Announcements: The syllabus has been updated to include additional topics at the end of semester in order to reflect the fact that we’re ahead of schedule. As mentioned, this does not change the problem set due dates, nor the exam content. The additional topics, presuming we have time to cover them, are for enriching your biostatistics toolbox!
Syllabus: The Spring 2021 course syllabus can be found here (updated 03/12/2021).
Lecture notes and recordings: It is possible that not all of the below material will be covered; these notes should be treated like a textbook in which certain subsections may be skimmed or skipped entirely. For folks who are enthusiastic about performing data analysis in R, the rightmost column contains supplementary notes that walk through examples using R code. You can find links to recordings of the course lectures here (you will need the password; also, please keep in mind that technology is not always 100% reliable).
| # | Topic | Slides | R examples |
|---|---|---|---|
| 1 | Welcome and review | Link | Link |
| 2 | Simple linear regression | Link | Link |
| 3 | Multiple linear regression | Link | Link |
| 4 | Regression with discrete outcomes | Link | Link |
| 5 | Regression with time-to-event outcomes | Link | Link |
| 6 | Prediction and flexible methods | Link | Link |
| 7 | Methods for correlated data | Link | Link |
| 8 | Bayesian methods | Link | Link |
| 9 | Methods for missing data | Link | N/A |
| 10 | Causal inference | Link | N/A |
| 11 | The bootstrap | Link | N/A |
| 12 | Sample size and power | Link | N/A |
| 13 | Approaching data analysis | Link | N/A |
Labs: Links to this year’s labs can be found below.
| # | Date | Topic | Lab | Key |
|---|---|---|---|---|
| 1 | 01/28 | Data management in Stata | Link | Link |
| 2 | 02/01 | Two-sample testing | Link | Link |
| 3 | 02/08 | Simple linear regression | Link | Link |
| 4 | 02/15 | Multiple linear regression (I) | Link | Link |
| 5 | 02/22 | Multiple linear regression (II) | Link | Link |
| 6 | 03/01 | Binary outcome regression | Link | Link |
| 7 | 03/08 | Discrete outcome regression | Link | Link |
| 8 | 03/15 | Survival analysis | Link | Link |
| 9 | 03/29 | The elastic net penalty | Link | Link |
| 10 | 04/05 | Correlated data | Link | Link |
| 11 | 04/12 | Bayesian regression | Link | Link |
| 12 | 04/19 | Multiple imputation | Link | Link |
Problem sets and keys: This year’s collection of problems can be found here and solutions will be posted in this document. Problems will be due in batches on a biweekly basis (see below for a breakdown and for keys to last year’s assignments).
| # | Due date | Required | Optional | Key (2020) |
|---|---|---|---|---|
| 1 | 02/04 | 1, 2 | 3 | Link |
| 2 | 02/18 | 4, 5, 6, 7 | 8 | Link |
| 3 | 03/04 | 9, 10, 11 | 12 | Link |
| 4 | 03/18 | 13, 14, 15, 16 | 17 | Link |
| 5 | 04/01 | 18, 19, 20 | 21 | Link |
| 6 | 04/15 | 22, 23 | 24 | Link |
| 7 | 04/29 | 25 | 26 | Link |
Data: You can find the data sets and documentation associated with the course here (you will need the password). Many in-class examples, and all problems sets will be based on these data sets. You are expected to read the documentation.
Exams: Previous exams are posted for practice. Current exams (with keys) will also be posted here when the time comes. Topics will not align perfectly from year to year, but the major function of the practice exam is to allow you to gain familiarity with the kinds of questions you’ll be expected to answer.
| Description | Study guide | Exam | Key |
|---|---|---|---|
| Previous in-class midterm | N/A | Link | Link |
| Previous take-home midterm | N/A | Link | Link |
| Exam 1 (2021) | Link | Link | Link |
| Exam 2 (2021) | Link | Link | Link |