This spring, the BMS-ANed organises an in-person meeting:

Hans van Houwelingen award ceremony and symposium
Date: June 19th 2025
Time: 13:00-18:00 (CEST)
Location: “Vredenburg 19”, Vredenburg 19, 3511 BB, Utrecht

Registration is required and is through the form below.

Public transport
From train station Utrecht Central, Vredenbrug 19 is a 3-4 minute walk.

Speakers

Tomasz Burzykowski – Hasselt University
Paul Blanche – University of Copenhagen
Jessica Barrett – University of Cambridge

Schedule

13:00-13:15             Coffee

13:15-13:55             BMS-ANed General Assembly (ALV)

13:55-14:10             Break

14:10-14:15             Opening

14:15-14:30             HvH award ceremony: Jessica Barrett (University of Cambridge)

14:30-15:15             Tomasz Burzykowski (UHasselt)

Cautionary remarks on the use of the net treatment benefit as a treatment-effect measure

15:15-15:35             Break

15:35-16:20            Paul Blanche (University of Copenhagen)

Sample size recalculation in adaptive group sequential study designs for comparing restricted mean survival times

16:20-17:05             Jessica Barrett (University of Cambridge)

Using electronic health records for healthcare research: Potentials and Pitfalls

17:05-17:10             Closing

17:10-18:00             Drinks

Abstracts

Paul Blanche – Sample size recalculation in adaptive group sequential study designs for comparing restricted mean survival times

Delayed treatment effects are frequently expected in clinical trials with time-to-event endpoints (e.g. cardiology, oncology). The relevance of hazard ratios to quantify the treatment effect is questionable and potentially misleading in this context. Hence, alternative methods comparing restricted mean survival times are increasingly promoted. Specific challenges arise when planning clinical trials for comparing restricted mean survival times, as several nuisance parameter estimates are needed for sample size calculation. Precise estimates might be difficult to obtain at the planning stage and might lead to underpowered trials (or, the other way around, to larger sample sizes than needed). One way of dealing with this insecurity is to apply adaptive group sequential study designs with the option to adapt the sample size during the trial, based on data observed at an interim analysis. In this talk, we consider such sample size adaptations in a specific context of expected delayed treatment effect that we met during consultation activities. A combination test approach is described to deal with the data-driven choice of sample size recalculation at interim analysis.

Tomasz Burzykowski – Cautionary remarks on the use of the net treatment benefit as a treatment-effect measure

Generalized pairwise comparisons (GPC) is a non-parametric method designed to quantify the benefit of a new treatment, as compared to a control one, by using a set of hierarchically-ordered endpoints. GPC yields an estimate of the treatment effect, the so-called net treatment benefit (NTB). For a single endpoint, NTB is simply the difference between the probability that the value of the endpoint for the experimental treatment is clinically “better” than for the control treatment and the probability that the value of the endpoint for the control treatment is “better”.

In the presentation, we take a critical look at some of the properties of NTB that may be important from a point of view of using it as a treatment-effect measure in randomized clinical trials. For instance, NTB may be trial‑specific, because it depends on the variability of endpoints. As a result, it may be difficult to compare the estimated values of NTB across different trials.

Jessica Barrett – Using electronic health records for healthcare research: Potentials and Pitfalls

Routinely collected healthcare data is becoming more commonly used for healthcare research. The increasing availability of such data promises advantages in the shape of largescale, representative data, but also brings many challenges which require statistical innovation. I will highlight some of these promises and challenges using four examples illustrating the use of routinely collected data, including modelling lung function trajectories of cystic fibrosis patients, dynamic prediction of cardiovascular disease, multi-state modelling of multimorbidity and predicting outcomes for intensive care patients.

Registration