Contingency management plus acceptance and commitment therapy for initial cocaine abstinence: Results of a sequential multiple assignment randomized trial (SMART)

BACKGROUND

This study tested an adaptive intervention for optimizing abstinence outcomes over phases of treatment for cocaine use disorder using a SMART design. Phase 1 assessed whether 4 weeks of contingency management (CM) improved response with the addition of Acceptance and Commitment Therapy (ACT). Phase 2 assessed pharmacological augmentation with modafinil (MOD) vs. placebo (PLA) for individuals not achieving abstinence during Phase 1.

METHOD

For Phase 1 of treatment, participants (N=118) were randomly allocated to ACT+CM or Drug Counseling (DC+CM), the comparison condition. At week 4, treatment response was defined as the submission of six consecutive cocaine-negative urine drug screens (UDS). Phase 1 non-responders were re-randomized to MOD or PLA as adjunct to their initial treatment. Phase 1 responders continued receiving their initial treatment. Primary outcomes included response rate and proportion of cocaine-negative UDS for Phase 1 and 2. Analyses used Bayesian inference with 80% pre-specified as the posterior probability (PP) threshold constituting moderate evidence that an effect exists.

RESULTS

Phase 1 response was higher in the ACT+CM group (24.5%) compared to the DC+CM group (17.5%; PP = 84.5%). In Phase 2, the proportion of cocaine-negative UDS among Phase 1 responders did not differ by initial treatment (PP = 61.8%) but remained higher overall compared to Phase 1 non-responders (PPs > 99%). No evidence of an effect favoring augmentation with MOD was observed.

DISCUSSION

Adding ACT to CM increased abstinence initiation. Initial responders were more likely to remain abstinent compared to initial non-responders, for whom modafinil was not an effective pharmacotherapy augmentation strategy.

Design of experiments with sequential randomizations on multiple timescales: the hybrid experimental design

Psychological interventions, especially those leveraging mobile and wireless technologies, often include multiple components that are delivered and adapted on multiple timescales (e.g., coaching sessions adapted monthly based on clinical progress, combined with motivational messages from a mobile device adapted daily based on the person's daily emotional state). The hybrid experimental design (HED) is a new experimental approach that enables researchers to answer scientific questions about the construction of psychological interventions in which components are delivered and adapted on different timescales. These designs involve sequential randomizations of study participants to intervention components, each at an appropriate timescale (e.g., monthly randomization to different intensities of coaching sessions and daily randomization to different forms of motivational messages). The goal of the current manuscript is twofold. The first is to highlight the flexibility of the HED by conceptualizing this experimental approach as a special form of a factorial design in which different factors are introduced at multiple timescales. We also discuss how the structure of the HED can vary depending on the scientific question(s) motivating the study. The second goal is to explain how data from various types of HEDs can be analyzed to answer a variety of scientific questions about the development of multicomponent psychological interventions. For illustration, we use a completed HED to inform the development of a technology-based weight loss intervention that integrates components that are delivered and adapted on multiple timescales.

Digital Adaptive Behavioral Interventions to Improve HIV Prevention and Care: Innovations in Intervention Approach and Experimental Design

PURPOSE OF REVIEW

Recent advances in digital technologies can be leveraged to adapt HIV prevention and treatment services to the rapidly changing needs of individuals in everyday life. However, to fully take advantage of these technologies, it is critical to effectively integrate them with human-delivered components. Here, we introduce a new experimental approach for optimizing the integration and adaptation of digital and human-delivered behavioral intervention components for HIV prevention and treatment.

RECENT FINDINGS

Typically, human-delivered components can be adapted on a relatively slow timescale (e.g., every few months or weeks), while digital components can be adapted much faster (e.g., every few days or hours). Thus, the systematic integration of these components requires an experimental approach that involves sequential randomizations on multiple timescales. Selecting an experimental approach should be motivated by the type of adaptive intervention investigators would like to develop, and the scientific questions they have about its construction.

Statistical Considerations and Software for Designing Sequential, Multiple Assignment, Randomized Trials (SMART) with a Survival Final Endpoint

Sequential, multiple assignment, randomized trial (SMART) designs are appropriate for comparing adaptive treatment interventions, in which intermediate outcomes (called tailoring variables) guide subsequent treatment decisions for individual patients. Within a SMART design, patients may be re-randomized to subsequent treatments following the outcomes of their intermediate assessments. In this paper, we provide an overview of statistical considerations necessary to design and implement a two-stage SMART design with a binary tailoring variable and a survival final endpoint. A chronic lymphocytic leukemia trial with a final endpoint of progression-free survival is used as an example for the simulations to assess how design parameters, including, choice of randomization ratios for each stage of randomization, and response rates of the tailoring variable affect the statistical power. We assess the choice of weights from restricted re-randomization on data analyses and appropriate hazard rate assumptions. Specifically, for a given first-stage therapy and prior to the tailoring variable assessment, we assume equal hazard rates for all patients randomized to a treatment arm. After the tailoring variable assessment, individual hazard rates are assumed for each intervention path. Simulation studies demonstrate that the response rate of the binary tailoring variable impacts power as it directly impacts the distribution of patients. We also confirm that when the first stage randomization is 1:1, it is not necessary to consider the first stage randomization ratio when applying the weights. We provide an R-shiny application for obtaining power for a given sample size for SMART designs.

A Sequential Multiple Assignment Randomized Trial of scalable interventions for ART delivery in South Africa: the SMART ART study

BACKGROUND

Of the 8 million people in South Africa living with HIV, 74% of persons living with HIV are on antiretroviral therapy (ART) and 65% are virally suppressed. Detectable viral load results in HIV-associated morbidity and mortality and HIV transmission. Patient barriers to care, such as missed wages, transport costs, and long wait times for clinic visits and ART refills, are associated with detectable viral load. HIV differentiated service delivery (DSD) has simplified ART delivery for clients who achieve viral suppression and engage in care. However, DSD needs adaptation to serve clients who are not engaged in care.

METHODS

A Sequential Multiple Assignment Randomized Trial will be undertaken in KwaZulu-Natal, South Africa, to test adaptive ART delivery for persons with detectable viral load and/or who are not engaged in care. The types of differentiated service delivery (DSD) which will be examined in this study are clinic-based incentives, community-based smart lockers, and home delivery. The study plans to enroll up to 900 participants-people living with HIV, eligible for ART, and who are not engaged in care. The study aims to assess the proportion of ART-eligible persons living with HIV who achieve viral suppression at 18 months. The study will also evaluate the preferences of clients and providers for differentiated service delivery and evaluate the cost-effectiveness of adaptive HIV treatment for those who are not engaged in care.

DISCUSSION

To increase population-level viral suppression, persons with detectable viral load need responsive DSD interventions. A Sequential Multiple Assignment Randomized Trial (SMART) design facilitates the evaluation of a stepped, adaptive approach to achieving viral suppression with "right-sized" interventions for patients most in need of effective and efficient HIV care delivery strategies.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05090150. Registered on October 22, 2021.

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

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.

Trial design and methodology for a non-restricted sequential multiple assignment randomized trial to evaluate combinations of perinatal interventions to optimize women's health

Pre-pregnancy overweight/obesity and excessive gestational weight gain (GWG) independently predict negative maternal and child health outcomes. To date, however, interventions that target GWG have not produced lasting improvements in maternal weight or health at 12-months postpartum. Given that interventions solely aimed at addressing GWG may not equip women with the skills needed for postpartum weight management, interventions that address health behaviors over the perinatal period might maximize maternal health in the first postpartum year. Thus, the current study leveraged a sequential multiple assignment randomized trial (SMART) design to evaluate sequences of prenatal (i.e., during pregnancy) and postpartum lifestyle interventions that optimize maternal weight, cardiometabolic health, and psychosocial outcomes at 12-months postpartum. Pregnant women (N = 300; ≤16 weeks pregnant) with overweight/obesity (BMI ≥ 25 kg/m2) are being recruited. Women are randomized to intervention or treatment as usual on two occasions: (1) early in pregnancy, and (2) prior to delivery, resulting in four intervention sequences. Intervention during pregnancy is designed to moderate GWG and introduce skills for management of weight as a chronic condition, while intervention in the postpartum period addresses weight loss. The primary outcome is weight at 12-months postpartum and secondary outcomes include variables of cardiometabolic health and psychosocial well-being. Analyses will evaluate the combination of prenatal and postpartum lifestyle interventions that optimizes maternal weight and secondary outcomes at 12-months postpartum. Optimizing the sequence of behavioral interventions to address specific needs during pregnancy and the first postpartum year can maximize intervention potency and mitigate longer-term cardiometabolic health risks for women.

Time-varying SMART design and data analysis methods for evaluating adaptive intervention effects

BACKGROUND

In a standard two-stage SMART design, the intermediate response to the first-stage intervention is measured at a fixed time point for all participants. Subsequently, responders and non-responders are re-randomized and the final outcome of interest is measured at the end of the study. To reduce the side effects and costs associated with first-stage interventions in a SMART design, we proposed a novel time-varying SMART design in which individuals are re-randomized to the second-stage interventions as soon as a pre-fixed intermediate response is observed. With this strategy, the duration of the first-stage intervention will vary.

METHODS

We developed a time-varying mixed effects model and a joint model that allows for modeling the outcomes of interest (intermediate and final) and the random durations of the first-stage interventions simultaneously. The joint model borrows strength from the survival sub-model in which the duration of the first-stage intervention (i.e., time to response to the first-stage intervention) is modeled. We performed a simulation study to evaluate the statistical properties of these models.

RESULTS

Our simulation results showed that the two modeling approaches were both able to provide good estimations of the means of the final outcomes of all the embedded interventions in a SMART. However, the joint modeling approach was more accurate for estimating the coefficients of first-stage interventions and time of the intervention.

CONCLUSION

We conclude that the joint modeling approach provides more accurate parameter estimates and a higher estimated coverage probability than the single time-varying mixed effects model, and we recommend the joint model for analyzing data generated from time-varying SMART designs. In addition, we showed that the proposed time-varying SMART design is cost-efficient and equally effective in selecting the optimal embedded adaptive intervention as the standard SMART design.