## Category: Data Analysis

- A Source of Error in Self-Reports of Pap Test Utilization
- Utilization This module shows a study whose purpose is to explore in a sample of low-income and minority women whether women over-report Pap testing because other gynecological procedures are mistaken for it.
- Analysis, Reporting, and Feedback of Surveillance Data Pt. 1
- This presentation concerns the analysis, reporting, and feedback of surveillance data.
- Analysis, Reporting, and Feedback of Surveillance Data Pt. 2
- This presentation looks at surveillance performance indicators the purpose of which is to monitor the quality of data collected by a surveillance system to identify parts of the surveillance and case investigation process that need improvement.
- Cox Regression II
- This module further explores Cox regression as discussed in the introduction to Cox regression.
- Determining the Clinical Importance of Trial Results
- This RLO demonstrates how to interpret and use clinical trial data (ARR, RRR, NNT, and confidence intervals) in practice.
- GEE and Mixed Models for Longitudinal Data Pt. 1
- This module is part one of a compilation of mixed models on longitudinal data.
- GEE and Mixed Models for Longitudinal Data Pt. 2
- This module is part two of a compilation of mixed models on longitudinal data.
- How to Conduct a Meta-Analysis
- At the end of this module the student will be able to define meta-analysis, select studies for a meta-analysis, identify different types of models, calculate summary effects, and interpret results of a meta-analysis.
- Intelligent Data Analysis (IDA)
- At the end of this module the learner will be able to understand the concept of the IDA; to meet web-sites and literature on IDA; to meet some tools of IDA; and to learn how to use IDA tools and to validate the IDA results.
- Introduction to Cox Regression Pt. 1
- This module introduces the student to the Cox Regression model.
- Introduction to Cox Regression Pt. 2
- This module is a continuation of the first part of the lecture.
- Introduction to Modeling Continuous Longitudinal Data and Repeated Measures ANOVA Pt. 1
- This module shows examples of continuous longitudinal data.
- Introduction to Modeling Continuous Longitudinal Data and Repeated Measures ANOVA Pt. 2
- This module is a continuation of part one of this lecture.
- Introduction to Survival Analysis Pt. 1
- At the conclusion of this module the learner will be able to understand what survival analysis is, know terminology and data structure, understand survival/hazard functions, understand parametric versus semi-parametric regression techniques, be introduced to Kaplan-Meier methods (non-parametric), and to understand relevant SAS procedures (PROCS).
- Introduction to Survival Analysis Pt. 2
- This module is a continuation of the first part of this lecture.
- Kaplan-Meier Methods and Parametric Regression Methods Pt. 1
- This module explains what the Kaplan-Meier method is used for: when there are no censored data, the KM estimator is simple and intuitive; when there are censored data, the KM provides estimate of S(t) that takes censoring into account; and the KM estimator is defined only at times when events occur (empirically defined).
- Kaplan-Meier Methods and Parametric Regression Methods Pt. 2
- This module is a continuation of the first part of the lecture.
- Meta-Analysis
- This RLO provides an introduction to the basic concepts of meta-analysis, which is an important and invaluable tool for summarizing data from multiple studies.
- Positive and Negative Predictive Value of Diagnostic Tests
- This RLO explains how diagnostic test results are a combination of true and false positive, or true and false negative.
- Presenting and Interpreting Meta-Analyses
- How to present and interpret the results of a meta-analysis using forest plots.
- Review of One-Way ANOVA
- This module defines ANOVA as a method to compare means between more than two groups.
- Sensitivity and Specificity
- This RLO explains how diagnostic test accuracy is described by the terms sensitivity and specificity. Sensitivity describes the accuracy of the test in detecting disease. Specificity describes the accuracy of the test in detecting health.
- Validation of Predictive Regression Models
- At the end of this module, the person should be able to know about common types of regression models, understand fundamental assumptions of regression models, understand performance criteria of predictive models, and to understand the principals of different types of validation.