Biostatistics development in theoretical and computational methods has grown rapidly in scientific research. Together with health informatics systems that curate multidimensional data at the population level, researchers now enjoy more complex and richer information than ever before to discover and invent new methods, tools, therapies, and medicines for vulnerable populations.
Recent developments in biostatistics have included novel design methods for laboratory experiments and randomized trials, analytical methods for large-dimensional data with a small number of participants, computational methods for data with a hierarchical structure, and analytical methods for systems biology and precision medicine. These developments also involve the use of Bayesian inference to integrate prior knowledge with current evidence.
Recent informatics developments have included novel system design in health informatics for data integration and linkage, enabling cross-system data inferences. For example, from health, sociology, education, and forensic science to solve unsolvable problems with multi-level factors and perhaps for difficult-to-reach or engage populations (i.e., populations experiencing suicide-related distress, Indigenous populations, LGBTQIA+, older people). Biotechnological advancements also facilitate researchers in using e-mental health devices and mobile apps for sensitive data collection and secure data management.
Molecular-focused bioinformatics development is a driving area in precision psychiatry. Bioinformatics methods integrate omics, such as genomics and proteomics, in the discovery of molecular markers for diagnosis and treatment. AI and machine learning have assisted in the discovery and facilitated the emergence of digital biomarkers.
The data curation involved in these research methods should consider ethical AI, algorithmic bias mitigation, Indigenous data governance, and sensitive data governance in the context of mental health. The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are critical for sensitive mental health data and underrepresented populations.
This Research Topic is a collection for mental health research studies using and/or creating advanced biostatistical methods, for observational and randomized control studies, and novel health informatics and bioinformatic methods. The purpose is to encourage more mental health research that utilizes advanced methods of biostatistics and informatics in research design optimization, thereby easing the recruitment process, advancing analytical methods for complex designs, finding solutions to new treatment discoveries, and supporting clinical decision-making.
The Topic Editors welcome submissions of various article types exploring mental health research using health informatics and bioinformatics, focusing on but not limited to:
• Data integration and linkage from the health system
• Health informatics system design and decision making for mental health
• Utilization of an informatics system for a cross-sectional and longitudinal study
• Bioinformatics methods for precision medicine in psychiatric medicines
• Molecular focus bioinformatic system integration (including cross-integration with the health system).
Methods and models of interest include:
• Large dimensional data analytical methods, for example, network methods, PCA, cluster methods, and machine learning
• Multilevel/hierarchical models in studies using complex design (e.g., schools/wards/centre clusters design, omics, multistage design with stratification and clusters)
• Advanced survival analysis (time-to-event) in a mental health study
• Innovative design or adaptive design for a randomized control trial in mental health research
• Bayesian inference utilizing prior information to inform hard-to-reach participants
• A new statistical method or a statistical method for a new insight with a mental health case study
• The use of PGx for psychotropic medication prescription with a personalized dosage regimen
• The use of AI in large-scale data
• Missing data imputation techniques (e.g., multiple imputation for longitudinal data)
• Validation methods and standards for digital biomarkers.
Biostatistics development in theoretical and computational methods has grown rapidly in scientific research. Together with health informatics systems that curate multidimensional data at the population level, researchers now enjoy more complex and richer information than ever before to discover and invent new methods, tools, therapies, and medicines for vulnerable populations.
Recent developments in biostatistics have included novel design methods for laboratory experiments and randomized trials, analytical methods for large-dimensional data with a small number of participants, computational methods for data with a hierarchical structure, and analytical methods for systems biology and precision medicine. These developments also involve the use of Bayesian inference to integrate prior knowledge with current evidence.
Recent informatics developments have included novel system design in health informatics for data integration and linkage, enabling cross-system data inferences. For example, from health, sociology, education, and forensic science to solve unsolvable problems with multi-level factors and perhaps for difficult-to-reach or engage populations (i.e., populations experiencing suicide-related distress, Indigenous populations, LGBTQIA+, older people). Biotechnological advancements also facilitate researchers in using e-mental health devices and mobile apps for sensitive data collection and secure data management.
Molecular-focused bioinformatics development is a driving area in precision psychiatry. Bioinformatics methods integrate omics, such as genomics and proteomics, in the discovery of molecular markers for diagnosis and treatment. AI and machine learning have assisted in the discovery and facilitated the emergence of digital biomarkers.
The data curation involved in these research methods should consider ethical AI, algorithmic bias mitigation, Indigenous data governance, and sensitive data governance in the context of mental health. The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are critical for sensitive mental health data and underrepresented populations.
This Research Topic is a collection for mental health research studies using and/or creating advanced biostatistical methods, for observational and randomized control studies, and novel health informatics and bioinformatic methods. The purpose is to encourage more mental health research that utilizes advanced methods of biostatistics and informatics in research design optimization, thereby easing the recruitment process, advancing analytical methods for complex designs, finding solutions to new treatment discoveries, and supporting clinical decision-making.
The Topic Editors welcome submissions of various article types exploring mental health research using health informatics and bioinformatics, focusing on but not limited to:
• Data integration and linkage from the health system
• Health informatics system design and decision making for mental health
• Utilization of an informatics system for a cross-sectional and longitudinal study
• Bioinformatics methods for precision medicine in psychiatric medicines
• Molecular focus bioinformatic system integration (including cross-integration with the health system).
Methods and models of interest include:
• Large dimensional data analytical methods, for example, network methods, PCA, cluster methods, and machine learning
• Multilevel/hierarchical models in studies using complex design (e.g., schools/wards/centre clusters design, omics, multistage design with stratification and clusters)
• Advanced survival analysis (time-to-event) in a mental health study
• Innovative design or adaptive design for a randomized control trial in mental health research
• Bayesian inference utilizing prior information to inform hard-to-reach participants
• A new statistical method or a statistical method for a new insight with a mental health case study
• The use of PGx for psychotropic medication prescription with a personalized dosage regimen
• The use of AI in large-scale data
• Missing data imputation techniques (e.g., multiple imputation for longitudinal data)
• Validation methods and standards for digital biomarkers.