The graph-based approach to multiple testing is an intuitive method that enables a study team to represent clearly, through a directed graph, its priorities for hierarchical testing of multiple hypot...

New article "The validation of prediction models deserves more recognition" published in BMC Medicine 2025.03.24 Begüm Irmak Ön and Ulrich M…

While well-established methods for time-to-event data are available when the proportional hazards assumption holds, there is no consensus on the best inferential approach under non-proportional hazar...

First schooling at Wissenschaftszentrum Schloss Reisensburg 2025.02.01 The 1st schooling of the SHARE-CTD project took place in person with internatio…

Large language models (LLMs) are being increasingly incorporated into scientific workflows. However, we have yet to fully grasp the implications of...

The graph based approach to multiple testing is an intuitive method that enables a study team to represent clearly, through a directed graph, its priorities for hierarchical testing of multiple hypoth...

Professor Dr. Amy Racine-Poon is best known for her interdisciplinary contributions as an applied Bayesian statistician in the pharmaceutical industry and healthcare. She was born in Hong Kong and ob...

To describe the priors and decision thresholds in phase 2 and 3 RCTs evaluating drug efficacy using Bayesian methods.

When developing a clinical prediction model, the sample size of the development dataset is a key consideration. Small sample sizes lead to greater concerns of overfitting, instability, poor performanc...

Conduct internal validation of a clinical prediction model for a binary outcome. Produce bias corrected performance metrics (c-statistic, calibration slope) via bootstrap (simple bootstrap, boots...

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YouTube video by Bennett Institute for Applied Data Science

Background: For RCTs with time-to-event endpoints, proportional hazard (PH) models are typically used to estimate treatment effects and logrank tests are commonly used for hypothesis testing. There is...

Error-correction feat shows quantum computers will get more accurate as they grow larger.

Stephen Senn Consultant Statistician Edinburgh The intellectual illness of clinical drug evaluation that I have discussed here can be cured, and it will be cured when we restore intellectual primac…

Background Clinical prediction models are widely used in health and medical research. The area under the receiver operating characteristic curve (AUC) is a frequently used estimate to describe the dis...

It’s not too late to register for our 2nd Annual OpenSAFELY Symposium, which is happening in London on 25/26 November.

Marginal structural models (MSMs) are often used to estimate causal effects of treatments on survival time outcomes from observational data when time-dependent confounding may be present. They can be...

Observing the Truth? If you wish to estimate the change in an outcome Y that accompanies a change in a predictor X and it is the relationship between the expected value of Y and the true value of X th...

In adaptive clinical trials, the conventional confidence interval (CI) for a treatment effect is prone to undesirable properties such as undercoverage and potential inconsistency with the final hypoth...

Regulatory guidance notes the need for caution in the interpretation of confidence intervals (CIs) constructed during and after an adaptive clinical trial. Conventional CIs of the treatment effects ar...