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Rainforest plots for the presentation of patient-subgroup analysis in clinical trials

  
@article{ATM17011,
	author = {Zhongheng Zhang and Michael Kossmeier and Ulrich S. Tran and Martin Voracek and Haoyang Zhang},
	title = {Rainforest plots for the presentation of patient-subgroup analysis in clinical trials},
	journal = {Annals of Translational Medicine},
	volume = {5},
	number = {24},
	year = {2017},
	keywords = {},
	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 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.},
	issn = {2305-5847},	url = {http://atm.amegroups.com/article/view/17011}
}