Geostatistical survival model with Gaussian random effect

Modeling geostatistical incomplete spatially correlated survival data with applications to COVID-19 mortality in Ghana

“…In health studies, often the event is infection or death, and usually survival time is defined in days (or years). If survival times may vary between locations, including spatial information in the survival model will be beneficial ( Motarjem et al, 2020 , Aswi et al, 2020 , Banerjee et al, 2003a , LeSage and Pace, 2004 , Su et al, 2020 , Zhou et al, 2017 , Hanson et al, 2012 ). This is due to the important role that geographical information plays in predicting survival and identifying high risk areas so that necessary action can be taken by policy makers ( Lawson et al, 2016 , Aswi et al, 2020 , Banerjee et al, 2003a ).…”

“…We usually model such strata spatial arrangement in two main settings: geostatistical approaches , where we use the exact geographic locations (for example, latitude and longitude) of the strata, and lattice approaches , where we only use the positions of the strata relative to each other (for example, which counties or regions neighbor which others) ( Banerjee et al, 2003a , Vaupel et al, 1979 , Su et al, 2020 , Zhou et al, 2017 ). Hierarchical models have been widely applied to analyze such strata spatial arrangement ( Vaupel et al, 1979 , Motarjem et al, 2020 , Li and Ryan, 2002 , Banerjee et al, 2003a , Hanson et al, 2012 ).…”

“…Most of these authors considered only complete data, even though most datasets are incomplete. For example, in Nasution et al, 2022 , Motarjem et al, 2020 , Hesam et al, 2018 , Banerjee et al, 2003b and Aswi et al (2020) , the authors did not address how the missing covariates or missing spatially information were handled. The authors simply dropped the missing values or overlooked the complication of the missing values and used a dataset with only observed cases.…”

“…The main objective of this paper is to propose a geostatistical modeling approach for incomplete spatially correlated survival data, applying and expanding on concepts from Banerjee et al, 2003a , Li and Ryan, 2002 , Motarjem et al, 2020 , Su et al, 2020 and Thamrin et al (2021) to mention but a few. Even though spatial survival models have already been discussed in literature, none of the studies have focus on appropriately handling missing covariate and spatial information in the data at the same time.…”

Regression Analysis of Spatially Correlated Event Durations With Missing Origins Annotated by Longitudinal Measures

“…Frailty models are commonly employed with spatially correlated time-to-event data (e.g. Banerjee, Wall, and Carlin, 2003;Li and Ryan, 2002;Motarjem, Mohammadzadeh, and Abyar, 2017). As in frailty models, we embed the spatial correlation in the 4 random effects.…”