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
)

## Arguments

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.

## Value

A simple two-arm design with covariate W.

## Details

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

See vignette online.

## Author

DeclareDesign Team

## Examples

#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)
if (FALSE) {
diagnose_design(prognostic)
}
# Design with confounding
confounding <- two_arm_covariate_designer(N = 40, ate = 0, rho_WZ = .9, rho_WY = .9, h = .5)
if (FALSE) {
diagnose_design(confounding, sims = 2000)
}

# 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)
if (FALSE) {
diagnoses <- diagnose_design(curses)
subset(diagnoses\$diagnosands_df, estimator == "No controls")[,c("N", "power")]
}