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

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 |

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.

## 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) # }# 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) # }# 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_label == "No controls")[,c("N", "power")] # }