Introduction
R Packages used in these notes
- General Theme for ggplot work
- Data used in these notes
1 Building Table 1
2 BRFSS SMART Data
3 Dealing with Missingness: Single Imputation
4 Summarizing the smart_cle1 data
5 Analysis of Variance with SMART
6 Analysis of Variance
7 Analysis of Covariance
8 Analysis of Covariance with the SMART data
9 Logistic Regression: The Foundations
10 Logistic Regression and the resect data
11 A Study of Prostate Cancer
12 “Best Subsets” Variable Selection in our Prostate Cancer Study
13 Adding Non-linear Terms to a Linear Regression Model
14 Using ols from the rms package to fit linear models
15 Other Variable Selection Strategies
16 Multiple Imputation and Linear Regression
17 Multiple Imputation and Logistic Regression
18 Colorectal Cancer Screening and Some Special Cases
- 18.1 Logistic Regression for Aggregated Data
- 18.2 Probit Regression
19 Modeling a Count Outcome in Ohio SMART
20 Modeling an Ordinal Categorical Outcome in Ohio SMART
21 Analyzing Literary Styles with Multinomial Logistic Regression
22 Exploring Time To Event / Survival Data
23 Cox Regression Models for Survival Data: Example 1
24 Cox Regression Models for Survival Data: Example 2
References

Data Science for Biological, Medical and Health Research: Notes for 432

Chapter 17 Multiple Imputation and Logistic Regression

Dr. Love may write a chapter on this subject. He’s not sure. For now, this is just a placeholder.