# Benefits of group sequential design and sample size re-estimation for randomised controlled trials evaluating the prevention of ventilator-associated pneumonia: a simulation study informed by real world data

## TL;DR

This study shows that adaptive trial designs can make VAP prevention trials more efficient by reducing the number of patients needed.

## Contribution

The paper demonstrates how group sequential and sample size re-estimation designs improve trial efficiency for VAP prevention.

## Key findings

- Pocock boundaries reduce expected sample size but increase maximum sample size compared to O’Brien Fleming boundaries.
- Sample size re-estimation maintains power better when initial prevention effect assumptions are incorrect.
- Adaptive designs can reduce expected sample size by 9-12% in VAP prevention trials.

## Abstract

Ventilator-associated pneumonia (VAP) is an important healthcare acquired infection, which is associated with high morbidity and mortality. Conducting conventional randomised controlled trials (RCTs) on VAP prevention is often challenging, due to low numbers of eligible patients and events per site, especially for pathogen-specific interventions. We explored how group sequential designs (GSD) and sample size re-estimation (SSR) trial designs could improve RCT efficiency in simulated superiority trials to prevent VAP.

Simulations were informed using data from the prospective observational Hospital Network Study – Preparation for a Randomised Evaluation of anti-Pneumonia Strategies (HONEST-PREPS). We tested the impact of different GSD and SSR designs on expected sample size (considering early stopping) and maximum sample size (no early stopping). We varied the type of stopping boundary, number and timepoint of interim analyses, and assumed and true prevention effect. We applied time-to-event analyses, with effect estimates expressed as hazard ratios, for the primary endpoint.

The estimated 28-day cumulative incidence of VAP in HONEST-PREPS was 15.5%. For a 30% reduction in VAP (hazard ratio of 0.68), a standard RCT (power 80%) would require a sample size of 1291 patients. For GSD, Pocock boundaries result in a smaller expected sample size (E[N] = 1128), but a larger maximum sample size (max(N) = 1578) than O’Brien Fleming boundaries (E[N] = 1170 and max(N) = 1389), when utilising the optimal placement of a single interim analysis, 48% and 64% of the maximum number of events for Pocock and O’Brien Fleming boundaries, respectively. SSR is more efficient compared to GSD when the incorrect prevention effect is initially used to plan the trial, as it maintains a power closer to the pre-specified desired power without substantial impact on the expected sample size.

GSD and SSR are effective adaptive designs, preferable to fixed RCTs in a superiority trial comparing the effectiveness of an investigational intervention with a standard of care in preventing VAP among critically ill, ventilated patients. They can reduce the expected sample size between 9% and 12% and should be considered at the trial design stage.

The online version contains supplementary material available at 10.1186/s12874-025-02681-4.