trialr (version 0.0.7)

stan_efftox: Fit an EffTox model

Description

Fit an EffTox model using Stan for full Bayesian inference.

Usage

stan_efftox(outcome_str = NULL, real_doses, efficacy_hurdle, toxicity_hurdle,
  p_e, p_t, eff0, tox1, eff_star, tox_star, alpha_mean, alpha_sd, beta_mean,
  beta_sd, gamma_mean, gamma_sd, zeta_mean, zeta_sd, eta_mean, eta_sd, psi_mean,
  psi_sd, doses_given = NULL, eff = NULL, tox = NULL, ...)

Arguments

outcome_str

A string representing the outcomes observed hitherto. See efftox_parse_outcomes for a description of syntax and examples. Alternatively, you may provide doses_given, eff and tox parameters. See Details.

real_doses

A vector of numbers.The doses under investigation. They should be ordered from lowest to highest and be in consistent units. E.g., #' to conduct a dose-finding trial of doses 10mg, 20mg and 50mg, use c(10, 20, 50).

efficacy_hurdle

Minimum acceptable efficacy probability. A number between 0 and 1.

toxicity_hurdle

Maximum acceptable toxicity probability. A number between 0 and 1.

p_e

Certainty required to infer a dose is acceptable with regards to being probably efficacious; a number between 0 and 1.

p_t

Certainty required to infer a dose is acceptable with regards to being probably tolerable; a number between 0 and 1.

eff0

Efficacy probability required when toxicity is impossible; a number between 0 and 1 (see Details).

tox1

Toxicity probability permitted when efficacy is guaranteed; a number between 0 and 1 (see Details).

eff_star

Efficacy probability of an equi-utility third point (see Details).

tox_star

Toxicity probability of an equi-utility third point (see Details).

alpha_mean

The prior normal mean of the intercept term in the toxicity logit model. A number.

alpha_sd

The prior normal standard deviation of the intercept term in the toxicity logit model. A number.

beta_mean

The prior normal mean of the slope term in the toxicity logit model. A number.

beta_sd

The prior normal standard deviation of the slope term in the toxicity logit model. A number.

gamma_mean

The prior normal mean of the intercept term in the efficacy logit model. A number.

gamma_sd

The prior normal standard deviation of the intercept term in the efficacy logit model. A number.

zeta_mean

The prior normal mean of the slope term in the efficacy logit model. A number.

zeta_sd

The prior normal standard deviation of the slope term in the efficacy logit model. A number.

eta_mean

The prior normal mean of the squared term coefficient in the efficacy logit model. A number.

eta_sd

The prior normal standard deviation of the squared term coefficient in the efficacy logit model. A number.

psi_mean

The prior normal mean of the association term in the combined efficacy-toxicity model. A number.

psi_sd

The prior normal standard deviation of the association term in the combined efficacy-toxicity model. A number.

doses_given

A optional vector of dose-levels given to patients 1:num_patients, where 1=lowest dose, 2=second dose, etc. Only required when outcome_str is not provided.

eff

An optional vector of efficacy outcomes for patients 1:num_patients, where 1=efficacy and 0=no efficacy. Only required when outcome_str is not provided.

tox

An optional vector of toxicity outcomes for patients 1:num_patients, where 1=toxicity and 0=no toxicity. Only required when outcome_str is not provided.

...

Extra parameters are passed to rstan::sampling. Commonly used options are iter, chains, warmup, cores, control. sampling.

Value

An object of class efftox_fit

Details

The quickest and easiest way to fit an EffTox model to some observed outcomes is to describe the outcomes using trialr's syntax for efficacy-toxicity dose-finding outcomes. See efftox_parse_outcomes for full details and examples.

Utility or attractivess scores are calculated in EffTox using L^p norms. Imagine the first quadrant of a scatter plot with prob_eff along the x-axis and prob_tox along the y-axis. The point (1, 0) (i.e. guaranteed efficacy & no toxicity) is the holy grail. The neutral contour intersects the points (eff0, 0), (1, tox1) and (eff_star, tox_star). A unique curve intersects these three points and identifies a value for p, the exponent in the L^p norm. On this neutral- utility contour, scores are equal to zero. A family of curves with different utility scores is defined that are "parallel" to this neutral curve. Points with probabilities of efficacy and toxicity that are nearer to (1, 0) will yield greater scores, and vice-versa.

References

Thall, P., & Cook, J. (2004). Dose-Finding Based on Efficacy-Toxicity Trade-Offs. Biometrics, 60(3), 684-693.

Thall, P., Herrick, R., Nguyen, H., Venier, J., & Norris, J. (2014). Effective sample size for computing prior hyperparameters in Bayesian phase I-II dose-finding. Clinical Trials, 11(6), 657-666. https://doi.org/10.1177/1740774514547397

Brock, K., Billingham, L., Copland, M., Siddique, S., Sirovica, M., & Yap, C. (2017). Implementing the EffTox dose-finding design in the Matchpoint trial. BMC Medical Research Methodology, 17(1), 112. https://doi.org/10.1186/s12874-017-0381-x

See Also

efftox_fit stan_efftox_demo efftox_process

Examples

# NOT RUN {
# This model is presented in Thall et al. (2014)
mod1 <- stan_efftox('1N 2E 3B',
                     real_doses = c(1.0, 2.0, 4.0, 6.6, 10.0),
                     efficacy_hurdle = 0.5, toxicity_hurdle = 0.3,
                     p_e = 0.1, p_t = 0.1,
                     eff0 = 0.5, tox1 = 0.65,
                     eff_star = 0.7, tox_star = 0.25,
                     alpha_mean = -7.9593, alpha_sd = 3.5487,
                     beta_mean = 1.5482, beta_sd = 3.5018,
                     gamma_mean = 0.7367, gamma_sd = 2.5423,
                     zeta_mean = 3.4181, zeta_sd = 2.4406,
                     eta_mean = 0, eta_sd = 0.2,
                     psi_mean = 0, psi_sd = 1, seed = 123)

# Shorthand for the above is:
mod2 <- stan_efftox_demo('1N 2E 3B', seed = 123)

# the seed is passed to the Stan sampler. The usual Stan sampler params like
# cores, iter, chains etc are passed on too via the ellipsis operator.
# }