1. Anja Schiel, PhD Statistician / Norwegian Medicines Agency
Time-to-event data analyses Differences in methods routinely used by Regulators and HTA
Anja Schiel, PhD
Statistician / Norwegian Medicines Agency

2. Disclaimer
The views and opinions expressed in this presentation are the author's own and do not necessarily reflect the official position of the Norwegian Medicines Agency or EMA.

3. Time-to-event, what’s so special?
We censor patients to allow the use of their information despite not having observed the event of interest.
In this situation standard logistic regression is not appropriate.
We need to keep in mind that
Time-to-event must be positive (> 0) and is right skewed
The probability of surviving past a certain point in time in itself is important information maybe more than observing the event
What kind of assumptions we make and which estimators we use

4. Regulators
Regulatory decision making is based on median survival times and HR

5. The data

6. ..and what we make of it

7. HTA / Cost-effectiveness analysis
Less interested in point estimators or the HR, but rather the area under the curve to allow Markov-modelling
Models should run with a lifetime horizon to capture all relevant differences

8. HTA / Cost-effectiveness analysis
Less interested in point estimators or the HR, but rather the area under the curve to allow Markov-modelling
Models should run with a lifetime horizon to capture all relevant differences

9. HTA / Cost-effectiveness analysis
Less interested in point estimators or the HR, but rather the area under the curve to allow Markov-modelling
Models should run with a lifetime horizon to capture all relevant differences

10. HTA / Cost-effectiveness analysis
Less interested in point estimators or the HR, but rather the area under the curve to allow Markov-modelling
Models should run with a lifetime horizon to capture all relevant differences

11. How to get from this…..

12. ….to this?

13. Main problems for Regulators and HTAs is
Decreasing follow-up time, in particular for OS
Uncertainty with decreasing numbers of subjects at risk

14. The solution used by the HTAs
Extrapolation by parametric modelling
Solves the problem of the median point estimator, we get mean survival times
Allows prediction beyond the actual observation time
Can help with artefacts such as the staircase phenomenon

15. The challenge Find the appropriate parametric model
The usual suspects:
Exponential
Weibull
Log-logistic
Log-normal
Gompertz
Generalised gamma
Proportional hazard model or Accelerated Failure time model?

16. The overlooked problem
The assumption of proportional hazard
The treatment effect is constant over time in the observed but also the un-observed period
This assumption needs to be tested!
Regulators do not routinely ask for confirmation of the PH assumption
HTAs start to request better documentation for the choice of modelling approach

17. Keep in mind… ….that this is not just a statistical exercise Biology
Underlying risk
Choice of distributions
Fit
Validation / Plausibility
Best fit

18. DSU TSD 14 Simple doesn’t always work
More complex but also more flexible models exist
There is no wrong or right by definition

19. Is this just some sort of voodoo?
No, it will not make our decisions better, but it will make them better informed