Simulate one or more clinical trials subject to known design parameters and treatment effect

Simulate multiple clinical trials with fixed input parameters, and tidily extract the relevant data to generate operating characteristics.

Usage

sim_trials(
  hazard_treatment,
  hazard_control = NULL,
  cutpoints = 0,
  N_total,
  lambda = 0.3,
  lambda_time = 0,
  interim_look = NULL,
  end_of_study,
  prior = c(0.1, 0.1),
  block = 2,
  rand_ratio = c(1, 1),
  prop_loss = 0,
  alternative = "two.sided",
  h0 = 0,
  Fn = 0.1,
  Sn = 0.9,
  prob_ha = 0.95,
  N_impute = 10,
  N_mcmc = 10,
  N_trials = 10,
  method = "logrank",
  imputed_final = FALSE,
  ncores = 1L
)

Arguments

hazard_treatment: vector. Constant hazard rates under the treatment arm.
hazard_control: vector. Constant hazard rates under the control arm.
cutpoints: vector. Times at which the baseline hazard changes. Default is cutpoints = 0, which corresponds to a simple (non-piecewise) exponential model.
N_total: integer. Maximum sample size allowable
lambda: vector. Enrollment rates across simulated enrollment times. See enrollment for more details.
lambda_time: vector. Enrollment time(s) at which the enrollment rates change. Must be same length as lambda. See enrollment for more details.
interim_look: vector. Sample size for each interim look. Note: the maximum sample size should not be included. For two-arm designs, each interim look must be at least the (largest) block size (see block), ensuring both treatment arms are present at every interim analysis; a smaller look could enrol subjects from a single arm only, leaving the interim posterior undefined for the missing arm.
end_of_study: scalar. Length of the study; i.e. time at which endpoint will be evaluated.
prior: vector. The prior distributions for the piecewise hazard rate parameters are each \(Gamma(a_0, b_0)\), where \(a_0\) is the shape parameter and \(b_0\) is the rate parameter (i.e., the inverse of the scale). This follows R's rgamma parameterization. The same prior is applied to all piecewise intervals and to both treatment arms. The default non-informative prior distribution used is Gamma(0.1, 0.1), which is specified by setting prior = c(0.1, 0.1).
block: scalar. Block size for generating the randomization schedule.
rand_ratio: vector. Randomization allocation for the ratio of control to treatment. Integer values mapping the size of the block. See randomization for more details.
prop_loss: scalar. Overall proportion of subjects lost to follow-up. Subjects are selected at random for LTFU regardless of treatment arm or event status. Each LTFU subject's observed time is drawn from a Uniform(0, t) distribution, where t is their potential event or censoring time. Since the LTFU time is always less than t, the event has not yet occurred at dropout and the subject is right-censored. Defaults to zero.
alternative: character. The string specifying the alternative hypothesis, must be one of "greater" (default), "less" or "two.sided". All three options are supported for method = "bayes", "logrank", and "cox". The chi-square test (method = "chisq") only supports "two.sided". For survival outcomes, "less" corresponds to the treatment group having a lower cumulative incidence (i.e., treatment is beneficial), and "greater" corresponds to the treatment group having a higher cumulative incidence.
h0: scalar. Null hypothesis value of \(p_\textrm{treatment} - p_\textrm{control}\) when method = "bayes". Default is h0 = 0. The argument is ignored when method = "logrank" or = "cox"; in those cases the usual test of non-equal hazards is assumed.
Fn: vector of [0, 1] values. Each element is the probability threshold to stop at the \(i\)-th look early for futility. If there are no interim looks (i.e. interim_look = NULL), then Fn is not used in the simulations or analysis. The length of Fn should be the same as interim_look, else the values are recycled.
Sn: vector of [0, 1] values. Each element is the probability threshold to stop at the \(i\)-th look early for expected success. If there are no interim looks (i.e. interim_look = NULL), then Sn is not used in the simulations or analysis. The length of Sn should be the same as interim_look, else the values are recycled.
prob_ha: scalar [0, 1]. Probability threshold of alternative hypothesis.
N_impute: integer. Number of imputations for Monte Carlo simulation of missing data.
N_mcmc: integer. Number of samples to draw from the posterior distribution when using a Bayesian test (method = "bayes").
N_trials: integer. Number of trials to simulate.
method: character. For an imputed data set (or the final data set after follow-up is complete), whether the analysis should be a log-rank (method = "logrank") test, Cox proportional hazards regression model Wald test (method = "cox"), a fully-Bayesian analysis (method = "bayes"), or a chi-square test (method = "chisq"). See Details section.
imputed_final: logical. Should the final analysis (after all subjects have been followed-up to the study end) be based on imputed outcomes for subjects who were LTFU (i.e. right-censored with time <end_of_study)? Default is TRUE. Setting to FALSE means that the final analysis would incorporate right-censoring.
ncores: integer. Number of cores to use for parallel processing.

Value

Data frame with 1 row per simulated trial and columns for key summary statistics. See survival_adapt for details of what is returned in each row.

Details

This is basically a wrapper function for survival_adapt, whereby we repeatedly run the function for a independent number of trials (all with the same input design parameters and treatment effect).

To use will multiple cores (where available), the argument ncores can be increased from the default of 1. Note: on Windows machines, it is not possible to use the mclapply function with ncores \(>1\).

Examples

hc <- prop_to_haz(c(0.20, 0.30), c(0, 12), 36)
ht <- prop_to_haz(c(0.05, 0.15), c(0, 12), 36)

out <- sim_trials(
  hazard_treatment = ht,
  hazard_control = hc,
  cutpoints = c(0, 12),
  N_total = 600,
  lambda = 20,
  lambda_time = 0,
  interim_look = c(400, 500),
  end_of_study = 36,
  prior = c(0.1, 0.1),
  block = 2,
  rand_ratio = c(1, 1),
  prop_loss = 0.30,
  alternative = "two.sided",
  h0 = 0,
  Fn = 0.05,
  Sn = 0.9,
  prob_ha = 0.975,
  N_impute = 5,
  N_mcmc = 5,
  method = "logrank",
  N_trials = 2,
  ncores = 1)