SMARTAR: an R package for designing and analyzing Sequential Multiple Assignment Randomized Trials

This article introduces an R package, SMARTAR (Sequential Multiple Assignment Randomized Trial with Adaptive Randomization), by which clinical investigators can design and analyze a sequential multiple assignment randomized trial (SMART) for comparing adaptive treatment strategies. Adaptive treatment strategies are commonly used in clinical practice to personalize healthcare in chronic disorder management. SMART is an efficient clinical design for selecting the best adaptive treatment strategy from a family of candidates. Although some R packages can help in adaptive treatment strategies research, they mainly focus on secondary data analysis for observational studies, instead of clinical trials. SMARTAR is the first R package provides functions that can support clinical investigators and data analysts at every step of the statistical work pipeline in clinical trial practice. In this article, we demonstrate how to use this package, using a real data example.

Adaptive treatment strategies for chronic conditions: shared-parameter G-estimation with an application to rheumatoid arthritis

Summary Most estimation algorithms for adaptive treatment strategies assume that treatment rules at each decision point are independent from one another in the sense that they do not possess any common parameters. This is often unrealistic, as the same decisions may be made repeatedly over time. Sharing treatment-decision parameters across decision points offers several advantages, including estimation of fewer parameters and the clinical ease of a single, time-invariant decision to implement. We propose a new computational approach to estimation of shared-parameter G-estimation, which is efficient and shares the double robustness of the “unshared” sequential G-estimation. We use this approach to analyze data from the Scottish Early Rheumatoid Arthritis (SERA) Inception Cohort.

Adaptive Treatment Strategies With Survival Outcomes: An Application to the Treatment of Type 2 Diabetes Using a Large Observational Database

Sequences of treatments that adapt to a patient’s changing condition over time are often needed for the management of chronic diseases. An adaptive treatment strategy (ATS) consists of personalized treatment rules to be applied through the course of a disease that input the patient’s characteristics at the time of decision-making and output a recommended treatment. An optimal ATS is the sequence of tailored treatments that yields the best clinical outcome for patients sharing similar characteristics. Methods for estimating optimal adaptive treatment strategies, which must disentangle short- and long-term treatment effects, can be theoretically involved and hard to explain to clinicians, especially when the outcome to be optimized is a survival time subject to right-censoring. In this paper, we describe dynamic weighted survival modeling, a method for estimating an optimal ATS with survival outcomes. Using data from the Clinical Practice Research Datalink, a large primary-care database, we illustrate how it can answer an important clinical question about the treatment of type 2 diabetes. We identify an ATS pertaining to which drug add-ons to recommend when metformin in monotherapy does not achieve the therapeutic goals.