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UID:20251105T182605EST-7432X91EBz@132.216.98.100
DTSTAMP:20251105T232605Z
DESCRIPTION:Title: Developing a New Rank-Based Quantile Regression Methodol
 ogy for Monitoring the Prevalence of Osteoporosis.\n\nIn osteoporosis stud
 ies\, quantiles of the distribution of bone mineral density (BMD) and thei
 r relationship with risk factors such as age\, family history\, gender\, e
 tc.\, are important. Low BMD is associated with higher probability of leg 
 and pelvis fractures due to falls and causes significant public health iss
 ues\, especially in elderly women\, leading to high medical cost\, inabili
 ty to live independently\, and even risk of death. In this study\, we use 
 a large cohort study in the Canadian province of Manitoba and develop a ne
 w methodology to monitor the prevalence of osteoporosis using more efficie
 nt and cost-efficient follow-ups. We propose to use expert knowledge and/o
 r suitable measurements from the base-line study to design follow-up sampl
 e selection procedures that are based on ranks. We obtain the ranks by com
 paring a patient with a small number of other patients\, randomly selected
  from the study cohort. We propose a new check-function to incorporate the
  rank information associated with selected (nominated) samples in the esti
 mation process. Strategies are given to design proper follow-up studies fo
 r a given population quantile. Numerical studies show that rank-based quan
 tile regression models are more efficient than those based on simple rando
 m sampling (SRS) for analyzing upper/lower tail quantiles of the distribut
 ion of BMD. Among other results\, we observe that in some cases\, our prop
 osed method requires about one‐tenth of the sample used in SRS to estimate
  the lower/upper tail conditional quantiles with comparable mean squared e
 rrors. This is a dramatic reduction in time and cost compared with the usu
 al approach and is very efficient to design better follow-ups in large coh
 ort studies.\n
DTSTART:20181120T203000Z
DTEND:20181120T213000Z
LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Prof. Mohammad Jafari Jozani (University of Manitoba) - (SPECIAL SE
 MINAR)
URL:/mathstat/channels/event/prof-mohammad-jafari-joza
 ni-university-manitoba-special-seminar-291799
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