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07_plot_fit.R
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07_plot_fit.R
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# Notes -------------------------------------------------------------------
# Plot calibration estimates and plot SCORAD
# Initialisation ----------------------------------------------------------
rm(list = ls()) # Clear Workspace (better to restart the session)
source(here::here("analysis", "00_init.R"))
#### OPTIONS
pid <- 3
####
model <- ScoradPred(independent_items = FALSE,
a0 = .04,
include_trend = FALSE,
include_calibration = TRUE,
include_treatment = TRUE,
treatment_names = c("localTreatment", "emollientCream"),
include_recommendations = FALSE)
file_dict <- get_results_files(outcome = "SCORAD",
model = model$name,
dataset = "PFDC",
root_dir = here())
# Load data ---------------------------------------------------------------
l <- load_PFDC()
POSCORAD <- l$POSCORAD %>%
rename(Time = Day)
df <- POSCORAD %>%
select(one_of("Patient", "Time", model$item_spec$Label)) %>%
pivot_longer(cols = all_of(model$item_spec$Label), names_to = "Label", values_to = "Score") %>%
drop_na() %>%
left_join(model$item_spec[, c("Label", "ItemID")], by = c("Label"))
# Prepare SCORAD calibration data
if (model$include_calibration) {
cal <- scorad <- l$SCORAD %>%
rename(Time = Day) %>%
select(one_of("Patient", "Time", model$item_spec$Label)) %>%
pivot_longer(cols = all_of(model$item_spec$Label), names_to = "Label", values_to = "Score") %>%
drop_na() %>%
left_join(model$item_spec[, c("Label", "ItemID")], by = c("Label"))
} else {
cal <- NULL
}
# Prepare treatment data
treatment_lbl <- paste0(model$treatment_names, "WithinThePast2Days")
if (model$include_treatment) {
treat <- POSCORAD %>%
select(all_of(c("Patient", "Time", treatment_lbl))) %>%
pivot_longer(cols = all_of(treatment_lbl), names_to = "Treatment", values_to = "UsageWithinThePast2Days") %>%
mutate(Treatment = vapply(Treatment, function(x) {which(x == treatment_lbl)}, numeric(1)) %>% as.numeric()) %>%
drop_na()
} else {
treat <- NULL
}
# NB: assume no recommendation (at least outside time-series)
pt <- unique(df[["Patient"]])
id <- get_index(bind_rows(df, cal, treat))
df <- left_join(df, id, by = c("Patient", "Time"))
# Results
fit <- readRDS(file_dict$Fit)
par <- readRDS(file_dict$FitPar)
# Correlation plot --------------------------------------------------------
x <- "Omega"
omg <- rstan::extract(fit, pars = x)[[1]]
tmp <- list(Mean = apply(omg, c(2, 3), mean),
SD = apply(omg, c(2, 3), sd),
Lower = apply(omg, c(2, 3), function(x) {quantile(x, probs = .05)}),
Upper = apply(omg, c(2, 3), function(x) {quantile(x, probs = .95)}),
pval = apply(omg, c(2, 3), function(x) {empirical_pval(x, 0)}))
tmp <- lapply(tmp,
function(x) {
colnames(x) <- model$item_spec$Name
rownames(x) <- model$item_spec$Name
return(x)
})
jpeg(here("results", paste0(x, "_", model$name, ".jpeg")),
width = 20, height = 20, units = "cm", res = 300, quality = 95, pointsize = 11)
corrplot::corrplot.mixed(tmp$Mean, lower = "number", upper = "ellipse")
dev.off()
# Combine with power prior plot in `plot_powerprior.R`
# Calibration plot --------------------------------------------------------------------
# Estimates
p1_cal <- par %>%
filter(Variable == "bias0") %>%
rename(ItemID = Index) %>%
left_join(model$item_spec, by = "ItemID") %>%
filter(!(Name %in% c("sleep", "itching"))) %>%
mutate(Name = factor(Name),
Name = factor(Name, levels = rev(levels(Name)))) %>%
ggplot(aes(x = Name, y = Mean, ymin = `5%`, ymax = `95%`)) +
facet_grid(rows = vars(Component), scales = "free", space = "free") +
geom_pointrange() +
geom_hline(yintercept = 0, linetype = "dashed") +
coord_flip() +
scale_y_continuous(limits = c(-.5, .5),
breaks = c(-.5, -.25, 0, .25, 0.5),
labels = c("-0.5\nPatient scores\nhigher than clinician",
-0.25, 0, 0.25,
"0.5\nClinician scores\n higher than patient")) +
labs(x = "",
y = "Initial bias (normalised)")
# Otherwise, post-process figures to give the interpretation of the direction of the effect
# ("patient scores higher than clinician" vs "clinician scores higher than patient")
aggcal <- rstan::extract(fit, pars = "agg_cal_rep")[[1]]
aggcal <- aggcal[, , 4] # SCORAD
### Plot observed PO-SCORAD and inferred SCORAD as a fanchart
tmp <- POSCORAD %>%
filter(Patient == pid)
p2_cal <- plot_post_traj_fanchart(aggcal,
id = id,
patient_id = pid,
legend_fill = "discrete",
CI_level = seq(0.1, 0.9, 0.2),
max_score = 60) +
add_broken_pointline(tmp, aes_x = "Time", aes_y = "SCORAD", colour = "Observed\nPO-SCORAD") +
scale_colour_manual(values = c("Observed\nPO-SCORAD" = "black")) +
labs(fill = "Inferred\nSCORAD\nprobabilities", colour = "") +
theme(legend.position = c(.9, .8),
legend.title = element_text(size = 11),
legend.spacing.y = unit(0, 'cm'))
plot_grid(p1_cal, p2_cal, nrow = 1, labels = "AUTO")
if (FALSE) {
ggsave(here("results", "plot_calibration.jpg"),
width = 18, height = 7, units = "cm", dpi = 300, scale = 2.5)
}
# Treatment ---------------------------------------------------------------
p_treat <- extract_par_indexes(par, var_name = "ATE", dim_names = c("ItemID", "Treatment")) %>%
filter(Variable == "ATE") %>%
mutate(Treatment = model$treatment_names[Treatment]) %>%
left_join(model$item_spec, by = "ItemID") %>%
mutate(Treatment = recode(Treatment,
emollientCream = "Emollient Cream",
localTreatment = "Topical Corticosteroids"),
Component = gsub(" ", "\n", Component),
Name = factor(Name),
Name = factor(Name, levels = rev(levels(Name)))) %>%
ggplot(aes(x = Name, y = Mean, ymin = `5%`, ymax = `95%`, colour = Treatment)) +
facet_grid(rows = vars(Component), scale = "free", space = "free") +
geom_pointrange(position = position_dodge(width = .5)) +
geom_hline(yintercept = 0, linetype = "dashed") +
coord_flip() +
scale_y_continuous(limits = c(-.05, .05),
breaks = c(-.05, -.025, 0, .025, 0.05),
labels = c("-0.05\nTreatment\nreduces severity", -0.025, 0, 0.025, "0.5\nTreatment\nincreases severity")) +
scale_colour_manual(values = cbbPalette[c(2, 1)]) +
labs(x = "", y = "Treatment effect (normalised)", colour = "") +
theme(legend.position = "top")
p_treat
# ggsave(here("results", "treatment_effects.jpg"), width = 13, height = 8, units = "cm", scale = 2)
# Combine with recommendation plot
p_rec <- readRDS(here("results", "subplot_recommendation.rds")) +
labs(title = "")
plot_grid(p_treat, p_rec, nrow = 1, labels = "AUTO")
if (FALSE) {
ggsave(here("results", "plot_treatment.jpg"),
width = 10, height = 5, units = "cm", dpi = 300, scale = 3.5)
}