In this work, we propose a novel joint frailty model assuming bivariate discretely- distributed non-parametric frailties, with an unknown finite number of mass points. This ap- proach allows to... Show moreIn this work, we propose a novel joint frailty model assuming bivariate discretely- distributed non-parametric frailties, with an unknown finite number of mass points. This ap- proach allows to detect a latent structure among subjects, clustering them in sub-populations where individuals are characterized by a common frailty value. Our method can be interpreted as an unsupervised classification tool and motivates further investigation into the reasons for similarities within the clustered subjects and dissimilarities across the clusters. This work is motivated by a study of patients with Heart Failure (HF) undergoing ACE inhibitors treatment in the Lombardia region of Italy. Recurrent events of interest are hos- pitalizations due to HF and terminal event is death for any cause. Show less
This dissertation focuses on developing new mathematical and statistical methods to properly represent time-varying covariates and model them within the context of time-to-event analysis. This... Show moreThis dissertation focuses on developing new mathematical and statistical methods to properly represent time-varying covariates and model them within the context of time-to-event analysis. This research topic is motivated by specific clinical questions aimed at gaining insights into personalised treatments for cardiological and oncological patients. The main purpose is to enrich the knowledge available for modelling patients’ survival with relevant features related to the time-varying processes of interest.The efforts of this work address the complexities of both (i) developing adequate dynamic characterizations of the processes under study (i.e., representation problem) and (ii) identifying and quantifying the association between time-varying processes and patient survival (i.e., time-to-event modelling problem). In both cases, the main issue is dealing with complex data sources while taking into account the nature of the processes and managing the complex trade-off between clinical interpretability and mathematical formulation.By solving the aforementioned statistical complexities, this work is not only impacting the community of researchers in mathematics and statistics. The development of these novel methodologies may represent a significant step forward in the definition of customized and flexible monitoring tools to support doctors and clinicians in their work.*********This doctoral dissertation was part of a cotutelle agreement between the Politecnico di Milano and Leiden University Show less
Time-varying covariates are of great interest in clinical research since they represent dynamic patterns which reflect disease progression. In cancer studies biomarkers values change as functions... Show moreTime-varying covariates are of great interest in clinical research since they represent dynamic patterns which reflect disease progression. In cancer studies biomarkers values change as functions of time and chemotherapy treatment is modified by delaying a course or reducing the dose intensity, according to patient's toxicity levels. In this work, a Functional covariate Cox Model (FunCM) to study the association between time-varying processes and a time-to-event outcome is proposed. FunCM first exploits functional data analysis techniques to represent time-varying processes in terms of functional data. Then, information related to the evolution of the functions over time is incorporated into functional regression models for survival data through functional principal component analysis. FunCM is compared to a standard time-varying covariate Cox model, commonly used despite its limiting assumptions that covariate values are constant in time and measured without errors. Data from MRC BO06/EORTC 80931 randomised controlled trial for treatment of osteosarcoma are analysed. Time-varying covariates related to alkaline phosphatase levels, white blood cell counts and chemotherapy dose during treatment are investigated. The proposed method allows to detect differences between patients with different biomarkers and treatment evolutions, and to include this information in the survival model. These aspects are seldom addressed in the literature and could provide new insights into the clinical research. Show less
Time-varying covariates are of great interest in clinical research since they represent dynamic patterns which reflect disease progression. In cancer studies biomarkers values change as functions... Show moreTime-varying covariates are of great interest in clinical research since they represent dynamic patterns which reflect disease progression. In cancer studies biomarkers values change as functions of time and chemotherapy treatment is modified by delaying a course or reducing the dose intensity, according to patient's toxicity levels. In this work, a Functional covariate Cox Model (FunCM) to study the association between time-varying processes and a time-to-event outcome is proposed. FunCM first exploits functional data analysis techniques to represent time-varying processes in terms of functional data. Then, information related to the evolution of the functions over time is incorporated into functional regression models for survival data through functional principal component analysis. FunCM is compared to a standard time-varying covariate Cox model, commonly used despite its limiting assumptions that covariate values are constant in time and measured without errors. Data from MRC BO06/EORTC 80931 randomised controlled trial for treatment of osteosarcoma are analysed. Time-varying covariates related to alkaline phosphatase levels, white blood cell counts and chemotherapy dose during treatment are investigated. The proposed method allows to detect differences between patients with different biomarkers and treatment evolutions, and to include this information in the survival model. These aspects are seldom addressed in the literature and could provide new insights into the clinical research. Show less
In myelodysplastic syndromes tailoring the timing of bone-marrow transplant according to patient’s genomic profile and characteristics is fundamental since it represents the only curative treatment... Show moreIn myelodysplastic syndromes tailoring the timing of bone-marrow transplant according to patient’s genomic profile and characteristics is fundamental since it represents the only curative treatment available in this disease and it can be per- formed only once. In this work we present a method based on a multi-state model to analytically optimize the timing of the transplant. This method can be used as an alternative to the microsimulation approach based on Monte Carlo. As objective function for the optimization, the Restricted Mean Survival Time was considered together with its first derivative. This decision analysis is the basis for a novel quantitative tool to assist clinicians in their decision process for the treatment of patients affected by this condition. Show less
Spreafico, M.; Ieva, F.; Arlati, F.; Capello, F.; Fatone, F.; Fedeli, F.; ... ; Fiocco, M. 2021
Objectives This study aims at exploring and quantifying multiple types of adverse events (AEs) experienced by patients during cancer treatment. A novel longitudinal score to evaluate the Multiple... Show moreObjectives This study aims at exploring and quantifying multiple types of adverse events (AEs) experienced by patients during cancer treatment. A novel longitudinal score to evaluate the Multiple Overall Toxicity (MOTox) burden is proposed. The MOTox approach investigates the personalised evolution of high overall toxicity (high-MOTox) during the treatment.Design Retrospective analysis of the MRC-BO06/EORTC-80931 randomised controlled trial for osteosarcoma.Setting International multicentre population-based study.Participants A total of 377 patients with resectable high-grade osteosarcoma, who completed treatment within 180 days after randomisation without abnormal dosages (+25% higher than planned).Interventions Patients were randomised to six cycles of conventional versus dose-intense regimens of doxorubicin and cisplatin. Non-haematological toxicity data were collected prospectively and graded according to the Common Terminology Criteria for Adverse Events (CTCAE).Main outcome measures The MOTox score described the overall toxicity burden in terms of multiple toxic AEs, maximum-severity episode and cycle time-dimension. Evolution of high-MOTox was assessed through multivariable models, that investigated the impact of personalised characteristics (eg, achieved chemotherapy dose, previous AEs or biochemical factors) cycle-by-cycle.Results A cycle-by-cycle analysis identifies different evolutions of MOTox levels during treatment, detecting differences in patients' health. Mean MOTox values and percentages of patients with high-MOTox decreased cycle-by-cycle from 2.626 to 1.953 and from 57.8% to 36.6%, respectively. High-MOTox conditions during previous cycles were prognostic risk factors for a new occurrence (ORs range from 1.522 to 4.439), showing that patient's history of toxicities played an important role in the evolution of overall toxicity burden during therapy. Conventional regimen may be preferred to dose-intense in terms of AEs at cycles 2-3 (p<0.05).Conclusions The novel longitudinal method developed can be applied to any cancer studies with CTCAE-graded toxicity data. After validation in other studies, the MOTox approach may lead to improvements in healthcare assessment and treatment planning. Show less
Spreafico, M.; Ieva, F.; Arlati, F.; Capello, F.; Fatone, F.; Fedeli, F.; ... ; Fiocco, M. 2021
In cancer trials, the analysis of longitudinal chemotherapy data is a difficult task due to the complex registration and evolution of toxicity levels during treatment. Models to deal with both the... Show moreIn cancer trials, the analysis of longitudinal chemotherapy data is a difficult task due to the complex registration and evolution of toxicity levels during treatment. Models to deal with both the longitudinal and the categorical aspects of toxicity level progression are necessary, still not well developed. In this work, a Latent Transition Analysis (LTA) procedure to identify and reconstruct the longitudinal latent profiles of toxicity evolution of each patient over time is proposed. The latent variables determining the progression of the observed toxicity levels can be thought of as the outcomes of an underlying latent process. This methodology has never been applied to osteosarcoma treatment and provides new insights for supporting decisions in childhood cancer therapy. Show less
In cancer trials, the analysis of longitudinal chemotherapy data is a difficult task due to the complex registration and evolution of toxicity levels during treatment. Models to deal with both the... Show moreIn cancer trials, the analysis of longitudinal chemotherapy data is a difficult task due to the complex registration and evolution of toxicity levels during treatment. Models to deal with both the longitudinal and the categorical aspects of toxicity level progression are necessary, still not well developed. In this work, a Latent Transition Analysis (LTA) procedure to identify and reconstruct the longitudinal latent profiles of toxicity evolution of each patient over time is proposed. The latent variables determining the progression of the observed toxicity levels can be thought of as the outcomes of an underlying latent process. This methodology has never been applied to osteosarcoma treatment and provides new insights for supporting decisions in childhood cancer therapy. Show less
In clinical research, associating dynamic time-varying covariates (e.g. biomarkers or drug assumption) with an event-time outcome represents a challenging task that could be tackled exploiting... Show moreIn clinical research, associating dynamic time-varying covariates (e.g. biomarkers or drug assumption) with an event-time outcome represents a challenging task that could be tackled exploiting Functional Data Analysis (FDA). In particular, FDA techniques can be used to represent dynamic time-varying covariates in terms of functions, which can be plugged into a Cox-type regression model to investigate the effect on survival outcomes. Data from MRC BO06/EORTC 80931 randomised controlled trial for treatment of osteosarcoma were analysed. Time-varying covariates related to alkaline phosphatase levels and chemotherapy dose during treatment were considered. Show less
