Cost: Industry: $1,200 / Academia+Government: $800 / Student: $400

Join us for a 2-day workshop following ACoP13!

Instructors

• Jim Rogers, PhD
• Andrew Tredennick, PhD
• Matthew Weins, MA
• Ramon Garcia, PhD

Learning Objectives

• Describe confounded exposure-response
• Analyze binary data (exploratory analysis and modeling using R and Stan)
• Analyze time-to-event data (exploratory analysis, semi-parametric and parametric modeling using R and Stan)
• Interpret TTE models with continuously time-varying hazard

Topic Outline

Day 1

• Study designs and confounded exposure-response
• General theory / background
• Binary data
• Models for binary data
• Bayesian models for binary data
  – Lunch Break –
• Time-to-event (TTE) data
  • What makes it different?
    • Describing a distribution w/o censoring (density, CDF)
    • Describing a distribution w/o censoring (hazard, CDF, survival function)
    • Non-parametric estimation of S(t), H(t) and h(t)
    • Study design implications: number of events vs number of subjects
  • Visualizing TTE data vs predictors: K-M plot (session 1)
    • How to interpret it in general?
    • Considerations for exposure metrics in TTE analyses
    • Utility and pitfalls of exposure quartiles
    • Hands-on example: visualizing TTE endpoint vs treatment or exposure
• TTE semi-parametric modeling
• Introduction to parametric survival analysis

Day 2

• TTE parametric modeling (Bayesian)
• Additional topics in TTE modeling

To register, visit the ACoP meeting website.