BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
DTSTART:20240310T030000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:EDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20231105T010000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:EST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20240311T232047Z
UID:1F48EDD7-768B-4975-93EF-585BDB8900BB
DTSTART;TZID=America/New_York:20240207T110000
DTEND;TZID=America/New_York:20240207T120000
DESCRIPTION:Reliability analysis and survival analysis both deal with the t
 ime-to-event data\, which is often censored and highly skewed. Medical res
 earchers try to predict the survival probabilities\, survival times and ot
 her important characteristics. Therefore\, it is not surprising to see man
 y of the reliability analysis tools being used in clinical trials and epid
 emic research. After all\, survival is the complementary event to failure.
 \n\nIn this talk I will focus on the parallels and distinctions between th
 e two statistical methods through some real-life examples. I will also dem
 onstrate modern predictive modeling tools that are useful to reliability e
 ngineers.\n\nSpeaker(s): Jian Cao\, \n\nAgenda: \n11:00 AM Technical Prese
 ntation\n\n11:45 AM Questions and Answers\n\n12:00 PM Adjournment\n\nVirtu
 al: https://events.vtools.ieee.org/m/401062
LOCATION:Virtual: https://events.vtools.ieee.org/m/401062
ORGANIZER:james.yakura@ieee.org 
SEQUENCE:32
SUMMARY:Webinar - Analyzing Time-to-Event Data: A Comparison Between Reliab
 ility and Survival Methods
URL;VALUE=URI:https://events.vtools.ieee.org/m/401062
X-ALT-DESC:Description: &lt;br /&gt;&lt;p class=&quot;western&quot;&gt;&lt;span style=&quot;color: #00000
 0\;&quot;&gt;&lt;span style=&quot;font-family: Verdana\, serif\;&quot;&gt;&lt;span style=&quot;font-size: 
 medium\;&quot;&gt;Reliability analysis and survival analysis both deal with the ti
 me-to-event data\, which is often censored and highly skewed. Medical rese
 archers try to &lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;span style=&quot;color: #000000\;&quot;&gt;&lt;span s
 tyle=&quot;font-family: Verdana\, serif\;&quot;&gt;&lt;span style=&quot;font-size: medium\;&quot;&gt;pr
 edict the survival probabilities\, survival times and other important char
 acteristics.&amp;nbsp\;Therefore\, it is not surprising to see many of the rel
 iability analysis tools being used in clinical trials and epidemic researc
 h. After all\, survival is the complementary event to failure. &lt;/span&gt;&lt;/sp
 an&gt;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;western&quot;&gt;&lt;span style=&quot;color: #000000\;&quot;&gt;&lt;span st
 yle=&quot;font-family: Verdana\, serif\;&quot;&gt;&lt;span style=&quot;font-size: medium\;&quot;&gt;In 
 this talk I will focus on the parallels and distinctions between the two s
 tatistical methods through some real-life examples. I will also demonstrat
 e modern predictive modeling tools that are useful to reliability engineer
 s. &lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;p&gt;&lt;strong&gt;11:00 AM&lt;
 /strong&gt;&amp;nbsp\;&amp;nbsp\;&amp;nbsp\;Technical Presentation&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;11:45 
 AM&lt;/strong&gt;&amp;nbsp\;&amp;nbsp\; Questions and Answers&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;12:00 PM&lt;/
 strong&gt;&amp;nbsp\;&amp;nbsp\;&amp;nbsp\;Adjournment&lt;/p&gt;
END:VEVENT
END:VCALENDAR