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The work introduces GOahead, a comprehensive web-based R Shiny tool that helps statisticians and collaborators prospectively plan and implement selected Go/No-Go decision approaches in real time. The paper surveys dual-criterion and confidence-interval methods, demonstrates examples for single, two-arm, and multi-arm designs, and provides access via a ShinyApps server.",{"@graph":69,"@context":113},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-comprehensive-toolbox-to-facilitate-quantitative-decision-science-in-drug-development-a-web-based-r-shiny-application-goahead/639500/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-comprehensive-toolbox-to-facilitate-quantitative-decision-science-in-drug-development-a-web-based-r-shiny-application-goahead/639500.png","ImageObject",300,407,{"name":92,"@type":93},"Mabel","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22",true,{"@type":101,"interactionType":102,"userInteractionCount":4},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107],{"name":108,"@type":109,"acceptedAnswer":110},"How does the GOahead tool support practical implementation?","Question",{"text":111,"@type":112},"GOahead is implemented as a user-friendly R Shiny web application, designed for real-time planning and calculation of Go/No-Go decisions, and it supports multiple decision-criterion constellations.","Answer","https://schema.org",{"og:url":83,"og:type":115,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":117,"canonical":83},"index,follow",{"doc_id":119,"site_id":62},639500,1792703922,{"code":4,"msg":5,"data":122},{"doc_id":119,"user_id":123,"nickname":92,"user_avatar":124,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":125,"file_id":126,"file_url":127,"file_type":128,"file_size":129,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":130,"language":131,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":132,"faqs":133,"seo_title":134,"seo_description":67,"update_tm":120,"read_time":135},7971474920318,"https://ap-avatar.wpscdn.com/avatar/74000ee537e6b0ca360?x-image-process=image/resize,m_fixed,w_180,h_180&k=1788430171632181674","A Comprehensive Toolbox to Facilitate Quantitative Decision Science in Drug  \nDevelopment: A web-based R shiny application GOahead  \nBo Wei¹ , Weibin Zhong² , Rong Liu*² , Alan Wu3 , Alan Chiang² , Michael Branson4 , Nanxiang Ge5  \n¹ University of Michigan, Ann Arbor, MI 48109 , ² Bristol-Myers Squibb, Berkeley Heights, NJ 07922, 3 Beigene, Ridgefield Park, NJ 07660 4 UCB Pharmaceuticals, Brussels, Switzerland, 5 Roivant, New York, NY 10036  \n*corresponding author  \nEmail: [rong.liu1@bms.com](rong.liu1@bms.com)  \n[Address: 300 Connell Drive](Address: 300 Connell Drive)  \nBerkeley Heights, NJ 07932, USA  \nAbstract  \nDecision making is critical at each stage of drug development and making informed and transparent Go/No-Go decision requires a “sound” quantitative decision framework. We designed and implemented GOahead, a comprehensive web-based tool to improve how statisticians and collaborators could prospectively plan and implement the selected Go/No-Go decision approach in real time. In the paper, we conduted comprehensive overview of dual-criterion and confidence interval-based approaches to enable quantitative decision-making. ilillustrative examples are demonstrated for single and two arms designs in both Bayesian and frequentist frameworks, multiple arms design with MCP-MOD is also demonstrated. GOahead can be found on [shinyapps.io](shinyapps.io) server  \n([https://goaheadtool.shinyapps.io/GOaheadv10/](https://goaheadtool.shinyapps.io/GOaheadv10/))  \nKeywords  \nGo/No-Go , Quantitative Decision-making, Decision criteria, R shiny, Dual-criterion, Confidence interval.  \n1. Introduction  \nDrug development naturally has several phases and each phase requires the trial sponsor to address key questions in relation to patient safety and treatment efficacy. The purpose in the early phases are to de-risk the late stage (phase 3) investment as these are usually very time intensive, requires many more patients and a significant financial investment. According to [1], between 2009 and 2018 the estimated mean Research and Development investment required to bring a new drug to market was $1.34 billion (95% CI, $1.04 billion- $1.64 billion), with an average time of more than 10 years. Amidst the huge cost and many years involved in developing a potentially new treatment , there is approximately a 15% probability of success for any new treatment first entering the clinical phase to be successfully registered: averaging across all therapeutic areas between 2003-2011 , oncology drugs have the lowest success rate of 7%[2] .  \nThe low success rate may be attributed to many reasons and one of the reasons is the high attrition rate of compounds in early development [3] . Thus, emphasis in the early phases of drug development should be to ensure that trials are:  \na. Prospectively designed with transparent, reasonable Go/ No-Go criteria  \nb. Clinical trials sized adequately to address these criteria supported by sound  \nstatistical principles  \nc. Analytic software available to calculate the level of evidence supporting the Go/ No-Go criteria for key drug development decisions.  \nIn following (a)-(c), it helps inform decision makers whether to progress to the next stage of development or to perhaps reallocate effort to other assets with higher anticipated probability of success.  \nImplementation of the quantitative decision approach requires close collaboration of clinical team members including clinicians, statistician and colleagues from other functions, to have a clear understanding of what the product target product profile (TPP) (the specification that defines the desired efficacy, safety and other characteristics of the drug product) [4] . Several quantitative decision-making approaches have been proposed and most of these approaches rely on a clearly defined set of rules for a threshold of treatment effect ( 􀟂 ) and its probability  \ndistribution 􀜲 (􀟂)([5],[6]) . For instance, the threshold 􀟂 may be the treatment effect of a drug relative","cbCaiq1kmasN4JDk","https://ap.wps.com/l/cbCaiq1kmasN4JDk","pdf",1146436,29,"English","# 1. Introduction\n# 2. Methods\n## 2.1. Background","[{\"question\":\"How does the GOahead tool support practical implementation?\",\"answer\":\"GOahead is implemented as a user-friendly R Shiny web application, designed for real-time planning and calculation of Go/No-Go decisions, and it supports multiple decision-criterion constellations.\"}]","A Comprehensive Toolbox to Facilitate Quantitative Decision Science in Drug Development: A web-based R shiny application GOahead | PDF",73]