Check a fitted model against the survival observed in each group

Compares, for each group of individuals, the proportion observed to survive through time with the proportion surviving in datasets simulated from the fitted model: a posterior predictive check. Where the model describes the data, the observed proportions lie within the band of the simulated ones.

Usage

check_survival(fit, by = NULL, prob = 0.95, n_draws = 200)

Arguments

fit

A brmsfit, a model fitted with fit_hazard().

by

An optional character string naming a variable of the fitted model with one value per individual (a grouping such as a trial, or a covariate with few values), to split the individuals of each trajectory by.

prob

A single number between 0 and 1, the coverage of the band of simulated proportions. Defaults to 0.95.

n_draws

A single number, the number of posterior draws to simulate from, evenly spaced through the posterior. Defaults to 200.

Value

A tibble with a row for each group and time: path_id, the by variable when given, n (the number of individuals in the group), time, Ta (the temperature of the trajectory in the interval ending at that time), observed (the Kaplan-Meier estimate of survival), and median, lower and upper of the simulated proportion surviving. Each group starts with survival 1 at time 0.

Details

A group is the set of individuals that followed one temperature trajectory (one path_id of the data), or, with by, a subset of them that share a value of another variable, such as a trial. Individuals on different trajectories are never pooled, since at the same elapsed time they are at different temperatures.

The observed proportion is the Kaplan-Meier estimate, which allows for individuals no longer observed; a failure known only to an interval is placed at the end of the interval. For the simulated proportion, each posterior draw is used to simulate every individual from the start of its trajectory to the last time any individual on that trajectory was observed, with its own covariates and the group-level effects estimated for its groups. The band therefore includes the variation expected among samples of this size, which is wide for a group of ten. The simulation is random: call set.seed() first for the same result again.

See also

plot_check() to draw it, hazard_residuals() for residuals by individual.

Examples

if (FALSE) { # \dontrun{
# a model fitted to a small ramping trial: 20 individuals warmed from 20 to 35
# degrees over 60 minutes and checked every 10, in two trials (see fit_hazard())
path   <- data.frame(path_id = "A", time = c(0, 60), temp = c(20, 35))
checks <- data.frame(path_id = "A", time = seq(0, 60, by = 10))
ind    <- data.frame(id = 1:20, path_id = "A", event = 1, mass = runif(20, 8, 12),
                     trial = rep(c("a", "b"), each = 10),
                     t_upper = sample(seq(10, 60, 10), 20, replace = TRUE))
long   <- build_hazard_data(ind, path, "interval", covariates = c("mass", "trial"),
                            checks = checks)
fit    <- fit_hazard(long, "exp", dpar_formulas = list(loga = ~ 1 + mass))

chk <- check_survival(fit)
plot_check(chk, time_unit = "min")

# each trial on its own
plot_check(check_survival(fit, by = "trial"))
} # }