.fit = TRUE, type = "response") %>%
data.frame() %>%
mutate(pred1 = fit,
lci1 = fit - 1.96*se.fit,
uci1 = fit + 1.96*se.fit) %>%
select(c("pred1","lci1","uci1"))
pred.df2 <- predict(mod, se.fit=TRUE, type = "response",
newdata=newdata) %>%
data.frame() %>%
mutate(pred2= fit,
lci2 = fit - 1.96*se.fit,
uci2 = fit + 1.96*se.fit) %>%
select(c("pred2","lci2","uci2"))
# Combined with original data
df_pred <- cbind(age_mos3 = df$age_mos3,
rate = df$out,
total = df$total,
pred.df1, pred.df2,
aic = AIC(mod)) %>%
mutate(pred1 = pred1 * 100000,
lci1 = lci1 * 100000,
uci1 = uci1 * 100000,
pred2 = pred2 * 100000,
lci2 = lci2 * 100000,
uci2 = uci2 * 100000,
start = start,
outcome = name)
return(df_pred)
}
covidcomp_nov <- mod_pred(data_nov, rate_covidcomposite, "Index date: November 26", "COVID admission/A&E/death")
covidcomp_sep <- mod_pred(data_sep, rate_covidcomposite, "Index date: September 3", "COVID admission/A&E/death")
covidadmit_nov <- mod_pred(data_nov, rate_covidadmitted, "Index date: November 26", "COVID admission")
covidadmit_sep <- mod_pred(data_sep, rate_covidadmitted, "Index date: September 3", "COVID admission")
covidemerg_nov <- mod_pred(data_nov, rate_covidemerg, "Index date: November 26", "COVID admission")
covidemerg_sep <- mod_pred(data_sep, rate_covidemerg, "Index date: September 3", "COVID admission")
respcomp_nov <- mod_pred(data_nov, rate_respcomposite, "Index date: November 26", "Respiratory admission/death")
respcomp_sep <- mod_pred(data_sep, rate_respcomposite, "Index date: September 3", "Respiratory admission/death")
respadmit_nov <- mod_pred(data_nov, rate_respadmitted, "Index date: November 26", "Respiratory admission")
respadmit_sep <- mod_pred(data_sep, rate_respadmitted, "Index date: September 3", "Respiratory admission")
anyadmit_nov <- mod_pred(data_nov, rate_anyadmitted, "Index date: November 26", "Any unplanned admission")
anyadmit_sep <- mod_pred(data_sep, rate_anyadmitted, "Index date: September 3", "Any unplanned admission")
covidcomp <- rbind(covidcomp_nov, covidcomp_sep)
covidcomp2 <- covidcomp %>% select(!rate)
write.csv(covidcomp2, here::here("output", "covid_outcomes", "predicted_covidcomp.csv"), row.names = FALSE)
covidadmit <- rbind(covidadmit_nov, covidadmit_sep)
covidadmit2 <- covidadmit %>% select(!rate)
write.csv(covidadmit2, here::here("output", "covid_outcomes", "predicted_covidadmit.csv"), row.names = FALSE)
covidemerg <- rbind(covidemerg_nov, covidemerg_sep)
covidemerg2 <- covidemerg %>% select(!rate)
write.csv(covidemerg2, here::here("output", "covid_outcomes", "predicted_covidemerg.csv"), row.names = FALSE)
respcomp <- rbind(respcomp_nov, respcomp_sep)
respcomp2 <- respcomp %>% select(!rate)
write.csv(respcomp2, here::here("output", "covid_outcomes", "predicted_respcomp.csv"), row.names = FALSE)
respadmit <- rbind(respadmit_nov, respadmit_sep)
respadmit2 <- respadmit %>% select(!rate)
write.csv(respadmit2, here::here("output", "covid_outcomes", "predicted_respadmit.csv"), row.names = FALSE)
anyadmit <- rbind(anyadmit_nov, anyadmit_sep)
anyadmit2 <- anyadmit %>% select(!rate)
write.csv(anyadmit2, here::here("output", "covid_outcomes", "predicted_anyadmit.csv"), row.names = FALSE)
################
plot_nopt <- function(outcome){
ggplot() +
geom_ribbon(data=subset(outcome, age_mos3 <= 600),
aes(x=age_mos3 / 12, ymin=lci2, ymax=uci2), alpha=0.2, fill = "gray50") +
geom_ribbon(data=subset(outcome, age_mos3 >= 600),
aes(x=age_mos3 / 12, ymin=lci1, ymax=uci1), alpha=0.2, fill = "gray50") +
geom_vline(data = outcome, aes(xintercept = 50), linetype = "longdash") +
geom_line(data=subset(outcome, age_mos3 <= 600),
aes(x=age_mos3 / 12, y = pred2, col = start), size = .8,
linetype = "longdash") +
geom_line(data=subset(outcome, age_mos3 >= 600),
aes(x=age_mos3 / 12, y = pred1, col = start), size = .8,
linetype = "longdash") +
scale_colour_manual(values = c("dodgerblue3", "maroon")) +
scale_y_continuous(expand = expansion(mult = c(.1, .1))) +
xlab("Age") + ylab("No. events per 100,000 (predicted)") +
facet_wrap(~ start, nrow = 2, scales = "free_y") +
theme_bw() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
strip.background = element_blank(),
strip.text = element_text(hjust = 0),
legend.title = element_blank(), legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1))
}
plot_nopt(covidcomp)
ggsave(here::here("output", "covid_outcomes", "figures", "plot_nopt_pred_covidcomp.png"),
dpi = 300, units = "in", width = 6, height = 6)
plot_nopt(covidadmit)
ggsave(here::here("output", "covid_outcomes", "figures", "plot_nopt_pred_covidadmit.png"),
dpi = 300, units = "in", width = 6, height = 6)
plot_nopt(covidemerg)
ggsave(here::here("output", "covid_outcomes", "figures", "plot_nopt_pred_covidemerg.png"),
dpi = 300, units = "in", width = 6, height = 6)
plot_nopt(respcomp)
ggsave(here::here("output", "covid_outcomes", "figures", "plot_nopt_pred_respcomp.png"),
dpi = 300, units = "in", width = 6, height = 6)
plot_nopt(respadmit)
ggsave(here::here("output", "covid_outcomes", "figures", "plot_nopt_pred_respadmit.png"),
dpi = 300, units = "in", width = 6, height = 6)
plot_nopt(anyadmit)
ggsave(here::here("output", "covid_outcomes", "figures", "plot_nopt_pred_anyadmit.png"),
dpi = 300, units = "in", width = 6, height = 6)
plot <- function(outcome){
ggplot() +
geom_ribbon(data=subset(outcome, age_mos3 <= 600),
aes(x=age_mos3 / 12, ymin=lci2, ymax=uci2), alpha=0.2, fill = "gray50") +
geom_ribbon(data=subset(outcome, age_mos3 >= 600),
aes(x=age_mos3 / 12, ymin=lci1, ymax=uci1), alpha=0.2, fill = "gray50") +
geom_point(data=outcome, aes(x = age_mos3 / 12, y = rate,
group = start, col = start), size = 1.25, alpha= .5) +
geom_vline(data = outcome, aes(xintercept = 50), linetype = "longdash") +
geom_line(data=subset(outcome, age_mos3 <= 600),
aes(x=age_mos3 / 12, y = pred2, col = start), size = .8,
linetype = "longdash") +
geom_line(data=subset(outcome, age_mos3 >= 600),
aes(x=age_mos3 / 12, y = pred1, col = start), size = .8,
linetype = "longdash") +
scale_colour_manual(values = c("dodgerblue3", "maroon")) +
scale_y_continuous(expand = expansion(mult = c(.1, .1))) +
xlab("Age") + ylab("No. events per 100,000 (predicted)") +
facet_wrap(~ start, nrow = 2, scales = "free_y") +
theme_bw() +
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
strip.background = element_blank(),
strip.text = element_text(hjust = 0),
legend.title = element_blank(), legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1))
}
plot(covidcomp)
ggsave(here::here("output", "covid_outcomes", "figures", "plot_pred_covidcomp.png"),
dpi = 300, units = "in", width = 6, height = 6)
plot(covidadmit)
ggsave(here::here("output", "covid_outcomes", "figures", "plot_pred_covidadmit.png"),
dpi = 300, units = "in", width = 6, height = 6)
plot(covidemerg)
ggsave(here::here("output", "covid_outcomes", "figures", "plot_pred_covidemerg.png"),
dpi = 300, units = "in", width = 6, height = 6)
plot(respcomp)
ggsave(here::here("output", "covid_outcomes", "figures", "plot_pred_respcomp.png"),
dpi = 300, units = "in", width = 6, height = 6)
plot(respadmit)
ggsave(here::here("output", "covid_outcomes", "figures", "plot_pred_respadmit.png"),
dpi = 300, units = "in", width = 6, height = 6)
plot(anyadmit)
ggsave(here::here("output", "covid_outcomes", "figures", "plot_pred_anyadmit.png"),
dpi = 300, units = "in", width = 6, height = 6)