09:00

collapsibility example in epidemiological setting

04:37

Cross validation vs bootstrap

01:05

Advantage of backward elimination over forward selection in regression methods

13:02

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

02:53

Reference for machine learning topics

14:11

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

32:46

Model Development Considerations for Machine Learning Implementations in Clinical Applications

09:55

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

24:22

Overview of supervised learning in machine learning

11:58

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

25:51

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

06:21

RHC data description

18:50

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

10:56

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

06:22

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

02:25:23

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

11:10

Lab 4h Supopulation in Complex Survey data (Subsetting)

10:23

Lab 4g Analyzing another the analytic dataset from NHANES

24:00

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

09:04

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

18:22

presenting at a conference or a seminar

25:59

RStudio + @GitHub: Manuscript Writing Collaboration Tools (Bookdown site + PDF via Rmarkdown)

08:33

Scientific writing Results Section

10:41

Scientific writing Discussion Section

07:20

Scientific writing Methods Section

06:33

Scientific writing Introduction Section

10:00

Scientific writing presenting findings via Tables and Figures

04:49

Different imputation methods in inferring per-protocol effects of sustained treatment strategies

12:06

RWCT Project: Developing & Evaluating Causal Inference Methods for Pragmatic Trials

20:24

The table 2 fallacy: how to present and interpret regression coefficients #confounder #adjustment

14:16

Tips and Tricks For Scientific Presentations

48:38

Identifiability conditions for causal inference framework

49:36

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

05:08

Collapsibility vs non-collapsibility in Big Data (when outcome is continuous vs binary & Lab 3C)

03:35

Centering and scaling and how that impacts interpretation

01:59

Outcome vs. multiple exposures (Goal 3)

06:54

Causal exploration: outcome vs. exposure of primary interest (Goal 2: Y-A | L relationship)

29:34

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

10:13

Inferential goals in an epidemiological study: Prediction, causal, important predictors, descriptive

13:34

Statistical modeling criteria and machine learning (more for prediction)

20:07

Identifying confounder through 5 empirical criteria

09:26

Data Analysis with dplyr (part 1)

10:59

Data Summary with tableone

07:37

Date & Time Data with lubridate (part 2)

11:25

Date & Time Data with lubridate (part 1)

05:52

Data Visualization with ggplot2 (part 3)

08:08

Data Visualization with ggplot2 (part 2)

08:37

Data Visualization with ggplot2 (part 1)

11:35

Data Analysis with dplyr (part 2)

06:11

Introduction to NHANES

09:14

Importing Data into R with readr

09:48

Introduction to R (part 3)

10:45

Introduction to R (part 2)

10:10

Introduction to R (part 1)

08:45

R and RStudio set up

32:07

Design-based analysis and NHANES

22:28

Statistical Analysis Plan (SAP)

21:36

Components of a Research Question: PICOT and FINER criteria

01:55:01

Understanding Propensity Score Matching (Post Conference Workshop for 2021 Conference - CSEB)

02:26

Lab 10 (part D) Poisson and negative binomial regression for survey data analysis