E
Emmanuel O. Ogundimu
Researcher at University of Oxford
Publications - 19
Citations - 932
Emmanuel O. Ogundimu is an academic researcher from University of Oxford. The author has contributed to research in topics: Sample size determination & Selection (genetic algorithm). The author has an hindex of 9, co-authored 18 publications receiving 676 citations. Previous affiliations of Emmanuel O. Ogundimu include Northumbria University & Durham University.
Papers
More filters
Journal ArticleDOI
Sample size considerations for the external validation of a multivariable prognostic model: a resampling study.
TL;DR: This study suggests that externally validating a prognostic model requires a minimum of 100 events and ideally 200 (or more) events, and provides guidance on sample size for investigators designing an external validation study.
Journal ArticleDOI
Adequate sample size for developing prediction models is not simply related to events per variable.
TL;DR: The results indicated that an EPV rule of thumb should be data driven and that EPV ≥ 20 generally eliminates bias in regression coefficients when many low-prevalence predictors are included in a Cox model.
Journal ArticleDOI
Quantifying the impact of different approaches for handling continuous predictors on the performance of a prognostic model.
TL;DR: Three broad approaches for handling continuous predictors are examined, including various methods of categorising predictors, modelling a linear relationship between the predictor and outcome and modelling a nonlinear relationship using fractional polynomials or restricted cubic splines.
Journal ArticleDOI
Lower limb arthroplasty: can we produce a tool to predict outcome and failure, and is it cost-effective? An epidemiological study
Nigel K Arden,Doug G Altman,David J Beard,Andrew Carr,Nicholas Clarke,Gary S. Collins,Cyrus Cooper,David Culliford,Antonella Delmestri,Stefanie Garden,Tinatin Griffin,Kassim Javaid,Andrew Judge,Latham Jm,Mark Mullee,David W. Murray,Emmanuel O. Ogundimu,Rafael Pinedo-Villanueva,Andrew Price,Daniel Prieto-Alhambra,James Raftery +20 more
TL;DR: The strongest predictors for poor outcomes were preoperative pain/function scores, deprivation, age, mental health score and radiographic variable pattern of joint space narrowing and osteoarthritis severity and migration pattern of the hip predicted patient-reported outcome measures.
Journal ArticleDOI
A Sample Selection Model with Skew-normal Distribution
TL;DR: In this article, a generalized Heckman selection model was proposed to capture spurious skewness in bounded scores, and in modelling data where logarithm transformation could not mitigate the effect of inherent skewness in the outcome variable.