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

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.

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

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

The data

..and what we make of it

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

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

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

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

How to get from this…..

….to this?

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

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

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?

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

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

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

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