Wrapper function performing simulation studies for a given set of scenarios (not parallelized)

This function performs a simulation study for a given set of scenarios, analyzing simulated data using different models as indicated by the user. Performs inference for indicated experimental treatment arms. Simulates the probability to reject \(H_0\) based on a given number of replications.

Usage

sim_study(
  nsim,
  scenarios,
  arms,
  models = c("fixmodel", "sepmodel", "poolmodel"),
  endpoint,
  verbose = TRUE
)

Arguments

nsim: Integer. Number of replications. Must be larger than 1.
scenarios: Data frame containing all parameters for scenarios that should be simulated.
arms: Integer vector with treatment arms to perform inference on. These arms are compared to the control group. Default - all arms except the first one.
models: Character vector with models that should be used for the analysis. Default=c("fixmodel", "sepmodel", "poolmodel"). Available models for continuous endpoints are: 'fixmodel', 'fixmodel_cal', 'gam', 'MAPprior', 'mixmodel', 'mixmodel_cal', 'mixmodel_AR1', 'mixmodel_AR1_cal', 'piecewise', 'piecewise_cal', 'poolmodel', 'sepmodel', 'sepmodel_adj', 'splines', 'splines_cal', 'timemachine'. Available models for binary endpoints are: 'fixmodel', 'fixmodel_cal', 'MAPprior', 'poolmodel', 'sepmodel', 'sepmodel_adj', 'timemachine'.
endpoint: Endpoint indicator. "cont" for continuous endpoints, "bin" for binary endpoints.
verbose: Logical. Indicates whether to print a message (system time and number of finished scenarios) after simulating each scenario in order to track the progress of the simulations. Default=TRUE.

Value

Data frame with all considered scenarios and corresponding results - the probability to reject \(H_0\).

Author

Pavla Krotka

Examples


# \donttest{
# Create data frame with all parameters:
sim_scenarios <- data.frame(num_arms = 4,
n_arm = 250,
d1 = 250*0,
d2 = 250*1,
d3 = 250*2,
d4 = 250*3,
period_blocks = 2,
mu0 = 0,
sigma = 1,
theta1 = 0,
theta2 = 0,
theta3 = 0,
theta4 = 0,
lambda0 = rep(seq(-0.15, 0.15, length.out = 9), 2),
lambda1 = rep(seq(-0.15, 0.15, length.out = 9), 2),
lambda2 = rep(seq(-0.15, 0.15, length.out = 9), 2),
lambda3 = rep(seq(-0.15, 0.15, length.out = 9), 2),
lambda4 = rep(seq(-0.15, 0.15, length.out = 9), 2),
trend = c(rep("linear", 9), rep("stepwise_2", 9)),
alpha = 0.025,
ncc = TRUE)

# Run simulation study:
sim_results <- sim_study(nsim = 100, scenarios = sim_scenarios, arms = c(3, 4),
models = c("fixmodel", "sepmodel", "poolmodel"), endpoint = "cont")
#> [1] "Starting the simulations. 18 scenarios will be simulated. Starting time: 2023-06-15 17:18:28.325615"
#> [1] "Scenario 1/18 done. Time: 2023-06-15 17:18:31.407138"
#> [1] "Scenario 2/18 done. Time: 2023-06-15 17:18:34.915403"
#> [1] "Scenario 3/18 done. Time: 2023-06-15 17:18:37.776169"
#> [1] "Scenario 4/18 done. Time: 2023-06-15 17:18:40.558491"
#> [1] "Scenario 5/18 done. Time: 2023-06-15 17:18:43.312991"
#> [1] "Scenario 6/18 done. Time: 2023-06-15 17:18:46.177698"
#> [1] "Scenario 7/18 done. Time: 2023-06-15 17:18:49.034121"
#> [1] "Scenario 8/18 done. Time: 2023-06-15 17:18:51.993325"
#> [1] "Scenario 9/18 done. Time: 2023-06-15 17:18:55.19815"
#> [1] "Scenario 10/18 done. Time: 2023-06-15 17:18:58.140603"
#> [1] "Scenario 11/18 done. Time: 2023-06-15 17:19:00.985676"
#> [1] "Scenario 12/18 done. Time: 2023-06-15 17:19:03.920289"
#> [1] "Scenario 13/18 done. Time: 2023-06-15 17:19:06.782618"
#> [1] "Scenario 14/18 done. Time: 2023-06-15 17:19:09.620936"
#> [1] "Scenario 15/18 done. Time: 2023-06-15 17:19:12.538929"
#> [1] "Scenario 16/18 done. Time: 2023-06-15 17:19:15.282896"
#> [1] "Scenario 17/18 done. Time: 2023-06-15 17:19:18.14485"
#> [1] "Scenario 18/18 done. Time: 2023-06-15 17:19:21.191437"
# }