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plots_binary.R
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pacman::p_load(dplyr, foreach, ggplot2, cowplot, tidyr, purrr, rlang, kableExtra)
## Read in data
full_sim_res <- readRDS("PATH for sim_res from simulations_binary.R") %>%
mutate(model = factor(model, levels = c("model_correct", "model_incorrect", "model_noise",
"model_no_strong_prog", "model_no_strong_prog_noise",
"model_correct_prior", "model_correct_prior_strong", "model_unadjusted"),
labels = c("correct", "no quad", "correct noise", "no strong prog",
"no strong prog noise", "correct prior", "correct strong prior", "unadjusted")))
summarize_results <- function(complete_sim_results){
complete_sim_results %>%
group_by(model, max_ss, effect_treatment, beta_1, beta_2, beta_3, beta_4, beta_5, control_risk) %>%
summarise(power = mean(superiority, na.rm = TRUE),
bias_post_median = mean(trt_est_median - effect_treatment, na.rm = TRUE),
bias_post_mean = mean(trt_est_mean - effect_treatment, na.rm = TRUE),
bias_post_median_lrr = mean(trt_est_median_log - log(effect_treatment), na.rm = TRUE),
bias_post_mean_lrr = mean(trt_est_mean_log - log(effect_treatment), na.rm = TRUE),
p_superiority = mean(superiority, na.rm = TRUE),
exp_sample_size = mean(n_total, na.rm = TRUE),
p_stop_early = mean(stopped_early, na.rm = TRUE),
.groups = "keep") %>%
ungroup()
}
full_res_summary <- summarize_results(full_sim_res)
plot_summary_metric <- function(.data_list,
metric = metric,
ylim = c(NA, NA),
round_digits = 2,
xlab = 'relative risk',
xscale = "response",
ylab = NULL,
scales = 'free_x',
dodge_width = 0.5,
legend_position = "top",
n_rows_legend = 2){
if(is.null(ylab)){
metric_name <- rlang::as_name(metric)
} else {
metric_name <- ylab
}
if(xscale == "link"){
.data_list <- .data_list %>%
mutate(effect_treatment = log(effect_treatment))
}
metric <- sym(metric)
max_ss_names <- c(`100` = "max ss = 100",
`200` = "max ss = 200",
`500` = "max ss = 500",
`1000` = "max ss = 1000")
ggplot(.data_list, aes(x = factor(round(effect_treatment, round_digits)),
y = !!metric,
group = model,
color = model,
shape = model)) +
coord_cartesian(ylim = ylim) +
facet_wrap(~ max_ss, scales = scales, nrow = 2,
labeller = as_labeller(max_ss_names)) +
geom_point(size = 1.5, position = position_dodge(width = dodge_width)) +
scale_color_brewer(palette = 'Set2') +
xlab(xlab) +
ylab(ylab) +
theme_bw() +
theme(axis.text=element_text(size=10),
axis.title=element_text(size=10),#,face="bold"),
panel.grid.minor.y = element_blank(),
panel.grid.minor.x = element_blank(),
strip.text.y = element_text(size = 8),
strip.text.x = element_text(size = 8),
legend.position = legend_position,
legend.text = element_text(size = 10),
legend.title = element_blank()) +
guides(colour = guide_legend(nrow = n_rows_legend))
}
### Manuscript plot with power and pse ###-------------------------------------------
plot_legend <- get_legend(plot_summary_metric(full_res_summary %>%
filter(model %in% c("correct", "no quad", "correct noise",
"correct prior", "correct strong prior", "unadjusted")),
metric = "power",
xlab = 'difference in means',
ylab = 'power',
dodge_width = 0.5,
legend_position = "top",
n_rows_legend = 1))
plot(plot_legend)
prow <- plot_grid(plot_summary_metric(full_res_summary %>%
filter(effect_treatment != 1,
model %in% c("correct", "no quad", "correct noise",
"correct prior", "correct strong prior", "unadjusted")),
metric = "power",
xlab = 'relative risk',
ylab = 'power',
scales = "free",
dodge_width = 0.5,
round_digits = 2,
legend_position = "none"),
plot_summary_metric(full_res_summary %>%
filter(effect_treatment != 1,
model %in% c("correct", "no quad", "correct noise",
"correct prior", "correct strong prior", "unadjusted")),
metric = "p_stop_early",
xlab = 'relative risk',
ylab = 'probability of stopping early',
scales = "free",
dodge_width = 0.5,
round_digits = 2,
legend_position = "none"),
labels = LETTERS[1:2],
align = "vh",
hjust = -1,
nrow = 1
)
power_pse <- plot_grid(prow, plot_legend, ncol = 1, rel_heights = c(1, .1))
ggsave2(filename = "PATH/FILENAME.eps",
plot = power_pse,
width = 8,
height = 3,
units = "in",
dpi = 800)
#----------------------------------------------------------------------------------#
### False positive rate table
fpr <- full_res_summary %>%
filter(effect_treatment == 1) %>%
arrange(max_ss) %>%
select(model, max_ss, power) %>%
pivot_wider(names_from = "max_ss",
values_from = "power")
## Bias under null
bias_null <- full_res_summary %>%
filter(effect_treatment == 1) %>%
arrange(max_ss) %>%
select(model, max_ss, bias_post_median) %>%
pivot_wider(names_from = "max_ss",
values_from = "bias_post_median") %>%
mutate(across(where(is.double), round, 3))
### Expected sample size
ess_100 <- full_res_summary %>%
filter(max_ss == 100) %>%
select(model, max_ss, effect_treatment, exp_sample_size) %>%
pivot_wider(names_from = "effect_treatment",
values_from = "exp_sample_size")
ess_200 <- full_res_summary %>%
filter(max_ss == 200) %>%
select(model, max_ss, effect_treatment, exp_sample_size) %>%
pivot_wider(names_from = "effect_treatment",
values_from = "exp_sample_size")
ess_500 <- full_res_summary %>%
filter(max_ss == 500) %>%
select(model, max_ss, effect_treatment, exp_sample_size) %>%
pivot_wider(names_from = "effect_treatment",
values_from = "exp_sample_size")
ess_1000 <- full_res_summary %>%
filter(max_ss == 1000) %>%
select(model, max_ss, effect_treatment, exp_sample_size) %>%
pivot_wider(names_from = "effect_treatment",
values_from = "exp_sample_size")
### Build table
### null is null, one is lowest power, two is highest power
n_100 <- fpr %>% select(model, frp = `100`) %>%
left_join(bias_null %>% select(model, bias_null = `100`), by = 'model') %>%
left_join(ess_100 %>% select(-max_ss, null = `1`, one = `0.518012`, two = `0.4447808`) %>%
relocate(model, null, one, two), by = "model")
n_200 <- fpr %>% select(model, frp = `200`) %>%
left_join(bias_null %>% select(model, bias_null = `200`), by = 'model') %>%
left_join(ess_200 %>% select(-max_ss, null = `1`, one = `0.5773397`, two = `0.4075299`) %>%
relocate(model, null, one, two), by = "model")
n_500 <- fpr %>% select(model, frp = `500`) %>%
left_join(bias_null %>% select(model, bias_null = `500`), by = 'model') %>%
left_join(ess_500 %>% select(-max_ss, null = `1`, one = `0.7136076`, two = `0.5976614`) %>%
relocate(model, null, one, two), by = "model")
n_1000 <- fpr %>% select(model, frp = `1000`) %>%
left_join(bias_null %>% select(model, bias_null = `1000`), by = 'model') %>%
left_join(ess_1000 %>% select(-max_ss, null = `1`, one = `0.7947651`, two = `0.7196724`) %>%
relocate(model, null, one, two), by = "model")
tbl_summary <- bind_rows(n_100 %>%
left_join(n_200, by = "model"),
n_500 %>%
left_join(n_1000, by = "model"))
tbl_summary <- tbl_summary %>%
filter(model %in% c("correct", "no quad", "correct noise", "correct prior",
"correct strong prior", "unadjusted"))
kbl(tbl_summary, booktabs = T, format = "latex") %>%
add_header_above(c(" "=3, "Expected sample size"=3,
" "=2, "Expected sample size"=3)) %>%
add_header_above(c(" "=1, "Maximum sample size = 100" = 5,
"Maximum sample size = 200" = 5)) %>%
kable_styling(latex_options = c("scale_down"))
### Visualize rmse as boxplots
# https://stackoverflow.com/questions/25124895/no-outliers-in-ggplot-boxplot-with-facet-wrap
calc_boxplot_stat <- function(x) {
coef <- 1.5
n <- sum(!is.na(x))
# calculate quantiles
stats <- quantile(x, probs = c(0.0, 0.25, 0.5, 0.75, 1.0))
names(stats) <- c("ymin", "lower", "middle", "upper", "ymax")
iqr <- diff(stats[c(2, 4)])
# set whiskers
outliers <- x < (stats[2] - coef * iqr) | x > (stats[4] + coef * iqr)
if (any(outliers)) {
stats[c(1, 5)] <- range(c(stats[2:4], x[!outliers]), na.rm = TRUE)
}
return(stats)
}
plot_trt_estimates_boxplot <- function(.data_list,
metric,
xlab = "treatment effect",
ylab = NULL,
scales = "free",
round_digits = 2,
ylim = c(NA, NA),
legend_position = "top",
n_rows_legend = 2){
n_models <- length(unique(.data_list$model))
if(is.null(ylab)){
metric_name <- rlang::as_name(metric)
} else {
metric_name <- ylab
}
metric <- sym(metric)
max_ss_names <- c(`100` = "max ss = 100",
`200` = "max ss = 200",
`500` = "max ss = 500",
`1000` = "max ss = 1000")
ggplot(.data_list, aes(x = factor(round(effect_treatment, round_digits)),
y = !!metric,
fill = model,
color = model)) +
stat_summary(fun.data = calc_boxplot_stat, geom="boxplot", position = "dodge",
lwd = 0.25) +
facet_wrap(~ max_ss, scales = scales, nrow = 2,
labeller = as_labeller(max_ss_names)) +
xlab(xlab) +
ylab(metric_name) +
scale_color_manual(values = rep("#003B46", n_models)) +
scale_fill_brewer(palette = 'Set2') +
theme_bw()+
theme(axis.text.x= element_text(size = 10),#, angle = -90, hjust = 0),
axis.title.x= element_text(size = 10),#, face = 'bold'),
axis.title=element_text(size=10),#,face="bold"),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_blank(),
strip.text.y = element_text(size = 8),
strip.text.x = element_text(size = 8),
legend.position = legend_position,
legend.text = element_text(size = 10),
legend.title = element_blank()) +
guides(colour = guide_legend(nrow = n_rows_legend))
}
### Manuscript appendix plot for bias and rmse
plot_legend <- get_legend(plot_summary_metric(full_res_summary %>%
filter(effect_treatment != 1,
model %in% c("correct", "no quad", "correct noise", "correct prior",
"correct strong prior", "unadjusted")),
metric = "bias_post_median",
xlab = 'difference in means',
ylab = 'power',
dodge_width = 0.5,
legend_position = "top",
n_rows_legend = 1))
plot(plot_legend)
prow <- plot_grid(plot_summary_metric(full_res_summary %>%
filter(effect_treatment != 1,
model %in% c("correct", "no quad", "correct noise", "correct prior",
"correct strong prior", "unadjusted")),
metric = "bias_post_median",
xlab = 'relative risk',
ylab = 'posterior median bias',
scales = "free",
dodge_width = 0.5,
round_digits = 2,
legend_position = "none"),
plot_trt_estimates_boxplot(full_sim_res %>%
filter(model %in% c("correct", "no quad", "correct noise", "correct prior",
"correct strong prior", "unadjusted")),
metric = "rmse",
ylab = "root mean squared error",
xlab = "relative risk",
scales = "free",
legend_position = "none"),
labels = LETTERS[1:2],
align = "vh",
hjust = -1,
nrow = 1
)
prow
bias_rmse <- plot_grid(prow, plot_legend, ncol = 1, rel_heights = c(1, .1))
ggsave2(filename = "PATH/FILENAME.eps",
plot = bias_rmse,
width = 8,
height = 3,
units = "in",
dpi = 800)