LauraBalzer

AdaptivePrespec

Code for "Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials"

DemystifyML

R code for the commentary "Demystifying Statistical Inference When Using Machine Learning in Causal Research" in AJE

Estimating-90-90-90-in-SEARCH

R code to evaluate the UNAIDS 90/90/90 Cascade Coverage in the SEARCH Study - Code by Laura Balzer & Joshua Schwab

HierarchicalTMLE

R code to generate simulated data and implement the hierarchical TMLEs described in "A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure", Balzer et al. SMMR 2018.

ISES_ISEE_Workshop

Introduction to Double Robust Estimation for Causal Inference

MachineLearningLove

Code for simulation study conducted in "Machine Learning in Causal Inference: How do I love thee? Let me count the ways."

On-Generalizability

R Code to implement simulations in the Invited Commentary: 'All generalizations are dangerous, even this one.' - Dumas # Written by Laura Balzer for Epidemiology 2017

SEARCH_Analysis_Adults

R code for evaluating adult HIV incidence, health, & implementation outcomes for the first phase of the SEARCH Study (https://www.searchendaids.com/). Full statistical analysis plan available at https://arxiv.org/abs/1808.03231

tmle4rcts

Analyze randomized trials with TMLE

TwoStageTMLE

LauraBalzer/HierarchicalTMLE

R code to generate simulated data and implement the hierarchical TMLEs described in "A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure", Balzer et al. SMMR 2018.

LauraBalzer/AdaptivePrespec

Code for "Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials"

LauraBalzer/Estimating-90-90-90-in-SEARCH

R code to evaluate the UNAIDS 90/90/90 Cascade Coverage in the SEARCH Study - Code by Laura Balzer & Joshua Schwab

LauraBalzer/DemystifyML

R code for the commentary "Demystifying Statistical Inference When Using Machine Learning in Causal Research" in AJE

LauraBalzer/On-Generalizability

R Code to implement simulations in the Invited Commentary: 'All generalizations are dangerous, even this one.' - Dumas # Written by Laura Balzer for Epidemiology 2017

LauraBalzer/TwoStageTMLE

LauraBalzer/ISES_ISEE_Workshop

Introduction to Double Robust Estimation for Causal Inference

LauraBalzer/MachineLearningLove

Code for simulation study conducted in "Machine Learning in Causal Inference: How do I love thee? Let me count the ways."

LauraBalzer/SEARCH_Analysis_Adults

R code for evaluating adult HIV incidence, health, & implementation outcomes for the first phase of the SEARCH Study (https://www.searchendaids.com/). Full statistical analysis plan available at https://arxiv.org/abs/1808.03231

LauraBalzer/Simulated_paradox

Simulated data to illustrate bias due to confounding

LauraBalzer/tmle4rcts

Analyze randomized trials with TMLE

LauraBalzer/AdaptivePrespecification-Old-2016-version-

Sample R code and simulations to illustrate estimation and inference for the PATE and SATE with the unadjusted estimator, MLE with a priori-specified adjustment set, TMLE with adaptive pre-specification for initial estimation of outcome regression, and C-TMLE including collaborative estimation of the known exposure mechanism.

LauraBalzer/Comparing_CRT_Methods

LauraBalzer/CovidShinyModel

Modeling COVID-19 epidemic

LauraBalzer/Far-From-MCAR

R code corresponding to "Far from MCAR: obtaining population-level estimates of HIV viral suppression"

LauraBalzer/search_severe_htn

LauraBalzer/test-github

learning how GitHub works

LauraBalzer/TMLE-for-SATE

Sample R code and Simulations to illustrate estimation and inference for the sample average treatment effect (SATE) in trials with and without pair-matching.