1. Solving Wide Predictive Modeling Problems With Clinical and Genomic Data
Kelci J. Miclaus, PhD
Advanced Analytics R&D Manager
JMP Life Sciences
SAS Institute, Inc.

2. Precision Medicine Initiative
Introduction
Outline
Precision Medicine Initiative
Predictive Models and the Impact of “Big” Data
Tools for Model Assessment
Subgroup Analysis
Live Demonstration

3. Biological data to drive research for tailored therapies
Introduction
Precision Medicine initiative
Biological data to drive research for tailored therapies
Wide-range of application areas, including oncology and pharmacogenomics
Better predict…
Treatment outcomes
Responders
Survival or Time-to-Event
Rich data mining environment

4. Rich set of methodology for prediction problems Popular methods:
Predictive Modeling
Methods and big data
Rich set of methodology for prediction problems
Popular methods:
Continuous: GLM, PLS, Kernel methods (e.g. Ridge, Radial-Basis), Trees (e.g. Forest, Gradient Boosting), Quantile Regression
Discrete: Logistic, Discriminant, KNN
Censored: Life Regression, Cox Proportional Hazards, Buckley-James
“Big” biological data => Wide prediction problem!
Serious risk of overfitting

5. Simple  Complex Filtering Techniques
Predictive Modeling
Predictor Reduction
Simple  Complex Filtering Techniques
Known biology
Statistical testing
Clustering
Forest models or linear regression model selection
Optimization
Combination of algorithms + predictor reduction = MILLIONS of potential models
Critical to perform filtering within a cross-validated framework to prevent OVERFITTING and generalization bias in your models

6. Data Hold Out: K-fold, leave L-out, leave P-percent-out, etc…
Model Assessment
Cross-validation Model comparison
Data Hold Out: K-fold, leave L-out, leave P-percent-out, etc…
Hold Out Methods: Simple Random, Random Partition, Stratified, etc..
Performance Metrics: RMSE, Harrell’s C, AUC, Correlation, etc…

7. Specialized Prediction problems
Subgroup analysis
Identify subjects most-likely to respond to treatment
Benefits in study design / safety / ethics
Subgroup Guidance (CPMP, 2014)
Classification and Regression Trees popular models (Zink et al., 2015)
0.5 1
P(Improve if NOT Treated)
P(Improve if Treated)
INCURABLE
GET WELL ANYWAY
DRUG MAKES YOU
WORSE
DRUG CURES YOU

8. JMP Genomics and JMP Clinical Predictive Modeling Reviews Example Data
JMP Life Sciences
Live Demonstration
JMP Genomics and JMP Clinical Predictive Modeling Reviews
Example Data
Sepsis prediction in hospitals with metabolite and protein data
Survival prediction in prostate cancer with clinical trials data

9. Discovery and prediction
Hospital Biomarker utility to predict sepsis survival

10. Subgroup Analysis Interaction Trees All Randomized Subjects
Linear, Logistic or Cox Model
f(yi) = β0 + β1xi + β2Treatmenti + β3Treatmenti*xi
Significant interaction implies
differential treatment effect between
subgroups defined by binary covariate
All Randomized Subjects
Biomarker 1 Absent
Biomarker 2 Absent
Biomarker 2 Present
Biomarker 3 Absent
Biomarker 3 Present
Biomarker 1 Present
Split based on p-value of
treatment by covariate
interaction term
Su et al. (2009)

11. Virtual Twins (Foster et al., 2011)
Subgroup analysis
Virtual twins
Virtual Twins (Foster et al., 2011)
Fit forest model and tree model to response and counter-factual data estimated treatment effects

12. Subgroup identification Optimal treatment regimes
Subgroup analysis
Optimal treatment Regimes
Subgroup identification
“the right patients for a given drug”
Optimal treatment regimes
“the best drug for a given patient”
Zhang et al. (2011) methodology to fit a response regression model and propensity score logistic model to create pseudo binary response and weight (augmented inverse probability weighted estimators or AIPWE)
Use as input into predictive modeling routines including cross-validated designs (Freidlan et al., 2009)