Landslide inventory dataset post Hurricane Stan (2005), Lake Atitlan region, Guatemala.

Landslides are a global hazard of devastating impacts resulting in thousands of fatalities every year, substantial economic losses, and long-term economic disruption. Examining the frequency and distribution of landslides is important to better understand their associated factors and to identify where future landslides may occur. The dataset described in this article consists of landslides digitized as polygons using high resolution aerial photography collected after Hurricane Stan (October 2005), which caused severe damages in Central America. In Guatemala, the total economic impact of Hurricane Stan has been estimated at US$983 Million dollars, with 719,000 ha of land lost due to flooding, landslides, and erosion. The dataset is provided in shapefile format and encompasses the areas located north and northwest of Lake Atitlan. The digitizing process involved the visual identification of the landslides in the areal imagery followed by manual delineation of the landslides' areal extent using ArcGIS desktop on-screen digitizing and editing tools. Additionally, a randomized verification procedure was completed to assess the completeness of the dataset.

A Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior.

Joint models of longitudinal and survival outcomes have gained much popularity in recent years, both in applications and in methodological development. This type of modelling is usually characterised by two submodels, one longitudinal (e.g., mixed-effects model) and one survival (e.g., Cox model), which are connected by some common term. Naturally, sharing information makes the inferential process highly time-consuming. In particular, the Bayesian framework requires even more time for Markov chains to reach stationarity. Hence, in order to reduce the modelling complexity while maintaining the accuracy of the estimates, we propose a two-stage strategy that first fits the longitudinal submodel and then plug the shared information into the survival submodel. Unlike a standard two-stage approach, we apply a correction by incorporating an individual and multiplicative fixed-effect with informative prior into the survival submodel. Based on simulation studies and sensitivity analyses, we empirically compare our proposal with joint specification and standard two-stage approaches. The results show that our methodology is very promising, since it reduces the estimation bias compared to the other two-stage method and requires less processing time than the joint specification approach.