arXiv subjects

Stefan Michiels

Publications and source records attributed to Stefan Michiels.

8 recordsLinked to original sources

arXiv2026

Estimating treatment duration effects via clone-censor-weight: a breast cancer case study

In this work, we study the estimation of treatment duration effects in observational survival data, where treatment and covariate histories evolve over time and longer observed durations are only attainable among individuals who remain event-free and under follow-up, leading to immortal time bias under naive analyses. The cloning-censoring-weighting (CCW) framework provides a practical approach to emulate target trials of treatment duration strategies, but several methodological aspects remain insufficiently understood. We focus on static treatment duration strategies under two settings of increasing complexity: baseline confounding only, and confounding with time-varying covariates. We formalize the assumptions underlying CCW, with particular emphasis on treatment admissibility, relaxed intervention rules, and the distinction between artificial and natural censoring. We then compare several estimation approaches after cloning and censoring, including inverse probability of censoring weighting (IPCW), the G-formula, and doubly robust estimators, through simulation studies assessing robustness, variability, and sensitivity to censoring model misspecification. Finally, we apply the framework to a Breast Cancer cohort to emulate a target trial comparing 2 versus 5 years of adjuvant tamoxifen in early stage breast cancer. Due to the small number of events and limited support for the 2-year strategy, estimates are associated with substantial uncertainty. These findings highlight both the practical relevance and the limitations of CCW, and underscore the importance of sensitivity analyses in complex longitudinal observational settings.

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arXiv2025

Evaluation of machine-learning models to measure individualized treatment effects from randomized clinical trial data with time-to-event outcomes

Objective: In randomized clinical trials, prediction models can be used to explore the relationships between patients' variables (e.g., clinical, pathological, or lifestyle variables, and also biomarker or genomic data) and treatment effect magnitude. Our aim was to evaluate flexible machine learning models capable of incorporating interactions and nonlinear effects from high-dimensional data to estimate individualized treatment recommendations in trials with time-to-event outcomes. Methods: We compared survival models based on neural networks (CoxCC and CoxTime) and random survival forests (Interaction Forests) against a Cox proportional hazards model with an adaptive LASSO (ALASSO) penalty as a benchmark. For individualized treatment recommendations in the survival setting, we adapted metrics originally designed for binary outcomes to accommodate time-to-event data with censoring. These adapted metrics included the C-for-Benefit, the E50-for-Benefit, and the root mean squared error for treatment benefit. An extensive simulation study was conducted using two different data generation processes incorporating nonlinearity and interactions. The models were applied to gene expression and clinical data from three cancer clinical trial data sets. Results: In the first data generation process, neural networks outperformed ALASSO in terms of calibration while the Interaction Forests showed superior C-for-benefit performance. In the second data generation process, both machine learning methods outperformed the benchmark linear ALASSO method across discrimination, calibration, and RMSE metrics. In the cancer trial data sets, the machine learning methods often performed better than ALASSO, particularly IF in terms of C-for-benefit, and either a neural network or IF for calibration measures addressing treatment benefit.

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arXiv2022

CustOmics: A versatile deep-learning based strategy for multi-omics integration

Recent advances in high-throughput sequencing technologies have enabled the extraction of multiple features that depict patient samples at diverse and complementary molecular levels. The generation of such data has led to new challenges in computational biology regarding the integration of high-dimensional and heterogeneous datasets that capture the interrelationships between multiple genes and their functions. Thanks to their versatility and ability to learn synthetic latent representations of complex data, deep learning methods offer promising perspectives for integrating multi-omics data. These methods have led to the conception of many original architectures that are primarily based on autoencoder models. However, due to the difficulty of the task, the integration strategy is fundamental to take full advantage of the sources' particularities without losing the global trends. This paper presents a novel strategy to build a customizable autoencoder model that adapts to the dataset used in the case of high-dimensional multi-source integration. We will assess the impact of integration strategies on the latent representation and combine the best strategies to propose a new method, CustOmics (https://github.com/HakimBenkirane/CustOmics). We focus here on the integration of data from multiple omics sources and demonstrate the performance of the proposed method on test cases for several tasks such as classification and survival analysis.

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arXiv2021

Penalized Poisson model for network meta-analysis of individual patient time-to-event data

Network meta-analysis (NMA) allows the combination of direct and indirect evidence from a set of randomized clinical trials. Performing NMA using individual patient data (IPD) is considered as a "gold standard" approach as it provides several advantages over NMA based on aggregate data. For example, it allows to perform advanced modelling of covariates or covariate-treatment interactions. An important issue in IPD NMA is the selection of influential parameters among terms that account for inconsistency, covariates, covariate-by-treatment interactions or non-proportionality of treatments effect for time to event data. This issue has not been deeply studied in the literature yet and in particular not for time-to-event data. A major difficulty is to jointly account for between-trial heterogeneity which could have a major influence on the selection process. The use of penalized generalized mixed effect model is a solution, but existing implementations have several shortcomings and an important computational cost that precludes their use for complex IPD NMA. In this article, we propose a penalized Poisson regression model to perform IPD NMA of time-to-event data. It is based only on fixed effect parameters which improve its computational cost over the use of random effects. It could be easily implemented using existing penalized regression package. Computer code is shared for implementation. The methods were applied on simulated data to illustrate the importance to take into account between trial heterogeneity during the selection procedure. Finally, it was applied to an IPD NMA of overall survival of chemotherapy and radiotherapy in nasopharyngeal carcinoma.

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arXiv2021

Evaluating methods for Lasso selective inference in biomedical research by a comparative simulation study

Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory which assumes a fixed set of covariates in the model. We review two interpretations of inference after selection: the full model view, in which the parameters of interest are those of the full model on all predictors, and then focus on the submodel view, in which the parameters of interest are those of the selected model only. In the context of L1-penalized regression we compare proposals for submodel inference (selective inference) via confidence intervals available to applied researchers via software packages using a simulation study inspired by real data commonly seen in biomedical studies. Furthermore, we present an exemplary application of these methods to a publicly available dataset to discuss their practical usability. Our findings indicate that the frequentist properties of selective confidence intervals are generally acceptable, but desired coverage levels are not guaranteed in all scenarios except for the most conservative methods. The choice of inference method potentially has a large impact on the resulting interval estimates, thereby necessitating that the user is acutely aware of the goal of inference in order to interpret and communicate the results. Currently available software packages are not yet very user friendly or robust which might affect their use in practice. In summary, we find submodel inference after selection useful for experienced statisticians to assess the importance of individual selected predictors in future applications.

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arXiv2020

Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention

Segmentation and accurate localization of nuclei in histopathological images is a very challenging problem, with most existing approaches adopting a supervised strategy. These methods usually rely on manual annotations that require a lot of time and effort from medical experts. In this study, we present a self-supervised approach for segmentation of nuclei for whole slide histopathology images. Our method works on the assumption that the size and texture of nuclei can determine the magnification at which a patch is extracted. We show that the identification of the magnification level for tiles can generate a preliminary self-supervision signal to locate nuclei. We further show that by appropriately constraining our model it is possible to retrieve meaningful segmentation maps as an auxiliary output to the primary magnification identification task. Our experiments show that with standard post-processing, our method can outperform other unsupervised nuclei segmentation approaches and report similar performance with supervised ones on the publicly available MoNuSeg dataset. Our code and models are available online to facilitate further research.

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arXiv2018

Estimating causal effects of time-dependent exposures on a binary endpoint in a high-dimensional setting

Recently, the intervention calculus when the DAG is absent (IDA) method was developed to estimate lower bounds of causal effects from observational high-dimensional data. Originally it was introduced to assess the effect of baseline biomarkers which do not vary over time. However, in many clinical settings, measurements of biomarkers are repeated at fixed time points during treatment exposure and, therefore, this method need to be extended. The purpose of this paper is then to extend the first step of the IDA, the Peter Clarks (PC)-algorithm, to a time-dependent exposure in the context of a binary outcome. We generalised the PC-algorithm for taking into account the chronological order of repeated measurements of the exposure and propose to apply the IDA with our new version, the chronologically ordered PC-algorithm (COPC-algorithm). A simulation study has been performed before applying the method for estimating causal effects of time-dependent immunological biomarkers on toxicity, death and progression in patients with metastatic melanoma. The simulation study showed that the completed partially directed acyclic graphs (CPDAGs) obtained using COPC-algorithm were structurally closer to the true CPDAG than CPDAGs obtained using PC-algorithm. Also, causal effects were more accurate when they were estimated based on CPDAGs obtained using COPC-algorithm. Moreover, CPDAGs obtained by COPC-algorithm allowed removing non-chronologic arrows with a variable measured at a time t pointing to a variable measured at a time t' where t'< t. Bidirected edges were less present in CPDAGs obtained with the COPC-algorithm, supporting the fact that there was less variability in causal effects estimated from these CPDAGs. The COPC-algorithm provided CPDAGs that keep the chronological structure present in the data, thus allowed to estimate lower bounds of the causal effect of time-dependent biomarkers.

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arXiv2017

Tutorial in Joint Modeling and Prediction: a Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event

Extensions in the field of joint modeling of correlated data and dynamic predictions improve the development of prognosis research. The R package frailtypack provides estimations of various joint models for longitudinal data and survival events. In particular, it fits models for recurrent events and a terminal event (frailtyPenal), models for two survival outcomes for clustered data (frailtyPenal), models for two types of recurrent events and a terminal event (multivPenal), models for a longitudinal biomarker and a terminal event (longiPenal) and models for a longitudinal biomarker, recurrent events and a terminal event (trivPenal). The estimators are obtained using a standard and penalized maximum likelihood approach, each model function allows to evaluate goodness-of-fit analyses and plots of baseline hazard functions. Finally, the package provides individual dynamic predictions of the terminal event and evaluation of predictive accuracy. This paper presents theoretical models with estimation techniques, applies the methods for predictions and illustrates frailtypack functions details with examples.

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