Methods for adjusting for covariate measurement error in flexible modelling of functional form: results of a blinded, controlled neutral comparison simulation study

Mohammed Sedki; Aris Perperoglou; Anne C. M. Thiébaut; Steve Ferreira Guerra; Paul Gustafson; Frank E. Harrell Jr; Willi Sauerbrei; Michal Abrahamowicz; Laurence S. Freedman

arXiv · 2606.10123

Methods for adjusting for covariate measurement error in flexible modelling of functional form: results of a blinded, controlled neutral comparison simulation study

Mohammed Sedki·Aris Perperoglou·Anne C. M. Thiébaut·Steve Ferreira Guerra·Paul Gustafson·Frank E. Harrell Jr·Willi Sauerbrei·Michal Abrahamowicz·Laurence S. Freedman

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Abstract

Covariate measurement error is pervasive in epidemiological research and distorts estimated exposure-outcome associations, yet correction methods have been studied almost exclusively under linear modelling assumptions. Their behaviour when the underlying association is non-linear and is itself estimated with flexible regression, remains poorly characterised. We report a blinded, multi-stage neutral comparison simulation study, conducted within the STRATOS initiative, evaluating measurement error correction coupled with flexible modelling of functional form. Six families of correction methods (pointwise and coefficient-based Simulation Extrapolation [SIMEX], Bayesian inference on the logit and risk scales, Multiple Imputation [MI], and Regression Calibration [RC]) were each combined with B-splines (BS), penalised splines (PS), fractional polynomials (FP), and natural splines (NS), yielding 23 analytic methods. Methods were applied to case-control data generated under five functional forms (J-shape, linear, two threshold models, and saturation) across simulated datasets spanning varying sample sizes, replication substudy sizes, error magnitudes, and error distributions, with classical additive error and a replication substudy for error calibration. Performance was assessed by the log mean squared error of the estimated function over the central 95 % of the exposure distribution. Pointwise SIMEX was the most accurate and most robust approach overall, followed by Bayesian methods and RC when paired with PS, FP, or NS; MI performed less well, and Bayesian estimation with unpenalised BS performed worst. PS, FP, and NS were near-equivalent, whereas BS was consistently inferior. No single method dominated across all scenarios, underscoring the value of sensitivity analyses.

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Mohammed Sedki, Aris Perperoglou, Anne C. M. Thiébaut, Steve Ferreira Guerra, Paul Gustafson, Frank E. Harrell Jr, Willi Sauerbrei, Michal Abrahamowicz, Laurence S. Freedman. 2026-06-08. Methods for adjusting for covariate measurement error in flexible modelling of functional form: results of a blinded, controlled neutral comparison simulation study. https://arxiv.org/abs/2606.10123

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