get_potential_outcomes: Get potential outcomes from a list of PatientSamples

In escalation: A Modular Approach to Dose-Finding Clinical Trials

Get potential outcomes from a list of PatientSamples

Description

An instance of PatientSample, or one of its subclasses like CorrelatedPatientSample, reflects one particular state of the world, where patient i would reliably experience a toxicity or efficacy event if treated at a particular dose. This function, given true toxicity and efficacy probabilities at doses 1, ..., num_doses, calculates 0/1 matrices to reflect whether the patients in those samples would have experienced toxicity and efficacy at the doses, had they been dosed as such. Using the vernacular of causal inference, these are _potential outcomes_. At any single instant, a patient can only be dosed at one dose, so only one of the outcomes for a patient would in reality have been observed; the rest are counterfactual.

Usage

get_potential_outcomes(patient_samples, true_prob_tox, true_prob_eff)

Arguments

patient_samples	list of PatientSample objects, or subclass thereof.
true_prob_tox	vector of probabilities of toxicity outcomes at doses
true_prob_eff	vector of probabilities of efficacy outcomes at doses

Value

a list of lists, with names tox and eff, each mapping to a matrix of the potential outcomes.

Examples

num_sims <- 10
ps <- lapply(1:num_sims, function(x) PatientSample$new())
# Set tox_u and eff_u for each simulation
set.seed(2024)
lapply(1:num_sims, function(x) {
  tox_u_new <- runif(n = 20)
  eff_u_new <- runif(n = 20)
  ps[[x]]$set_eff_and_tox(tox_u = tox_u_new, eff_u = eff_u_new)
})
true_prob_tox <- c(0.05, 0.10, 0.15, 0.18, 0.45)
true_prob_eff <- c(0.40, 0.50, 0.52, 0.53, 0.53)
get_potential_outcomes(
  patient_samples = ps,
  true_prob_tox = true_prob_tox,
  true_prob_eff = true_prob_eff
)

escalation documentation built on June 27, 2024, 5:09 p.m.