New Statistics

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New Statistics

• Personalized Medicine

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Patent

• This presentation is based on a patent
  application on personalized medication held by
  George Mason University. Scientists and
  government organizations have free access to this
  patent

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Acknowledgement

• Farrokh Alemi, Ph.D.
• Harold Erdman, Ph.D.
• Igor Griva, Ph.D.
• Charles H. Evans, Jr., M.D., Ph.D.
• Jee Vang, Ph.D.
• Seminar on data mining

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Case of George

• Military service
• Industrial manager, polite, defines himself a
  medieval knight.
• First depression episode at 26, treated with
  clomipramine, dose unknown.
• At 30 married with a daughter
• At 45 return of depressive symptoms, treated with
  fluvoxamine 200300 mg and mirtazapine 15 mg
• Depression continues, loss of interest in work,
  difficulty with bi-polar daughter
• Loss of daughter, divorce and loss of work
• At 48, suicide

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Potential for Guided Treatmentto Increase
Remission
Data is based on 1,933 cases in STARD with
genetic profiles. Remission was defined as
score less than 5 in the Quick Inventory of
Depressive SymptomsClinical Version.
Cumulative in remission rate at previous
level (1 rate at previous level) rate at
current level.
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Clincial Success Has Been Elusive
Examined 68 candidate genes, 768
single-nucleotidepolymorphism in 1953 patients
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Clinical Success Has Been Elusive
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Make Genetic Progress Relevant to Patient at Hand

• New Statistics

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Patients Like Me Algorithm

• For a patient, select cases in the database in
  order of their similarity to the patient
• Check if the number of failures exceed Walds
  Sequential Probability Ratio Test

Additional steps on selection of features not
presented in this slide.
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Patients Like Me Algorithm

• For a patient, select cases in the database in
  order of their similarity to the patient
• Check if the number of failures exceed Walds
  Sequential Probability Ratio Test

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Enables Personalized Medicine

• No one is the average patient (Duncan Neuhauser)
• Different recommendations to different groups
• Subgroup analysis is not practical in traditional
  statistics
• For example, different markers for male/females

These findings are consistent with the
hypothesis that major depression may be related
to some distinct genes in male versus female
patients. Paddock et al., Am J Psychiatry 2007
164 1168
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Patients Like Me Algorithm
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Simulated Data of Treatment Success in Two
Dimensions
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Simulated Data of Treatment Success in Two
Dimensions
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Simulated Data of Treatment Success in Two
Dimensions
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STARD Data

• Citalopram
• 1933 cases
• Remission of symptoms
• Hamilton score of less than 7
• 50 decrease in Inventory of Depressive
  Symptomatology Clinician-Rated
• Many independent predictors
• 45 phenotypic features
• 216 Single Nucleotide Polymorphisms

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Success Has Been Elusive

• SNPs in STARD not predictive of outcomes
• Random forest
• Logistic regression
• BESt

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5-fold Cross Validation of Patients Like Me
Algorithm in 386 Cases

• Select 1/5 data for validation
• Use training data to estimate parameters
• Use SKNN to predict validation set
• Repeat the process

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5-fold Cross Validation of Patients Like Me
Algorithm in 386 Cases
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5-fold Cross Validation of Patients Like Me
Algorithm in 386 Cases
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Optimization of SKNN

• Use training data to set SKNN parameters
• Percent of predicted cases correctly classified
  92

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SKNN helps some patients using data otherwise
ignored

• Conclusions

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Medications withdrawn from market could benefit
some patients

• Implications

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Scientists should publish databases not just
their papers

• Implications

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Questions or Comments

• Discussion

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SKNN Feature Selection
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SKNN Feature Selection
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5-fold Cross Validation Confusion Matrices (no
optimizaton)