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.

翻译：协变量测量误差在流行病学研究中普遍存在，并会导致暴露-结局关联估计的偏倚，然而校正方法几乎仅在线性建模假设下得到研究。当潜在关联呈非线性且其本身通过灵活回归进行估计时，这些方法的性能仍缺乏充分表征。我们报告了一项在STRATOS计划框架内开展的多阶段盲法中立的比较模拟研究，旨在评估结合灵活函数形式建模的测量误差校正方法。六类校正方法（逐点法与基于系数的模拟外推法[SIMEX]、在logit和风险尺度上的贝叶斯推断、多重插补法[MI]和回归校准法[RC]）分别与B样条（BS）、惩罚样条（PS）、分数多项式（FP）和自然样条（NS）组合，共形成23种分析方法。这些方法应用于五种函数形式（J形、线性、两种阈值模型和饱和模型）生成的病例-对照数据，模拟数据集涵盖不同样本量、重复子研究规模、误差幅度和误差分布，采用经典加性误差模型和重复子研究进行误差校准。通过暴露分布中间95%区间内估计函数的对数均方误差评估性能。逐点SIMEX在整体准确性和稳健性方面表现最优，其次为与PS、FP或NS联合使用的贝叶斯方法及RC；MI表现较差，而基于未惩罚BS的贝叶斯估计表现最差。PS、FP和NS性能近似，而BS始终处于劣势。在所有场景中，没有任何单一方法占绝对优势，这凸显了敏感性分析的重要性。