[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-657407-105":3,"detail-sidebar-cat-0-en-105":80,"doc-detail-657407-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"en","waterfall-charts-in-oncology-trials-ride-the-wave","Waterfall Charts in Oncology Trials - Ride the Wave","","Oncology trials often summarize overall drug response using RECIST categories, typically yielding multiple response groups that can reduce interpretability when collapsed into discrete labels. Waterfall charts address this by visualizing tumor-size changes across the full result spectrum and showing increases or decreases in a parameter of interest. The paper highlights SAS® solutions and focuses on the SGPLOT procedure for creating waterfall plots and presenting response performance clearly.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/waterfall-charts-in-oncology-trials-ride-the-wave/657407/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/waterfall-charts-in-oncology-trials-ride-the-wave/657407.png","ImageObject",300,407,{"name":42,"@type":43},"Blitz","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-10-26",true,{"@type":52,"interactionType":53,"userInteractionCount":22},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"Why are waterfall charts useful in oncology trials compared with collapsing RECIST categories?","Question",{"text":62,"@type":63},"Collapsing tumor response into discrete RECIST categories limits data available for interpretation. Waterfall charts visualize the full range of response by plotting tumor-size changes across all patients, improving understanding of trends and overall performance.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"How does the paper define response categories for waterfall chart grouping?",{"text":67,"@type":63},"Patients are assigned to categories based on overall response and status: CR (Complete Response), PR (Partial Response), PD (Progressive Disease), SD (Stable Disease), and ED (Early Death). These categories can be used for color coding groups.",{"name":69,"@type":60,"acceptedAnswer":70},"What SAS procedure does the article focus on for generating waterfall charts?",{"text":71,"@type":63},"The article focuses on the recently introduced SGPLOT procedure (PROC SGPLOT) to create waterfall plots using a dataset with one record per patient and the value to be plotted.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},657407,1792978681,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":22,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":111,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":79,"read_time":117},24464137899374,"https://us-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2","PharmaSUG 2012-Paper DG13  \nWaterfall Charts in Oncology Trials-Ride the Wave  \nNiraj J. Pandya, Element Technologies Inc. , Piscataway, NJ  \nABSTRACT  \nIn oncology trials, overall drug response is usually analyzed using Response Evaluation Criteria in Solid Tumors (RECIST) criteria. Using these criteria, results are expressed in 4 to 5 different categories. By collapsing tumor response in to different categories, it reduces the amount of data available for interpretation. Waterfall chart is oneway to address this issue. It is a way of plotting the tumor size across the entire spectrum of much broader view of results. Waterfall chart is a data visualization technique that depicts how a value increases or decreases for parameter of interest. Waterfall chart is gaining recognition recently for the reporting of overall drug response in Oncology clinical trials because of its simple yet powerful representation of results. It can be used for performance or response analysis, especially for understanding or explaining the overall response of a parameter which can vary with the effect of multiple factors. SAS® offers multiple solutions to produce waterfall charts and in this article we will focus on the use of recently introduced SGPLOT procedure for creation of waterfall chart.  \nINTRODUCTION  \nApplication of waterfall charts is different in various industries and is often presented/interpreted differently in different type of data analysis. It is gaining popularity because of its ability to include large data spectrum and its ability to present complete response and trend of interested parameter. In finance industry, it is often used to determine cumulative effect of different positive and negative intermediate values on an initial value of a parameter and how it leads to a final value. Initial and final values of an entity are presented by full bars on the chart and intermediate values are presented by floating bars. Because of this, such chart is also called‘Floating Bar Chart’or ‘Mario Chart’. Sometimes, it is also referred as ‘The Bridge’. In pharmaceutical industry, this chart is usually used in oncology clinical trials data analysis. It is used for performance analysis of drug. In such chart, each patient in the trial is presented by a vertical bar on the chart which represents the change or % change from baseline in the diameter of tumor. In controlled trials, such chart can be used to compare the performance of active drug versus placebo.  \nWATERFALL CHART  \nWhen a patient is enrolled in a clinical trial for the treatment of tumor, the diameter of tumor site is measured on different visits. Then collected data is compared to the data taken at baseline to determine if drug has some activity or not. Also each patient is assigned in to different categories based on overall response. Based on response and patient status, patient can be assigned in to any of the categories mentioned in following table.  \n\n| CR | Complete Response |\n| --- | --- |\n| PR | Partial Response |\n| PD | Progressive Disease |\n| SD | Stable Disease |\n| ED | Early Death |\n\nPlotting the maximum change(+ve or –ve) in the diameter of tumor site for each patient and color code each patient in to different groups based on above mentioned categories gives a very clear picture about the performance of drug. When the patient population in a particular trial is large, waterfall chart is the perfect presentation to give a good idea about drug activity.  \nTo get the effect of waterfall, the flow of the plot has to be from worst value among the population on left to best value on the right side of the plot (descending order) . As mentioned earlier, with some programming efforts, SAS offers multiple solutions to get required waterfall chart, but currently there is no inbuilt procedure available that can create waterfall chart readily. We will look at the use of PROC SGPLOT in this paper to generate waterfall plot. For that, the required input dataset should contain one ","cbCaiuBy3Nzs7SCI","https://ap.wps.com/l/cbCaiuBy3Nzs7SCI","pdf",300677,"English","# Abstract\n# Introduction\n# Waterfall Chart\n## Response categories (CR/PR/PD/SD/ED)\n## Creating the chart with PROC SGPLOT\n## Dataset description (LSPCHG, LSRESP)\n## Plot ordering and waterfall flow\n## Customizing bar color and transparency","[{\"question\":\"Why are waterfall charts useful in oncology trials compared with collapsing RECIST categories?\",\"answer\":\"Collapsing tumor response into discrete RECIST categories limits data available for interpretation. Waterfall charts visualize the full range of response by plotting tumor-size changes across all patients, improving understanding of trends and overall performance.\"},{\"question\":\"How does the paper define response categories for waterfall chart grouping?\",\"answer\":\"Patients are assigned to categories based on overall response and status: CR (Complete Response), PR (Partial Response), PD (Progressive Disease), SD (Stable Disease), and ED (Early Death). These categories can be used for color coding groups.\"},{\"question\":\"What SAS procedure does the article focus on for generating waterfall charts?\",\"answer\":\"The article focuses on the recently introduced SGPLOT procedure (PROC SGPLOT) to create waterfall plots using a dataset with one record per patient and the value to be plotted.\"}]","Waterfall Charts in Oncology Trials - Ride the Wave | PDF"]