CIEE: generate_data – R documentation

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generate_data

Data generation function

Description

Function to generate data with n observations of a primary outcome Y, secondary outcome K, exposure X, and measured as well as unmeasured confounders L and U, where the primary outcome is a quantitative normally-distributed variable (setting = "GLM") or censored time-to-event outcome under an accelerated failure time (AFT) model (setting = "AFT"). Under the AFT setting, the observed time-to-event variable T=exp(Y) as well as the censoring indicator C are also computed. X is generated as a genetic exposure variable in the form of a single nucleotide variant (SNV) in 0-1-2 additive coding with minor allele frequency maf. X can be generated independently of U (X_orth_U = TRUE) or dependent on U (X_orth_U = FALSE). For more details regarding the underlying model, see the vignette.

Usage

generate_data(setting = "GLM", n = 1000, maf = 0.2, cens = 0.3,
  a = NULL, b = NULL, aXK = 0.2, aXY = 0.1, aXL = 0, aKY = 0.3,
  aLK = 0, aLY = 0, aUY = 0, aUL = 0, mu_X = NULL, sd_X = NULL,
  X_orth_U = TRUE, mu_U = 0, sd_U = 1, mu_K = 0, sd_K = 1, mu_L = 0,
  sd_L = 1, mu_Y = 0, sd_Y = 1)

Arguments

`setting`	String with value `"GLM"` or `"AFT"` indicating whether the primary outcome is generated as a normally-distributed quantitative outcome (`"GLM"`) or censored time-to-event outcome (`"AFT"`).
`n`	Numeric. Sample size.
`maf`	Numeric. Minor allele frequency of the genetic exposure variable.
`cens`	Numeric. Desired percentage of censored individuals and has to be specified under the AFT setting. Note that the actual censoring rate is generated through specification of the parameters `a` and `b`, and `cens` is mostly used as a check whether the desired censoring rate is obtained through `a` and `b` (otherwise, a warning is issued).
`a`	Integer for generating the desired censoring rate under the AFT setting. Has to be specified under the AFT setting.
`b`	Integer for generating the desired censoring rate under the AFT setting. Has to be specified under the AFT setting.
`aXK`	Numeric. Size of the effect of `X` on `K`.
`aXY`	Numeric. Size of the effect of `X` on `Y`.
`aXL`	Numeric. Size of the effect of `X` on `L`.
`aKY`	Numeric. Size of the effect of `K` on `Y`.
`aLK`	Numeric. Size of the effect of `L` on `K`.
`aLY`	Numeric. Size of the effect of `L` on `Y`.
`aUY`	Numeric. Size of the effect of `U` on `Y`.
`aUL`	Numeric. Size of the effect of `U` on `L`.
`mu_X`	Numeric. Expected value of `X`.
`sd_X`	Numeric. Standard deviation of `X`.
`X_orth_U`	Logical. Indicator whether `X` should be generated independently of `U` (`X_orth_U` = `TRUE`) or dependent on `U` (`X_orth_U` = `FALSE`).
`mu_U`	Numeric. Expected value of `U`.
`sd_U`	Numeric. Standard deviation of `U`.
`mu_K`	Numeric. Expected value of `K`.
`sd_K`	Numeric. Standard deviation of `K`.
`mu_L`	Numeric. Expected value of `L`.
`sd_L`	Numeric. Standard deviation of `L`.
`mu_Y`	Numeric. Expected value of `Y`.
`sd_Y`	Numeric. Standard deviation of `Y`.

Value

A dataframe containing n observations of the variables Y, K, X, L, U. Under the AFT setting, T=exp(Y) and the censoring indicator C (0 = censored, 1 = uncensored) are also computed.

Examples

# Generate data under the GLM setting with default values
dat_GLM <- generate_data()
head(dat_GLM)

# Generate data under the AFT setting with default values
dat_AFT <- generate_data(setting = "AFT", a = 0.2, b = 4.75)
head(dat_AFT)

CIEE

Estimating and Testing Direct Effects in Directed Acyclic Graphs using Estimating Equations

v0.1.1

GPL-2

Authors

Stefan Konigorski [aut, cre], Yildiz E. Yilmaz [ctb]

Initial release