Effectiveness of the smart program: Stepped-care HIV prevention for gay and bisexual adolescent boys.

Objective: Adolescent men who have sex with men (AMSM) have high HIV incidence and low rates of HIV testing and pre-exposure prophylaxis (PrEP) use. There are few evidence-based HIV prevention programs that meet the unique needs of AMSM. The sequential, multiple assignment, randomized trial (SMART) program is a stepped care package of increasingly intensive eHealth interventions that were developed specifically for English- and Spanish-speaking AMSM.

Adaptive randomization methods for sequential multiple assignment randomized trials (smarts) via thompson sampling.

Response-adaptive randomization (RAR) has been studied extensively in conventional, single-stage clinical trials, where it has been shown to yield ethical and statistical benefits, especially in trials with many treatment arms. However, RAR and its potential benefits are understudied in sequential multiple assignment randomized trials (SMARTs), which are the gold-standard trial design for evaluation of multi-stage treatment regimes. We propose a suite of RAR algorithms for SMARTs based on Thompson Sampling (TS), a widely used RAR method in single-stage trials in which treatment randomization probabilities are aligned with the estimated probability that the treatment is optimal.

Electronic Clinical Decision Support Tools: Strategies to Improve the Management of Lower Respiratory Tract Infections in Low-Resource Settings.

Lower respiratory tract infection (LRTI) is a common reason for hospitalization and antibacterial use globally. There is considerable overlap in the clinical presentation of bacterial and viral LRTIs. Low- or middle-income countries (LMICs) face the dual challenge of appropriately targeting antibacterials for bacterial LRTI while reducing inappropriate antibacterials for viral LRTI.

Assessing the utility of the PC-PTSD-5 as a screening tool among a cancer survivor sample.

Introduction: Hematopoietic stem cell transplantation (HCT) is an intensive and invasive procedure used in cancer treatment that can lead to posttraumatic stress disorder (PTSD) symptoms. These symptoms are frequently overlooked in oncology and general health care settings. The suitability and utility of the Primary Care PTSD Screen for DSM-5 (PC-PTSD-5) within the cancer population remains uncertain.

Reinforced risk prediction with budget constraint using irregularly measured data from electronic health records.

Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs.

Cost-Effectiveness Analysis Evaluating Delivery Strategies for Pain Coping Skills Training in Women With Breast Cancer.

Pain coping skills training (PCST) is efficacious in patients with cancer, but clinical access is limited. To inform implementation, as a secondary outcome, we estimated the cost-effectiveness of 8 dosing strategies of PCST evaluated in a sequential multiple assignment randomized trial among women with breast cancer and pain (N = 327). Women were randomized to initial doses and re-randomized to subsequent doses based on their initial response (ie, ≥30% pain reduction).

Behavioral cancer pain intervention dosing: results of a Sequential Multiple Assignment Randomized Trial.

Behavioral pain management interventions are efficacious for reducing pain in patients with cancer. However, optimal dosing of behavioral pain interventions for pain reduction is unknown, and this hinders routine clinical use. A Sequential Multiple Assignment Randomized Trial (SMART) was used to evaluate whether varying doses of Pain Coping Skills Training (PCST) and response-based dose adaptation can improve pain management in women with breast cancer.

Interim monitoring of sequential multiple assignment randomized trials using partial information.

The sequential multiple assignment randomized trial (SMART) is the gold standard trial design to generate data for the evaluation of multistage treatment regimes. As with conventional (single-stage) randomized clinical trials, interim monitoring allows early stopping; however, there are few methods for principled interim analysis in SMARTs. Because SMARTs involve multiple stages of treatment, a key challenge is that not all enrolled participants will have progressed through all treatment stages at the time of an interim analysis.

Hand Grip Strength Predicts Mortality and Quality of Life in Heart Failure: Insights From the Singapore Cohort of Patients With Advanced Heart Failure.

Background: Frailty is prevalent among patients with heart failure (HF) and is associated with increased mortality rates and worse patient-centered outcomes. Hand grip strength (GS) has been proposed as a single-item marker of frailty and a potential screening tool to identify patients most likely to benefit from therapies that target frailty so as to improve quality of life (QoL) and clinical outcomes. We assessed the association of longitudinal decline in GS with all-cause mortality and QoL.

Understanding heterogeneity of responses to, and optimizing clinical efficacy of, exercise training in older adults: NIH NIA Workshop summary.

Exercise is a cornerstone of preventive medicine and a promising strategy to intervene on the biology of aging. Variation in the response to exercise is a widely accepted concept that dates back to the 1980s with classic genetic studies identifying sequence variations as modifiers of the VOmax response to training. Since that time, the literature of exercise response variance has been populated with retrospective analyses of existing datasets that are limited by a lack of statistical power from technical error of the measurements and small sample sizes, as well as diffuse outcomes, very few of which have included older adults.

Effectiveness of the SMART Sex Ed program among 13-18 year old English and Spanish speaking adolescent men who have sex with men.

Adolescent men who have sex with men (AMSM) have a high HIV incidence and low utilization of testing and prevention services. However, very few HIV prevention programs exist that focus on the unique sexual health needs of AMSM. SMART is a stepped care package of eHealth interventions that comprehensively address the sexual and HIV prevention needs of AMSM.

Telehealth and racial disparities in colorectal cancer screening: A pilot study of how virtual clinician characteristics influence screening intentions.

Introduction: Racial disparities in colorectal cancer (CRC) can be addressed through increased adherence to screening guidelines. In real-life encounters, patients may be more willing to follow screening recommendations delivered by a race concordant clinician. The growth of telehealth to deliver care provides an opportunity to explore whether these effects translate to a virtual setting.

Reinforcement Learning Methods in Public Health.

Purpose: Reinforcement learning (RL) is the subfield of machine learning focused on optimal sequential decision making under uncertainty. An optimal RL strategy maximizes cumulative utility by experimenting only if and when the information generated by experimentation is likely to outweigh associated short-term costs. RL represents a holistic approach to decision making that evaluates the impact of every action (ie, data collection, allocation of resources, and treatment assignment) in terms of short-term and long-term utility to stakeholders.

Estimation and Optimization of Composite Outcomes.

There is tremendous interest in precision medicine as a means to improve patient outcomes by tailoring treatment to individual characteristics. An individualized treatment rule formalizes precision medicine as a map from patient information to a recommended treatment. A treatment rule is defined to be optimal if it maximizes the mean of a scalar outcome in a population of interest, e.

High-Dimensional Precision Medicine From Patient-Derived Xenografts.

The complexity of human cancer often results in significant heterogeneity in response to treatment. Precision medicine offers the potential to improve patient outcomes by leveraging this heterogeneity. Individualized treatment rules (ITRs) formalize precision medicine as maps from the patient covariate space into the space of allowable treatments.

A SMART approach to optimizing delivery of an mHealth intervention among cancer survivors with posttraumatic stress symptoms.

Background/aims: Many cancer survivors who received intensive treatment such as hematopoietic stem cell transplantation (HCT) experience posttraumatic stress disorder (PTSD) symptoms. PTSD is associated with lower quality of life and other symptoms that require clinical treatment. The iterative treatment decisions that happen in clinical practice are not adequately represented in traditional randomized controlled trials (RCT) of PTSD treatments.

A Pilot Study Examining the Efficacy of Delivering Colorectal Cancer Screening Messages via Virtual Health Assistants.

Introduction: Patients are more likely to complete colorectal cancer screening when recommended by a race-concordant healthcare provider. Leveraging virtual healthcare assistants to deliver tailored screening interventions may promote adherence to colorectal cancer screening guidelines among diverse patient populations. The purpose of this pilot study is to determine the efficacy of the Agent Leveraging Empathy for eXams virtual healthcare assistant intervention to increase patient intentions to talk to their doctor about colorectal cancer screening.

A spatiotemporal recommendation engine for malaria control.

Malaria is an infectious disease affecting a large population across the world, and interventions need to be efficiently applied to reduce the burden of malaria. We develop a framework to help policy-makers decide how to allocate limited resources in realtime for malaria control. We formalize a policy for the resource allocation as a sequence of decisions, one per intervention decision, that map up-to-date disease related information to a resource allocation.

Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions.

Osteoarthritis (OA) is a chronic condition often associated with pain, affecting approximately fourteen percent of the population, and increasing in prevalence. A globally aging population have made treating OA-associated pain as well as maintaining mobility and activity a public health priority. OA affects all mammals, and the use of spontaneous animal models is one promising approach for improving translational pain research and the development of effective treatment strategies.

Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals.

Tooth loss from periodontal disease is a major public health burden in the United States. Standard clinical practice is to recommend a dental visit every six months; however, this practice is not evidence-based, and poor dental outcomes and increasing dental insurance premiums indicate room for improvement. We consider a tailored approach that recommends recall time based on patient characteristics and medical history to minimize disease progression without increasing resource expenditures.

Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning.

The vision for precision medicine is to use individual patient characteristics to inform a personalized treatment plan that leads to the best possible health-care for each patient. Mobile technologies have an important role to play in this vision as they offer a means to monitor a patient's health status in real-time and subsequently to deliver interventions if, when, and in the dose that they are needed. Dynamic treatment regimes formalize individualized treatment plans as sequences of decision rules, one per stage of clinical intervention, that map current patient information to a recommended treatment.

Receiver operating characteristic curves and confidence bands for support vector machines.

Many problems that appear in biomedical decision-making, such as diagnosing disease and predicting response to treatment, can be expressed as binary classification problems. The support vector machine (SVM) is a popular classification technique that is robust to model misspecification and effectively handles high-dimensional data. The relative costs of false positives and false negatives can vary across application domains.

Evaluation of a Stepped-Care eHealth HIV Prevention Program for Diverse Adolescent Men Who Have Sex With Men: Protocol for a Hybrid Type 1 Effectiveness Implementation Trial of SMART.

Background: Adolescent men who have sex with men (AMSM), aged 13 to 18 years, account for more than 80% of teen HIV occurrences. Despite this disproportionate burden, there is a conspicuous lack of evidence-based HIV prevention programs. Implementation issues are critical as traditional HIV prevention delivery channels (eg, community-based organizations, schools) have significant access limitations for AMSM.

Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness.

Dynamic treatment regimes operationalize precision medicine as a sequence of decision rules, one per stage of clinical intervention, that map up-to-date patient information to a recommended intervention. An optimal treatment regime maximizes the mean utility when applied to the population of interest. Methods for estimating an optimal treatment regime assume the data to be fully observed, which rarely occurs in practice.

Use of standardized bioinformatics for the analysis of fungal DNA signatures applied to sample provenance.

The use of environmental trace material to aid criminal investigations is an ongoing field of research within forensic science. The application of environmental material thus far has focused upon a variety of different objectives relevant to forensic biology, including sample provenance (also referred to as sample attribution). The capability to predict the provenance or origin of an environmental DNA sample would be an advantageous addition to the suite of investigative tools currently available.