ArticleStatistical methods in medical research2017
Bayesian accelerated failure time model for space-time dependency in a geographically augmented survival model.
Article in Statistical methods in medical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 17 citations in OpenAlex.
- Spatial-temporal Bayesian accelerated failure time models for survival endpoints with applications to prostate cancer registry data.BMC medical research methodology · 2024Article
- Article
- Survival of epithelial ovarian cancer in Black women: a society to cell approach in the African American cancer epidemiology study (AACES).Cancer causes & control : CCC · 2023Article
- Temporally dependent accelerated failure time model for capturing the impact of events that alter survival in disease mapping.Biostatistics (Oxford, England) · 2019Article
- Spatially-explicit survival modeling with discrete grouping of cancer predictors.Spatial and spatio-temporal epidemiology · 2019Article
- Trends in Colorectal Cancer Incidence and Survival in Iowa SEER Data: The Timing of It All.Clinical colorectal cancer · 2019Article
- Bayesian cure-rate survival model with spatially structured censoring.Spatial statistics · 2018Article
- Spatially explicit survival modeling for small area cancer data.Journal of applied statistics · 2018Article
- Spatial-temporal analysis of prostate cancer incidence from the Pennsylvania Cancer Registry, 2000-2011.Geospatial health · 2017Article
- Assessment of spatial variation in breast cancer-specific mortality using Louisiana SEER data.Social science & medicine (1982) · 2017Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
In this paper, we extend the spatially explicit survival model for small area cancer data by allowing dependency between space and time and using accelerated failure time models. Spatial dependency is modeled directly in the definition of the survival, density, and hazard functions. The models are developed in the context of county level aggregated data. Two cases are considered: the first assumes that the spatial and temporal distributions are independent; the second allows for dependency between the spatial and temporal components. We apply the models to prostate cancer data from the Louisiana SEER cancer registry.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.