arXiv subjects

Virginie Rondeau

Publications and source records attributed to Virginie Rondeau.

8 recordsLinked to original sources

arXiv2026

Correlated frailty model for analysis of genetic association in family studies

Family-based study designs allow the investigation of gene mutation effects on a disease risk by considering related family members. Some methods have been developed for testing sets of genetic variants in family studies but only very few can handle right-censored time-to-event data. We propose here a correlated frailty model for the analysis of a survival outcome related to cancer in presence of familial correlations. These familial correlations are explained by a residual familial component specified by a kinship matrix and a region- or gene-based specific correlation structure modeled via identical-by-descent (IBD) probability matrix. The proposed approach is used to quantify and evaluate the association between a set of common single nucleotide polymorphism (SNPs) or rare variants (or both) from the same genomic region and a survival outcome, e.g. time to disease onset. The model's marginal likelihood is maximized using the Marquardt algorithm. We evaluated the method by simulations under various scenarios where we varied the family size, the strength of genetic associations from multiple rare variants and the presence or not of residual familial correlation. The results indicate that the correlated frailty model can be valuable in family cancer studies, for example to identify genomic regions significantly associated with the time to cancer onset.

stat.AP↗

arXiv2026

Empirical comparison of win ratio and joint frailty models for recurrent event endpoints with applications in oncology and cardiology

Composite endpoints that combine recurrent non-fatal events with a terminal event are increasingly used in randomized clinical trials, yet conventional time-to-first event analyses may obscure clinically relevant information. We compared two statistical frameworks tailored to such endpoints: the joint frailty model (JFM) and the last-event assisted recurrent-event win ratio (LWR). The JFM specifies proportional hazards for the recurrent and terminal events linked through a shared frailty, yielding covariate-adjusted, component-specific hazard ratios that account for informative recurrences and dependence with death. The LWR is a nonparametric, prioritized pairwise comparison that incorporates all observed events over follow-up and summarizes a population-level benefit of treatment while respecting a pre-specified hierarchy between death and recurrences. We first assessed the performance of the methods using simulations that varied both the gamma-frailty variance and the event rates. We next illustrated both approaches using two clinical application examples in oncology and cardiology, highlighting how conclusions depend on whether treatment primarily affects recurrent events, mortality, or both. The JFM provided component-specific estimates, while the LWR led to a summary measure of treatment effect with direction. Power was systematically improved with JFM, which thus appeared as the most reliable approach for inference and sample size estimation. Methodological extensions of the LWR to appropriately handle censoring and to formalize causal estimands remain a promising direction for future research.

stat.ME↗

arXiv2025

The DeepJoint algorithm: An innovative approach for studying the longitudinal evolution of quantitative mammographic density and its association with screen-detected breast cancer risk

Mammographic density is a dynamic risk factor for breast cancer and affects the sensitivity of mammography-based screening. While automated machine and deep learning-based methods provide more consistent and precise measurements compared to subjective BI-RADS assessments, they often fail to account for the longitudinal evolution of density. Many of these methods assess mammographic density in a cross-sectional manner, overlooking correlations in repeated measures, irregular visit intervals, missing data, and informative dropouts. Joint models, however, are well-suited for capturing the longitudinal relationship between biomarkers and survival outcomes. We present the DeepJoint algorithm, an open-source solution that integrates deep learning for quantitative mammographic density estimation with joint modeling to assess the longitudinal relationship between mammographic density and breast cancer risk. Our method efficiently analyzes processed mammograms from various manufacturers, estimating both dense area and percent density--established risk factors for breast cancer. We utilize a joint model to explore their association with breast cancer risk and provide individualized risk predictions. Bayesian inference and the Monte Carlo consensus algorithm make the approach reliable for large screening datasets. Our method allows for accurate analysis of processed mammograms from multiple manufacturers, offering a comprehensive view of breast cancer risk based on individual longitudinal density profiles. The complete pipeline is publicly available, promoting broader application and comparison with other methods.

stat.AP↗

arXiv2025

The LongiMam model for improved breast cancer risk prediction using longitudinal mammograms

Risk-adapted breast cancer screening requires robust models that leverage longitudinal imaging data. Most current deep learning models use single or limited prior mammograms and lack adaptation for real-world settings marked by imbalanced outcome distribution and heterogeneous follow-up. We developed LongiMam, an end-to-end deep learning model that integrates both current and up to four prior mammograms. LongiMam combines a convolutional and a recurrent neural network to capture spatial and temporal patterns predictive of breast cancer. The model was trained and evaluated using a large, population-based screening dataset with disproportionate case-to-control ratio typical of clinical screening. Across several scenarios that varied in the number and composition of prior exams, LongiMam consistently improved prediction when prior mammograms were included. The addition of prior and current visits outperformed single-visit models, while priors alone performed less well, highlighting the importance of combining historical and recent information. Subgroup analyses confirmed the model's efficacy across key risk groups, including women with dense breasts and those aged 55 years or older. Moreover, the model performed best in women with observed changes in mammographic density over time. These findings demonstrate that longitudinal modeling enhances breast cancer prediction and support the use of repeated mammograms to refine risk stratification in screening programs. LongiMam is publicly available as open-source software.

cs.CV↗

arXiv2025

Tutorial for Surrogate Endpoint Validation Using Joint modeling and Mediation Analysis

The use of valid surrogate endpoints is an important stake in clinical research to help reduce both the duration and cost of a clinical trial and speed up the evaluation of interesting treatments. Several methods have been proposed in the statistical literature to validate putative surrogate endpoints. Two main approaches have been proposed: the meta-analytic approach and the mediation analysis approach. The former uses data from meta-analyses to derive associations measures between the surrogate and the final endpoint at the individual and trial levels. The latter rather uses the proportion of the treatment effect on the final endpoint through the surrogate as a measure of surrogacy in a causal inference framework. Both approaches have remained separated as the meta-analytic approach does not estimate the treatment effect on the final endpoint through the surrogate while the mediation analysis approach have been limited to single-trial setting. However, these two approaches are complementary. In this work we propose an approach that combines the meta-analytic and mediation analysis approaches using joint modeling for surrogate validation. We focus on the cases where the final endpoint is a time-to-event endpoint (such as time-to-death) and the surrogate is either a time-to-event or a longitudinal biomarker. Two new joint models were proposed depending on the nature of the surrogate. These model are implemented in the R package frailtypack. We illustrate the developed approaches in three applications on real datasets in oncology.

stat.ME↗

arXiv2023

Bayesian Estimation of Two-Part Joint Models for a Longitudinal Semicontinuous Biomarker and a Terminal Event with R-INLA: Interests for Cancer Clinical Trial Evaluation

Two-part joint models for a longitudinal semicontinuous biomarker and a terminal event have been recently introduced based on frequentist estimation. The biomarker distribution is decomposed into a probability of positive value and the expected value among positive values. Shared random effects can represent the association structure between the biomarker and the terminal event. The computational burden increases compared to standard joint models with a single regression model for the biomarker. In this context, the frequentist estimation implemented in the R package frailtypack can be challenging for complex models (i.e., large number of parameters and dimension of the random effects). As an alternative, we propose a Bayesian estimation of two-part joint models based on the Integrated Nested Laplace Approximation (INLA) algorithm to alleviate the computational burden and fit more complex models. Our simulation studies confirm that INLA provides accurate approximation of posterior estimates and to reduced computation time and variability of estimates compared to frailtypack in the situations considered. We contrast the Bayesian and frequentist approaches in the analysis of two randomized cancer clinical trials (GERCOR and PRIME studies), where INLA has a reduced variability for the association between the biomarker and the risk of event. Moreover, the Bayesian approach was able to characterize subgroups of patients associated with different responses to treatment in the PRIME study. Our study suggests that the Bayesian approach using INLA algorithm enables to fit complex joint models that might be of interest in a wide range of clinical applications.

stat.ME↗

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.

stat.CO↗

arXiv2015

Joint modelling of longitudinal and multi-state processes: application to clinical progressions in prostate cancer

Joint modelling of longitudinal and survival data is increasingly used in clinical trials on cancer. In prostate cancer for example, these models permit to account for the link between longitudinal measures of prostate-specific antigen (PSA) and the time of clinical recurrence when studying the risk of relapse. In practice, multiple types of relapse may occur successively. Distinguishing these transitions between health states would allow to evaluate, for example, how PSA trajectory and classical covariates impact the risk of dying after a distant recurrence post-radiotherapy, or to predict the risk of one specific type of clinical recurrence post-radiotherapy, from the PSA history. In this context, we present a joint model for a longitudinal process and a multi-state process which is divided into two sub-models: a linear mixed sub-model for longitudinal data, and a multi-state sub-model with proportional hazards for transition times, both linked by shared random effects. Parameters of this joint multi-state model are estimated within the maximum likelihood framework using an EM algorithm coupled to a quasi-Newton algorithm in case of slow convergence. It is implemented under R, by combining and extending the mstate and JM packages. The estimation program is validated by simulations and applied on pooled data from two cohorts of men with localized prostate cancer and treated by radiotherapy. Thanks to the classical covariates available at baseline and the PSA measurements collected repeatedly during the follow-up, we are able to assess the biomarker's trajectory, define the risks of transitions between health states, and quantify the impact of the PSA dynamics on each transition intensity.

stat.AP↗