Ehsan Karim

Introduction to NHANES

Scientific writing Discussion Section

Dummy Variables in Regression in SPSS

Scientific writing presenting findings via Tables and Figures

collapsibility example in epidemiological setting

Sweave (latex + R) tutorial

Prediction continuous outcome, Design Matrix, Measure prediction error: rmse/ r2, Overfit/ Optimism

Overview of supervised learning in machine learning

Lab 3d definition of collapsibility, and examples in RD, RR and OR: marginal & conditional estimates

Advantage of backward elimination over forward selection in regression methods

Unsupervised Learning: K-means, Optimal number of clusters, choose different initial means (nstart)

interaction vs effect measure modification in observational epidemiological studies (w/confounding)

Lab 4h Supopulation in Complex Survey data (Subsetting)

Prediction binary outcome, Measuring prediction error: AUC / Brier Score

Statistics with R (part 2: help search commands tutorial)

Introduction to Rmarkdown for writing an article with code + text + citation + table + figures

Statistics with R (part 3: plot and history tutorial)

Supervised Learning: CART vs Ensemble method (bagging/ boosting/ Super Learner); Variable importance

R Guide for TMLE in Medical Research (R/Medicine Conference Short Course)

RHC data description

Cross validation in machine learning: k-fold, 1-by-1, automated caret package, performance measure

Reference for machine learning topics

Lab 4g Analyzing another the analytic dataset from NHANES

Lab3c problem with change-in-estimate method for odds ratios

presenting at a conference or a seminar

Data Visualization with ggplot2 (part 2)

Data Summary with tableone

RStudio +

Scientific writing Results Section

Cross validation vs bootstrap

Model Development Considerations for Machine Learning Implementations in Clinical Applications

Critical Appraisal of Published Articles using Machine Learning methods in clinical research

Introduction to NHANES

Lab 6 (part 1b) Single Imputation of Missing Data

Survey Data Analysis: NHANES sampling, survey features, weights, inference, variance, subpopulation

More Discussion and Reporting guideline of Missing Data Analysis

Data Visualization with ggplot2 (part 3)

Prediction model, discrimination, calibration, overfitting, validation, model selection (Goal 1)

Statistical Analysis Plan (SAP)

Identifiability conditions for causal inference framework

Lab 11 (part B) gee::gee vs geepack::geeglm, compare via QIC & QICu, marginal vs conditional

Data splitting in machine learning; test, train and performance measures