two_arm_covariate_designer {DesignLibrary} R Documentation

## Create a simple two arm design with a possibly prognostic covariate

### Description

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.

### Usage

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.

### 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.

### Value

A simple two-arm design with covariate W.

### 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)
## 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)


[Package DesignLibrary version 0.1.10 Index]