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cumulative_incidence.R
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cumulative_incidence.R
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######################################
# This script
# - produces a cumulative incidence plot of follow-up-time
# - saves plot as svg
######################################
# Preliminaries ----
## Import libraries
library('tidyverse')
library('lubridate')
library('reshape2')
library('here')
library('survival')
library('survminer')
## Create output directory
dir.create(here::here("output", "figures"), showWarnings = FALSE, recursive=TRUE)
## Custome function
fct_case_when <- function(...) {
# uses dplyr::case_when but converts the output to a factor,
# with factors ordered as they appear in the case_when's ... argument
args <- as.list(match.call())
levels <- sapply(args[-1], function(f) f[[3]]) # extract RHS of formula
levels <- levels[!is.na(levels)]
factor(dplyr::case_when(...), levels=levels)
}
## Import data
data_processed <- read_rds(here::here("output", "data", "data_processed.rds"))
## Format groups
data_processed <- data_processed %>%
mutate(group = ifelse(care_home_65plus == 1, 1, NA),
group = ifelse(is.na(group) & ageband == 3, 2, group),
group = ifelse(is.na(group) & hscworker == 1, 3, group),
group = ifelse(is.na(group) & ageband == 2, 4, group),
group = ifelse(is.na(group) & shielded == 1, 5, group),
group = ifelse(is.na(group) & age >=50 & age <70, 6, group),
group = ifelse(is.na(group), 7, group),
group = fct_case_when(
group == "1" ~ "Care home (priority group 1)",
group == "2" ~ "80+ (priority group 2)",
group == "3" ~ "Health/care workers (priority groups 1-2)",
group == "4" ~ "70-79 (priority groups 3-4)",
group == "5" ~ "Shielding (age 16-69) (priority group 4)",
group == "6" ~ "50-69 (priority groups 5-9)",
group == "7" ~ "Others not in the above groups (under 50)",
#TRUE ~ "Unknown"
TRUE ~ NA_character_)) %>%
mutate(time_to_positive_test = ifelse(time_to_positive_test < 0, follow_up_time_vax2, time_to_positive_test))
# Plot ----
threshold <- 7
## Data
surv_data_all <- survfit(Surv(time = time_to_positive_test, event = covid_positive_test) ~ 1,
data = data_processed) %>%
broom::tidy() %>%
filter(estimate > 0) %>%
mutate(
estimate = pmin(1,plyr::round_any(estimate, threshold/max(n.risk)), na.rm=TRUE),
conf.low = pmin(1, plyr::round_any(conf.low, threshold/max(n.risk)), na.rm=TRUE),
conf.high = pmin(1, plyr::round_any(conf.high, threshold/max(n.risk)), na.rm=TRUE),
cum.in = 1 - estimate,
lci = 1- conf.high,
uci = 1 - conf.low
)
surv_data_groups <- survfit(Surv(time = time_to_positive_test, event = covid_positive_test) ~ group,
data = data_processed) %>%
broom::tidy() %>%
filter(estimate > 0) %>%
mutate(
estimate = pmin(1,plyr::round_any(estimate, threshold/max(n.risk)), na.rm=TRUE),
conf.low = pmin(1, plyr::round_any(conf.low, threshold/max(n.risk)), na.rm=TRUE),
conf.high = pmin(1, plyr::round_any(conf.high, threshold/max(n.risk)), na.rm=TRUE),
cum.in = 1 - estimate,
lci = 1- conf.high,
uci = 1 - conf.low
) %>%
mutate(group = gsub(".*=","", strata),
group = factor(group, levels = c("All",
"Care home (priority group 1)",
"80+ (priority group 2)",
"Health/care workers (priority groups 1-2)",
"70-79 (priority groups 3-4)",
"Shielding (age 16-69) (priority group 4)",
"50-69 (priority groups 5-9)",
"Others not in the above groups (under 50)")))
surv_data_risk_table <- ggsurvplot(survfit(Surv(time = time_to_positive_test, event = covid_positive_test) ~ group,
data = data_processed), risk.table = TRUE)$data.survtable %>%
select(group, time, n.risk) %>%
mutate(`n.risk` = ifelse(`n.risk` < 8, "<8", `n.risk`),
group = factor(group, levels = c("Care home (priority group 1)",
"80+ (priority group 2)",
"Health/care workers (priority groups 1-2)",
"70-79 (priority groups 3-4)",
"Shielding (age 16-69) (priority group 4)",
"50-69 (priority groups 5-9)",
"Others not in the above groups (under 50)")))
## Plot
surv_plot <- surv_data_groups %>%
ggplot(aes(x = time, y = cum.in, colour = group)) +
geom_step(data = surv_data_all, aes(x = time, y = cum.in, colour = "All"), size = 1, linetype = 1) +
geom_step(size = 0.5) +
#geom_ribbon(aes(ymin = lci, ymax = uci, fill = group), alpha=0.2, colour = "transparent") +
#geom_ribbon(data = surv_data_all, aes(ymin = lci, ymax = uci), alpha=0.2, colour="transparent") +
scale_x_continuous(breaks = seq(0, max(surv_data_groups$time),50)) +
scale_y_continuous(expand = expansion(mult=c(0,0.01))) +
coord_cartesian(xlim=c(0, max(surv_data_groups$time))) +
labs(
x = "Days since being fully vaccinated",
y = "Cumulative incidence of positive SARS-CoV-2 test",
colour = "Priority Group",
title = "") +
theme_minimal(base_size = 9) +
theme(
legend.position = c(0.2,0.65),
legend.background = element_rect(colour = "white"),
legend.box.background = element_rect(colour = "black"),
axis.line.x = element_line(colour = "black"),
panel.grid.minor.x = element_blank(),
legend.title = element_blank(),
legend.box.margin = margin(t = 1, l = 1, b = 1, r = 1)) +
scale_color_manual(values = c("All" = "black",
"Care home (priority group 1)" = "#00BFC4",
"80+ (priority group 2)" = "#7CAE00",
"Health/care workers (priority groups 1-2)" = "#00A9FF",
"70-79 (priority groups 3-4)" = "#CD9600",
"Shielding (age 16-69) (priority group 4)" = "#FF61CC",
"50-69 (priority groups 5-9)" = "#F8766D",
"Others not in the above groups (under 50)" = "#C77CFF")) +
guides(col = guide_legend(order = 1))
surv_table <- ggplot(surv_data_risk_table, aes(time, group)) +
geom_text(aes(label = n.risk), size = 2) +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_rect(fill= "white", size = 1),
axis.text.x = element_text(color = "black", size = 6),
axis.text.y = element_text(color = c("#00BFC4", "#7CAE00", "#00A9FF", "#CD9600", "#FF61CC",
"#F8766D", "#C77CFF"), size = 4),
axis.title = element_text(color = "black", size = 8),
strip.text = element_text(color = "black", size = 3)) +
scale_y_discrete(limits = c("Others not in the above groups (under 50)",
"50-69 (priority groups 5-9)",
"Shielding (age 16-69) (priority group 4)",
"70-79 (priority groups 3-4)",
"Health/care workers (priority groups 1-2)",
"80+ (priority group 2)",
"Care home (priority group 1)")) +
xlab("") +
ylab("Priority group") +
ggtitle("Number at Risk")
gA <- ggplotGrob(surv_plot)
gB <- ggplotGrob(surv_table)
maxWidth = grid::unit.pmax(gA$widths[2:5], gB$widths[2:5])
gA$widths[2:5] <- as.list(maxWidth)
gB$widths[2:5] <- as.list(maxWidth)
#gridExtra::grid.arrange(gA, gB, ncol=1,heights = c(4, 1))
surv_plot_table <- gridExtra::arrangeGrob(gA, gB, ncol=1,heights = c(4, 1))
surv_plot_ci <- surv_data_groups %>%
ggplot(aes(x = time, y = cum.in, colour = group)) +
geom_step(size = 0.5) +
geom_ribbon(aes(ymin = lci, ymax = uci, fill = group), alpha=0.2, colour = "transparent") +
geom_step(data = surv_data_all, aes(x = time, y = cum.in, colour = "All"), size = 0.5, linetype = 2) +
geom_ribbon(data = surv_data_all, aes(ymin = lci, ymax = uci), alpha=0.2, colour="transparent") +
scale_x_continuous(breaks = seq(0,140,25)) +
scale_y_continuous(expand = expansion(mult=c(0,0.01))) +
coord_cartesian(xlim=c(0, 100)) +
labs(
x = "Days since being fully vaccinated",
y = "Cumulative incidence of COVID-19 infection",
colour = "Priority Group",
title = "") +
theme_minimal(base_size = 9) +
theme(
legend.position = "right",
axis.line.x = element_line(colour = "black"),
panel.grid.minor.x = element_blank(),
legend.title = element_blank()) +
guides(fill = "none") +
scale_color_manual(values = c("All" = "black",
"Care home (priority group 1)" = "#00BFC4",
"80+ (priority group 2)" = "#7CAE00",
"Health/care workers (priority groups 1-2)" = "#00A9FF",
"70-79 (priority groups 3-4)" = "#CD9600",
"Shielding (age 16-69) (priority group 4)" = "#FF61CC",
"50-69 (priority groups 5-9)" = "#F8766D",
"Others not in the above groups (under 50)" = "#C77CFF"))
## CI
surv_data_groups %>%
filter(time == 140) %>%
select(cum.in, group)
## Save plot
ggsave(
here::here("output", "figures", "figure1.png"),
surv_plot_table,
units = "cm", width = 35, height = 20
)
ggsave(
here::here("output", "figures", "figure1_cis.svg"),
surv_plot_ci,
units = "cm", width = 30, height = 15
)