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Additional info for Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine
For a complete description of the STAR*D study design, see Fava et al. (2003) and Rush et al. (2004). We will re-visit this study in Chap. 8 in the context of making inference about the parameters indexing the optimal DTRs. 5 Discussion In this chapter, we have described the two sources of data that are commonly used for estimating DTRs: observational follow-up studies and SMARTs. The use of observational data adds an element of complexity to the problem of estimation and requires careful handling and additional assumptions, due to the possibility of confounding.
To use the formula, one needs to postulate the effect size δ , as is the case in standard two-group randomized controlled trials (RCTs). ”. In other words, the researcher wants to compare the mean primary outcomes of two groups of responders (those who get TM versus TMC as the secondary treatment). As before, standard formula can be used. e. δ= E(Y |Response, A2 = TM) − E(Y |Response, A2 = TMC) [Var(Y |Response, A2 = TM) + Var(Y |Response, A2 = TMC)]/2 . Let γ denote the overall response rate to initial treatment.
Efficacy and toxicity) into a single reward is an open question. Finally, policy is synonymous with dynamic treatment regime, and the value of a policy is the same as the expected primary outcome under a dynamic regime. While the problem of constructing DTRs from patient data seems to be a special case of the classical RL, it has several unique features that distinguishes it from the classical RL problem. Below we list the major distinctions: Unknown System Dynamics and the Presence of Unknown Causes: In many RL problems, the system dynamics (multivariate distribution of the data, including state transition probabilities) are known from the physical laws or other subjectmatter knowledge.