two_arm_covariate_designer: Create a simple two arm design with a possibly prognostic...
In DesignLibrary: Library of Research Designs

Description Usage Arguments Details Value Author(s) Examples

View source: R/two_arm_covariate_designer.R

Builds a design with one treatment and one control arm. Treatment effects can be specified either by providing control_mean and treatment_mean or by specifying a control_mean and ate. Non random assignment is specified by a possible correlation, rho_WZ, between W and a latent variable that determines the probability of Z. Nonignorability is specified by a possible correlation, rho_WY, between W and outcome Y.

two_arm_covariate_designer(
  N = 100,
  prob = 0.5,
  control_mean = 0,
  sd = 1,
  ate = 1,
  h = 0,
  treatment_mean = control_mean + ate,
  rho_WY = 0,
  rho_WZ = 0,
  args_to_fix = NULL
)

`N`	An integer. Sample size.
`prob`	A number in [0,1]. Probability of assignment to treatment.
`control_mean`	A number. Average outcome in control.
`sd`	A positive number. Standard deviation of shock on Y.
`ate`	A number. Average treatment effect.
`h`	A number. Controls heterogeneous treatment effects by W. Defaults to 0.
`treatment_mean`	A number. Average outcome in treatment. Overrides `ate` if both specified.
`rho_WY`	A number in [-1,1]. Correlation between W and Y.
`rho_WZ`	A number in [-1,1]. Correlation between W and Z.
`args_to_fix`	A character vector. Names of arguments to be args_to_fix in design.

Units are assigned to treatment using complete random assignment. Potential outcomes are normally distributed according to the mean and sd arguments.

See vignette online.

A simple two-arm design with covariate W.

DeclareDesign Team

#Generate a simple two-arm design using default arguments
two_arm_covariate_design <- two_arm_covariate_designer()
# Design with no confounding but a prognostic covariate 
prognostic <- two_arm_covariate_designer(N = 40, ate = .2, rho_WY = .9, h = .5)
## Not run: 
diagnose_design(prognostic)

## End(Not run)
# Design with confounding 
confounding <- two_arm_covariate_designer(N = 40, ate = 0, rho_WZ = .9, rho_WY = .9, h = .5)
## Not run: 
diagnose_design(confounding, sims = 2000)

## End(Not run)

# Curse of power: A biased design may be more likely to mislead the larger it is 
curses <- expand_design(two_arm_covariate_designer, 
                        N = c(50, 500, 5000), ate = 0, rho_WZ = .2, rho_WY = .2)
## Not run: 
diagnoses <- diagnose_design(curses)
subset(diagnoses$diagnosands_df, estimator == "No controls")[,c("N", "power")]

## End(Not run)