Biostat 406 - Spring 2023 - Syllabus.pdf

• Instructor: Donatello Telesca (dtelesca at ucla dot edu)

• Lecture: TR - 4:00PM-5:20PM - [PUB HLT 41-268] - Zoom

• Lab 1A : M - 9:00AM - 9:50AM - [PUB HLT 61-235] - Zoom

• Lab 1B : F - 12:00PM - 12:50PM - [PUB HLT A1-241] - Zoom

• Office Hours: [TR: 5:20PM - 6:00PM] - Zoom

• Teaching Assistant:

• Kristen McGreevy : Office hour [M 10:00AM-11:00AM - Zoom]

Assignments

• HW1 [Due:4/17] (HW1.Rmd, HW1.pdf )

• HW2 [Due:5/01] (HW2.Rmd, HW2.pdf)

• HW3 [Due:5/15] (HW3.Rmd, HW3.pdf)

• HW4 [Due:6/01] (HW4.Rmd, HW4.pdf)

• HW5 [Due:6/12] (HW5.Rmd, HW5.pdf)

Submission Document Example: [HWexample.pdf, HWexample.Rmd]

Schedule of Lectures

• [04/04] Introduction - Review of Linear Models [Slides.Rmd, Slides.pdf - recording] [AMRC Ch 7 - 8]

• [04/06] Factorial Predictors, Interactions and Transformations [Slides.Rmd, Slides.pdf - recording] [AMRC Ch 8]

• [04/11] Introduction to Model Selection [Slides.Rmd, Slides.pdf - recording] [AMRC Ch 9]

• [04/13] Model Selection and Regularized Estimation [Slides.Rmd, Slides.pdf - recording]

• [04/18] Introduction to Statistical Machine Learning [Slides.Rmd, Slides.pdf - recording]

• [04/20] Generalized Additive Models and Tree-based Regression [Slides.Rmd, Slides.pdf - recording]

• [04/25] Introduction to Generalized Linear Models [Slides.Rmd - Slides.pdf - recording]

• [04/27] Regression Models for Binary Data [Slides.Rmd - Slides.pdf - recording]

• [05/02] Regression Models for Counting Processes [Slides.Rmd - Slides.pdf - recording]

• [05/04] Regression Models for Time-to-Event Data [Slides.Rmd - Slides.pdf - recording]

• [05/09] Cox and AFT Models [Slides.Rmd - Slides.pdf - recording]

• [05/11] Missing Data [Slides.Rmd - Slides.pdf - recording]

• [05/16] Missing Data [Slides.Rmd - Slides.pdf - recording]

• [05/18] Linear Dimension Reduction (PCA) [Slides.Rmd - Slides.pdf - recording]

• [05/23] Latent Variables and Factor Analysis [Slides.Rmd - Slides.pdf - recording]

• [05/25] Multilevel Models [Slides.Rmd - Slides.pdf - recording]

• [05/30] Unsupervised Learning and Clustering [Slides.Rmd - Slides.pdf - recording]

• [06/01] Supervised Learning and Classification [Slides.pdf - recording]

• [06/06] Practical ML in R [Slides.Rmd - Slides.pdf - recording]

• [06/08] Extra Office Hour [zoom only]

Data

• Forced Expiratory Volume (fev.txt, fev.info.txt)

• Poverty (poverty.dat)

• Body Fat and Weight (fat.dat.txt)

• Money and Democracy (CAFE.csv)

• Students Performance (student-por.csv, student-mat.csv)

• Diabetis (diabetis.csv)

• Biochemistry (biochem.csv)

• Food and Nutrition (food.txt)

• Medicare (medpar.scv)

• Personality (personality0.txt)

• Melanoma (melanoma.csv)

• Medicare (medicare.csv)

• RPPA (rppax.csv , rppay.csv)

• Breast Cancer (gbcs.csv)

• Donut (donut.txt)

• NMMAPS (chicago-nmmaps.csv)

• Exposome (Exposure.csv, Phenotype.csv)

• Afgan Terror (Afgan_terror.txt)

• Divorce (divorce.txt)

• Body (body.txt)

• Cells (cellbc.txt)

• Places (places.txt)

• NHANES (fast_nhanes.Rdta)

Coursework

Reading List

Computing and Software

Biostatistics 406 will be taught using the R computational framework, which is a powerful and freely available tool for computation, analytics and graphics.

To make the most of your first lab please follow the following steps:

1. Install R-studio on your machine. You simply need the open-source desktop version downloadable here.

2. The course will focus on reproducible research through dynamic/smart documentation. Please get somewhat familiar with R Markdown as a platform for document creation here. Simple installation instructions are available here. A comprehensive reference is available here.