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

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

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

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

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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).

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

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

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

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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)

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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).

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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

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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)

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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.

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

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

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

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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

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

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