R/two_arm_covariate_designer.R
two_arm_covariate_designer.Rd
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 |
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. |
A simple two-arm design with covariate W.
Units are assigned to treatment using complete random assignment. Potential outcomes are normally distributed according to the mean and sd arguments.
See vignette online.
#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_label == "No controls")[,c("N", "power")] }