Recurrent Event Studies - Model choices, pitfalls and group sequential design

Designing Studies with Recurrent Events
Model choices, pitfalls and group sequential design

We recently ran a webinar on Designing studies with recurrent events. Recurring events are common in clinical trials (e.g. COPD exacerbations, MS relapses) but have often been analysed using survival models or other approximations. But these simple approaches fail to use every event.

This has led to increasing interest in recurring event and count models and how these allow us to analyse all recurring events or counts and thus provide additional insight and power. Below are the summarized notes from are webinar

1. Recurrent Events Overview
2. Recurrent Event Analysis Approaches
3. Issues & Pitfalls in Recurrent Event Analysis
4. Discussion and Conclusions

1. Recurrent Events Overview 

• Recurrent event processes are endpoints where subject can have >1 informative event in time period 
• Common endpoint in clinical trials esp. chronic diseases
• COPD/asthma exacerbations, MS relapses, migraines, seizures
• Similar considerations and methods also relevant in case of count data e.g. imaging, epidemiology
• Should use events/counts “as-is” for better estimation but historically often “simplified” to other endpoints

2. Recurrent Event Analysis Approaches

• Event Rate Models: Estimate # of events per time unit
  • Parametric models for (constant over time) event rate ratio
  • e.g. Poisson (incl. quasi-P), negative binomial (both ZI, ZT)
• Time-to-Events Models: Time(s) between/til next event
  • Semi-parametric models for hazard from each event
  • e.g. Andersen-Gill, Wei-Lin-Weissfeld, Prentice-Williams-Peterson

• Mean Cumulative Event Function: E(Events) at time t
  • Non-parametric method for cumulative E’s e.g. Nelson-Aale

3. Issues & Pitfalls in Recurrent Event Analysis  

• What is the question/target of interest in study?
  • Use event rate, time between events, # of events?
  • Compare groups via rate ratio, # events, intensity/HR? 
  • Interested in first K events, later events, more intense events?

• What assumptions are reasonable for data?
  • Independent events, non-informative censoring,
  • Event process differs per subject, process changes over time
  • Some events more important, effect of terminal events

• Recurrent Events Group Sequential Design Issues
  • Appropriate MLE and test for GST
    • Normal approx. if t same per subject?
    • For NB, no closed form if t differs
      
      
  • How turn info. time into “real” time
    • Info. time related to follow-up times, n, rates and dispersion parameter
    • Equal t ~ Means, Unequal ~ TTE
  • Type I Error ↑ if low N/high κ
    • No Exact, some proposed altered stats
    • t-distribution GSD better for low N

4. Discussion and Conclusions 

• Recurrent Events/Count data common in clinical trials
  • Move away from approximations to modelling process fully
• Variety of models depending on scientific question
  • Parametric: event rate; Semi-parametric: t between/to events

• SSD methods available for most common models
  • Negative Binomial, Poisson, Andersen-Gill; one less barrier

• GSD methods available for recurrent events growing
  
  • Approx. work adequately for standard Phase III assumptions
    

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To watch the recording of this webinar, just click the image below.