activity
20082025
most cited- and -Learning Methods for Estimating Optimal Dynamic Treatment Regimes

183 citations · 259 across the 10 of their papers we have counts for

collaborators

11 papers

stat.ME2025

Independent increments and group sequential tests

Anastasios A. Tsiatis, Marie Davidian

Widely used methods and software for group sequential tests of a null hypothesis of no treatment difference that allow for early stopping of a clinical trial depend primarily on th…

stat.ME2025

Bayesian adaptive randomization in the I-SPY2 sequential multiple assignment randomized trial

Peter Norwood, Christina Yau, Denise Wolf +4

The I-SPY2 phase 2 clinical trial is a long-running platform trial that evaluates neoadjuvant treatments for locally advanced breast cancer, assigning subjects to novel agents usin…

stat.ME2024

A Generalized Logrank-type Test for Comparison of Treatment Regimes in Sequential Multiple Assignment Randomized Trials

Anastasios A. Tsiatis, Marie Davidian

The sequential multiple assignment randomized trial (SMART) is the ideal study design for the evaluation of multistage treatment regimes, which comprise sequential decision rules t…

stat.ME2022★ 1 cited

Group sequential methods for interim monitoring of randomized clinical trials with time-lagged outcome

Anastasios A. Tsiatis, Marie Davidian

The primary analysis in two-arm clinical trials usually involves inference on a scalar treatment effect parameter; e.g., depending on the outcome, the difference of treatment-speci…

stat.ME2021★ 1 cited

Estimation of the odds ratio in a proportional odds model with censored time-lagged outcome in a randomized clinical trial

Anastasios A. Tsiatis, Marie Davidian, Shannon T. Holloway

In many randomized clinical trials of therapeutics for COVID-19, the primary outcome is an ordinal categorical variable, and interest focuses on the odds ratio (active agent vs. co…

stat.AP2021★ 1 cited

Estimating Vaccine Efficacy Over Time After a Randomized Study is Unblinded

Anastasios A. Tsiatis, Marie Davidian

The COVID-19 pandemic due to the novel coronavirus SARS CoV-2 has inspired remarkable breakthroughs in development of vaccines against the virus and the launch of several phase 3 v…

stat.ME2019★ 2 cited

Sample Size Calculations for SMARTs

Eric J. Rose, Eric B. Laber, Marie Davidian +3

Sequential Multiple Assignment Randomized Trials (SMARTs) are considered the gold standard for estimation and evaluation of treatment regimes. SMARTs are typically sized to ensure…

stat.ME2016★ 4 cited

Interpretable Dynamic Treatment Regimes

Yichi Zhang, Eric B. Laber, Anastasios Tsiatis +1

Precision medicine is currently a topic of great interest in clinical and intervention science. One way to formalize precision medicine is through a treatment regime, which is a se…

stat.ME2015★ 5 cited

Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes

Yichi Zhang, Eric B. Laber, Anastasios Tsiatis +1

A treatment regime formalizes personalized medicine as a function from individual patient characteristics to a recommended treatment. A high-quality treatment regime can improve pa…

stat.ME2012★ 183 cited

- and -Learning Methods for Estimating Optimal Dynamic Treatment Regimes

Phillip J. Schulte, Anastasios A. Tsiatis, Eric B. Laber +1

In clinical practice, physicians make a series of treatment decisions over the course of a patient's disease based on his/her baseline and evolving characteristics. A dynamic treat…

stat.ME2008★ 62 cited

Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data

Anastasios A. Tsiatis, Marie Davidian

Comment on ``Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data'' [arXiv:0804.2958]