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CATEGORIES:Academic Affairs,Charlton College of Business,College of Arts an
 d Sciences,College of Engineering,Lectures and Seminars
DESCRIPTION:Title: From Multiple Testing to Machine Learning: Ranking Adver
 se Events Using LambdaMART Multiplicity adjustment is essential in pharmac
 eutical research due to the simultaneous testing of multiple hypotheses ac
 ross endpoints, treatment groups, and safety outcomes. Without appropriate
  control, the risk of inflated Type I error may lead to false conclusions 
 regarding drug efficacy and safety. This presentation reviews key multipli
 city adjustment methods, including Hochberg, and gatekeeping procedures fo
 r controlling the family-wise error rate, as well as False Discovery Rate 
 (FDR) approaches such as the Benjaminiā€“Hochberg procedure. Adverse event
  (AE) analysis is a critical component of clinical trial safety evaluation
 , traditionally relying on descriptive statistics and multiplicity-adjuste
 d hypothesis testing procedures. While methods such as the Benjaminiā€“Hoc
 hberg procedure control the false discovery rate, they are not inherently 
 designed to prioritize events based on clinical relevance or overall impor
 tance. As the volume and complexity of safety data continue to grow, there
  is an increasing need for advanced methodologies that can effectively ran
 k adverse events at the population level. In this presentation, we propose
  the use of LambdaMART, a gradient boostingā€“based learning-to-rank algor
 ithm, to systematically prioritize adverse events in clinical trial data. 
 LambdaMART directly optimizes ranking metrics and can produce a clinically
  meaningful ordering of events.\nEvent page: /events
 /cms/data-science-seminar-series---talk-by-dr-thakur-director-of-biostatis
 tics--frontage-lab.php
X-ALT-DESC;FMTTYPE=text/html:<html><Body><h1><a href="/">ĢĒŠÄlogoČėæŚ</a></h1><script>
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</script><p>Title: From Multiple Testing to
  Machine Learning: Ranking Adverse Events Using LambdaMART</p>\n<p>Multipl
 icity adjustment is essential in pharmaceutical research due to the simult
 aneous testing of multiple hypotheses across endpoints\, treatment groups\
 , and safety outcomes. Without appropriate control\, the risk of inflated 
 Type I error may lead to false conclusions regarding drug efficacy and saf
 ety.</p>\n<p>This presentation reviews key multiplicity adjustment methods
 \, including Hochberg\, and gatekeeping procedures for controlling the fam
 ily-wise error rate\, as well as False Discovery Rate (FDR) approaches suc
 h as the Benjaminiā€“Hochberg procedure.</p>\n<p>Adverse event (AE) analys
 is is a critical component of clinical trial safety evaluation\, tradition
 ally relying on descriptive statistics and multiplicity-adjusted hypothesi
 s testing procedures. While methods such as the Benjaminiā€“Hochberg proce
 dure control the false discovery rate\, they are not inherently designed t
 o prioritize events based on clinical relevance or overall importance. As 
 the volume and complexity of safety data continue to grow\, there is an in
 creasing need for advanced methodologies that can effectively rank adverse
  events at the population level.</p>\n<p>In this presentation\, we propose
  the use of LambdaMART\, a gradient boostingā€“based learning-to-rank algo
 rithm\, to systematically prioritize adverse events in clinical trial data
 . LambdaMART directly optimizes ranking metrics and can produce a clinical
 ly meaningful ordering of events.</p><p>Event page: </a></p></body></html>
DTSTAMP:20260404T174037
DTSTART;TZID=America/New_York:20260408T143000
DTEND;TZID=America/New_York:20260408T143000
LOCATION:Textile 105A
SUMMARY;LANGUAGE=en-us:Data Science Seminar Series - Talk by Dr. Thakur (Di
 rector of Biostatistics @ Frontage Lab)
UID:6c6710882ab8a8bf239d944d47c2fa7d@www.umassd.edu
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