[Data analysis: challenges and specifics in neuroscience and psychiatry]

Area of Science:

• Biostatistics
• Clinical Medicine
• Psychiatry

Background:

• The volume of clinical data is rapidly increasing, outpacing analytical capabilities.
• This data overload hinders the effective use of information for patient diagnosis, therapy, and prognosis.
• Biostatistical analysis is crucial but faces significant challenges, especially for non-statisticians.

Purpose of the Study:

• To discuss principles of big data statistical analysis in clinical medicine research and practice.
• To highlight the specific challenges and required expertise for biostatistical analysis in psychiatry.
• To present and discuss key research questions in psychiatric big data from a biostatistical perspective.

Main Methods:

• Review and discussion of biostatistical principles for big data analysis.
• Focus on the unique aspects of psychiatric data analysis.
• Illustration of common complications in psychiatric data analysis.

Main Results:

• Big data analysis in psychiatry requires specialized approaches and experience distinct from other clinical fields.
• Standard multivariate statistics and machine learning methods are often insufficient for complex psychiatric data.
• Analysis of big data in psychiatric research and practice is a complex endeavor, not a routine service.

Conclusions:

• Effective utilization of clinical big data necessitates sophisticated biostatistical methodologies.
• Psychiatric big data analysis presents unique challenges demanding tailored statistical expertise.
• Addressing these challenges is vital for advancing psychiatric research and improving clinical decision-making.