Understanding Waterfall Plots

Author's disclosures of potential conflict of interest are found at the end of this article. C linical trials in oncology continue to offer hope for tumor control. Such hope intensifies as the rapidly advancing knowledge from molecular biology, genetics, and immunology is more readily applied to targeted therapies. The ability to discern genomic or genetic risks and likelihood of response on a biologic basis has enhanced the field's capacity to tailor therapeutic interventions specific to the individual's tumor biology and possible host response. The impact of the tumor's characteristics on outcome, as well as the effect of the patient's characteristics, e.g. smoking status, become increasingly important to measure and interpret as these variables relate to response, progression-free survival (PFS), and ultimately, overall survival (OS). For decades, overall survival has been the " gold standard " for interpreting clinical trials, as often results of PFS have not translated into long-term survival. In a similar way, Kaplan-Mei-er curves have long been used to illustrate survival analyses for clinical trials and other statistical evaluations of survival over time. More recently, different types of graphs called waterfall plots have begun to gain in popularity in serving as the means to visually depict results of response to, and types of survival from, oncology clinical trials. An example of a recent publication that relied on waterfall plots for data presentation is Kwak et al.'s New England Journal of Medicine article entitled, " Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer " (Kwak et al., 2010), discussed on page 103 by Beth Eaby-Sandy, MSN, CRNP, OCN®. Since these graphic presentations may be unfamiliar to some readers, this article will comment on their interpretation and application herein. Waterfall plots are graphic illustrations of data that can vary from audio frequencies to clinical trial patient information and results. In oncology, for example, a waterfall plot may be used to present each individual patient's response to a particular drug based on a parameter, such as tumor burden. The horizontal (x) axis across the plot may serve as a baseline measure; vertical bars are drawn for each patient, either above or below the baseline. The vertical (y) axis may be used to measure maximum percent change from baseline, e.g., percent growth or reduction of the tumor by radiologic measurement. Those vertical bars that are above the line represent nonresponders or progressive disease. Vertical bars below the baseline (x) axis are drawn …