Efficient radio resource allocation scheme for 5G networks with device-to-device communication

<p>A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases.</p>

Multitiered Systems of Support, Adaptive Interventions, and SMART Designs

This article introduces the special section on adaptive interventions and sequential multiple-assignment randomized trial (SMART) research designs. In addition to describing the two accompanying articles, we discuss features of adaptive interventions (AIs) and describe the use of SMART design to optimize AIs in the context of multitiered systems of support (MTSS) and integrated MTSS. AI is a treatment delivery model that explicitly specifies how information about individuals should be used to decide which treatment to provide in practice. Principles that apply to the design of AIs may help to more clearly operationalize MTSS-based programs, improve their implementation in school settings, and increase their efficacy when used according to evidence-based decision rules. A SMART is a research design for developing and optimizing MTSS-based programs. We provide a running example of a SMART design to optimize an MTSS-aligned AI that integrates academic and behavioral interventions.

Credibility of Causal Estimates from Regression Discontinuity Designs with Multiple Assignment Variables

In this paper, we determine treatment effects when the treatment assignment is based on two or more cut-off points of covariates rather than on one cut-off point of one assignment variable. using methods that are referred to as multivariate regression discontinuity designs (MRDD). One major finding of this paper is the discovery of new evidence that both matric points and household income have a huge impact on the probability of eligibility for funding from the National Student Financial Aid Scheme (NSFAS) to study for a bachelor’s degree program at universities in South Africa. This evidence will inform policymakers and educational practitioners on the effects of matric points and household income on the eligibility for NSFAS funding. The availability of the NSFAS grant impacts greatly students’ decisions to attend university or seek other opportunities elsewhere. Using the frontier MRDD analytical results, barely scoring matric points greater than or equal to 25 points compared to scoring matric points less than 25 for students whose household income is less than R350,000 (≈US$2500) increases the probability of eligibility for NSFAS funding by a significant 3.75 ( p-value = 0.0001 < 0.05) percentage points. Therefore, we have shown that the frontier MRDD can be employed to determine the causal effects of barely meeting the requirements of one assignment variable, among the subjects that either meet or fail to meet the requirements of the other assignment variable.

Sequential multiple assignment randomised trial of a brief contact intervention for suicide risk management among discharged psychiatric patients: an implementation study protocol

IntroductionThe postdischarge suicide risk among psychiatric patients is significantly higher than it is among patients with other diseases and general population. The brief contact interventions (BCIs) are recommended to decrease suicide risk in areas with limited mental health service resources like China. This study aims to develop a postdischarge suicide intervention strategy based on BCIs and evaluate its implementability under the implementation outcome framework.Methods and analysisThis study will invite psychiatric patients and family members, clinical and community mental health service providers as the community team to develop a postdischarge suicide intervention strategy. The study will recruit 312 patients with psychotic symptoms and 312 patients with major depressive disorder discharged from Shenzhen Kangning Hospital (SKH) in a Sequential Multiple Assignment Randomised Trial. Participants will be initially randomised into two intervention groups to receive BCIs monthly and weekly, and they will be rerandomised into three intervention groups to receive BCIs monthly, biweekly and weekly at 3 months after discharge according to the change of their suicide risk. Follow-ups are scheduled at 1, 3, 6 and 12 months after discharge. With the intention-to-treat approach, generalised estimating equation and survival analysis will be applied. This study will also collect qualitative and quantitative information on implementation and service outcomes from the community team.Ethics/disseminationThis study has received ethical approval from the Ethics Committee Review Board of SKH. All participants will provide written informed consent prior to enrolment. The findings of the study will be disseminated through peer-reviewed scientific journals, conference presentations. A project report will be submitted to the National Natural Science Foundation of China as the concluding report of this funded project, and to the mental health authorities in the Shenzhen to refine and apply evidence-based and pragmatic interventions into health systems for postdischarge suicide prevention.Trial registration numberNCT04907669.

A technology-based intervention to improve safety, mental health and empowerment outcomes for immigrant women with intimate partner violence experiences: it’s weWomen plus sequential multiple assignment randomized trial (SMART) protocol

Background Intimate partner violence (IPV) disproportionately affects immigrant women, an understudied and underserved population in need for evidence-based rigorously evaluated culturally competent interventions that can effectively address their health and safety needs. Methods This study uses a sequential, multiple assignment, randomized trial (SMART) design to rigorously evaluate an adaptive, trauma-informed, culturally tailored technology-delivered intervention tailored to the needs of immigrant women who have experienced IPV. In the first stage randomization, participants are randomly assigned to an online safety decision and planning or a usual care control arm and safety, mental health and empowerment outcomes are assessed at 3-, 6- and 12-months post-baseline. For the second stage randomization, women who do not report significant improvements in safety (i.e., reduction in IPV) and empowerment from baseline to 3 months follow up (i.e., non-responders) are re- randomized to safety and empowerment strategies delivered via text only or a combination of text and phone calls with trained advocates. Data on outcomes (safety, mental health, and empowerment) for early non-responders is assessed at 6 and 12 months post re-randomization. Discussion The study’s SMART design provides an opportunity to implement and evaluate an individualized intervention protocol for immigrant women based on their response to type or intensity of intervention. The findings will be useful for identifying what works for whom and characteristics of participants needing a particular type or intensity level of intervention for improved outcomes. If found to be effective, the study will result in an evidence-based trauma-informed culturally tailored technology-based safety decision and planning intervention for immigrant survivors of IPV that can be implemented by practitioners serving immigrant women in diverse settings. Trial registration This trial was registered with ClinicalTrials.gov as NCT04098276 on September 13, 2019.

Dimensions of Skill Use in the Unified Protocol: Exploring Unique Effects on Anxiety and Depression

Objective: Although the Unified Protocol contains multiple distinct skills to target anxiety, depression, and related conditions, researchers have yet to establish if patients' use of these skills contributes to symptom change. Using data from the first-stage randomization of a sequential multiple assignment randomized trial, we tested whether general skillfulness, defined by skill knowledge, frequency, quality, and effectiveness, predicted within- or between-person changes in anxiety and depression. We further tested whether use of particular UP skills (e.g., mindfulness, behavior change) predicted changes in these outcomes. Method: Participants (N = 70; Mage = 33.74, 67% female, 74% white) completed six sessions of core UP modules and reported on their skillfulness and specific skill use using two novel measures, along with their symptoms of anxiety (OASIS) and depression (ODSIS) before each session, and their alliance (WAI) after each session. We disaggregated between- from within-person effects to test session- to-session effects of skillfulness, skill use, and the alliance on symptom change. Results: Greater within-person skillfulness predicted decreases in anxiety and depression. More frequent within- person use of all UP skills predicted decreases in anxiety, whereas more frequent within-person use of alternative actions specifically predicted decreases in depression. Conclusions: This pattern of results provides guidance on the specific aspects of skill training to prioritize in transdiagnostic treatments depending on whether therapists are targeting anxiety or depression.

Sequential Multiple Assignment Randomized Trial (SMART) to identify optimal sequences of telemedicine interventions for improving initiation of insulin therapy: A simulation study

Background To examine the value of a Sequential Multiple Assignment Randomized Trial (SMART) design compared to a conventional randomized control trial (RCT) for telemedicine strategies to support titration of insulin therapy for Type 2 Diabetes Mellitus (T2DM) patients new to insulin. Methods Microsimulation models were created in R using a synthetic sample based on primary data from 63 subjects enrolled in a pilot study of a smartphone application (App), Diabetes Pal compared to a nurse-based telemedicine strategy (Nurse). For comparability, the SMART and an RCT design were constructed to allow comparison of four (embedded) adaptive interventions (AIs). Results In the base case scenario, the SMART has similar overall mean expected HbA1c and cost per subject compared with RCT, for sample size of n = 100 over 10,000 simulations. SMART has lower (better) standard deviations of the mean expected HbA1c per AI, and higher efficiency of choosing the correct AI across various sample sizes. The differences between SMART and RCT become apparent as sample size decreases. For both trial designs, the threshold value at which a subject was deemed to have been responsive at an intermediate point in the trial had an optimal choice (i.e., the sensitivity curve had a U-shape). SMART design dominates the RCT, in the overall mean HbA1c (lower value) when the threshold value is close to optimal. Conclusions SMART is suited to evaluating the efficacy of different sequences of treatment options, in addition to the advantage of providing information on optimal treatment sequences.

Optimal allocation to treatments in a sequential multiple assignment randomized trial

One of the main questions in the design of a trial is how many subjects should be assigned to each treatment condition. Previous research has shown that equal randomization is not necessarily the best choice. We study the optimal allocation for a novel trial design, the sequential multiple assignment randomized trial, where subjects receive a sequence of treatments across various stages. A subject's randomization probabilities to treatments in the next stage depend on whether he or she responded to treatment in the current stage. We consider a prototypical sequential multiple assignment randomized trial design with two stages. Within such a design, many pairwise comparisons of treatment sequences can be made, and a multiple-objective optimal design strategy is proposed to consider all such comparisons simultaneously. The optimal design is sought under either a fixed total sample size or a fixed budget. A Shiny App is made available to find the optimal allocations and to evaluate the efficiency of competing designs. As the optimal design depends on the response rates to first-stage treatments, maximin optimal design methodology is used to find robust optimal designs. The proposed methodology is illustrated using a sequential multiple assignment randomized trial example on weight loss management.