Dylan Spicker
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Opportunity for current undergraduate and masters students…

I am currently recruiting graduate students at both the Masters and PhD level.

If you are interested in pursuing either a Masters or PhD in Statistics, and are interested in any of the research topics that I work on, please reach out. Even if you have a somewhat non-traditional background, but think that you would be a good fit, please get in touch with me.

These are funded opportunities with a lot of flexibility for you to grow and learn as a Statistician. Do not hesitate to reach out!

STAT 437

Statistical Methods for Life History Analysis

Administrative Information

  1. Course Outline - PDF Version
  2. STAT 437 - Lectures Overview

Assignments and Solutions

  1. Assignment 1 (Solution)
  2. Assignment 2 (Solution)
  3. Assignment 3 (Solution)
  4. Midterm Test (Solution)
  5. Paper Review Assignment
  6. Final Project

Helper Code and Supplementary Notes

  1. Quasi-Likelihood Theory in Full (Supplementary Notes)
  2. data_import_helper.R
  3. helper_functions.R

Lecture Videos, Slides, Notes, and Code

  1. (Lecture 001) Welcome to STAT 437
  2. (Lecture 002) What are longitudinal data?
    • Lecture Slides
  3. (Lecture 003) Exploring Longitudinal Data (Application)
    • Lecture Code
  4. (Lecture 004) Notation for Longitudinal Data (Theory)
    • Lecture Notes
  5. (Lecture 005) What is Linear Regression (Review; Theory)
    • Lecture Slides
  6. (Lecture 006) Continuous Longitudinal Data: Why Can’t we Just Use Regression? (Linear Marginal Models)
    • Lecture Slides
  7. (Lecture 007) Linear Marginal Models: Likelihood, Inference, and Asymptotics (Theory)
    • Lecture Notes
  8. (Lecture 008) Linear Marginal Models: Implementation in R (Application)
    • Lecture Code
  9. (Lecture 009) What are generalized linear models? (Review; Theory)
    • Lecture Slides
  10. (Lecture 010) Marginal Models: Accommodating non-continuous outcomes
    • Lecture Slides
  11. (Lecture 011) M-Estimation: A Practicing Statistician’s Best Friend (Conceptual, Theory, and Application)
    • Lecture Slides
    • Lecture Notes
  12. (Lecture 012) Generalized Estimating Equations: Estimating parameters from Marginal Models
    • Lecture Slides
  13. (Lecture 013) Generalized Estimating Equations: Examples of GEEs (Theory)
    • Lecture Notes
  14. (Lecture 014) Generalized Estimating Equations: Details of Asymptotic Inference (Theory)
  15. (Lecture 015) Generalized Estimating Equations: COVID-19 Example
    • Lecture Code
  16. (Lecture 016) Generalized Estimating Equations: Epilepsy Trial Example
    • Lecture Code
  17. (Lecture 017) From the Population to the Individual: Mixed Effects Models
    • Lecture Slides
  18. (Lecture 018) Linear Mixed Effects Models
    • Lecture Slides
  19. (Lecture 019) Linear Mixed Effects Models (Theory)
    • Lecture Notes
  20. (Lecture 020) Variance Testing Considerations: Constrained LRT
  21. (Lecture 021) Linear Mixed Effects Models (Application)
    • Lecture Code
  22. (Lecture 022) Transition Models for Longitudinal Data
    • Lecture Slides
  23. (Lecture 023) Transition Models (Theory)
    • Lecture Notes
  24. (Lecture 024) Transition Models (Application)
    • Lecture Code
  25. (Lecture 025) Handling Missing Data in Longitudinal Models
    • Lecture Notes
    • Lecture Slides
  26. (Lecture 026) Handling Missing Data in Longitudinal Models - MCAR, NMAR, and Likelihood Techniques
    • Lecture Code
  27. (Lecture 027) Handling Missing Data in Longitudinal Models - Imputation and Weighting
    • Lecture Code
  28. (Lecture 028) Recap of Longitudinal Methods
    • Lecture Slides
  29. (Lecture 029) Introduction to Time-to-Event Data
    • Lecture Slides
  30. (Lecture 030) Quantities of Interest for Survival Analysis
    • Lecture Notes
  31. (Lecture 031) Discrete Time to Event Data
    • Lecture Slides
  32. (Lecture 032) Discrete Time to Event Data (Theory)
    • Lecture Notes
  33. (Lecture 033) Discrete Time to Event Data Exploration (Application)
    • Lecture Code
  34. (Lecture 034) Logistic Regression and Proportional Odds Models
    • Lecture Slides
  35. (Lecture 035) Logistic Regression and Proportional Odds Models (Application)
    • Lecture Code
  36. (Lecture 036) Introduction to Continuous Time Survival Analysis
    • Lecture Slides
  37. (Lecture 037) Continuous Time Survival Analysis, Likelihood Construction (Theory)
    • Lecture Notes
  38. (Lecture 038) Location Scale Family Distributions, with log-linear Regression (Theory)
  39. (Lecture 039) Continuous Time Regression Models using Survreg (Application)
    • Lecture Code
  40. (Lecture 040) Accelerated Failure Time Models
    • Lecture Slides
  41. (Lecture 041) Accelerated Failure Time Models (Theory)
    • Lecture Notes
  42. (Lecture 042) Accelerated Failure Time Models (Application)
    • Lecture Code
  43. (Lecture 043) Proportional Hazards Models
    • Lecture Slides
  44. (Lecture 044) Proportional Hazards Models (Theory)
  45. (Lecture 045) Proportional Hazards Models (Application)
    • Lecture Code

Data Files

To save these data files, right-click and save as. The data are presented for educational purposes only. Sources for the data are available in the data_import_helper.R script, and data should be used only to follow along with the relevant lectures. Any use of this data beyond these purposes needs to be cleared with the data owners.

File Name Modified
DogOwners.csv 7/4/23
Korea Income and Welfare.csv 7/4/23
TLC.csv 7/4/23
TLC_mar.csv 7/4/23
air_pollution.csv 7/4/23
air_pollution_NMAR.csv 7/4/23
aml.csv 7/4/23
customer_churn.csv 7/4/23
dental.csv 7/4/23
gold_medal_instagram.csv 7/4/23
job_code_table.csv 7/4/23
mort.csv 7/4/23
oasis_longitudinal.csv 7/4/23
ontario_by_phu.csv 7/4/23
pasta_sales.csv 7/4/23
schoolgirls.csv 7/4/23
seizures_full.csv 7/4/23
seizures_mcar.csv 7/4/23
stroke.csv 7/4/23
teachers.csv 7/4/23
wallstreet_sentiment.csv 7/4/23
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