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Survival analysis and machine learning
Survival analysis and machine learning
Conference Schedule
Conference Schedule
Registration begins at 8:00 am on 14 July. The workshop runs from 8:30 am to 6:00 pm each day.
10:30 tea, 1pm lunch, and 4pm tea will be provided at EKA hotel throughout the workshop.
Day 1
Day 1
Morning
8.30 Introductions and welcoming remarks
Objectives of the workshop
8.45 Opening remarks
Assoc Dean SPGSR&I, Moi University
9.00 Opening remarks
DVC ARESA, Moi University
9.15 Technical session 1
Bernard Osang'ir, Hasselt
R Studio and R Markdown
Downloads for Session 1
Slides for Session 1 - PowerPoint Version
Slides for Session 1 - PDF version
10.30 Tea Break & Group Photo
11.00 Technical session 2
Joseph Hogan, Brown U
Introduction to datasets and event time data
12.00 Technical session 3
Joseph Hogan, Brown U
Regression for event time data
Downloads for Sessions 2 and 3
(you need to download these to the same directory on your computer)
ZIP file with all materials for Sessions 2 & 3
13.00 Lunch break
Afternoon
14.00 Hands-on session 1
Prepare summary statistics and exploratory analysis
Fit regression for event time data
Check assumptions in regression models
Downloads
16.00 Tea break
Day 2
Day 2
Morning
8.30 Registration
9.00 Technical session 4
Richard Mugo, Moi University & AMPATH
Pooled logistic regression
Prediction for survival data using pooled logistic regression
Downloads
Lab exercise - R markdown file
Link to paper on pooled logistic regression
10.30 Tea break
11.00 Technical session 5
Joseph Hogan, Brown U
Prediction from Cox regression
Discussion and Q&A
Downloads
Slides (updated from Sessions 2 & 3)
** Please install package pec for this session
13.00 Lunch break
Afternoon
14.00 Hands-on session 2
Fit a pooled logistic regression model
Generate individual-specific predictions
Validate predictions against actual outcomes
Use different methods for handling missing covariates
16.00 Tea break
Day 3
Day 3
Morning 8.30 Registration
9.00 Case Study 1Richard Mugo, Moi U and AMPATHMachine learning with pooled logistic regression
9.45 Case Study 2Joseph Hogan, Brown UMortality prediction using random forest
Downloads Slides for case study 2 (updated from previous sessions) R code for case study 2
Please install these packages: nanair, VIM, mice
10.30 Tea break
11.00 Case Study 3 Ziv Shkedy, Hasselt UDevelopment of high dimensional microbiome biomarkers using machine learning methods for survival data Downloads Slides for Case Study 3
12.30 Discussion, Q&A Fairness and equity Technical questions
13.00 Lunch break
Afternoon 14.00 Hands-on session 3Use machine learning for mortality predictionCompare performance to standard regression models
15.15 Q&A, Group discussion
16.00 Tea break
Day 4
Day 4
MorningIntroductions Overview of the group’s goalsUpdates from the Indexing GroupUpdates from the Scoping Review GroupLogistics for future meetings
Downloads Powerpoint Slides Scoping Review Protocol on Social Determinants of Health Data in Sub-Saharan Africa: Progress and the Work Ahead of Us
AfternoonOpen DiscussionsClosing CeremonyGroup Photo
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