MEGH: A parametric class of general hazard models for clustered survival data

Rubio, Francisco J.; Drikvandi, Reza

doi:10.1177/09622802221102620

Cited by 8 publications

(9 citation statements)

References 43 publications

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“…lognormal) or a simpler hazard structure (such as the AFT or PH) might be favoured by the data. In such cases, a model selection tool (such as AIC or BIC) would help identify the best model for the data (see Rubio et al [34] and Rubio and Drikvandi [33] for a discussion).…”

Section: Discussionmentioning

confidence: 99%

Individual frailty excess hazard models in cancer epidemiology

Rubio¹,

Putter²,

Belot³

2023

Preprint

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Unobserved individual heterogeneity is a common challenge in population cancer survival studies. This heterogeneity is usually associated with the combination of model misspecification and the failure to record truly relevant variables. We investigate the effects of unobserved individual heterogeneity in the context of excess hazard models, one of the main tools in cancer epidemiology. We propose an individual excess hazard frailty model to account for individual heterogeneity. This represents an extension of frailty modelling to the relative survival framework. In order to facilitate the inference on the parameters of the proposed model, we select frailty distributions which produce closed-form expressions of the marginal hazard and survival functions. The resulting model allows for an intuitive interpretation, in which the frailties induce a selection of the healthier individuals among survivors. We model the excess hazard using a flexible parametric model with a general hazard structure which facilitates the inclusion of time-dependent effects. We illustrate the performance of the proposed methodology through a simulation study. We present a real-data example using data from lung cancer patients diagnosed in England, and discuss the impact of not accounting for unobserved heterogeneity on the estimation of net survival. The methodology is implemented in the R package IFNS.

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Section: Discussionmentioning

confidence: 99%

Individual frailty excess hazard models in cancer epidemiology

Rubio¹,

Putter²,

Belot³

2023

Preprint

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“…The spatial models used to define G false~ and G will be introduced in Section 2.2. Thus, our proposal can be seen as an extension of the MEGH model proposed by Rubio and Drikvandi 16 to the relative survival framework, but also with the incorporation of spatial effects.…”

Section: Spatial Modelsmentioning

confidence: 96%

“…Let x ij ∈ ℝ p be the vector of available covariates. Similar to the mixed effects survival regression model (for overall survival) proposed by Rubio and Drikvandi, 16 we consider the excess hazard model…”

Section: Excess Hazard Modelmentioning

confidence: 99%

“…In that case, the hazard function is written as a product of the baseline hazard component and a non-parametric component modeled as a soft Bayesian additive regression tree (sBART) that is used to incorporate the possible clustering effects. Rubio and Drikvandi 16 consider the GH structure with random effects at the cluster level; however, they do not account for the spatial structure and limit their proposal to modeling the clustering components.…”

Section: Introductionmentioning

confidence: 99%

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Extended excess hazard models for spatially dependent survival data

Amaral,

Rubio,

Quaresma

et al. 2024

Stat Methods Med Res

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Relative survival represents the preferred framework for the analysis of population cancer survival data. The aim is to model the survival probability associated with cancer in the absence of information about the cause of death. Recent data linkage developments have allowed for incorporating the place of residence into the population cancer databases; however, modeling this spatial information has received little attention in the relative survival setting. We propose a flexible parametric class of spatial excess hazard models (along with inference tools), named “Relative Survival Spatial General Hazard,” that allows for the inclusion of fixed and spatial effects in both time-level and hazard-level components. We illustrate the performance of the proposed model using an extensive simulation study, and provide guidelines about the interplay of sample size, censoring, and model misspecification. We present a case study using real data from colon cancer patients in England. This case study illustrates how a spatial model can be used to identify geographical areas with low cancer survival, as well as how to summarize such a model through marginal survival quantities and spatial effects.

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“…This sort of models has been used in survival analysis applications, as they represent a simple alternative for including time‐level effects (through the coefficients

\bm {\alpha }

), while allowing for a tractable estimation. These applications include the analysis of the survival of cancer patients (Li et al., 2015; Rubio et al., 2021; Rubio & Drikvandi, 2022; Ribeiro‐Amaral et al., 2023), recurrent event data (Xu et al., 2020), joint models for longitudinal and survival data (Alvares & Rubio, 2021; Rubio et al., 2019), reliability analysis (Gámiz et al., 2011), and as a mean for comparing competing nested submodels (e.g., PH and AFT) of interest (Zhao et al., 2009).…”

Section: Introductionmentioning

confidence: 99%

On near‐redundancy and identifiability of parametric hazard regression models under censoring

Rubio

Espíndola²,

Montoya³

2023

Biometrical J

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We study parametric inference on a rich class of hazard regression models in the presence of right‐censoring. Previous literature has reported some inferential challenges, such as multimodal or flat likelihood surfaces, in this class of models for some particular data sets. We formalize the study of these inferential problems by linking them to the concepts of near‐redundancy and practical nonidentifiability of parameters. We show that the maximum likelihood estimators of the parameters in this class of models are consistent and asymptotically normal. Thus, the inferential problems in this class of models are related to the finite‐sample scenario, where it is difficult to distinguish between the fitted model and a nested nonidentifiable (i.e., parameter‐redundant) model. We propose a method for detecting near‐redundancy, based on distances between probability distributions. We also employ methods used in other areas for detecting practical nonidentifiability and near‐redundancy, including the inspection of the profile likelihood function and the Hessian method. For cases where inferential problems are detected, we discuss alternatives such as using model selection tools to identify simpler models that do not exhibit these inferential problems, increasing the sample size, or extending the follow‐up time. We illustrate the performance of the proposed methods through a simulation study. Our simulation study reveals a link between the presence of near‐redundancy and practical nonidentifiability. Two illustrative applications using real data, with and without inferential problems, are presented.

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MEGH: A parametric class of general hazard models for clustered survival data

Cited by 8 publications

References 43 publications

Individual frailty excess hazard models in cancer epidemiology

Individual frailty excess hazard models in cancer epidemiology

Extended excess hazard models for spatially dependent survival data

On near‐redundancy and identifiability of parametric hazard regression models under censoring

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