A cluster model for space–time disease counts

Yan, Ping; Clayton, Murray K.

doi:10.1002/sim.2424

Cited by 25 publications

(31 citation statements)

References 26 publications

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“…First, in geographical statistics, clustering temporal data with respect to geographical constraints is a major interest (18)(19)(20). A variation of this class of methods is to identify clusters with geometric constraints (21)(22)(23)(24)(25)(26).…”

Section: Clustering Of Temporal Datamentioning

confidence: 99%

Time-variant clustering model for understanding cell fate decisions

Huang

Cao

Biase

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Both spatial characteristics and temporal features are often the subjects of concern in physical, social, and biological studies. This work tackles the clustering problems for time course data in which the cluster number and clustering structure change with respect to time, dubbed time-variant clustering. We developed a hierarchical model that simultaneously clusters the objects at every time point and describes the relationships of the clusters between time points. The hidden layer of this model is a generalized form of branching processes. A reversible-jump Markov Chain Monte Carlo method was implemented for model inference, and a feature selection procedure was developed. We applied this method to explore an open question in preimplantation embryonic development. Our analyses using single-cell gene expression data suggested that the earliest cell fate decision could start at the 4-cell stage in mice, earlier than the commonly thought 8-to 16-cell stage. These results together with independent experimental data from singlecell RNA-seq provided support against a prevailing hypothesis in mammalian development.clustering | time | branching process | embryonic development | cell fate

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Section: Clustering Of Temporal Datamentioning

confidence: 99%

Time-variant clustering model for understanding cell fate decisions

Huang

Cao

Biase

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…Simultaneous inference on multiple scales in spatial epidemiology was illustrated by Tuscan gastric cancer data at various levels of aggregation [567,568]. A cluster model for space-time disease counts used reversible jump Markov chain Monte Carlo in Japanese breast cancer mortality [569]. The impact of prior choice on local Bayes factor for cluster detection was compared on breast cancer incidence from Wisconsin [570].…”

Section: Spatial and Spatio-temporal Modellingmentioning

confidence: 99%

Bayesian statistics in medicine: a 25 year review

2006

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SUMMARYThis review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

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“…Note that the fixed covariate effects do not interact with space or time in this particular model, although it would be easy to extend the model in this direction. Multiple authors have considered variations of this basic framework, including extensions for multiple health outcomes [47] and spatial clustering [48], among others.…”

Section: Review Of Spatiotemporal Modelingmentioning

confidence: 99%

Methodologic implications of social inequalities for analyzing health disparities in large spatiotemporal data sets: An example using breast cancer incidence data (Northern and Southern California, 1988–2002)

Chen

Coull

Waterman

et al. 2008

Statistics in Medicine

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SUMMARYEfforts to monitor, investigate, and ultimately eliminate health disparities across racial/ethnic and socioeconomic groups can benefit greatly from spatiotemporal models that enable exploration of spatial and temporal variation in health. Hierarchical Bayes methods are well-established tools in the statistical literature for fitting such models, as they permit smoothing of unstable small-area rates. However, issues presented by 'real-life' surveillance data can be a barrier to routine use of these models by epidemiologists. These include (1) shifting of regional boundaries over time, (2) social inequalities in racial/ethnic residential segregation, which imply differential spatial structuring across different racial/ethnic groups, and (3) heavy computational burdens for large spatiotemporal data sets. Using data from a study of changing socioeconomic gradients in female breast cancer incidence in two population-based cancer registries covering the San Francisco Bay Area and Los Angeles County, CA (1988CA ( -2002, we illustrate a two-stage approach to modeling health disparities and census tract (CT) variation in incidence over time. In the first stage, we fit race-and year-specific spatial models using CT boundaries normalized to the U.S. Census 2000. In stage 2, temporal patterns in the race-and year-specific estimates of racial/ethnic and socioeconomic effects are explored using a variety of methods. Our approach provides a straightforward means of fitting spatiotemporal models in large data sets, while highlighting differences in spatial patterning across racial/ethnic population and across time.

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A cluster model for space–time disease counts

Cited by 25 publications

References 26 publications

Time-variant clustering model for understanding cell fate decisions

Time-variant clustering model for understanding cell fate decisions

Bayesian statistics in medicine: a 25 year review

Methodologic implications of social inequalities for analyzing health disparities in large spatiotemporal data sets: An example using breast cancer incidence data (Northern and Southern California, 1988–2002)

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