Statistical Modeling

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

2. Identify Outcomes of Interestand Potential Predictors • Pain control: yes-no • Quality of Life • Pain scores • Hospitalization • Other

3. Current Prediction Modelsfor Pain-Related Outcomes • Clinician’s experience • Statistical Models • Other

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

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

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

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

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

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

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

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