1. 1 Statistical Modeling  To develop predictive Models by using sophisticated statistical techniques on large databases

2. 2 Identify Outcomes of Interest and Potential Predictors  Pain control: yes-no  Quality of Life  Pain scores  Hospitalization  Other

3. 3 Current Prediction Models for Pain-Related Outcomes  Clinician’s experience  Statistical Models  Other

4. 4 Available Databases  To identify all databases containing relevant information on pain management, and potential predictors of pain- related outcomes

5. 5 Data Mining  “The process of secondary analysis of large databases aimed at finding unsuspected relationships…” (Hand, 1998)  “Seeks to build statistical models that allow the prediction of one variable in terms of known values of other” (Hand, 2001).

6. 6 Data Mining  Select pain score measurement, pain-related outcomes, and all potential predictors from existing databases  Develop Predictive Models  Pattern Recognition

7. 7 Statistical Methods  Choice of statistical technique dictated by type of response  Continuous (Gaussian)  Dichotomous  Categorical

8. 8 Model Building  Multivariate linear regression  Logistic regression  Classification and regression trees  Neural networks  Generalized additive models  Structural equation models  Other

9. 9 Logistic Regression  Models the probability of a dichotomous outcome (e.g. pain control, yes/no) as a function of other variables  Used in Demography, Epidemiology (cohort studies, case-control, matched case-control studies)

10. 10 Classification and Regression Trees (CART)  An exploratory technique for uncovering structure in the data  Useful for classification and regression problems where one has a set of classification or predictor variables and a single-response variable (Clark & Pregibon, in Statistical Models in S).

11. 11 Neural Networks (NN)  Artificial neural networks refer to computing algorithms that use large, highly connected networks of relatively simple elements (neurons) to perform complex tasks, such as pattern recognition  NN were originally intended as realistic models of neural activity in the human brain

12. 12 Essential Features of NN  Basic computing elements, referred to as neurons, nodes, or computing units  Network architecture describing the connections between computing units  The training algorithm used to find of the network parameters for performing a particular task  (Stern, Technometrics 1996)

13. 13 Computer Intensive Methods  Refer to methods that involve the computation of a statistic form many artificially constructed data sets (Noreem, 1989)  Bootstrap methods involve repeated sampling from the sample itself and are used for hypothesis testing, and model variability, validity, stability, building.

14. 14 Selection of Predictive Factors  Expert-opinion  Automated variable selection (e.g. stepwise regression, “chunk-wise” regression, etc)  Computer intensive methods (e.g. bootstrapping)

15. 15 Model Assessment and Validation  Data splitting  Cross-validation  Bootstrapping  External  Receiver Operating Characteristic (ROC) curves

16. 16 Create Large Database  New database is designed  Including  Pain score measurement(s)  Pain-related outcomes  Potential outcome predictors  Repeated Measures

17. 17 Update Predictive Models  Refine existing predictive models  Develop new predictive models to accommodate additional information collected (e.g. new pain scores, repeated measures, etc).  Integrate qualitative and quantitative predictors into prediction models

18. 18 Uses of Database  Clinical Decision Making  Patient/caregiver feedback  Epidemiologic research  Data mining