arXiv subjects

Marta Spreafico

Publications and source records attributed to Marta Spreafico.

5 recordsLinked to original sources

arXiv2025

Impact of Near-Positivity Violations on IPTW-Estimated Marginal Structural Survival Models With Time-Dependent Confounding

In longitudinal observational studies, marginal structural models (MSMs) are a class of causal models used to analyse the effect of an exposure on the (time-to-event) outcome of interest, while accounting for exposure-affected time-dependent confounding. In the applied literature, inverse probability of treatment weighting (IPTW) has been widely adopted to estimate MSMs. An essential assumption for IPTW-based MSMs is the positivity assumption, which ensures that, for any combination of measured confounders among individuals, there is a non-zero probability of receiving each possible treatment strategy. Positivity is crucial for valid causal inference through IPTW-based MSMs, but is often overlooked compared to confounding bias. Positivity violations may also arise due to randomness, in situations where the assignment to a specific treatment is theoretically possible but is either absent or rarely observed in the data, leading to near violations. These situations are common in practical applications, particularly when the sample size is small, and they pose significant challenges for causal inference. This study investigates the impact of near-positivity violations on estimates from IPTW-based MSMs in survival analysis. Two algorithms are proposed for simulating longitudinal data from hazard-MSMs, accommodating near-positivity violations, a time-varying binary exposure, and a time-to-event outcome. Cases of near-positivity violations, where remaining unexposed is rare within certain confounder levels, are analysed across various scenarios and weight truncation (WT) strategies. This work aims to serve as a critical warning against overlooking the positivity assumption or naively applying WT in causal studies using longitudinal observational data and IPTW.

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arXiv2025

Discrimination performance in illness-death models with interval-censored disease data

In clinical studies, the illness-death model is often used to describe disease progression. A subject starts disease-free, may develop the disease and then die, or die directly. In clinical practice, disease can only be diagnosed at pre-specified follow-up visits, so the exact time of disease onset is often unknown, resulting in interval-censored data. This study examines the impact of ignoring this interval-censored nature of disease data on the discrimination performance of illness-death models, focusing on the time-specific Area Under the receiver operating characteristic Curve (AUC) in both incident/dynamic and cumulative/dynamic definitions. A simulation study with data simulated from Weibull transition hazards and disease state censored at regular intervals is conducted. Estimates are derived using different methods: the Cox model with a time-dependent binary disease marker, which ignores interval-censoring, and the illness-death model for interval-censored data estimated with three implementations - the piecewise-constant model from the msm package, the Weibull and M-spline models from the SmoothHazard package. These methods are also applied to a dataset of 2232 patients with high-grade soft tissue sarcoma, where the interval-censored disease state is the post-operative development of distant metastases. The results suggest that, in the presence of interval-censored disease times, it is important to account for interval-censoring not only when estimating the parameters of the model but also when evaluating the discrimination performance of the disease.

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arXiv2023

Causal effect of chemotherapy received dose intensity on survival outcome: a retrospective study in osteosarcoma

This study aims to analyse the effects of reducing Received Dose Intensity (RDI) in chemotherapy treatment for osteosarcoma patients on their survival by using a novel approach. In this scenario, toxic side effects are risk factors for mortality and predictors of future exposure levels, introducing post-assignment confounding. Chemotherapy administration data from BO03 and BO06 Randomized Clinical Trials (RCTs) in ostosarcoma are employed to emulate a target trial with three RDI-based exposure strategies: 1) standard, 2) reduced, and 3) highly-reduced RDI. Investigations are conducted between subgroups of patients characterised by poor or good Histological Responses (HRe). Inverse Probability of Treatment Weighting (IPTW) is first used to transform the original population into a pseudo-population which mimics the target randomized cohort. Then, a Marginal Structural Cox Model with effect modification is employed. Conditional Average Treatment Effects (CATEs) are ultimately measured as the difference between the Restricted Mean Survival Time of reduced/highly-reduced RDI strategy and the standard one. Confidence Intervals for CATEs are obtained using a novel IPTW-based bootstrap procedure. Significant effect modifications based on HRe were found. Increasing RDI-reductions led to contrasting trends for poor and good responders: the higher the reduction, the better (worsen) was the survival in poor (good) reponders. This study introduces a novel approach to (i) comprehensively address the challenges related to the analysis of chemotherapy data, (ii) mitigate the toxicity-treatment-adjustment bias, and (iii) repurpose existing RCT data for retrospective analyses extending beyond the original trials' intended scopes.

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arXiv2023

Joint modelling of recurrent and terminal events with discretely-distributed non-parametric frailty: application on re-hospitalizations and death in heart failure patients

In the context of clinical and biomedical studies, joint frailty models have been developed to study the joint temporal evolution of recurrent and terminal events, capturing both the heterogeneous susceptibility to experiencing a new episode and the dependence between the two processes. While discretely-distributed frailty is usually more exploitable by clinicians and healthcare providers, existing literature on joint frailty models predominantly assumes continuous distributions for the random effects. In this article, we present a novel joint frailty model that assumes bivariate discretely-distributed non-parametric frailties, with an unknown finite number of mass points. This approach facilitates the identification of latent structures among subjects, grouping them into sub-populations defined by a shared frailty value. We propose an estimation routine via Expectation-Maximization algorithm, which not only estimates the number of subgroups but also serves as an unsupervised classification tool. This work is motivated by a study of patients with Heart Failure (HF) receiving ACE inhibitors treatment in the Lombardia region of Italy. Recurrent events of interest are hospitalizations due to HF and terminal event is death for any cause.

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arXiv2023

Longitudinal Latent Overall Toxicity (LOTox) profiles in osteosarcoma: a new taxonomy based on latent Markov models

Due to the presence of multiple types of adverse events with different levels of severity, the analysis of longitudinal toxicity data is a difficult task in cancer studies. In this work, a novel approach based on latent Markov models and compositional data techniques is proposed. The latent status of interest is the Latent Overall Toxicity (LOTox) condition of each patient. The main objectives consist in identifying different latent states of overall toxicity burden and investigating the evolution of individual toxicity risk during cancer treatment. This methodology is applied to osteosarcoma treatment data to provide novel techniques that may support medical decisions in childhood cancer therapy.

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