plt.noncomp: Plotting noncompliance rates for a given dataset

In BayesCACE: Bayesian Model for CACE Analysis

Plotting noncompliance rates for a given dataset

Description

Provides a forest plot of noncompliance rates in an R plot window.

Usage

plt.noncomp(data, overall = TRUE, ...)

Arguments

data	a dataset with structure like the example epidural_c or epidural_ic
overall	a logical value indicating whether a summary estimate of the compliance rates per randomization group is provided. The default is TRUE. This overall rate is estimated using a logit generalized linear mixed model.
...	optional parameters passed into the forestplot function from the forestplot library

Details

This function provides a visual overview (forest plot) of study-specific noncompliance rates in both randomization arms.

Only studies with full compliance information are included in this plot because noncompliance rates cannot be calculated without compliance data. In the generated plot, the red dot with its horizontal line shows the study-specific noncompliance rate with its 95% exact confidence interval for the patients randomized to the treatment arm, and the blue square with its horizontal line represents that rate and interval for those in the control arm. The confidence intervals are calculated by the Clopper–Pearson exact method, which is based on the cumulative distribution function of the binomial distribution.

Value

A forest plot of noncompliance rates in an R plot window

Examples

data("epidural_c", package = "BayesCACE")
plt.noncomp(data=epidural_c, overall = TRUE)

BayesCACE documentation built on Oct. 2, 2022, 5:08 p.m.