SMARTer Discontinuation Trial Designs for Developing an Adaptive Treatment Strategy

Abstract Objective: Developing evidenced-based practices for the management of childhood psychiatric disorders requires research studies that address how to treat children during both the acute phase of the disorder and beyond. Given the selection of a medication for acute treatment, discontinuation trials are used to evaluate the effects of treatment duration (e.g., time on medication) and/or maintenance strategies following successful acute-phase treatment. Recently, sequential multiple assignment randomized trials (SMART) have been proposed for use in informing sequences of critical clinical decisions such as those mentioned. The objective of this article is to illustrate how a SMART study is related to the standard discontinuation trial design, while addressing additional clinically important questions with similar trial resources. Method: The recently completed Child/Adolescent Anxiety Multimodal Study (CAMS), a randomized trial that examined the relative efficacy of three acute-phase treatments for pediatric anxiety disorders, along with a next logical step, a standard discontinuation trial design, is used to clarify the ideas. This example is used to compare the discontinuation trial design relative to the SMART design. Results: We find that the standard discontinuation trial can be modified slightly using a SMART design to yield high-quality data that can be used to address a wider variety of questions in addition to the impact of treatment duration. We discuss how this innovative trial design is ultimately more efficient and less costly than the standard discontinuation trial, and may result in more representative comparisons between treatments. Conclusions: Mental health researchers who are interested in addressing questions concerning the effects of continued treatment (for different durations) following successful acute-phase treatment should consider SMART designs in place of discontinuation trial designs in their research. SMART designs can be used to address these and other questions concerning individualized sequences of treatment, such as the choice of a rescue treatment in case of postacute phase relapse.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98496/1/cap%2E2011%2E0073.pd

Optimal treatment allocations in space and time for on-line control of an emerging infectious disease

A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America

The effect of timing and frequency of push notifications on usage of a smartphone-based stress management intervention: An exploratory trial

Push notifications offer a promising strategy for enhancing engagement with smartphone-based health interventions. Intelligent sensor-driven machine learning models may improve the timeliness of notifications by adapting delivery to a user's current context (e.g. location). This exploratory mixed-methods study examined the potential impact of timing and frequency on notification response and usage of Healthy Mind, a smartphone-based stress management intervention. 77 participants were randomised to use one of three versions of Healthy Mind that provided: intelligent notifications; daily notifications within pre-defined time frames; or occasional notifications within pre-defined time frames. Notification response and Healthy Mind usage were automatically recorded. Telephone interviews explored participants' experiences of using Healthy Mind. Participants in the intelligent and daily conditions viewed (d = .47, .44 respectively) and actioned (d = .50, .43 respectively) more notifications compared to the occasional group. Notification group had no meaningful effects on percentage of notifications viewed or usage of Healthy Mind. No meaningful differences were indicated between the intelligent and non-intelligent groups. Our findings suggest that frequent notifications may encourage greater exposure to intervention content without deterring engagement, but adaptive tailoring of notification timing does not always enhance their use. Hypotheses generated from this study require testing in future work. Trial registration number: ISRCTN67177737 © 2017 Morrison et al

Statistical design and analysis in trials of proportionate interventions: a systematic review

Background: In proportionate or adaptive interventions, the dose or intensity can be adjusted based on individual need at predefined decision stages during the delivery of the intervention. The development of such interventions may require an evaluation of the effectiveness of the individual stages in addition to the whole intervention. However, evaluating individual stages of an intervention has various challenges, particularly the statistical design and analysis. This review aimed to identify the use of trials of proportionate interventions and how they are being designed and analysed in current practice. Methods: We searched MEDLINE, Web of Science and PsycINFO for articles published between 2010 and 2015 inclusive. We considered trials of proportionate interventions in all fields of research. For each trial, its aims, design and analysis were extracted. The data synthesis was conducted using summary statistics and a narrative format. Results: Our review identified 44 proportionate intervention trials, comprising 28 trial results, 13 protocols and three secondary analyses. These were mostly described as stepped care (n=37) and mainly focussed on mental health research (n=30). The other studies were aimed at finding an optimal adaptive treatment strategy (n=7) in a variety of therapeutic areas. Further terminology used included adaptive intervention, staged intervention, sequentially multiple assignment trial or a two-phase design. The median number of decision stages in the interventions was two and only one study explicitly evaluated the effect of the individual stages. Conclusions: Trials of proportionate staged interventions are being used predominantly within the mental health field. However, few studies consider the different stages of the interventions, either at the design or the analysis phase, and how they may interact with one another. There is a need for further guidance on the design, analyses and reporting across trials of proportionate interventions

Mathematical models for immunology:current state of the art and future research directions

The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years

Supervisor support: Does supervisor support buffer or exacerbate the adverse effects of supervisor undermining?

10.1037/a0035313Journal of Applied Psychology993484-503JAPG