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10 reasons why precision psychiatry will not be based on classical null-hypothesis testing

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The nature of mental illness remains a conundrum. Traditional
disease categories are increasingly suspected to misrepresent the causes underlying mental disturbance. Yet, psychiatrists and investigators now have an unprecedented opportunity to benefit from complex patterns in brain, behavior, and genes using methods from machine learning (e.g., support vector machines, modern neural-network algorithms, crossvalidation procedures). Combining these analysis techniques with a wealth of data from consortia and repositories has the potential to advance a biologically grounded redefinition of major psychiatric disorders. Increasing evidence suggests that data-derived subgroups of psychiatric patient can better predict treatment outcomes than DSM/ICD diagnoses. In a new era of evidence-based psychiatry tailored to single patients, objectively measurable endophenotypes could allow for early disease detection, individualized treatment selection and dosage adjustment to reduce the burden of disease.

Veröffentlicht in: Gesundheit & Medizin
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10 reasons why precision psychiatry will not be based on classical null-hypothesis testing

  1. 1. 10 reasons why precision psychiatry will not be based on classical null-hypothesis testing Prof. Danilo Bzdok, MD, PhD
  2. 2. 1 Missing focus on prediction Bzdok, 2017 „Classical Statistics and Statistical Learning in Imaging Neuroscience“ Shmueli, 2010 „To explain or to predict?“ Yarkoni & Westfall, 2016. Choosing prediction over explanation in psychology: Lessons from machine learning.
  3. 3. 1 Missing focus on prediction Bzdok, 2017 „Classical Statistics and Statistical Learning in Imaging Neuroscience“ Shmueli, 2010 „To explain or to predict?“ Yarkoni & Westfall, 2016. Choosing prediction over explanation in psychology: Lessons from machine learning.
  4. 4. 2 P-values cannot be applied to single patients Null hypothesis testing Out-of-sample generalization Bzdok, Krzywinski, Altman, 2017 „Machine learning: A primer“ Nature Methods Casella & Berger, 2002 „Statistical Inference“
  5. 5. 3 P-values cannot be applied to future individuals Casella & Berger, 2002 „Statistical Inference“
  6. 6. 4 Hypothesis testing was not made for high-n setting Berkson, 1938. „Some difficulties of interpretation encountered in the application of the chi-square test“ Miller, et al., 2016. „Multimodal population brain imaging in the UK Biobank prospective epidemiological study.“
  7. 7. 5 Hypothesis testing was not made for high-p setting Breiman, 2001 (page 203) Breiman, 2001. „Statistical Modeling: The two cultures“
  8. 8. 5 Hypothesis testing was not made for high-p setting James et al., 2013 (page 247) James, 2013. „Introduction to Statistical Learning“
  9. 9. 6 Inferential thinking is often dichotomic in nature Casella & Berger, 2002 „Statistical Inference“ Casella & Berger, 2002
  10. 10. 7 Small tradition in modeling many outcomes at once ADHD vs. healthy working memory performance , attention scores, IQ, socioeconomic status, significant life events, social interaction capacities, gender, smoking behavior, caffeine intake, mood assessment,… Caruana, 1998. „Multitask learning“
  11. 11. 8 Small tradition in combining heterogeneous data + + +
  12. 12. 9 Precision medicine will be driven by observational data Henke, et al., 2016. „The age of analytics: Competing in a data-driven world“ McKinsey Global Institute Manyika, et al., 2015. „Unlocking the Potential of the Internet of Things“ McKinsey Global Institute vs Controlled experiment Internet of Things
  13. 13. 10 Small tradition in phenotype discovery Insel, et al., 2015. „Brain disorders? Precisely.“ Science Bengio et al., 2013. „Representation learning: A review and new perspectives“
  14. 14. Conclusion Not a matter of tradition or taste. Different modeling paradigms serve distinct statistical goals.

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