3 citations · 6 across the 3 of their papers we have counts for
4 papers
Lord's 'paradox' explained: the 50-year warning on the use of 'change scores' in observational data
Peter W. G. Tennant, Georgia D. Tomova, Eleanor J. Murray +3
In 1967, Frederick Lord posed a conundrum that has confused scientists for over half a century. Subsequently named Lord's 'paradox', the puzzle centres on the observation that two…
Application of Cox Model to predict the survival of patients with Chronic Heart Failure: A latent class regression approach
John Mbotwa, Marc de Kamps, Paul D. Baxter +1
Most prediction models that are used in medical research fail to accurately predict health outcomes due to methodological limitations. Using routinely collected patient data, we ex…
Analyses of 'change scores' do not estimate causal effects in observational data
Peter W. G. Tennant, Kellyn F. Arnold, George T. H. Ellison +1
Background: In longitudinal data, it is common to create 'change scores' by subtracting measurements taken at baseline from those taken at follow-up, and then to analyse the result…
Generalised linear models for prognosis and intervention: Theory, practice, and implications for machine learning
Kellyn F. Arnold, Vinny Davies, Marc de Kamps +3
Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between pr…