BIOSTAT 210: Biostatistical Methods for Clinical Research IV (Fall 2013)
Section outline
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Lecture: Introduction
Review of regression; major model questions, properties of a good regression model.Faculty:Â David Glidden
Location:Â China Basin 6704 -
Lecture: Functional Form
Splines, smoothing, categorizing continuous predictorsFaculty:Â David Glidden
Location:Â China Basin 6704 -
Lecture: Repeated Measures Data
Mixed models, generalized estimating eqnsFaculty:Â David Glidden
Location:Â China Basin 6704 -
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Lecture: Missing Values
Types of missing data, MCAR/MAR/NAR, maximum likelihoodFaculty:Â David Glidden
Location:Â CB 6704 -
Lecture: Multiple Imputation
Choice of imputation model, mi packageFaculty: David Glidden
Location: China Basin 6704 -
Computer Lab
Faculty: Â David Glidden, Barbara Grimes
Location:Â Glidden/Grimes CB 6704 -
Lecture: Missing Data
Multiple imputation, maximum likelihood, inverse weightingFaculty: David Glidden
Location: China Basin 6704 -
Lecture: Causal Inference
Strengthening causal inference using regressionFaculty:Â David Glidden
Location:Â China Basin 6704 -
Computer Lab
Faculty: Â David Glidden, Barbara Grimes
Location:Â Glidden/Grimes CB 6704 -
Lecture: Propensity Scores
Propensity scores for causal inference and contrast with regression models.Faculty: David Glidden
Location: China Basin 6704 -
Lecture: Predictive Models
Building Models for PredictionFaculty: David Glidden
Location: China Basin 6704 -
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Lecture: Model Validation
Assessing PredictionFaculty: David Glidden
Location: China Basin 6704 -
Computer Lab
Faculty: Â David Glidden, Barbara Grimes
Location:Â Glidden/Grimes CB 6704 -
Lecture: Model Comparison
Quantifying Improvement in PredictionFaculty: David Glidden
Location: China Basin 6704
