Socio-economic inequality, interregional mobility and mortality among cancer patients: A mediation analysis approach

2022

This paper investigates the effect of socio-economic status on interregional mobility and mortality among cancer patients. The cohort under analysis comprises patients residing in Sicily (Italy), who were diagnosed with lung and colon cancer between 2010 and 2011. The data was collated from the hospital discharge records of the Sicilian Region and the Regional register of the causes of death, by considering all those patients for whom information relating to socio-economic status was available. First, graphical models were applied to highlight the multivariate structure of association among socio-economic status, interregional mobility and 3-year mortality. Secondly, mediation analysis quan…

Analysing the mediating role of a network: a Bayesian latent space approach

2020

The use of network analysis for the investigation of social structures has recently seen a rise, due both to the high availability of data and to the numerous insights it can provide into different fields. Most analyses focus on the topological characteristics of networks and the estimation of relationships between the nodes. We adopt a different point of view, by considering the whole network as a random variable conveying the effect of an exposure on a response. This point of view represents a classical mediation setting, where the interest lies in the estimation of the indirect effect, that is, the effect propagated through the mediating variable. We introduce a latent space model mappin…

Structural multilevel models for longitudinal mediation analysis: a definition variable approach

2022

Mediation analysis is used to assess the direct effect of an exposure on an outcome, and the indirect effect transmitted by a third intermediate variable. Longitudinal data are the most suited to address mediation, since they allow mediational effects to manifest over time. There exist several approaches to deal with longitudinal mediation analysis, and one of the most widely spread, especially in social and behavioural sciences, consists of using multilevel models. However, when applied to mediational settings, these models present some limitations that can be overcome moving to a structural perspective. In this paper we propose a new formalisation of multilevel models within a structural …

Does self-efficacy influence academic results? A separable-effect mediation analysis

2021

In causal mediation analysis, natural effects are identified only under strict assumptions involving cross-world counterfactuals. An alternative approach recently developed, called separable, allows for identification of mediational effects in a wide range of models, since it relies on weaker assumptions than those required by natural effects. In this paper, the separable-effect approach is revised and an application to data is presented.

Longitudinal mediation analysis with structural and multilevel models: associational and causal perspectives

2022

Estimating the Bayesian posterior distribution of indirect effects in causal longitudinal mediation analysis

Many research studies aim to unveil the causal mechanism underlying a particular phenomenon; mediation analysis is increasingly used for this scope, and longitudinal data are particularly suited for mediation since they ensure the correct temporal order among variables and the time spanning allows the causal effects to unfold. This explains the rise of interest in the topic of longitudinal mediation analysis. Many approaches have been proposed to cope with longitudinal mediation (Fosen et al., 2005; Lin et al., 2017), among which mixed-effect modelling. In a recent paper, Bind et al. (Biostatistics, 2016) made use of generalised mixed effect models and provided conditions for the identifiab…

Bayesian causal mediation analysis through linear mixed-effect models

2022

In mediational settings, the main focus is on the estimation of the indirect effect of an exposure on an outcome through a third variable called mediator. The traditional maximum likelihood estimation method presents several problems in the estimation of the standard error and the confidence interval of the indirect effect. In this paper, we propose a Bayesian approach to obtain the posterior distribution of the indirect effect through MCMC, in the context of mediational mixed models for longitudinal data. A simulation study shows that our method outperforms the traditional maximum likelihood approach in terms of bias and coverage rates.

Insights into the derivative-based method for nonlinear mediation models

2022

Associational mediation analysis has generally relied on the linearity of models to estimate the indirect effect as a product of regression coefficients. Very few examples of generalisations to nonlinear settings have been proposed, including a derivative-based method that, however, is far from being widely spread among scholars. In this paper, we clarify some aspects of such an approach to nonlinear mediation models, which have not been addressed by the previous literature. In addition, we run a simulation study to compare confidence intervals for the indirect effect obtained through different approaches.

Networks as mediating variables: a Bayesian latent space approach

2022

AbstractThe use of network analysis to investigate social structures has recently seen a rise due to the high availability of data and the numerous insights it can provide into different fields. Most analyses focus on the topological characteristics of networks and the estimation of relationships between the nodes. We adopt a different perspective by considering the whole network as a random variable conveying the effect of an exposure on a response. This point of view represents a classical mediation setting, where the interest lies in estimating the indirect effect, that is, the effect propagated through the mediating variable. We introduce a latent space model mapping the network into a …