psborrow2 (version 0.0.4.0)

sim_data_list: Input generated data for a simulation study

Description

A function for defining generated data for use as part of a simulation study.

Usage

sim_data_list(data_list, guide, effect, drift, index)

Value

Object of class SimDataList.

Arguments

data_list

list of lists of matrices. The lists at the highest level differ in that the parameters used to generate the data. The matrices at lowest level are different iterations of the same data generation parameters. See details.

guide

data.frame. guide contains information on the parameters that differ at the highest level of data_list. See details.

effect

character. The column in guide that corresponds to the true treatment effect estimate (hazard ratio or odds ratio).

drift

character. The column in guide that corresponds to the true drift effect estimate (hazard ratio or odds ratio). A drift >1 means the external arm experiences greater effects.

index

character. The column in guide that corresponds to the index column.

Details

In this function, you are providing generated data for analysis in a simulation study in psborrow2. Note that this function does not do any data generation on your behalf; it assumes that you have generated the data already. For a full working example, refer to the relevant vignette: vignette('simulation_study', package = 'psborrow2').

More information on the inputs is provided below.

Matrix requirements in data_list

Each matrix embedded in data_list must have:

1. a flag for whether the patient is an external control

2. a flag for whether the patient is in the experimental treatment arm

3. outcome information (time and censorship for survival, flag for outcome in binary endpoints)

Optionally, the matrices may also contain covariates. See examples.

data_list

Each set of distinct data generation parameters should be represented by a single list of matrices. Because multiple scenarios may want to be compared, a list of list of matrices is preferred. See examples.

guide

The guide should be a data.frame with one row per scenario. As a consquence of this, the length of the list should equal the number of rows in the guide. See examples.

See Also

Other simulation classes: sim_borrowing_list(), sim_covariate_list(), sim_outcome_list(), sim_treatment_list()

Examples

base_mat <- matrix(
  c(
    rep(0, 200), rep(0, 200), rep(1, 200),
    rep(1, 200), rep(0, 200), rep(0, 200),
    rep(0, 600)
  ),
  ncol = 3,
  dimnames = list(NULL, c("ext", "trt", "driftOR"))
)

add_binary_endpoint <- function(odds_ratio,
                                base_matrix = base_mat) {
  linear_predictor <- base_matrix[, "trt"] * log(odds_ratio)
  prob <- 1 / (1 + exp(-linear_predictor))

  bin_endpoint <- rbinom(
    NROW(base_matrix),
    1,
    prob
  )

  cbind(base_matrix, matrix(bin_endpoint, ncol = 1, dimnames = list(NULL, "ep")))
}

data_list <- list(
  list(add_binary_endpoint(1.5), add_binary_endpoint(1.5)),
  list(add_binary_endpoint(2.5), add_binary_endpoint(2.5))
)

guide <- data.frame(
  trueOR = c(1.5, 2.5),
  driftOR = c(1.0, 1.0),
  ind = c(1, 2)
)

sdl <- sim_data_list(
  data_list = data_list,
  guide = guide,
  effect = "trueOR",
  drift = "driftOR",
  index = "ind"
)