Analytics and Statistics for Population Research Panel A – ASPA

Topics

• Development and validation of statistical methods, and computational algorithms for the analysis of high-dimensional biological or biomedical data from human population health studies
• Development and validation of statistical methods and computational algorithms or other big data approaches to extract, summarize, analyze or model genetic, genomic, metagenomic, other omic, medical imaging and other diagnostic modality, biomarker and other biological or biomedical data in human population health studies
• Development and validation of machine learning and artificial intelligence procedures for the analysis of high-dimensional biological or biomedical data to advance human population health studies
• Development and validation of statistical genetic and genomic methods for assessment of biological genetic risk and protective factors of diseases in human populations
• Development and validation of statistical methods for metagenomic data to assess the role of the human microbiome in health outcomes measured in human population health studies
• Development and validation of statistical methods for scalable analysis of medical imaging and other biological diagnostic modality data from human population health studies
• Development and validation of statistical methods for design and analysis of clinical trials using population health data
• Development and validation of statistical methods for meta-analysis for use in population health studies
• Adaptation, novel assembly and novel application of existing statistical and computational methodology for biological and biomedical data analysis in population health studies

Shared Interests and Overlaps

There are shared interests in the development and application of statistical and computational methods to human population health research with Analytics and Statistics for Population Research Panel B (ASPB).  Applications focused on biological or biomarker data, particularly those that characterize complex biological/biomedical processes are reviewed in ASPA. Applications focused on observational, environmental, infectious disease, or social/behavioral data and modeling approaches for application to human population health are reviewed in ASPB.

There are shared interests in clinical data analysis using biological or biomedical data from human populations with the Clinical Data Management and Analysis (CDMA) and Clinical Informatics and Digital Health (CIDH) study sections. Applications that emphasize the development of this statistical methodology to support epidemiological studies and public health decision making using clinical biological or biomedical data are reviewed in ASPA. Applications that emphasize the clinical data analysis and methodology development for eventual translation to clinical use are reviewed in CDMA. Applications that emphasize informatics and computing methodology development using clinical data for clinical decision support and healthcare delivery are reviewed in CIDH.

There are shared interests in statistical genetics and genomics with Biodata Management and Analysis (BDMA). Applications that emphasize the development of statistical genetic and genomic methods and the development of statistical methods for scalable analysis of medical imaging and other diagnostic modality data for use in population-based research are reviewed in ASPA.  Applications that focus on computational methods for acquisition, management, querying, sharing and analysis of biological data, particularly software or computing hardware for the analysis of large genomic datasets, medical and cellular imaging are reviewed in BDMA.

There are shared interests in the analysis of omic/genetic data with Genomics, Computational Biology and Technology (GCAT).  Applications that focus on the development of statistical methodology for analysis of human omic/genetic data from population-based studies are reviewed in ASPA.  Applications that focus on development of new computational methodologies, algorithms and software as applied to –omic/genetic data are reviewed in GCAT.

There are shared interests in development of imaging tools and computational analysis of ‘-omics’ data with Neuro Infomatics, Computational and Data Analysis (NICD). Applications that emphasize developing computational methods and tools to support population-based research or focusing on statistical methodology development using neurological data are reviewed in ASPA. Applications that emphasize developing computational methods and tools to support basic biological research are reviewed in NICD.