Rainforest plots for the presentation of patient-subgroup analysis in clinical trials
Zhang, Zhongheng ; Kossmeier, Michael ; Tran, Ulrich S ; Voracek, Martin and Zhang, Haoyang LU
(2017)
In Annals of Translational Medicine
5(24).
p.1-6
- Abstract
While the conventional forest plot is useful to present results within subgroups of patients in clinical studies, it has been criticized for several reasons. First, small subgroups are visually overemphasized by long confidence interval lines, which is misleading. Second, the point estimates of large subgroups are difficult to discern because of the large box representing the precision of the estimate within subgroups. Third, confidence intervals depicted by lines might incorrectly convey the impression that all points within the interval are equally likely. Rainforest plots have been proposed to overcome these potentially misleading aspects of conventional forest plots. The metaviz package enables to generate rainforest plots for... (More)
While the conventional forest plot is useful to present results within subgroups of patients in clinical studies, it has been criticized for several reasons. First, small subgroups are visually overemphasized by long confidence interval lines, which is misleading. Second, the point estimates of large subgroups are difficult to discern because of the large box representing the precision of the estimate within subgroups. Third, confidence intervals depicted by lines might incorrectly convey the impression that all points within the interval are equally likely. Rainforest plots have been proposed to overcome these potentially misleading aspects of conventional forest plots. The metaviz package enables to generate rainforest plots for meta-analysis within the statistical computing environment R. We suggest the application of rainforest plots for the depiction of subgroup analysis in clinical trials. In this tutorial, detailed step-by-step guidance on the generation of rainforest plot for this purpose is provided.
(Less)
- author
- Zhang, Zhongheng
; Kossmeier, Michael
; Tran, Ulrich S
; Voracek, Martin
and Zhang, Haoyang
LU
- publishing date
- 2017-12
- type
- Contribution to journal
- publication status
- published
- in
- Annals of Translational Medicine
- volume
- 5
- issue
- 24
- article number
- 485
- pages
- 1 - 6
- publisher
- AME Publishing Company
- external identifiers
-
- pmid:29299447
- scopus:85038024212
- ISSN
- 2305-5839
- DOI
- 10.21037/atm.2017.10.07
- language
- English
- LU publication?
- no
- id
- 923528e3-e4e2-4848-92ce-5b65cfcc5259
- date added to LUP
- 2024-02-05 16:14:36
- date last changed
- 2024-06-03 15:09:52
@article{923528e3-e4e2-4848-92ce-5b65cfcc5259,
abstract = {{<p>While the conventional forest plot is useful to present results within subgroups of patients in clinical studies, it has been criticized for several reasons. First, small subgroups are visually overemphasized by long confidence interval lines, which is misleading. Second, the point estimates of large subgroups are difficult to discern because of the large box representing the precision of the estimate within subgroups. Third, confidence intervals depicted by lines might incorrectly convey the impression that all points within the interval are equally likely. Rainforest plots have been proposed to overcome these potentially misleading aspects of conventional forest plots. The metaviz package enables to generate rainforest plots for meta-analysis within the statistical computing environment R. We suggest the application of rainforest plots for the depiction of subgroup analysis in clinical trials. In this tutorial, detailed step-by-step guidance on the generation of rainforest plot for this purpose is provided.</p>}},
author = {{Zhang, Zhongheng and Kossmeier, Michael and Tran, Ulrich S and Voracek, Martin and Zhang, Haoyang}},
issn = {{2305-5839}},
language = {{eng}},
number = {{24}},
pages = {{1--6}},
publisher = {{AME Publishing Company}},
series = {{Annals of Translational Medicine}},
title = {{Rainforest plots for the presentation of patient-subgroup analysis in clinical trials}},
url = {{http://dx.doi.org/10.21037/atm.2017.10.07}},
doi = {{10.21037/atm.2017.10.07}},
volume = {{5}},
year = {{2017}},
}