Many trials are affected by unforeseen events after recruitment has commenced. The aim of this study is to explore a hypothetical strategy for dealing with an intercurrent event that occurred during trial follow-up; COVID-19 restrictions. Analysis was conducted using multiple imputation.

This article conducted a secondary analysis of the VANISH RCT, which compared the early use of vasopressin with norepinephrine on renal failure-free survival for patients with septic shock at 28 days. This was done using classical (separate tests for interaction with Bonferroni correction), data-adaptive (hierarchical lasso regression), and non-parametric causal machine learning (causal forest) methods to analyse HTEs for the primary outcome of being alive at 28 days. The modal initial (root) splits of the causal forest were extracted, and the mean value was used as a threshold to partition the population into subgroups with different treatment effects.

This paper formulates and describes two algorithms for implementing reference-based multiple imputation for longitudinal binary outcome data using: (i) joint modeling with the multivariate normal distribution and an adaptive rounding algorithm and (ii) joint modeling with a latent multivariate normal model. A simulation study was performed to compare the properties of the two methods.

Five years on from the publication of the ICH E9 (R1), this fun but informative article uses a trial conducted by Santa Clause to explain the importance of estimands to clearly specifying the precise research targeted in a trial.

This paper provides an overview of the evolution of Phase 3 vaccine trial design and statistical analysis methods from traditional to more innovative contemporary methods.

This work provides a starting point on preferred terminology by patients and the public to describe potential harmful intervention effects. Whilst researchers have tried to seek agreement, public partners endorsed use of different terms for different situations. We highlight some key areas for improvement in public facing materials that are necessary to avoid miscommunication and incorrect perception of harm.

A systematic review to evaluate how user engagement data is reported and considered in the efficacy analysis of digital mental health interventions during randomised controlled trials.

The objectives of this research were threefold: first, to develop a framework for identifying spin associated with harms in systematic reviews of interventions; second, to apply the framework to a set of reviews, thereby pinpointing instances where spin may be present; and finally, to revise the spin examples, offering guidance on how spin can be rectified.

This paper introduces a novel multiple imputation method for estimating a treatment policy estimand with missing data, referred to as retrieved dropout reference-base centred multiple imputation.

Statistical methods for signal detection of adverse drug reactions (ADRs) in electronic health records (EHRs) need information about optimal significance levels and sample sizes to achieve sufficient power. Sauzet and Cornelius proposed tests for signal detection based on the hazard functions of Weibull type distributions (WSP tests) which use the time-to-event information available in EHRs. Optimal significance levels and sample sizes for the application of the WPS tests are derived.