two_arm_covariate_designer {DesignLibrary} | R Documentation |
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. |
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.
#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)