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Centre for Statistical Methodology Seminar
Causal Inference Theme

Friday 1 July 2016, 12:45-2:00pm
LG24, Keppel Street
Recent advances in causal mediation analysis
Rhian Daniel (LSHTM)

Abstract: In diverse fields of empirical research, including epidemiology, public health and the social sciences, attempts are made to decompose the effect of an exposure on an outcome into its effects via different pathways. For example, it is well-established that breast cancer survival rates in the UK differ by socio-economic status. But how much of this effect is due to differential adherence to screening programmes? How much is explained by treatment choices? And so on. These enquiries, traditionally tackled using simple regression methods, have been given much recent attention in the causal inference literature, specifically in the fruitful area known as Casual Mediation Analysis. The focus has mainly been on so-called natural direct and indirect effects, with flexible estimation methods that allow their estimation in the presence of non-linearities and interactions, and careful consideration given to the need for controlling confounding. Despite these many developments, the estimation of natural direct and indirect effects is still plagued by one major limitation, namely its reliance on a type of assumption known as “cross-world” assumptions; this type of assumption is so strong that no experiment could even hypothetically be designed under which its validity would be guaranteed. Moreover, these assumptions are violated (or at best are implausible) when confounders of the mediator-outcome association are affected by the exposure, and thus in particular in settings that involve repeatedly measured mediators, or multiple correlated mediators. In this talk, I will discuss alternative mediation effects known as interventional direct and indirect effects, (VanderWeele et al, Epidemiology, 2014), and a novel extension to the multiple mediator setting. This is joint work with Stijn Vansteelandt, University of Gent. We argue that interventional direct and indirect effects are policy-relevant and can be identified under much weaker conditions than natural direct and indirect effects. In particular, they can be used to capture the path-specific effects of an exposure on an outcome that are mediated by distinct mediators, even when, as often, the structural dependence between the multiple mediators is unknown. The approach will be illustrated using data on breast cancer survival.

Centre for Statistical Methodology Seminar
Survival Analysis Theme

Thursday 14 July 2016, 12:45-2:00pm
LG80, Keppel Street
A multilevel excess hazard model to estimate net survival on hierarchical data allowing for non-linear and non-proportional effects of covariates
Aurelien Belot (LSHTM)

Abstract: The excess hazard regression model is an approach developed for the analysis of cancer registry data to estimate net survival, that is, the survival of cancer patients that would be observed if cancer was the only cause of death. Cancer registry data typically possess a hierarchical structure: individuals from the same geographical unit share common characteristics such as proximity to a large hospital that may influence access to and quality of health care, so that their survival times might be correlated. As a consequence, correct statistical inference regarding the estimation of net survival and the effect of covariates should take this hierarchical structure into account. It becomes particularly important as many studies in cancer epidemiology aim at studying the effect on the excess mortality hazard of variables, such as deprivation indexes, often available only at the ecological level rather than at the individual level. We developed here an approach to fit a flexible excess hazard model including a random effect to describe the unobserved heterogeneity existing between different clusters of individuals, and with the possibility to estimate non-linear and time-dependent effects of covariates. We demonstrated the overall good performance of the proposed approach in a simulation study that assessed the impact on parameter estimates of the number of clusters, their size and their level of unbalance. We then used this multilevel model to describe the effect of a deprivation index defined at the geographical level on the excess mortality hazard of patients diagnosed with cancer of the oral cavity.

Centre for Statistical Methodology Seminar
Friday 30 September 2016, 12:45-2:00pm
Room TBC
Title TBC
Clemence Leyrat (LSHTM)