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Severe obesity is a complex, chronic disease affecting nearly 9% of adolescents in the U.S. Although the current mainstay of treatment is lifestyle therapy, pediatric clinical practice guidelines recommend the addition of adjunct anti-obesity medication (AOM), such as phentermine and topiramate. However, guidance regarding when adjunct AOM should be started and how AOM should be used is unclear. Furthermore, an inherent limitation of current treatment guidelines is their "one-size-fits-all" approach, which does not account for the heterogeneous nature of obesity and high degree of patient variability in response to all interventions. This paper describes the study design and methods of a sequential multiple assignment randomized trial (SMART), "SMART Use of Medications for the Treatment of Adolescent Severe Obesity." The trial will examine 1) when to start AOM (specifically phentermine) in adolescents who are not responding to lifestyle therapy and 2) how to modify AOM when there is a sub-optimal response to the initial pharmacological intervention (specifically, for phentermine non-responders, is it better to add topiramate to phentermine or switch to topiramate monotherapy). Critically, participant characteristics that may differentially affect response to treatment will be assessed and evaluated as potential moderators of intervention efficacy. Data from this study will be used to inform the development of an adaptive intervention for the treatment of adolescent severe obesity that includes empirically-derived decision rules regarding when and how to use AOM. Future research will test this adaptive intervention against standard "one-size-fits-all" treatments. [ABSTRACT FROM AUTHOR]

Primary care is a frequent source of pain treatment and opioid prescribing. The objective of the Prescribing Interventions for Chronic Pain using the Electronic health record (PRINCE) study is to assess the effects of two behavioral economics-informed interventions embedded within the electronic health record (EHR) on guideline-concordant pain treatment and opioid prescribing decisions in primary care settings. Setting : The setting for this study is 43 primary care clinics in Minnesota. Design: The PRINCE study uses a cluster-randomized 2 × 2 factorial design to test the effects of two interventions. An adaptive design allows for the possibility of secondary randomization to test if interventions can be titrated while maintaining efficacy. Interventions: One intervention alters the "choice architecture" within the EHR to nudge clinicians toward non-opioid treatments for opioid-naïve patients and toward tapering for patients currently receiving a "high risk" opioid. The other intervention integrates the prescription drug monitoring program (PDMP) directly within the EHR. Outcome : The primary outcome for opioid-naïve patients is whether an opioid is prescribed in a primary care visit without a non-opioid alternative pain treatment. The primary outcome for current opioid-using patients is whether opioid prescriptions were tapered with a documented rationale. The PRINCE study will provide real-world evidence on two approaches to improving pain treatment in primary care using the EHR. The adaptive study design strikes a balance between establishing intervention efficacy and testing whether efficacy varies with intervention intensity. [ABSTRACT FROM AUTHOR]

An individualized treatment rule (ITR) formalizes personalized medicine by assigning treatment as a function of patients' clinical information, which contrasts with a static treatment rule that assigns everyone the same treatment. ITR identification has become a common aim in randomized clinical trials but sample size considerations for this aim are lacking. One approach is to select a sample size that will reliably identify an ITR with a performance close to the theoretical optimal rule. However, this approach could still lead to identifying ITRs that perform worse than the optimal static rule, particularly in the absence of substantial effect heterogeneity. This limitation motivates sample size considerations aimed at reliable identification of a beneficial ITR, which outperforms the optimal static rule, and analysis methods that identify the estimated optimal static rule when there is substantial uncertainty about whether an ITR will improve outcomes. To address these limitations, we propose a sample size approach based on the probability of identifying a beneficial ITR and introduce an approach for selecting the LASSO penalty parameter such that in the absence of treatment effect heterogeneity the estimated optimal static rule is identified with high probability. We apply these approaches to the PLUTO trial aimed at developing methods to assist with smoking cessation. [ABSTRACT FROM AUTHOR]