ov_sim {OVtool}

ov_sim

Description

This function will create the simulation grid. The simulation will iterate over effects sizes and absolute correlations with the outcome (rho) and see how the treatment effect and relevant p-value changes

Usage

ov_sim(model_results, plot_covariates, es_grid = seq(-.4, .4, by = 0.05),
rho_grid = seq(0, .4, by = 0.05), n_reps = 50, progress = TRUE, add = FALSE,
sim_archive = NULL)

Arguments

model_results

object returned from outcome_model

plot_covariates

vector of column names representing the covariates that will be plotted on the graphic as observed covariates. Most users will include the variables on the right-hand side of the propensity score model

es_grid

Not required. A grid of effect sizes to simulate over

rho_grid

Not required. A grid of correlations to simulate over; rho relates the correlation to the effect size.

n_reps

Number of repetitions to simulate over

progress

Whether or not the function progress should print to screen. The default value is TRUE. If the user does not want the output to print to screen, they should set to FALSE.

add

Default is FALSE. This is set to true if the user is running additional repetitions after the first call to ov_sim

sim_archive

Default is NULL

Value

ov_sim returns a list containing the following components:

p_val

matrix of pvalues for each grid point

trt_effect

matrix of effect sizes for each grid point

es_grid

vector of the effect size grid

rho_grid

vector of the rho grid

cov

vector of covariates used to estimate propensity score weights

data

the initial data frame containing data with new weights

tx

column name in data representing the treatment indicator

y

column name in data representing the outcome

estimand

estimand used

n_reps

number of repetitions to simulate over

std.error

matrix of standard errors for each grid point

es_se_raw

matrix that stores each repetitions results at every grid point

Examples

data(sud)
sud = data.frame(sud)
sud$treat = ifelse(sud$treat == "A", 1, 0)
sud$wts = sample(seq(1, 10, by=.01), size=nrow(sud), replace = TRUE)
outcome_mod = outcome_model(data = sud,
                            weights = "wts",
                            treatment = "treat",
                            outcome = "eps7p_3",
                            model_covariates = c("sfs8p_0"),
                            estimand = "ATE")

ovtool_results = ov_sim(model_results=outcome_mod,
                        plot_covariates=c("sfs8p_0"),
                        es_grid = NULL,
                        rho_grid = NULL,
                        n_reps = 2,
                        progress=FALSE)