arXiv subjects

Michal Abrahamowicz

Publications and source records attributed to Michal Abrahamowicz.

6 recordsLinked to original sources

arXiv2026

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

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.

stat.ME↗

arXiv2026

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

This article describes the design of a neutral comparison study in the context of empirical studies where the interest is in learning the functional relationship between a continuous errorprone exposure variable and a binary outcome. The performance of combinations of measurement error correction methods and flexible regression modeling techniques was compared using a simulation study. The project involved four independent teams, one devoted to data generation and evaluation, the other three to specific methods of measurement error correction (Simulation-Extrapolation, Regression-Calibration and Multiple imputation, Bayesian method). The study was conducted in three successive stages. In Stage 1, the first team simulated five datasets differing only by the true exposure-outcome functional form and distribution of true exposure. Furthermore, the implementation of flexible modeling methods (B-splines, P-splines, and fractional polynomials) was standardized. The three methods teams, blinded to the underlying data generation process, created the codes to implement their methods, and provided their results to the first team who evaluated them. These codes were then used by this team in the next Stages of the project. In Stage 2, the team simulated 150 additional datasets where other design parameters varied while using the same five exposureoutcome functions. Stage 3 consisted of simulating independent replications of each of the 150 scenarios considered in Stage 2 to quantify the sampling variance of the estimates. This work emphasizes the relevance of neutral comparison studies to fairly evaluate statistical methods aimed at addressing a complex analytical challenge, and demonstrates their feasibility through a large collaborative project.

stat.ME↗

arXiv2025

A categorization of performance measures for estimated non-linear associations between an outcome and continuous predictors

In regression analysis, associations between continuous predictors and the outcome are often assumed to be linear. However, modeling the associations as non-linear can improve model fit. Many flexible modeling techniques, like (fractional) polynomials and spline-based approaches, are available. Such methods can be systematically compared in simulation studies, which require suitable performance measures to evaluate the accuracy of the estimated curves against the true data-generating functions. Although various measures have been proposed in the literature, no systematic overview exists so far. To fill this gap, we introduce a categorization of performance measures for evaluating estimated non-linear associations between an outcome and continuous predictors. This categorization includes many commonly used measures. The measures can not only be used in simulation studies, but also in application studies to compare different estimates to each other. We further illustrate and compare the behavior of different performance measures through some examples and a Shiny app.

stat.ME↗

arXiv2025

An Overview and Recent Developments in the Analysis of Multistate Processes

Multistate models offer a powerful framework for studying disease processes and can be used to formulate intensity-based and more descriptive marginal regression models. They also represent a natural foundation for the construction of joint models for disease processes and dynamic marker processes, as well as joint models incorporating random censoring and intermittent observation times. This article reviews the ways multistate models can be formed and fitted to life history data. Recent work on pseudo-values and the incorporation of random effects to model dependence on the process history and between-process heterogeneity are also discussed. The software available to facilitate such analyses is listed.

stat.ME↗

arXiv2020

Analysis of time-to-event for observational studies: Guidance to the use of intensity models

This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.

stat.ME↗

arXiv2019

State-of-the-art in selection of variables and functional forms in multivariable analysis -- outstanding issues

How to select variables and identify functional forms for continuous variables is a key concern when creating a multivariable model. Ad hoc 'traditional' approaches to variable selection have been in use for at least 50 years. Similarly, methods for determining functional forms for continuous variables were first suggested many years ago. More recently, many alternative approaches to address these two challenges have been proposed, but knowledge of their properties and meaningful comparisons between them are scarce. To define a state-of-the-art and to provide evidence-supported guidance to researchers who have only a basic level of statistical knowledge many outstanding issues in multivariable modelling remain. Our main aims are to identify and illustrate such gaps in the literature and present them at a moderate technical level to the wide community of practitioners, researchers and students of statistics. We briefly discuss general issues in building descriptive regression models, strategies for variable selection, different ways of choosing functional forms for continuous variables, and methods for combining the selection of variables and functions. We discuss two examples, taken from the medical literature, to illustrate problems in the practice of modelling. Our overview revealed that there is not yet enough evidence on which to base recommendations for the selection of variables and functional forms in multivariable analysis. Such evidence may come from comparisons between alternative methods. In particular, we highlight seven important topics that require further investigation and make suggestions for the direction of further research.

stat.ME↗