Introduction to Applied Biostatistics

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Advanced biostatistics in clinical research: 

Advanced biostatistics in clinical research

Analysis of clustered data: 

Analysis of clustered data Examples Repeated measures of individual patients Measures of different patients treated by the same doctor

Repeated measures are often correlated: 

Repeated measures are often correlated A patient’s measurements will tend to run high (or low) over time Patients managed by the same physician may be managed more similarly than patients managed by different physicians

Common objectives: 

Common objectives Describe changes in the response over time Understand how the changes are related to covariates of interest

Paired t-test: Stable characteristics do not confound results: 

Paired t-test: Stable characteristics do not confound results Lecture 1: Fixed effects models

Extensions of fixed effects models: 

Extensions of fixed effects models What if there is one pre- and two post-measurements? What if there are four measurements but no treatment (a comparison of two trends over time)

PowerPoint Presentation: 

Which is best? Lecture 2: Discrete choice models

Lecture 2: Discrete time logistic regression: 

Lecture 2: Discrete time logistic regression

Oral treatment to reduce blood lead levels: 

Oral treatment to reduce blood lead levels Lecture 3: Introduction to longitudinal analysis and repeated measures analysis

PowerPoint Presentation: 

Lecture 4: Clinical examples of longitudinal analysis and repeated measures analysis

Lecture 5: competing risks: 

Lecture 5: competing risks

Lecture 6: multi-state models: 

Lecture 6: multi-state models

Lecture 7: overview of multi-level models: 

Lecture 7: overview of multi-level models

Lecture 8: Clinical examples of multi-level models: 

Lecture 8: Clinical examples of multi-level models Multi-membership model Example: patients (t’s) seeing different patients (m’s)

PowerPoint Presentation: 

Lecture 9: Discrete time multi-process models

Semivariance: 

Semivariance Lecture 10: Overview of spatial analysis

Interpolated map: 

Interpolated map

Spatial analysis: 

Spatial analysis

Nearest neighborhood analysis: 

Nearest neighborhood analysis

Clinical accessibility: 

Clinical accessibility Lecture 11: Selected applications using spatial analysis

Examine distributions: 

Examine distributions

Statistically adjusted mortality rates: 

Statistically adjusted mortality rates

Lecture 12: Overview of structural equation modeling: 

Lecture 12: Overview of structural equation modeling

Latent variables f1 and f2 Predictors x1, x2, and x3: 

Latent variables f1 and f2 Predictors x1, x2, and x3

Lecture 13: Growth models using structural equation models: 

Lecture 13: Growth models using structural equation models

Parallel growth processes: 

Parallel growth processes

Latent class analysis: 

Latent class analysis Lecture 14: Advanced topics using structural equation models

Prostate specific antigen and development prostate cancer: 

Prostate specific antigen and development prostate cancer

Multilevel latent variables: 

Multilevel latent variables

Lecture 15: Intensive longitudinal designs : 

Lecture 15: Intensive longitudinal designs Urges to smoke in a smoking cessation program Participants were beeped on approximately 250 occasions on a handheld palmtop computer When beeped they answered a questionnaire on their current setting and activities, and their mood and urges to smoke

PowerPoint Presentation: 

Regulation of intimacy and disclosure in marriage

Coupled trajectories: 

Coupled trajectories