Patterns of primary and specialty care among children with sickle cell anemia.

BACKGROUND AND OBJECTIVE: National guidelines recommend that children with sickle cell anemia (SCA) be seen regularly by primary care providers (PCPs) as well as hematologists to receive comprehensive, multidisciplinary care. The objective is to characterize the patterns of primary and hematology care for children with SCA in Michigan. METHODS: Using validated claims definitions, children ages 1-17 years with SCA were identified using Michigan Medicaid administrative claims from 2010 to 2018. We calculated the number of outpatient PCP and hematologist visits per person-year, as well as the proportion of children with at least one visit to a PCP, hematologist, or both a PCP and hematologist annually. Negative binomial regression was used to calculate annual rates of visits for each provider type. RESULTS: A total of 875 children contributed 2889 person-years. Of the total 22,570 outpatient visits, 52% were with a PCP and 34% with a hematologist. Annually, 87%-93% of children had a visit with a PCP, and 63%-85% had a visit with a hematologist. Approximately 66% of total person-years had both visit types within a year. The annual rate ranged from 2.3 to 2.5 for hematologist visits and from 3.7 to 4.1 for PCP visits. CONCLUSIONS: Substantial gaps exist in the receipt of annual hematology care. Given that the majority of children with SCA see a PCP annually, strategies to leverage primary care visits experienced by this population may be needed to increase receipt of SCA-specific services.

Comparing Self-Reported Dietary Intake to Provided Diet during a Randomized Controlled Feeding Intervention: A Pilot Study.

Systematic and random errors based on self-reported diet may bias estimates of dietary intake. The objective of this pilot study was to describe errors in self-reported dietary intake by comparing 24 h dietary recalls to provided menu items in a controlled feeding study. This feeding study was a parallel randomized block design consisting of a standard diet (STD; 15% protein, 50% carbohydrate, 35% fat) followed by either a high-fat (HF; 15% protein, 25% carbohydrate, 60% fat) or a high-carbohydrate (HC; 15% protein, 75% carbohydrate, 10% fat) diet. During the intervention, participants reported dietary intake in 24 h recalls. Participants included 12 males (seven HC, five HF) and 12 females (six HC, six HF). The Nutrition Data System for Research was utilized to quantify energy, macronutrients, and serving size of food groups. Statistical analyses assessed differences in 24 h dietary recalls vs. provided menu items, considering intervention type (STD vs. HF vs. HC) (Student's t-test). Caloric intake was consistent between self-reported intake and provided meals. Participants in the HF diet underreported energy-adjusted dietary fat and participants in the HC diet underreported energy-adjusted dietary carbohydrates. Energy-adjusted protein intake was overreported in each dietary intervention, specifically overreporting beef and poultry. Classifying misreported dietary components can lead to strategies to mitigate self-report errors for accurate dietary assessment.

Concurrent dose-finding of a novel cancer drug with and without a second agent.

Introduction: More complex research questions are being posed in early-phase oncology clinical trials, necessitating design strategies tailored to contemporary study objectives. This paper describes the proposed design of a Phase I trial concurrently evaluating the safety of a hematopoietic progenitor kinase-1 inhibitor (Agent A) as a single agent and in combination with an anti-PD-1 agent in patients with advanced malignancies. The study's primary objective was to concurrently determine the maximum tolerated dose (MTD) of Agent A with and without anti-PD-1 therapy among seven possible study dose levels. Methods: Our solution to this challenge was to apply a continual reassessment method shift model to meet the research objectives of the study. Results: The application of this method is described herein, and a simulation study of the design's operating characteristics is conducted. This work was developed through collaboration and mentoring between the authors at the American Association for Cancer Research (AACR) and the American Society of Clinical Oncology (ASCO) annual AACR/ASCO Methods in Clinical Cancer Research Workshop. Conclusions: The aim of this manuscript is to highlight examples of novel design applications as a means of augmenting the implementation of innovative designs in the future and to demonstrate the flexibility of adaptive designs in satisfying modern design conditions. Although the design is presented using an investigation of Agent A with and without anti-PD-1 therapy as an illustrative example, the approach described is not specific to these agents and could be applied to other concurrent monotherapy and combination therapy studies with well-defined binary safety endpoints.

Isotonic design for phase I cancer clinical trials with late-onset toxicities.

This article addresses the problem of identifying the maximum tolerated dose (MTD) in Phase I dose-finding clinical trials with late-onset toxicities. The main design challenge is how best to adaptively allocate study participants to tolerable doses when the evaluation window for the toxicity endpoint is long relative to the accrual rate of new participants. We propose a new design framework based on order-restricted statistical inference that addresses this challenge in sequential dose assignments. We illustrate the proposed method on real data from a Phase I trial of bortezomib in lymphoma patients and apply it to a Phase I trial of radiotherapy in prostate cancer patients. We conduct extensive simulation studies to compare our design's operating characteristics to existing published methods. Overall, our proposed design demonstrates good performance relative to existing methods in allocating participants at and around the MTD during the study and accurately recommending the MTD at the study conclusion.

Power prior models for estimating response rates in a small n, sequential, multiple assignment, randomized trial.

A small n, sequential, multiple assignment, randomized trial (snSMART) is a small sample, two-stage design where participants receive up to two treatments sequentially, but the second treatment depends on response to the first treatment. The parameters of interest in an snSMART are the first-stage response rates of the treatments, but outcomes from both stages can be used to obtain more information from a small sample. A novel way to incorporate the outcomes from both stages uses power prior models, in which first stage outcomes from an snSMART are regarded as the primary (internal) data and second stage outcomes are regarded as supplemental data (co-data). We apply existing power prior models to snSMART data, and we also develop new extensions of power prior models. All methods are compared to each other and to the Bayesian joint stage model (BJSM) via simulation studies. By comparing the biases and the efficiency of the response rate estimates among all proposed power prior methods, we suggest application of Fisher's Exact Test or the Bhattacharyya's overlap measure to an snSMART to estimate the response rates in an snSMART, which both have performance mostly as good or better than the BJSM. We describe the situations where each of these suggested approaches is preferred.

Extending the Continual Reassessment Method to accommodate step-up dosing in Phase I trials.

The Continual Reassessment Method (CRM) was developed for Phase I trials to identify a maximum-tolerated dose of an agent using a design in which each participant is treated with a single administration of the agent. We propose an extension of the CRM in which participants receive multiple administrations of an agent using a so-called step-up dosing procedure in which participants receive one or more administrations of lower doses of the agent before they receive their penultimate dose. We use methods developed for the CRM to model the probability of DLT for each administration, which leads to the use of conditional probability models to model the joint probability of DLT across multiple administrations. We compare our approach to two existing methods that use time-to-event modeling methods for modeling the probability of DLT. We demonstrate through simulations that our approach has operating characteristics similar to existing methods, but due to its foundations in the CRM, ours is simpler to implement than existing approaches and is therefore more likely to be adopted in practice.

Defibrotide Therapy for SARS-CoV-2 ARDS.

BACKGROUND: SARS-CoV-2-related ARDS is associated with endothelial dysfunction and profound dysregulation of the thrombotic-fibrinolytic pathway. Defibrotide is a polyanionic compound with fibrinolytic, antithrombotic, and antiinflammatory properties. RESEARCH QUESTION: What is the safety and tolerability of defibrotide in patients with severe SARS-CoV-2 infections? STUDY DESIGN AND METHODS: We report a prospective, open-label, single-center safety trial of defibrotide for the management of SARS-CoV-2-related ARDS. Eligible participants were 18 years of age or older with clinical and radiographic signs of ARDS, no signs of active bleeding, a serum D-dimer of more than twice upper limit of normal, and positive polymerase chain reaction-based results for SARS-CoV-2. Defibrotide (6.25 mg/kg/dose IV q6h) was administered for a planned 7-day course, with serum D-dimer levels and respiratory function monitored daily during therapy. RESULTS: Twelve patients (median age, 63 years) were treated, with 10 patients receiving mechanical ventilation and 6 receiving vasopressor support at study entry. The median D-dimer was 3.25 µg/ml (range, 1.33-12.3) at study entry. The median duration of therapy was 7 days. No hemorrhagic or thrombotic complications occurred during therapy. No other adverse events attributable to defibrotide were noted. Four patients met the day 7 pulmonary response parameter, all four showing a decrease in serum D-dimer levels within the initial 72 h of defibrotide therapy. Three patients died of progressive pulmonary disease 11, 17, and 34 days after study entry. Nine patients (75%) remain alive 64 to 174 days after initiation of defibrotide. Day 30 all-cause mortality was 17% (95% CI, 0%-35%). All patients with a baseline Pao2 to Fio2 ratio of ≥ 125 mm Hg survived, whereas the three patients with a baseline Pao2 to Fio2 ratio of < 125 mm Hg died. INTERPRETATION: The use of defibrotide for management of SARS-CoV-2-related ARDS proved safe and tolerable. No hemorrhagic or thrombotic complications were reported during therapy, with promising outcomes in a patient population with a historically high mortality rate. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT04530604; URL: www. CLINICALTRIALS: gov.

Employment outcomes in family supporters of patients with early stage breast cancer and their association with patients' health-related quality of life and financial burden.

BACKGROUND: Little is known about how cancer impacts the employment status of patients' family supporters, or about associations between patients' health-related quality of life, perceived financial burden, and supporters' employment trajectory. METHODS: We surveyed patients with early stage breast cancer reported to the Georgia and Los Angeles SEER registries in 2014-15, and their spouse/partner or other family supporters. Patients and supporters were asked about employment impacts of the patient's cancer, and descriptive analyses of supporters' employment trajectories were generated. We measured patients' health-related quality of life (HRQoL) using the PROMIS scale for global health. We measured patients' perceived financial burden attributed to cancer by asking them two questions regarding (i) their financial status since their breast cancer diagnosis and (ii) how much it was impacted by their breast cancer and treatment. Associations between patients' HRQoL, perceived financial burden, and supporters' employment status were assessed using linear mixed model regression analyses. RESULTS: In total, 2502 patients (68% response rate) and 1203 supporters (70% response rate) responded; 1057 paired patient-supporter dyads were included. Similar proportions of spouse/partner and other family supporters reported missed work and lost employment due to patients' cancer. After adjustment, lower HRQoL and an increased odds of perceived financial burden among patients were associated with changes in other family supporters' employment (both p < 0.05), but not with changes in spouses'/partners' employment. Lower HRQoL was also associated with changes in patients' own employment among patients with both types of supporters (both p < 0.001). An increased odds of perceived financial burden among patients was associated with changes in patients' employment only in those supported by other family members (p < 0.001). CONCLUSIONS: Both spouse/partner and other family supporters faced adverse employment outcomes due to patients' cancer. This contributes to worse HRQoL and greater perception of financial burden among patients, especially those whose supporter is not a spouse/partner.

TrueNTH Sexual Recovery Intervention for couples coping with prostate cancer: Randomized controlled trial results.

BACKGROUND: Despite significant sexual dysfunction and distress after localized prostate cancer treatment, patients typically receive only physiologic erectile dysfunction management. The authors performed a randomized controlled trial of an online intervention supporting couples' posttreatment recovery of sexual intimacy. METHODS: Patients treated with surgery, radiation, or combined radiation and androgen deprivation therapy who had partners were recruited and randomized to an online intervention or a control group. The intervention, tailored to treatment type and sexual orientation, comprised 6 modules addressing expectations for sexual and emotional sequelae of treatment, rehabilitation, and guidance toward sexual intimacy recovery. Couples, recruited from 6 sites nationally, completed validated measures at the baseline and 3 and 6 months after treatment. Primary outcome group differences were assessed with t tests for individual outcomes. RESULTS: Among 142 randomized couples, 105 patients (mostly surgery) and 87 partners completed the 6-month survey; this reflected challenges with recruitment and attrition. There were no differences between the intervention and control arms in Patient-Reported Outcomes Measurement Information System Global Satisfaction With Sex Life scores 6 months after treatment (the primary outcome). Three months after treatment, intervention patients and partners reported more engagement in penetrative and nonpenetrative sexual activities than controls. More than 73% of the intervention participants reported high or moderate satisfaction with module content; more than 85% would recommend the intervention to other couples. CONCLUSIONS: Online psychosexual support for couples can help couples to connect and experience sexual pleasure early after treatment despite patients' sexual dysfunction. Participants' high endorsement of the intervention reflects the importance of sexual health support to couples after prostate cancer treatment. LAY SUMMARY: This study tested a web-based program supporting couples' sexual recovery of sexual intimacy after prostate cancer treatment. One hundred forty-two couples were recruited and randomly assigned to the program (n = 60) or to a control group (n = 82). The program did not result in improvements in participants' satisfaction with their sex life 6 months after treatment, but couples in the intervention group engaged in sexual activity sooner after treatment than couples in the control group. Couples evaluated the program positively and would recommend it to others facing prostate cancer treatment.

Childhood vaccination timeliness following maternal migration to an informal urban settlement in Kenya.

INTRODUCTION: Timely receipt of recommended vaccines is a proven strategy to reduce preventable under-five deaths. Kenya has experienced impressive declines in child mortality from 111 to 43 deaths per 1000 live births between 1980 and 2019. However, considerable inequities in timely vaccination remain, which unnecessarily increases risk for serious illness and death. Maternal migration is a potentially important driver of timeliness inequities, as the social and financial stressors of moving to a new community may require a woman to delay her child's immunizations. This analysis examined how maternal migration to informal urban settlements in Nairobi, Kenya influenced childhood vaccination timeliness. METHODS: Data came from the Nairobi Urban Health and Demographic Surveillance System, 2002-2018. Migration exposures were migrant status (migrant, non-migrant), migrant origin (rural, urban), and migrant type (first-time, circular [previously resided in settlement]). Age at vaccine receipt (vaccination timeliness) was calculated for all basic vaccinations. Accelerated failure time models were used to investigate relationships between migration exposures and vaccination timeliness. Confounding was addressed using propensity score weighting. RESULTS: Over one-third of the children of both migrants and non-migrants received at least one dose late or not at all. Unweighted models showed the children of migrants had shorter time to OPV1 and DPT1 vaccine receipt compared to the children of non-migrants. After accounting for confounding only differences in timeliness for DPT1 remained, with the children of migrants receiving DPT1 significantly earlier than the children of non-migrants. Timeliness was comparable among migrants with rural and urban origins and among first-time and circular migrants. CONCLUSION: Although a substantial proportion of children in Nairobi's informal urban settlements do not receive timely vaccination, this analysis found limited evidence that maternal migration and migration characteristics were associated with delays for most doses. Future research should seek to elucidate potential drivers of low vaccination timeliness in Kenya.

Depression in Female Adolescents with Heavy Menstrual Bleeding.

OBJECTIVE: To assess the degree to which heavy menstrual bleeding is associated with depression, independent of hormonal contraception. STUDY DESIGN: We performed a retrospective cohort study of 1168 female adolescents 9-18 years old presenting to general pediatricians for heavy menstrual bleeding or well visits. Depression was the primary outcome and defined as a diagnosis in the health record. Univariable and multivariable regression models were fit to the data to identify factors associated with depression diagnosis. RESULTS: In total, 581 adolescents with heavy menstrual bleeding and 587 without heavy menstrual bleeding were included. Depression diagnoses occurred with greater frequency in youth with heavy menstrual bleeding compared with those without heavy menstrual bleeding (50.9% vs 24.2% P < .001; risk ratio 1.67, 95% CI 1.39-2.01) but did not significantly differ between those taking vs not taking hormonal contraception (risk ratio 0.99; 95% CI 0.84-1.17). Most patients with depression and heavy menstrual bleeding developed depression following or concurrent with heavy menstrual bleeding (261/296, 88%). Of these, 199 of 261 (76%) were treated with hormonal contraception, but the majority (168/199; 84%) were diagnosed with depression before initiation. CONCLUSIONS: Heavy menstrual bleeding is associated with depression diagnosis in female adolescents. The use of hormonal contraception was not associated with depression diagnosis in multivariable analysis, covarying heavy menstrual bleeding, age, body mass index, anxiety, sexual activity, and substance use. As hormonal contraception is often used to treat heavy menstrual bleeding, heavy menstrual bleeding may be partially driving previous reports of increased depression risk in those taking hormonal contraception.

A modular framework for early-phase seamless oncology trials.

BACKGROUND: As our understanding of the etiology and mechanisms of cancer becomes more sophisticated and the number of therapeutic options increases, phase I oncology trials today have multiple primary objectives. Many such designs are now "seamless," meaning that the trial estimates both the maximum tolerated dose and the efficacy at this dose level. Sponsors often proceed with further study only with this additional efficacy evidence. However, with this increasing complexity in trial design, it becomes challenging to articulate fundamental operating characteristics of these trials, such as (1) what is the probability that the design will identify an acceptable, that is., safe and efficacious, dose level? or (2) how many patients will be assigned to an acceptable dose level on average? METHODS: In this manuscript, we propose a new modular framework for designing and evaluating seamless oncology trials. Each module is comprised of either a dose assignment step or a dose-response evaluation, and multiple such modules can be implemented sequentially. We develop modules from existing phase I/II designs as well as a novel module for evaluating dose-response using a Bayesian isotonic regression scheme. RESULTS: We also demonstrate a freely available R package called seamlesssim to numerically estimate, by means of simulation, the operating characteristics of these modular trials. CONCLUSIONS: Together, this design framework and its accompanying simulator allow the clinical trialist to compare multiple different candidate designs, more rigorously assess performance, better justify sample sizes, and ultimately select a higher quality design.

Bayesian methods to compare dose levels with placebo in a small n, sequential, multiple assignment, randomized trial.

Clinical trials studying treatments for rare diseases are challenging to design and conduct due to the limited number of patients eligible for the trial. One design used to address this challenge is the small n, sequential, multiple assignment, randomized trial (snSMART). We propose a new snSMART design that investigates the response rates of a drug tested at a low and high dose compared with placebo. Patients are randomized to an initial treatment (stage 1). In stage 2, patients are rerandomized, depending on their initial treatment and their response to that treatment in stage 1, to either the same or a different dose of treatment. Data from both stages are used to determine the efficacy of the active treatment. We present a Bayesian approach where information is borrowed between stage 1 and stage 2. We compare our approach to standard methods using only stage 1 data and a log-linear Poisson model that uses data from both stages where parameters are estimated using generalized estimating equations. We observe that the Bayesian method has smaller root-mean-square-error and 95% credible interval widths than standard methods in the tested scenarios. We conclude that it is advantageous to utilize data from both stages for a primary efficacy analysis and that the specific snSMART design shown here can be used in the registration of a drug for the treatment of rare diseases.

Choosing the right time granularity for analysis of digital biomarker trajectories.

INTRODUCTION: The use of digital biomarker data in dementia research provides the opportunity for frequent cognitive and functional assessments that was not previously available using conventional approaches. Assessing high-frequency digital biomarker data can potentially increase the opportunities for early detection of cognitive and functional decline because of improved precision of person-specific trajectories. However, we often face a decision to condense time-stamped data into a coarser time granularity, defined as the frequency at which measurements are observed or summarized, for statistical analyses. It is important to find a balance between ease of analysis by condensing data and the integrity of the data, which is reflected in a chosen time granularity. METHODS: In this paper, we discuss factors that need to be considered when faced with a time granularity decision. These factors include follow-up time, variables of interest, pattern detection, and signal-to-noise ratio. RESULTS: We applied our procedure to real-world data which include longitudinal in-home monitored walking speed. The example shed lights on typical problems that data present and how we could use the above factors in exploratory analysis to choose an appropriate time granularity. DISCUSSION: Further work is required to explore issues with missing data and computational efficiency.

A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).

Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web of Science, and IEEE Xplore databases was performed. Search terms included "hematopoietic cell transplantation (HCT)," "autologous HCT," "allogeneic HCT," "machine learning," and "artificial intelligence." Only full-text studies reported between January 2015 and July 2020 were included. Data were extracted by two authors using predefined data fields. Following PRISMA guidelines, a total of 242 studies were identified, of which 27 studies met the inclusion criteria. These studies were sub-categorized into three broad topics and the type of ML techniques used included ensemble learning (63%), regression (44%), Bayesian learning (30%), and support vector machine (30%). The majority of studies examined models to predict HCT outcomes (e.g., survival, relapse, graft-versus-host disease). Clinical and genetic data were the most commonly used predictors in the modeling process. Overall, this review provided a systematic review of ML techniques applied in the context of HCT. The evidence is not sufficiently robust to determine the optimal ML technique to use in the HCT setting and/or what minimal data variables are required.

Are Improvements Still Needed to the Modified Hospital Readmissions Reduction Program: a Health and Retirement Study (2000-2014)?

BACKGROUND: To address concerns that the Hospital Readmissions Reduction Program (HRRP) unfairly penalized safety net hospitals treating patients with high social and functional risks, Medicare recently modified HRRP to compare hospitals with similar proportions of high-risk, dual-eligible patients ("peer group hospitals"). Whether the change fully accounts for patients' social and functional risks is unknown. OBJECTIVE: Examine risk-standardized readmission rates (RSRRs) and hospital penalties after adding patient-level social and functional and community-level risk factors. DESIGN: Using 2000-2014 Medicare hospital discharge, Health and Retirement Study, and community-level data, latent factors for patient social and functional factors and community factors were identified. We estimated RSRRs for peer groups and by safety net status using four hierarchical logistic regression models: "base" (HRRP model); "patient" (base plus patient factors); "community" (base plus community factors); and "full" (all factors). The proportion of hospitals penalized was calculated by safety net status. PATIENTS: 20,255 fee-for-service Medicare beneficiaries (65+) with eligible index hospitalizations MAIN MEASURES: RSRRs KEY RESULTS: Half of safety net hospitals are in peer group 5. Compared with other hospitals, peer group 5 hospitals (most dual-eligibles) treated sicker, more functionally limited patients from socially disadvantaged groups. RSRRs decreased by 0.7% for peer groups 2 and 4 and 1.3% for peer group 5 under the patient and full (versus base) models. Measured performance improved after adjusting for patient risk factors for hospitals in peer group 4 and 5 hospitals, but worsened for those in peer groups 1, 2, and 3. Under the patient (versus base) model, fewer safety net hospitals (48.7% versus 51.3%) but more non-safety net hospitals (50.0% versus 49.1%) were penalized. CONCLUSIONS: Patient-level risk adjustment decreased RSRRs for hospitals serving more at-risk patients and proportion of safety net hospitals penalized, while modestly increasing RSRRs and proportion of non-safety net hospitals penalized. Results suggest HRRP modifications may not fully account for hospital variation in patient-level risk.

A simulation-free approach to assessing the performance of the continual reassessment method.

The continual reassessment method (CRM) is an adaptive design for Phase I trials whose operating characteristics, including appropriate sample size, probability of correctly identifying the maximum tolerated dose, and the expected proportion of participants assigned to each dose, can only be determined via simulation. The actual time to determine a final "best" design can take several hours or days, depending on the number of scenarios that are examined. The computational cost increases as the kernel of the one-parameter CRM design is expanded to other settings, including additional parameters, monitoring of both toxicity and efficacy, and studies of combinations of two agents. For a given vector of true DLT probabilities, we have developed an approach that replaces a simulation study of thousands of hypothetical trials with a single simulation. Our approach, which is founded on the consistency of the CRM, very accurately reflects the results produced by the simulation study, but does so in a fraction of time required by the simulation study. Relative to traditional simulations, we extensively examine how our method is able to assess the operating characteristics of a CRM design for a hypothetical trial whose characteristics are based upon a previously published Phase I trial. We also provide a metric of nonconsistency and demonstrate that although nonconsistency can impact the operating characteristics of our method, the degree of over- or under-estimation is unpredictable. As a solution, we provide an algorithm for maintaining the consistency of a chosen CRM design so that our method is applicable for any trial.

Sample size determination for Bayesian analysis of small n sequential, multiple assignment, randomized trials (snSMARTs) with three agents.

The small n, Sequential, Multiple Assignment, Randomized Trial (snSMART) is a two-stage clinical trial design for rare diseases motivated by the comparison of three active treatments for isolated skin vasculitis in the ongoing clinical trial ARAMIS (a randomized multicenter study for isolated skin vasculitis, NCT09239573). In Stage 1, all patients are randomized to one of three treatments. In Stage 2, patients who respond to their initial treatment receive the same treatment again, while those who fail to respond are re-randomized to one of the two remaining treatments. A Bayesian method for estimating the response rate of each individual treatment in a three-arm snSMART demonstrated efficiency gains for a given sample size relative to other existing frequentist approaches. However, these efficiency gains are dependent upon knowing how many subjects are required to determine a specific difference in the treatment response rates. Because few sample size calculation methods for snSMARTs exist, we propose a Bayesian sample size calculation for an snSMART designed to distinguish the best treatment from the second-best treatment. Although our methods are based on asymptotic approximations, we demonstrate via simulations that our proposed sample size calculation approach produces the desired statistical power, even in small samples. Moreover, our methods and applet produce sample sizes quickly, thereby saving time relative to using simulations to determine the appropriate sample size. We compare our proposed sample size to an existing frequentist method based upon a weighted Z-statistic and demonstrate that the Bayesian method requires far fewer patients than the frequentist method for a study with the same design parameters.