Dataset Information

Towards a Multidimensional Approach to Bayesian Disease Mapping.

ABSTRACT: Multivariate disease mapping enriches traditional disease mapping studies by analysing several diseases jointly. This yields improved estimates of the geographical distribution of risk from the diseases by enabling borrowing of information across diseases. Beyond multivariate smoothing for several diseases, several other variables, such as sex, age group, race, time period, and so on, could also be jointly considered to derive multivariate estimates. The resulting multivariate structures should induce an appropriate covariance model for the data. In this paper, we introduce a formal framework for the analysis of multivariate data arising from the combination of more than two variables (geographical units and at least two more variables), what we have called Multidimensional Disease Mapping. We develop a theoretical framework containing both separable and non-separable dependence structures and illustrate its performance on the study of real mortality data in Comunitat Valenciana (Spain).

SUBMITTER: Martinez-Beneito MA

PROVIDER: S-EPMC5918309 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Towards a Multidimensional Approach to Bayesian Disease Mapping.

Martinez-Beneito Miguel A MA Botella-Rocamora Paloma P Banerjee Sudipto S

Bayesian analysis 20160318 1

Multivariate disease mapping enriches traditional disease mapping studies by analysing several diseases jointly. This yields improved estimates of the geographical distribution of risk from the diseases by enabling borrowing of information across diseases. Beyond multivariate smoothing for several diseases, several other variables, such as sex, age group, race, time period, and so on, could also be jointly considered to derive multivariate estimates. The resulting multivariate structures should ...[more]

PMID: 29707101

Similar Datasets

Project description:Post-stroke cognitive and linguistic impairments are debilitating conditions, with limited therapeutic options. Domain-general brain networks play an important role in stroke recovery and characterizing their residual function with functional MRI has the potential to yield biomarkers capable of guiding patient-specific rehabilitation. However, this is challenging as such detailed characterization requires testing patients on multitudes of cognitive tasks in the scanner, rendering experimental sessions unfeasibly lengthy. Thus, the current status quo in clinical neuroimaging research involves testing patients on a very limited number of tasks, in the hope that it will reveal a useful neuroimaging biomarker for the whole cohort. Given the great heterogeneity among stroke patients and the volume of possible tasks this approach is unsustainable. Advancing task-based functional MRI biomarker discovery requires a paradigm shift in order to be able to swiftly characterize residual network activity in individual patients using a diverse range of cognitive tasks. Here, we overcome this problem by leveraging neuroadaptive Bayesian optimization, an approach combining real-time functional MRI with machine-learning, by intelligently searching across many tasks, this approach rapidly maps out patient-specific profiles of residual domain-general network function. We used this technique in a cross-sectional study with 11 left-hemispheric stroke patients with chronic aphasia (four female, age ± standard deviation: 59 ± 10.9 years) and 14 healthy, age-matched control subjects (eight female, age ± standard deviation: 55.6 ± 6.8 years). To assess intra-subject reliability of the functional profiles obtained, we conducted two independent runs per subject, for which the algorithm was entirely reinitialized. Our results demonstrate that this technique is both feasible and robust, yielding reliable patient-specific functional profiles. Moreover, we show that group-level results are not representative of patient-specific results. Whereas controls have highly similar profiles, patients show idiosyncratic profiles of network abnormalities that are associated with behavioural performance. In summary, our study highlights the importance of moving beyond traditional 'one-size-fits-all' approaches where patients are treated as one group and single tasks are used. Our approach can be extended to diverse brain networks and combined with brain stimulation or other therapeutics, thereby opening new avenues for precision medicine targeting a diverse range of neurological and psychiatric conditions.

Dataset Information

Towards a Multidimensional Approach to Bayesian Disease Mapping.

Publications

Towards a Multidimensional Approach to Bayesian Disease Mapping.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets