srpmodel {oncomsm}

A stable-response-progression model

Description

create_model() takes one or more prior-specifications for an SRP multi-state model and combines them into a single model object. Groups are still treated as independent.

Usage

define_srp_prior(
  p_mean = 0.5,
  p_n = 3,
  p_eta = 0,
  p_min = 0,
  p_max = 1,
  median_t_q05 = c(1, 1, 1),
  median_t_q95 = c(60, 60, 60),
  shape_q05 = rep(0.9, 3),
  shape_q95 = rep(2.5, 3),
  visit_spacing = 1,
  recruitment_rate = 1
)

create_srpmodel(
  ...,
  maximal_time = 10 * 12,
  states = c("stable", "response", "progression"),
  censored = "EOF"
)

Arguments

p_mean

numeric, mean of the beta prior for the response probability

p_n

numeric, beta prior equivalent sample size (a + b)

p_eta

numeric, robustification parameter for beta prior; actual prior is (1 - eta) beta + eta; i.e., eta is the non-informative weight.

p_min

numeric, minimal response probability

p_max

numeric, maximal response probability

median_t_q05

numeric of length three, 5% quantiles of the log-normal distributions for the median time-to-next-event for the three transitions s->r, s->p, r->p.

median_t_q95

numeric of length three, 95% quantiles of the log-normal distributions for the median time-to-next-event for the three transitions s->r, s->p, r->p.

shape_q05

numeric of length three, 5% quantiles of the log-normal distributions for the shapes of the time-to-next-event distributions for the three transitions s->r, s->p, r->p.

shape_q95

numeric of length three, 95% quantiles of the log-normal distributions for the shapes of the time-to-next-event distributions for the three transitions s->r, s->p, r->p.

visit_spacing

numeric, fixed duration between visits

recruitment_rate

numeric, constant recruitment rate

...

named srp_prior objects; the argument names serve as group labels

maximal_time

the maximal overall runtime of the trial as measured from the first visit of any group. No visits past this point are sampled.

states

character vector of three states (initial, intermediate, terminal)

censored

string, indicator of premature censoring events; no data is imputed after this point.

Details

define_srp_prior() specifies a prior distribution for a three state model (stable, response, progression) for a single group.

Value

define_srp_prior() returns an object of class srp_prior, all inputs are accessible via ⁠$x⁠ where x is the name of the input argument in the function call except for the two parameters visit_spacing and recruitment_rate. These two parameters are saved as attributes and can be retrieved directly using attr(mdl, "visit_spacing") and attr(mdl, "recruitment_rate").

create_srpmodel() returns an object of class c("srpmodel", "list") that holds information about potentially multiple groups in a compact format and can be accessed using the list operator ⁠$name⁠. group_id is a character vector with the group names, maximal_time is the maximal follow-up time since the first visit in the study, visit_spacing is the vector of per-group difference between visits (only relevant for forward sampling), recruitment_rate is the vector of per-group recruitment rates, stan_model is the pre-compiled 'stan' model used for inference, states is the vector of state names in the multi-state model, and prior is a list of hyperparamters for the model prior with elements p, vector, for the response probability per group, median_t is an c(n_groups, 3, 2) dimensional array where median_t[i,j,1] holds the 5% quantile of the the lognormal prior on median transition time for group i and transition j and median_t[i,j,2] the corresponding upper 95% quantile. The shape hyperparamter has the same format and specified the corresponding quantiles for the Weibull shape parameter.

Examples

# a model with prior 25% response rate and variance equivalent to
# 10 data points (i.e. a Beta(2.5, 7.5) distribution).
grp <- define_srp_prior(p_mean = 0.25, p_n = 10)
attr(grp, "recruitment_rate")

# a model with two groups and different priors on the respective response
# probabilities
mdl <- create_srpmodel(
  A = define_srp_prior(),
  B = define_srp_prior(p_mean = 0.33, p_n = 10)
)
mdl$median_t