60 citations · 64 across the 5 of their papers we have counts for
5 papers
A non-parametric Bayesian approach for adjusting partial compliance in sequential decision making
Indrabati Bhattacharya, Brent A. Johnson, William Artman +4
Existing methods in estimating the mean outcome under a given dynamic treatment regime rely on intention-to-treat analyses which estimate the effect of following a certain dynamic…
Sample size estimation for comparing dynamic treatment regimens in a SMART: a Monte Carlo-based approach and case study with longitudinal overdispersed count outcomes
Jamie Yap, John J. Dziak, Raju Maiti +4
Dynamic treatment regimens (DTRs), also known as treatment algorithms or adaptive interventions, play an increasingly important role in many health domains. DTRs are motivated to a…
Bayesian Set of Best Dynamic Treatment Regimes and Sample Size Determination for SMARTs with Binary Outcomes
William J. Artman, Ashkan Ertefaie, Kevin G. Lynch +1
One of the main goals of sequential, multiple assignment, randomized trials (SMART) is to find the most efficacious design embedded dynamic treatment regimes. The analysis method k…
Adjusting for Partial Compliance in SMARTs: a Bayesian Semiparametric Approach
William J. Artman, Ashkan Ertefaie, Kevin G. Lynch +2
The cyclical and heterogeneous nature of many substance use disorders highlights the need to adapt the type or the dose of treatment to accommodate the specific and changing needs…
Hidden Markov models for alcoholism treatment trial data
Kenneth E. Shirley, Dylan S. Small, Kevin G. Lynch +2
In a clinical trial of a treatment for alcoholism, a common response variable of interest is the number of alcoholic drinks consumed by each subject each day, or an ordinal version…