Webinar - Analyzing Time-to-Event Data: A Comparison Between Reliability and Survival Methods

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Sponsor:   IEEE Boston/Providence/New Hampshire Reliability Chapter

                Please visit https://r1.ieee.org/boston-rl/

Host:        IEEE Boston/Providence/New Hampshire Reliability Chapter

Reliability analysis and survival analysis both deal with the time-to-event data, which is often censored and highly skewed. Medical researchers try to predict the survival probabilities, survival times and other important characteristics. Therefore, it is not surprising to see many of the reliability analysis tools being used in clinical trials and epidemic research. After all, survival is the complementary event to failure.

In this talk I will focus on the parallels and distinctions between the two statistical methods through some real-life examples. I will also demonstrate modern predictive modeling tools that are useful to reliability engineers.


  Date and Time

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  • Date: 07 Feb 2024
  • Time: 11:00 AM to 12:00 PM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
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  • James P. (Jay) Yakura, Chair

    IEEE Boston/Providence/New Hampshire Reliability Chapter

  • Starts 21 January 2024 12:00 AM
  • Ends 06 February 2024 05:30 PM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
  • No Admission Charge

  Speakers

Jian Cao Jian Cao of JMP

Topic:

Analyzing Time-to-Event Data: A Comparison Between Reliability and Survival Methods

Reliability analysis and survival analysis both deal with the time-to-event data, which is often censored and highly skewed. Medical researchers try to predict the survival probabilities, survival times and other important characteristics. Therefore, it is not surprising to see many of the reliability analysis tools being used in clinical trials and epidemic research. After all, survival is the complementary event to failure.

In this talk I will focus on the parallels and distinctions between the two statistical methods through some real-life examples. I will also demonstrate modern predictive modeling tools that are useful to reliability engineers.

Biography:

Jian Cao, PhD, is a principal systems engineer at JMP, a statistical software company headquartered in Cary, North Carolina.

Jian started his career at AT&T Bell Labs as a researcher in New Jersey and has since worked in the pharmaceutical industry as well as the consultancy and software business before joining JMP in 2006. His current role at JMP is technical enablement.

Jian’s specialties include advanced statistical analysis & modeling, and their applications.




Agenda

11:00 AM   Technical Presentation

11:45 AM   Questions and Answers

12:00 PM   Adjournment


The meeting is open to all.  You do not need to belong to the IEEE to attend this event; however, we welcome your consideration of IEEE membership as a career enhancing technical affiliation.

There is no cost to register or attend, but registration is required.


  Media

IEEE_Boston_Reliability__02.07.2024 Slides. The ZOOM recording is at https://us02web.zoom.us/rec/share/5cnk7Qw3bfAda04kE8d48XDkbyBlrITxiWZmjTagb2634erSKRUbimE3UYwPj1Lx.9fOqBorbQjGcGqul Passcode: D.S?0dWn 1.03 MiB
Additional related links from JMP Hyperlinks to JMP traning 60.32 KiB