@article{brown_paul_m_composite_2017,
	title = {Composite {End} {Points} in {Clinical} {Trials} of {Heart} {Failure} {Therapy}},
	volume = {10},
	url = {https://www.ahajournals.org/doi/full/10.1161/circheartfailure.116.003222},
	doi = {10.1161/CIRCHEARTFAILURE.116.003222},
	abstract = {Composite end points are popular outcomes in clinical trials of heart failure therapies. For example, a global rank composite is typically analyzed using a Mann–Whitney U test, and the results are summarized by the mean of ranks and a corresponding P value. The mean of ranks is uninformative, and a clinically meaningful estimate of the treatment effect is needed to communicate study results and facilitate an assessment of heterogeneity (the consistency of the effect across outcomes). The probability index is intuitive for clinicians, easy to calculate, and may be applied to various composites. We suggest a simple and familiar plot to assess heterogeneity across outcomes, which should be routine when analyzing composites. We think that the probability index provides an immediate and simple solution to an overt problem.},
	number = {1},
	urldate = {2020-11-25},
	journal = {Circulation: Heart Failure},
	author = {{Brown Paul M.} and {Ezekowitz Justin A.}},
	month = jan,
	year = {2017},
	note = {Publisher: American Heart Association},
	keywords = {c-index, global-rank, multiple-endpoints, probability-index, rct},
	pages = {e003222},
}

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