Dynamic treatment regimes: practical design considerations

By Philip W. Lavori, R. Dawson. 2004

227 citations recorded by the source corpus.

Background Clinical management of chronic disease requires a dynamic treatment regime (DTR): rules for choosing the new treatment based on the history of response to past treatments. Estimating and comparing the effects of DTRs from a sample of observed trajectories of treatment and outcome depends on the untestable assumption that new treatments are assigned independently of potential future responses to treatment, conditional on the history of treatments and response to date (“sequential ignorability”). In longitudinal observational studies, sequential ignorability must be assumed, while randomization of dynamic regimes can guarantee it. Methods Using several clinical examples, we describe the simplest randomized experimental designs for comparing DTRs. We begin by considering an initial treatment A and a second treatment B, and discuss how a dynamic treatment regime that starts with A and leads (sometimes) to B, might be compared to either fixed treatment A or B. We also illustrate the problem of finding the optimal sequence of treatments in a DTR, when there are several choices. We describe and contrast two ways of incorporating randomization into studies to compare such regimes: baseline randomization among DTRs versus randomization at the decision points (sequentially randomized designs). Conclusions We discuss estimation and inference from both baseline randomized and sequentially randomized designs and conclude with a discussion of the differences between the experimental and observational approaches to optimizing and comparing dynamic treatment regimes.

Subfields: Economics and Econometrics, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Statistics and Probability

Citations in the corpus

Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data (2017, background): “Methods to identify the optimal DTRs have recently received attentions in the statistical community [7], [8], [9], [10], [11], [12], [13], [14], and most of exsiting methods focused on data from randomized clinical trials.”
Adaptive treatment strategies in chronic disease. (2008, background): “There are broadly speaking three modes of inquiry into the relative merits of different ATS (4,6,7).”
Interactive model building for Q-learning (2014, methodology): “Treatments are randomly assigned at each stage as in a sequential multiple assignment randomized trial design (Murphy, 2005b; see also Lavori and Dawson, 2000, 2004).”
A Pilot SMART for Developing an Adaptive Treatment Strategy for Adolescent Depression (2015, unknown): “ATSs can be developed and refined within the context of a sequential multiple assignment randomized trial (SMART; Lavori & Dawson, 2000, 2003; Murphy, 2005).”
Estimating Response-Maximized Decision Rules With Applications to Breastfeeding (2009, background): “Other work in the area includes consideration of randomization, sample size calculations, and variable selection (Lavori 2000; Dawson and Lavori 2002; Lavori and Dawson 2004; Murphy 2004; Murphy et al. 2007; Oetting, Levy, Weiss, and Murphy 2008).”
Adaptive Interventions in Child and Adolescent Mental Health (2016, background): “…literature, they are commonly referred to as “dynamic treatment regimens” (Chakraborty & Moodie, 2013; Laber, Lizotte, Qian, Pelham, & Murphy, 2014; Lavori & Dawson, 2004; Murphy, 2003; Murphy & Almirall, 2009; Shortreed & Moodie, 2012), “treatment policies” (Lunceford, Davidian, & Tsiatis,…”
Subgroup Identification and Variable Selection from Randomized Clinical Trial Data. (2013, unknown): “Additionally, we briefly argue that, following the results of Mammen [1991], the oracle properties may also hold for the constrained approach, and it would be interesting to investigate this more formally.”
Constructing evidence-based treatment strategies using methods from computer science. (2007, background): “Adaptive treatment strategies (Lavori and Dawson, 1998,2003;Lavori et al., 2000;Murphy, 2003;Murphy and McKay, 2004;Collins et al., 2004) are operationalized clinical guidelines.”
Dynamic treatment regimes (2009, background): “Lavori and Dawson (2004) discussed practical considerations for the design.”
Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimens (2016, methodology): “Parametric methods based on G-computation and Monte Carlo simulation (Lavori & Dawson, 2004) and Bayesian approaches (Wathen & Thall, 2008; Arjas & Andreev, 2000; Arjas & Saarela, 2010) were proposed.”
New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes (2015, methodology): “The foregoing assumptions are satisfied when data are collected in a SMART (Lavori and Dawson 2004).”
Interactive Q-learning for Quantiles (2017, methodology): “Our motivation for this restriction is to mimic a two-stage SMART where treatments are randomized at both stages, thus simplifying our illustration.”
Designing a Pilot Sequential Multiple Assignment Randomized Trial for Developing an Adaptive Treatment Strategy (2012, unknown): “ATSs are also known as adaptive interventions and dynamic treatment regimes [7–22].”
Q-LEARNING WITH CENSORED DATA (2012, background): “The first two challenges are shared with general multistage decision optimization (Lavori and Dawson, 2004; Moodie et al., 2007).”
Adaptive Interventions Treatment Modelling and Regimen Optimization Using Sequential Multiple Assignment Randomized Trials (Smart) and Q-Learning (2018, background): “For this purpose, numerous literature on clinical trial design employing experimental data are present [5, 29-32, 42, 43] that defines construction of sequential multiple assignment randomized trial (SMART) where a patient is randomized more than one time through all possible treatment options at ea”