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ITS_repeat_incident.R
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ITS_repeat_incident.R
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### load library ###
library("tidyverse")
library(foreign)
library(tsModel)
library(lmtest)
library(Epi)
library(multcomp)
library(splines)
library(vcd)
library(here)
library(lubridate)
library(stringr)
library(ggplot2)
library(patchwork)
library(dplyr)
library(tidyr)
### import data ###
all_files <- list.files(here::here("output"), pattern = "repeat_incidental_")
outcomes <- stringr::str_remove_all(all_files, c("repeat_incidental_|.csv"))
outcome_of_interest_namematch <- bind_cols("outcome" = outcomes,
"outcome_name" = (c("Cold","COPD","Cough",
"LRTI","Otitis externa","Otitis media",
"Sinusitis","Sore throat","URTI","UTI"))
)
bkg_colour <- "gray99"
# load data ---------------------------------------------------------------
for(ii in 1:length(outcomes)){
load_file <- read.csv(here::here("output", paste0("repeat_incidental_", outcomes[ii], ".csv")))
assign(outcomes[ii], load_file)
}
its_function <- function(outcomes_vec = outcomes,
display_from = as.Date("2019-01-01")){
plot_its <- function(outcome){
df_outcome <- get(outcome)
## model binomial
# Change in level + slope:
### include interaction with time (centred at end of Lockdown adjustment period)
ldn_centre <- df_outcome$time[min(which(df_outcome$covid == 1))]
## fit model, calculate lagged residuals to fit in final model
binom_model1 <- glm(as.matrix(cbind(numOutcome, numEligible)) ~ covid + I(time-ldn_centre) + I(time-ldn_centre):covid + as.factor(mon) , family=binomial, data = filter(df_outcome, !is.na(covid)))
### confidence intervals for the coefficients
ci.exp(binom_model1)
binom_lagres <- lag(residuals(binom_model1)) %>% as.numeric()
res1 <- residuals(binom_model1,type="deviance")
## manipulate data so output looks cleaner
model_data <- df_outcome %>%
mutate(timeC = time - ldn_centre) %>%
mutate_at("mon", ~as.factor(.))
## fit model with lagged residuals
binom_model2 <- glm(as.matrix(cbind(numOutcome, numEligible)) ~ covid + timeC + timeC:covid + as.factor(mon) + binom_lagres, family=binomial, data = filter(model_data, !is.na(covid)))
ci.exp(binom_model2)
summary.glm(binom_model2)
## calculate dispersion adjustment parameter -- https://online.stat.psu.edu/stat504/node/162/
#Pearson Goodness-of-fit statistic
pearson_gof <- sum(residuals(binom_model2, type = "pearson")^2)
df <- binom_model2$df.residual
deviance_adjustment <- pearson_gof/df
## some manual manipulation to merge the lagged residuals varaible back with the original data
missing_data_start <- min(which(is.na(model_data$covid)))
missing_data_end <- max(which(is.na(model_data$covid)))
missing_data_restart <- max(which(is.na(model_data$covid)))
binom_lagres_timing <- bind_cols("time" = model_data$time[!is.na(model_data$covid)],
"binom_lagres" = binom_lagres)
## set up data frame to calculate linear predictions
outcome_pred <- model_data %>%
left_join(binom_lagres_timing, by = "time") %>%
mutate_at("binom_lagres", ~(. = 0))
## predict values adjusted for overdispersion
pred1 <- predict(binom_model2, newdata = outcome_pred, se.fit = TRUE, interval="confidence", dispersion = deviance_adjustment)
predicted_vals <- pred1$fit
stbp <- pred1$se.fit
## set up data frame to calculate linear predictions with no covid and predict values
outcome_pred_nointervention <- outcome_pred %>%
mutate_at("covid", ~(.=0))
pred_noCovid <- predict(binom_model2, newdata = outcome_pred_nointervention, se.fit = TRUE, interval="confidence", dispersion = deviance_adjustment)
pred_noCov <- pred_noCovid$fit
stbp_noCov <- pred_noCovid$se.fit
## combine all those predictions and convert from log odds to percentage reporting
df_se <- bind_cols(stbp = stbp, stbp_noCov = stbp_noCov,
pred = predicted_vals, pred_noCov = pred_noCov) %>%
mutate(
#CIs
upp = pred + (1.96*stbp),
low = pred - (1.96*stbp),
upp_noCov = pred_noCov + (1.96*stbp_noCov),
low0_noCov = pred_noCov - (1.96*stbp_noCov),
# probline
predicted_vals = exp(pred)/(1+exp(pred)),
probline_noCov = exp(pred_noCov)/(1+exp(pred_noCov)),
#
uci = exp(upp)/(1+exp(upp)),
lci = exp(low)/(1+exp(low)),
#
uci_noCov = exp(upp_noCov)/(1+exp(upp_noCov)),
lci_noCov = exp(low0_noCov)/(1+exp(low0_noCov))
)
## combine data set and predictions
outcome_plot <- bind_cols(outcome_pred, df_se) %>%
mutate(var = outcome)
## Get ORs for effect of covid
paramter_estimates <- as.data.frame(ci.exp(binom_model2))
vals_to_print <- paramter_estimates %>%
mutate(var = rownames(paramter_estimates)) %>%
filter(var == "covid") %>%
mutate(var = outcome)
## Get ORs for effect of time on outcome after covid happened (time + interaction of time:covid)
interaction_lincom <- glht(binom_model2, linfct = c("timeC + covid:timeC = 0"))
summary(interaction_lincom)
out <- confint(interaction_lincom)
time_grad_postCov <- out$confint[1,] %>% exp() %>% t() %>% as.data.frame()
interaction_to_print <- time_grad_postCov %>%
mutate(var = outcome)
## output
return(list(df_1 = outcome_plot, vals_to_print = vals_to_print, interaction_to_print = interaction_to_print))
}
# the plot ----------------------------------------------------------------
main_plot_data <- NULL
forest_plot_data <- NULL
interaction_tbl_data <- NULL
for(ii in 1:length(outcomes_vec)){
main_plot_data <- main_plot_data %>%
bind_rows(
plot_its(outcomes_vec[ii])$df_1
)
forest_plot_data <- forest_plot_data %>%
bind_rows(
plot_its(outcomes_vec[ii])$vals_to_print
)
interaction_tbl_data <- interaction_tbl_data %>%
bind_rows(
plot_its(outcomes_vec[ii])$interaction_to_print
)
}
## convert proportions into percentage
main_plot_data <- main_plot_data %>%
mutate(pc_broad = (numOutcome/numEligible)*100) %>%
mutate_at(.vars = c("predicted_vals", "lci", "uci", "probline_noCov", "uci_noCov", "lci_noCov"),
~.*100) %>%
left_join(outcome_of_interest_namematch, by = c("var" = "outcome"))
## replace outcome name with the pretty name for printing on results
main_plot_data$outcome_name <- factor(main_plot_data$outcome_name, levels = outcome_of_interest_namematch$outcome_name)
abline_max <- main_plot_data$monPlot[max(which(is.na(main_plot_data$covid)))+1]
abline_min <- main_plot_data$monPlot[min(which(is.na(main_plot_data$covid)))-1]
if(is.na(abline_min) & is.na(abline_max)){
abline_min <- start_covid
abline_max <- start_covid
}
write_csv(main_plot_data, here::here("output", "its_repeat_incident_predicted_data_table.csv"))
main_plot_data$monPlot <- as.Date(main_plot_data$monPlot)
plot1 <- ggplot(main_plot_data, aes(x = monPlot, y = pc_broad, group = outcome_name)) +
# the data
geom_line(col = "gray60") +
### the probability if therer was no Covid
geom_line(data = main_plot_data, aes(y = probline_noCov), col = 2, lty = 2) +
### probability with model (inc. std. error)
geom_line(aes(y = predicted_vals), col = 4, lty = 2) +
geom_ribbon(aes(ymin = lci, ymax=uci), fill = alpha(4,0.4), lty = 0) +
### format the plot
facet_wrap(~outcome_name, scales = "free", ncol = 4) +
geom_vline(xintercept = c(as.Date(abline_min),
as.Date(abline_max)), col = 1, lwd = 1) + # 2020-04-05 is first week/data After lockdown gap
labs(x = "", y = "% of repeat prescription", title = "A") +
theme_classic() +
theme(axis.title = element_text(size =16),
axis.text.x = element_text(angle = 60, hjust = 1, size = 12),
legend.position = "top",
plot.background = element_rect(fill = bkg_colour, colour = NA),
panel.background = element_rect(fill = bkg_colour, colour = NA),
legend.background = element_rect(fill = bkg_colour, colour = NA),
legend.text = element_text(size = 12),
legend.title = element_text(size = 12),
strip.text = element_text(size = 12, hjust = 0),
strip.background = element_rect(fill = bkg_colour, colour = NA),
panel.grid.major = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_line(size=.2, color=rgb(0,0,0,0.2)) ,
panel.grid.major.y = element_line(size=.2, color=rgb(0,0,0,0.3)))
plot1
ggsave(
plot= plot1,
filename="its_repeat_incident_predicted.jpeg", path=here::here("output"),
)
# Forest plot of interaction terms ------------------------------------------------------
## clean up the names
interaction_tbl_data <- interaction_tbl_data %>%
rename("Est" = "Estimate", lci = lwr, uci = upr) %>%
left_join(outcome_of_interest_namematch, by = c("var" = "outcome"))
# changes the names of outcomes to full names
interaction_tbl_data$outcome_name <- factor(interaction_tbl_data$outcome_name, levels = outcome_of_interest_namematch$outcome_name)
write_csv(interaction_tbl_data, here::here("output", "its_repeat_incident_recovery_data_table.csv"))
# forest plot of estiamtes
fp2 <- ggplot(data=interaction_tbl_data, aes(x=outcome_name, y=Est, ymin=lci, ymax=uci)) +
geom_point(size = 2.5, pch = 16, colour = "orange") +
geom_linerange(lwd = 1.5, colour = "orange") +
geom_hline(yintercept=1, lty=2) + # add a dotted line at x=1 after flip
coord_flip() + # flip coordinates (puts labels on y axis)
labs(x = "", y = '95% CI', title = "C") +
theme_classic() +
theme(axis.title = element_text(size = 16),
#axis.text.x = element_text(angle = 45),
axis.line.y.left = element_blank(),
axis.line.y.right = element_line(),
axis.text.y = element_blank(),
legend.position = "top",
plot.background = element_rect(fill = bkg_colour, colour = NA),
panel.background = element_rect(fill = bkg_colour, colour = NA),
legend.background = element_rect(fill = bkg_colour, colour = NA),
strip.background = element_rect(fill = bkg_colour, colour = NA),
panel.grid.major = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_line(size=.2, color=rgb(0,0,0,0.2)) ,
panel.grid.major.y = element_line(size=.2, color=rgb(0,0,0,0.3))) +
scale_x_discrete(limits = rev(levels(as.factor(interaction_tbl_data$outcome_name))))
fp2
ggsave(
plot= fp2,
filename="forest_repeat_incident_B.jpeg", path=here::here("output"),
)
# Forest plot of ORs ------------------------------------------------------
## clean up the names
forest_plot_df <- forest_plot_data %>%
rename("Est" = "exp(Est.)", "lci" = "2.5%", "uci" = "97.5%") %>%
left_join(outcome_of_interest_namematch, by = c("var" = "outcome"))
# changes the names of outcomes to full names
forest_plot_df$outcome_name <- factor(forest_plot_df$outcome_name, levels = outcome_of_interest_namematch$outcome_name)
# export table of results for the appendix
write_csv(forest_plot_df, here::here("output", "its_repeat_incident_changes_data_table.csv"))
forest_plot_df <- forest_plot_df %>%
mutate(dummy_facet = "A")
## Forest plot
fp <- ggplot(data=forest_plot_df, aes(x=dummy_facet, y=Est, ymin=lci, ymax=uci)) +
geom_point(size = 2.5, pch = 16, colour = "darkred") +
geom_linerange(lwd = 1.5, colour = "darkred") +
geom_hline(yintercept=1, lty=2) + # add a dotted line at x=1 after flip
coord_flip() + # flip coordinates (puts labels on y axis)
labs(x = "", y = "95% CI", title = "B") +
facet_wrap(~outcome_name, ncol = 1, dir = "h", strip.position = "right") +
theme_classic() +
theme(axis.title = element_text(size = 16),
axis.text.y = element_blank(),
axis.line.y.left = element_blank(),
axis.line.y.right = element_line(),
axis.ticks.y = element_blank(),
axis.text.x = element_text(angle = 0),
legend.position = "top",
plot.background = element_rect(fill = bkg_colour, colour = NA),
panel.background = element_rect(fill = bkg_colour, colour = NA),
legend.background = element_rect(fill = bkg_colour, colour = NA),
strip.background = element_rect(fill = bkg_colour, colour = NA),
strip.text.y = element_text(hjust=0.5, vjust = 0, angle=0, size = 10),
panel.grid.major = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_line(size=.2, color=rgb(0,0,0,0.2)) ,
panel.grid.major.y = element_line(size=.2, color=rgb(0,0,0,0.3)))
fp
ggsave(
plot= fp,
filename="forest_repeat_incident_A.jpeg", path=here::here("output"),
)
layout = "
AAABBB
AAABBB
AAABBB
"
ggsave(
plot= fp + fp2 +
plot_layout(design = layout) ,
filename="conbined_repeat_incident.jpeg", path=here::here("output"),
)
}
its_function(outcomes_vec = outcomes,
display_from <- as.Date("2019-01-01")
)