26 citations · 29 across the 4 of their papers we have counts for
6 papers
DeepJoint: Robust Survival Modelling Under Clinical Presence Shift
Vincent Jeanselme, Glen Martin, Niels Peek +3
Observational data in medicine arise as a result of the complex interaction between patients and the healthcare system. The sampling process is often highly irregular and itself co…
A scoping review of causal methods enabling predictions under hypothetical interventions
Lijing Lin, Matthew Sperrin, David A. Jenkins +2
Background and Aims: The methods with which prediction models are usually developed mean that neither the parameters nor the predictions should be interpreted causally. However, wh…
Clinical Prediction Models to Predict the Risk of Multiple Binary Outcomes: a comparison of approaches
Glen P. Martin, Matthew Sperrin, Kym I. E. Snell +2
Clinical prediction models (CPMs) are used to predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome.…
Towards a Framework for the Design, Implementation and Reporting of Methodology Scoping Reviews
Glen P. Martin, David Jenkins, Lucy Bull +10
Background: In view of the growth of published papers, there is an increasing need for studies that summarise scientific research. An increasingly common review is a 'Methodology s…
Examining the impact of data quality and completeness of electronic health records on predictions of patients risks of cardiovascular disease
Yan Li, Matthew Sperrin, Glen P. Martin +2
The objective is to assess the extent of variation of data quality and completeness of electronic health records and impact on the robustness of risk predictions of incident cardio…
Using marginal structural models to adjust for treatment drop-in when developing clinical prediction models
Matthew Sperrin, Glen Martin, Tjeerd Van Staa +2
Objectives: Clinical prediction models (CPMs) can inform decision-making concerning treatment initiation. Here, one requires predicted risks assuming that no treatment is given. Th…