Nothing
## ----message=FALSE, warning=FALSE---------------------------------------------
library(rtables)
library(dplyr)
## -----------------------------------------------------------------------------
adtte <- ex_adtte
anl <- adtte %>%
dplyr::filter(PARAMCD == "OS") %>%
dplyr::filter(ARM %in% c("A: Drug X", "B: Placebo")) %>%
dplyr::filter(RACE %in% c("ASIAN", "BLACK OR AFRICAN AMERICAN", "WHITE")) %>%
dplyr::mutate(RACE = droplevels(RACE)) %>%
dplyr::mutate(ARM = droplevels(stats::relevel(ARM, "B: Placebo"))) %>%
dplyr::mutate(EVENT = 1 - CNSR)
## -----------------------------------------------------------------------------
tidy.summary.coxph <- function(x, ...) {
is(x, "summary.coxph")
pval <- x$coefficients
confint <- x$conf.int
levels <- rownames(pval)
pval <- tibble::as_tibble(pval)
confint <- tibble::as_tibble(confint)
ret <- cbind(pval[, grepl("Pr", names(pval))], confint)
ret$level <- levels
ret$n <- x[["n"]]
ret
}
## -----------------------------------------------------------------------------
h_coxreg_inter_effect <- function(x,
effect,
covar,
mod,
label,
control,
data) {
if (is.numeric(x)) {
betas <- stats::coef(mod)
attrs <- attr(stats::terms(mod), "term.labels")
term_indices <- grep(pattern = effect, x = attrs[!grepl("strata\\(", attrs)])
betas <- betas[term_indices]
betas_var <- diag(stats::vcov(mod))[term_indices]
betas_cov <- stats::vcov(mod)[term_indices[1], term_indices[2]]
xval <- stats::median(x)
effect_index <- !grepl(covar, names(betas))
coef_hat <- betas[effect_index] + xval * betas[!effect_index]
coef_se <- sqrt(betas_var[effect_index] + xval^2 * betas_var[!effect_index] + 2 * xval * betas_cov)
q_norm <- stats::qnorm((1 + control$conf_level) / 2)
} else {
var_lvl <- paste0(effect, levels(data[[effect]])[-1]) # [-1]: reference level
giv_lvl <- paste0(covar, levels(data[[covar]]))
design_mat <- expand.grid(effect = var_lvl, covar = giv_lvl)
design_mat <- design_mat[order(design_mat$effect, design_mat$covar), ]
design_mat <- within(data = design_mat, expr = {
inter <- paste0(effect, ":", covar)
rev_inter <- paste0(covar, ":", effect)
})
split_by_variable <- design_mat$effect
interaction_names <- paste(design_mat$effect, design_mat$covar, sep = "/")
mmat <- stats::model.matrix(mod)[1, ]
mmat[!mmat == 0] <- 0
design_mat <- apply(X = design_mat, MARGIN = 1, FUN = function(x) {
mmat[names(mmat) %in% x[-which(names(x) == "covar")]] <- 1
mmat
})
colnames(design_mat) <- interaction_names
coef <- stats::coef(mod)
vcov <- stats::vcov(mod)
betas <- as.matrix(coef)
coef_hat <- t(design_mat) %*% betas
dimnames(coef_hat)[2] <- "coef"
coef_se <- apply(design_mat, 2, function(x) {
vcov_el <- as.logical(x)
y <- vcov[vcov_el, vcov_el]
y <- sum(y)
y <- sqrt(y)
y
})
q_norm <- stats::qnorm((1 + control$conf_level) / 2)
y <- cbind(coef_hat, `se(coef)` = coef_se)
y <- apply(y, 1, function(x) {
x["hr"] <- exp(x["coef"])
x["lcl"] <- exp(x["coef"] - q_norm * x["se(coef)"])
x["ucl"] <- exp(x["coef"] + q_norm * x["se(coef)"])
x
})
y <- t(y)
y <- by(y, split_by_variable, identity)
y <- lapply(y, as.matrix)
attr(y, "details") <- paste0(
"Estimations of ", effect, " hazard ratio given the level of ", covar, " compared to ",
effect, " level ", levels(data[[effect]])[1], "."
)
xval <- levels(data[[covar]])
}
data.frame(
effect = "Covariate:",
term = rep(covar, length(xval)),
term_label = as.character(paste0(" ", xval)),
level = as.character(xval),
n = NA,
hr = if (is.numeric(x)) exp(coef_hat) else y[[1]][, "hr"],
lcl = if (is.numeric(x)) exp(coef_hat - q_norm * coef_se) else y[[1]][, "lcl"],
ucl = if (is.numeric(x)) exp(coef_hat + q_norm * coef_se) else y[[1]][, "ucl"],
pval = NA,
pval_inter = NA,
stringsAsFactors = FALSE
)
}
## -----------------------------------------------------------------------------
h_coxreg_extract_interaction <- function(effect, covar, mod, data) {
control <- list(pval_method = "wald", ties = "exact", conf_level = 0.95, interaction = FALSE)
test_statistic <- c(wald = "Wald", likelihood = "LR")[control$pval_method]
mod_aov <- withCallingHandlers(
expr = car::Anova(mod, test.statistic = test_statistic, type = "III"),
message = function(m) invokeRestart("muffleMessage")
)
msum <- if (!any(attr(stats::terms(mod), "order") == 2)) summary(mod, conf.int = control$conf_level) else mod_aov
sum_anova <- broom::tidy(msum)
if (!any(attr(stats::terms(mod), "order") == 2)) {
effect_aov <- mod_aov[effect, , drop = TRUE]
pval <- effect_aov[[grep(pattern = "Pr", x = names(effect_aov)), drop = TRUE]]
sum_main <- sum_anova[grepl(effect, sum_anova$level), ]
term_label <- if (effect == covar) {
paste0(levels(data[[covar]])[2], " vs control (", levels(data[[covar]])[1], ")")
} else {
unname(formatters::var_labels(data, fill = TRUE)[[covar]])
}
y <- data.frame(
effect = ifelse(covar == effect, "Treatment:", "Covariate:"),
term = covar, term_label = term_label,
level = levels(data[[effect]])[2],
n = mod[["n"]], hr = unname(sum_main["exp(coef)"]), lcl = unname(sum_main[grep("lower", names(sum_main))]),
ucl = unname(sum_main[grep("upper", names(sum_main))]), pval = pval,
stringsAsFactors = FALSE
)
y$pval_inter <- NA
y
} else {
pval <- sum_anova[sum_anova$term == effect, ][["p.value"]]
## Test the interaction effect
pval_inter <- sum_anova[grep(":", sum_anova$term), ][["p.value"]]
covar_test <- data.frame(
effect = "Covariate:",
term = covar, term_label = unname(formatters::var_labels(data, fill = TRUE)[[covar]]),
level = "",
n = mod$n, hr = NA, lcl = NA, ucl = NA, pval = pval,
pval_inter = pval_inter,
stringsAsFactors = FALSE
)
## Estimate the interaction
y <- h_coxreg_inter_effect(
data[[covar]],
covar = covar,
effect = effect,
mod = mod,
label = unname(formatters::var_labels(data, fill = TRUE)[[covar]]),
control = control,
data = data
)
rbind(covar_test, y)
}
}
## -----------------------------------------------------------------------------
cached_model <- function(df, cov, cache_env) {
## Check if a model already exists for
## `cov` in the caching environment
if (!is.null(cache_env[[cov]])) {
## If model already exists, retrieve it from cache_env
model <- cache_env[[cov]]
} else {
## Build model formula
model_form <- paste0("survival::Surv(AVAL, EVENT) ~ ARM")
if (length(cov) > 0) {
model_form <- paste(c(model_form, cov), collapse = " * ")
} else {
cov <- "ARM"
}
## Calculate Cox regression model
model <- survival::coxph(
formula = stats::as.formula(model_form),
data = df,
ties = "exact"
)
## Store model in the caching environment
cache_env[[cov]] <- model
}
model
}
## -----------------------------------------------------------------------------
a_cox_summary <- function(df,
labelstr = "",
.spl_context,
stat,
format,
cache_env,
cov_main = FALSE) {
## Get current covariate (variable used in latest row split)
cov <- tail(.spl_context$value, 1)
## If currently analyzing treatment effect (ARM) replace empty
## value of cov with "ARM" so the correct model row is analyzed
if (length(cov) == 0) cov <- "ARM"
## Use cached_model to get the fitted Cox regression
## model for the current covariate
model <- cached_model(df = df, cov = cov, cache_env = cache_env)
## Extract levels of cov to be used as row labels for interaction effects.
## If cov is numeric, the median value of cov is used as a row label instead
cov_lvls <- if (is.factor(df[[cov]])) levels(df[[cov]]) else as.character(median(df[[cov]]))
## Use function to calculate and extract information relevant to cov from the model
cov_rows <- h_coxreg_extract_interaction(effect = "ARM", covar = cov, mod = model, data = df)
## Effect p-value is only printed for treatment effect row
if (!cov == "ARM") cov_rows[, "pval"] <- NA_real_
## Extract rows containing statistics for cov from model information
if (!cov_main) {
## Extract rows for main effect
cov_rows <- cov_rows[cov_rows$level %in% cov_lvls, ]
} else {
## Extract all non-main effect rows
cov_rows <- cov_rows[nchar(cov_rows$level) == 0, ]
}
## Extract value(s) of statistic for current column and variable/levels
stat_vals <- as.list(apply(cov_rows[stat], 1, function(x) x, simplify = FALSE))
## Assign labels: covariate name for main effect (content) rows, ARM comparison description
## for treatment effect (content) row, cov_lvls for interaction effect (data) rows
nms <- if (cov_main) labelstr else if (cov == "ARM") cov_rows$term_label else cov_lvls
## Return formatted/labelled row
in_rows(
.list = stat_vals,
.names = nms,
.labels = nms,
.formats = setNames(rep(format, length(nms)), nms),
.format_na_strs = setNames(rep("", length(nms)), nms)
)
}
## -----------------------------------------------------------------------------
my_covs <- c("AGE", "RACE") ## Covariates
my_cov_labs <- c("Age", "Race") ## Covariate labels
my_stats <- list("n", "hr", c("lcl", "ucl"), "pval", "pval_inter") ## Statistics
my_stat_labs <- c("n", "Hazard Ratio", "95% CI", "p-value\n(effect)", "p-value\n(interaction)") ## Statistic labels
my_formats <- c(
n = "xx", hr = "xx.xx", lcl = "(xx.xx, xx.xx)", pval = "xx.xxxx", pval_inter = "xx.xxxx" ## Statistic formats
)
my_env <- new.env()
ny_cache_env <- replicate(length(my_stats), list(my_env)) ## Caching environment
## -----------------------------------------------------------------------------
lyt <- basic_table() %>%
## Column split: one column for each statistic
split_cols_by_multivar(
vars = rep("STUDYID", length(my_stats)),
varlabels = my_stat_labs,
extra_args = list(
stat = my_stats,
format = my_formats,
cache_env = ny_cache_env
)
) %>%
## Create content row for treatment effect
summarize_row_groups(cfun = a_cox_summary) %>%
## Row split: one content row for each covariate
split_rows_by_multivar(
vars = my_covs,
varlabels = my_cov_labs,
split_label = "Covariate:",
indent_mod = -1 ## Align split label left
) %>%
## Create content rows for covariate main effects
summarize_row_groups(
cfun = a_cox_summary,
extra_args = list(cov_main = TRUE)
) %>%
## Create data rows for covariate interaction effects
analyze_colvars(afun = a_cox_summary)
## -----------------------------------------------------------------------------
cox_tbl <- build_table(lyt, anl)
cox_tbl
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