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UID:20251122T230534EST-37278Uwb2H@132.216.98.100
DTSTAMP:20251123T040534Z
DESCRIPTION:Latent class analysis: An indispensable tool for diagnostic res
 earch\n\nThe lack of an adequate diagnostic test is a fairly common proble
 m in many disease areas including childhood tuberculosis\, pneumonia and A
 lzheimer’s disease. Latent class analysis has been applied to estimate dis
 ease prevalence or sensitivity and specificity of a new diagnostic test in
  these settings. It can also be used to develop prediction models\, estima
 te overdiagnosis and support cost-effectiveness analyses. Despite its util
 ity\, the number of applications of latent class analysis in diagnostic re
 search remains limited. This presentation will discuss some of the reasons
  for this and some solutions to make latent class models more accessible. 
 One particular impediment has been the promotion of simple but biased alte
 rnative approaches\, particularly composite reference standards. Other cha
 llenges include the handling of non-identifiability and conditional depend
 ence\, which are common in these problems\, and model validation in the ab
 sence of a perfect reference test.\n
DTSTART:20170120T203000Z
DTEND:20170120T213000Z
LOCATION:Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue des Pins 
 Ouest
SUMMARY:Nandini Dendukuri\, PhD\, 9IÖÆ×÷³§Ãâ·Ñ
URL:/mathstat/channels/event/nandini-dendukuri-phd-mcg
 ill-university-265100
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