Pinned Repositories
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's Repositories
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.