Browsing by Author "Touloumi, Giota"

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    Impact of covariate omission and categorization from the Fine–Gray model in randomized-controlled trials
    (Springer, 2021-12) Bakoyannis, Giorgos; Chu, Fang-I.; Babiker, Abdel G. A.; Touloumi, Giota; Biostatistics, School of Public Health
    In this paper, we study the statistical issues related to the omission and categorization of important covariates in the context of the Fine–Gray model in randomized-controlled trials with competing risks. We show that the omission of an important covariate from the Fine–Gray model leads to attenuated estimates for treatment effect and loss of proportionality in general. Our simulation studies reveal substantial attenuation in the estimate for treatment effect and the loss of statistical power, while dichotomizing a continuous covariate leads to similar but less pronounced impact. Our results are illustrated using data from a randomized clinical trial of HIV-infected individuals. The relative merits of conducting an adjusted versus an unadjusted analysis of treatment effect in light of both statistical and practical considerations are discussed.
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    Impact of dependent left truncation in semiparametric competing risks methods: A simulation study
    (Taylor & Francis, 2017) Bakoyannis, Giorgos; Touloumi, Giota; Department of Biostatistics, School of Public Health
    In this study, we investigated the robustness of the methods that account for independent left truncation when applied to competing risks settings with dependent left truncation. We specifically focused on the methods for the proportional cause-specific hazards model and the Fine–Gray model. Simulation experiments showed that these methods are not in general robust against dependent left truncation. The magnitude of the bias was analogous to the strength of the association between left truncation and failure times, the effect of the covariate on the competing cause of failure, and the baseline hazard of left truncation time.
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    Joint modeling of longitudinal and competing-risk data using cumulative incidence functions for the failure submodels accounting for potential failure cause misclassification through double sampling
    (Oxford University Press, 2023) Thomadakis, Christos; Meligkotsidou, Loukia; Yiannoutsos, Constantin T.; Touloumi, Giota; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Most of the literature on joint modeling of longitudinal and competing-risk data is based on cause-specific hazards, although modeling of the cumulative incidence function (CIF) is an easier and more direct approach to evaluate the prognosis of an event. We propose a flexible class of shared parameter models to jointly model a normally distributed marker over time and multiple causes of failure using CIFs for the survival submodels, with CIFs depending on the “true” marker value over time (i.e., removing the measurement error). The generalized odds rate transformation is applied, thus a proportional subdistribution hazards model is a special case. The requirement that the all-cause CIF should be bounded by 1 is formally considered. The proposed models are extended to account for potential failure cause misclassification, where the true failure causes are available in a small random sample of individuals. We also provide a multistate representation of the whole population by defining mutually exclusive states based on the marker values and the competing risks. Based solely on the assumed joint model, we derive fully Bayesian posterior samples for state occupation and transition probabilities. The proposed approach is evaluated in a simulation study and, as an illustration, it is fitted to real data from people with HIV.