kennethataylor Goto Github PK

2022_ser_competing_events

Causal inference with competing events

beyond-bar-and-box-plots

Hands–on code to create and style chart alternatives with ggplot2

causal-inference-rstats

causalsurvivalanalysisworkshop

competingevents_young_sim_2020

R Code used in https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.8471

data-viz-ggplot2

Data Visualization for Environmental Epidemiology with ggplot2: Mastering Presentation-Grade Figures

gformula

The gfoRmula package implements the parametric g-formula in R. The parametric g-formula (Robins, 1986) uses longitudinal data with time-varying treatments and confounders to estimate the risk or mean of an outcome under hypothetical treatment strategies specified by the user.

gformula-sas

The GFORMULA macro implements the parametric g-formula in SAS. The parametric g-formula (Robins, 1986) uses longitudinal data with time-varying treatments and confounders to estimate the risk or mean of an outcome under hypothetical treatment strategies specified by the user.

iv-bounds

kennethataylor.github.io

mediationpowertutorial

medshift

:package: :game_die: R/medshift: Causal Mediation Analysis for Stochastic Interventions

mlworkshop2022

SER 2022 Workshop: ABCs of Machine Learning

ow_survival

Estimating Counterfactual Survival Functions via the Overlap Weights

rworkshopser2022

Files for the 2022 R workshop for the Society for Epidemiologic Research

sas-violinplot

Violin Plots in SAS

sas_packages

[PRODUCTION READY] - SAS Packages Framework and Repository

ser2022-workshop

Materials for two half-day workshops on Targeted Learning with the tlverse at the 2022 Society for Epidemiologic Research Meeting

ser2022_mediation_workshop

Materials for the workshop "Modern Causal Mediation Analysis" at the 2022 Society for Epidemiologic Research Meeting

ser_workshop_did_scm_2022

This is a Society for Epidemiology Research (SER) 2022 workshop on "An introduction to Difference-in-difference and Synthetic control methods for Epidemiologists" by Roch Nianogo ([email protected]) & Tarik Benmarhnia ([email protected])

simulationforepi

Epidemiology Population Simulation Files

stat_rethinking_2022

Statistical Rethinking course winter 2022

tidyverse_workshop_oscr

This is a 90' workshop to learn about tidyverse

tmleworkshop

Targeted maximum likelihood estimation (TMLE) enables the integration of machine learning approaches in comparative effectiveness studies. It is a doubly robust method, making use of both the outcome model and propensity score model to generate an unbiased estimate as long as at least one of the models is correctly specified.

transport

Transporting intervention effects from one population to another with targeted learning

transportability-workshop

Transporting treatment effects from randomized trials to real-world populations, Karolinska Institutet, 16th Nov 21

validationcomprisks

Code repository for the manuscript 'Validation of the performance of competing risks prediction models: a guide through modern methods' (published in BMJ)

veteran-pain-trends

Code for veteran pain trends