Restricted sub‐tree learning to estimate an optimal dynamic treatment regime using observational data

2021 ◽  
Author(s):  
Kelly Speth ◽  
Lu Wang
Keyword(s):  
Observational Data ◽  
Treatment Regime ◽  
Dynamic Treatment Regime ◽  
Dynamic Treatment ◽  
Optimal Dynamic

Model validation and selection for personalized medicine using dynamic-weighted ordinary least squares

2017 ◽  
Vol 26 (4) ◽  
pp. 1641-1653 ◽  
Author(s):  
Michael P Wallace ◽  
Erica EM Moodie ◽  
David A Stephens
Keyword(s):  
Least Squares ◽  
Ordinary Least Squares ◽  
Treatment Regime ◽  
Model Assessment ◽  
Double Robustness ◽  
Dynamic Treatment Regime ◽  
Dynamic Treatment ◽  
Optimal Dynamic ◽  
Treatment Alternatives ◽  
Least Squares Approach

Model assessment is a standard component of statistical analysis, but it has received relatively little attention within the dynamic treatment regime literature. In this paper, we focus on the dynamic-weighted ordinary least squares approach to optimal dynamic treatment regime estimation, introducing how its double-robustness property may be leveraged for model assessment, and how quasilikelihood may be used for model selection. These ideas are demonstrated through simulation studies, as well as through application to data from the sequenced treatment alternatives to relieve depression study.

scholarly journals Power analysis in a SMART design: sample size estimation for determining the best embedded dynamic treatment regime

Biostatistics ◽  
2018 ◽  
Vol 21 (3) ◽  
pp. 432-448 ◽  
Author(s):  
William J Artman ◽  
Inbal Nahum-Shani ◽  
Tianshuang Wu ◽  
James R Mckay ◽  
Ashkan Ertefaie
Keyword(s):  
Power Analysis ◽  
Ad Hoc ◽  
Treatment Effectiveness ◽  
R Package ◽  
Treatment Regime ◽  
Size Estimation ◽  
Dynamic Treatment Regime ◽  
Dynamic Treatment ◽  
The Individual ◽  
Number Of Individuals

Summary Sequential, multiple assignment, randomized trial (SMART) designs have become increasingly popular in the field of precision medicine by providing a means for comparing more than two sequences of treatments tailored to the individual patient, i.e., dynamic treatment regime (DTR). The construction of evidence-based DTRs promises a replacement to ad hoc one-size-fits-all decisions pervasive in patient care. However, there are substantial statistical challenges in sizing SMART designs due to the correlation structure between the DTRs embedded in the design (EDTR). Since a primary goal of SMARTs is the construction of an optimal EDTR, investigators are interested in sizing SMARTs based on the ability to screen out EDTRs inferior to the optimal EDTR by a given amount which cannot be done using existing methods. In this article, we fill this gap by developing a rigorous power analysis framework that leverages the multiple comparisons with the best methodology. Our method employs Monte Carlo simulation to compute the number of individuals to enroll in an arbitrary SMART. We evaluate our method through extensive simulation studies. We illustrate our method by retrospectively computing the power in the Extending Treatment Effectiveness of Naltrexone (EXTEND) trial. An R package implementing our methodology is available to download from the Comprehensive R Archive Network.

scholarly journals Influence Re-weighted G-Estimation

2016 ◽  
Vol 12 (1) ◽  
pp. 157-177
Author(s):  
Benjamin Rich ◽  
Erica E. M. Moodie ◽  
David A. Stephens
Keyword(s):  
Individualized Medicine ◽  
Treatment Strategies ◽  
Treatment Regimens ◽  
Dynamic Treatment Regime ◽  
Dynamic Treatment ◽  
Optimal Dynamic ◽  
Data Adaptive ◽  
Doubly Robust ◽  
The Cost ◽  
The Impact

Abstract Individualized medicine is an area that is growing, both in clinical and statistical settings, where in the latter, personalized treatment strategies are often referred to as dynamic treatment regimens. Estimation of the optimal dynamic treatment regime has focused primarily on semi-parametric approaches, some of which are said to be doubly robust in that they give rise to consistent estimators provided at least one of two models is correctly specified. In particular, the locally efficient doubly robust g-estimation is robust to misspecification of the treatment-free outcome model so long as the propensity model is specified correctly, at the cost of an increase in variability. In this paper, we propose data-adaptive weighting schemes that serve to decrease the impact of influential points and thus stabilize the estimator. In doing so, we provide a doubly robust g-estimator that is also robust in the sense of Hampel (15).

scholarly journals Inference about the expected performance of a data-driven dynamic treatment regime

2014 ◽  
Vol 11 (4) ◽  
pp. 408-417 ◽  
Author(s):  
Bibhas Chakraborty ◽  
Eric B Laber ◽  
Ying-Qi Zhao
Keyword(s):  
Treatment Regime ◽  
Data Driven ◽  
Dynamic Treatment Regime ◽  
Expected Performance ◽  
Dynamic Treatment

scholarly journals Q-learning for estimating optimal dynamic treatment rules from observational data

2012 ◽  
Vol 40 (4) ◽  
pp. 629-645 ◽  
Author(s):  
Erica E. M. Moodie ◽  
Bibhas Chakraborty ◽  
Michael S. Kramer
Keyword(s):  
Observational Data ◽  
Q Learning ◽  
Dynamic Treatment ◽  
Optimal Dynamic ◽  
Treatment Rules
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