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The explore_pi_tq_surv() function allows you to
understand how the prognostic index (linear predictor) affects survival
at a given quantile.
data_sim <- explore_pi_tq_surv(
pi = seq(-3, 3, by = 0.1),
mu = -1,
shape = seq(0.9, 1.1, by = 0.1),
end_time = 200,
type = "weibull"
)
head(data_sim)library(ggplot2)
ggplot(data_sim, aes(x = exp(lp), y = survdiff_tq)) +
geom_line(aes(color = factor(shape), group = shape)) +
scale_x_log10() +
labs(
x = "Hazard Ratio",
y = expression(Delta ~ "Survival at" ~ t[50]),
color = "Shape"
) +
geom_vline(xintercept = 1, linetype = 2) +
geom_hline(yintercept = 0, linetype = 2) +
theme_bw()If you have a custom mrgsolve model that outputs a survival
probability column, you can use sim_tte_df() directly to
perform inverse transform sampling on the output:
# Example with a mock survival data frame (no mrgsolve needed)
set.seed(123)
mrg_output <- data.frame(
ID = rep(1:5, each = 100),
time = rep(seq(0.1, 10, length.out = 100), 5),
p11 = rep(exp(-0.3 * seq(0.1, 10, length.out = 100)), 5)
)
result <- sim_tte_df(mrg_output, surv_var = "p11", id_var = "ID",
time_var = "time")
head(result)
#> # A tibble: 5 × 3
#> sim_time sim_status ID
#> <dbl> <dbl> <int>
#> 1 4.2 1 1
#> 2 0.8 1 2
#> 3 3 1 3
#> 4 0.5 1 4
#> 5 0.3 1 5You can simulate different treatment arms by specifying different prognostic indices:
set.seed(42)
n_per_arm <- 50
times <- seq(0.1, 50, by = 0.1)
# Control arm
lp_ctrl <- matrix(rep(0, n_per_arm), nrow = n_per_arm)
ctrl <- sim_tte(pi = lp_ctrl, mu = -1, coefs = 1.1,
time = times, type = "weibull", end_time = 50)
ctrl$arm <- "Control"
# Treatment arm (lower hazard)
lp_trt <- matrix(rep(-0.5, n_per_arm), nrow = n_per_arm)
trt <- sim_tte(pi = lp_trt, mu = -1, coefs = 1.1,
time = times, type = "weibull", end_time = 50)
trt$arm <- "Treatment"
combined <- rbind(ctrl, trt)
head(combined)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.