| Title: | Visualize ROBUST-RCT Risk of Bias Assessments |
|---|---|
| Description: | Provides functions to visualize ROBUST-RCT assessments, as introduced by Wang et al. (2025) <doi:10.1136/bmj-2024-081199>. Through a two-step workflow (step 1 and step 2), the package generates bar plots and traffic-light plots that match standard Cochrane styles. |
| Authors: | Guang Chen [aut, cre], Fanrong Liang [aut] |
| Maintainer: | Guang Chen <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.2 |
| Built: | 2026-09-25 09:52:21 UTC |
| Source: | https://github.com/cran/RobustVis |
A dataset containing the step 1 risk of bias assessment items (Item 1 to Item 5).
data_step1data_step1
A data frame with 19 rows and 6 variables:
Study identifier and publication year.
Random sequence generation assessment.
Allocation concealment assessment.
Blinding of participants and personnel assessment.
Blinding of outcome assessment.
Additional bias domain assessment.
Abbreviations used for the assessment items:
DY: Definitely Yes
PY: Probably Yes
PN: Probably No
DN: Definitely No
Data from Li, Q. et al.'s systematic review and meta-analysis on antibiotic use as the primary outcome.
Li, Q., Zhou, Q., Fan, J., Feng, X., Lai, H., Chen, Y., ... & Zeng, L. (2026). Impact of molecular point-of-care testing for respiratory pathogens on antibiotic use and clinical outcomes in acute respiratory tract infections: a systematic review and meta-analysis. EClinicalMedicine, 92.
A dataset containing the step 2 risk of bias assessment items (Item 1 to Item 6).
data_step2data_step2
A data frame with 19 rows and 7 variables:
Study identifier and publication year.
Random sequence generation assessment.
Allocation concealment assessment.
Blinding of participants and personnel assessment.
Blinding of outcome assessment.
Additional bias domain assessment.
Outcome data not included in analysis.
Abbreviations used for the assessment items:
DH: Definitely High risk
PH: Probably High risk
PL: Probably Low risk
DL: Definitely Low risk
Data from Li, Q. et al.'s systematic review and meta-analysis on antibiotic use as the primary outcome.
Li, Q., Zhou, Q., Fan, J., Feng, X., Lai, H., Chen, Y., ... & Zeng, L. (2026). Impact of molecular point-of-care testing for respiratory pathogens on antibiotic use and clinical outcomes in acute respiratory tract infections: a systematic review and meta-analysis. EClinicalMedicine, 92.
A function to convert risk-of-bias assessment data for Step 1 or Step 2 into tidy data and plot a summary stacked barplot matching the standard Cochrane style with a boxed legend and custom labels.
rob_bar(data, step = 1, colour = NULL, ...)rob_bar(data, step = 1, colour = NULL, ...)
data |
A dataframe containing the study IDs in the first column, followed by item evaluation columns (columns B to F for Step 1, or columns B to G for Step 2). |
step |
An integer or character specifying the evaluation step, either |
colour |
A character vector specifying the colour scheme. Defaults to |
... |
Additional arguments to be passed to internal functions. |
A ggplot2 risk-of-bias summary barplot figure.
rob_bar(data_step1, step = 1) rob_bar(data_step2, step = 2)rob_bar(data_step1, step = 1) rob_bar(data_step2, step = 2)
Draw a robvis-style traffic-light grid for step 1 or step 2 assessments. The first column of 'data' contains study labels; the remaining columns map by position to Item 1–5 ('step = 1') or Item 1–6 ('step = 2').
rob_traffic_light(data, step, colour = NULL, psize = 10)rob_traffic_light(data, step, colour = NULL, psize = 10)
data |
A data frame. Column 1 contains study labels. Step 1 requires five assessment columns; step 2 requires six assessment columns. |
step |
Assessment step, either '1' or '2'. |
colour |
A character vector of four colours. It may be unnamed (in the documented level order) or named with full labels/abbreviations. Defaults to green, yellow, red, and blue. |
psize |
Diameter of the traffic-light circles in millimetres. |
A 'ggplot2' object.
rob_traffic_light(data = data_step1, step = 1) rob_traffic_light(data = data_step2, step = 2)rob_traffic_light(data = data_step1, step = 1) rob_traffic_light(data = data_step2, step = 2)