The Experts below are selected from a list of 38262 Experts worldwide ranked by ideXlab platform

Daniel Almirall - One of the best experts on this subject based on the ideXlab platform.

  • The Balanced Opioid Initiative: protocol for a clustered, sequential, Multiple-Assignment randomized trial to construct an adaptive implementation strategy to improve guideline-concordant opioid prescribing in primary care.
    Implementation science : IS, 2020
    Co-Authors: Andrew Quanbeck, Daniel Almirall, Nora Jacobson, Randall Brown, Jillian K. Landeck, Lynn M. Madden, Andrew S. Cohen, Brienna Deyo, James Robinson, Roberta A. Johnson
    Abstract:

    Rates of opioid prescribing tripled in the USA between 1999 and 2015 and were associated with significant increases in opioid misuse and overdose death. Roughly half of all opioids are prescribed in primary care. Although clinical guidelines describe recommended opioid prescribing practices, implementing these guidelines in a way that balances safety and effectiveness vs. risk remains a challenge. The literature offers little help about which implementation strategies work best in different clinical settings or how strategies could be tailored to optimize their effectiveness in different contexts. Systems consultation consists of (1) educational/engagement meetings with audit and feedback reports, (2) practice facilitation, and (3) prescriber peer consulting. The study is designed to discover the most cost-effective sequence and combination of strategies for improving opioid prescribing practices in diverse primary care clinics. The study is a hybrid type 3 clustered, sequential, Multiple-Assignment randomized trial (SMART) that randomizes clinics from two health systems at two points, months 3 and 9, of a 21-month intervention. Clinics are provided one of four sequences of implementation strategies: a condition consisting of educational/engagement meetings and audit and feedback alone (EM/AF), EM/AF plus practice facilitation (PF), EM/AF + prescriber peer consulting (PPC), and EM/AF + PF + PPC. The study’s primary outcome is morphine-milligram equivalent (MME) dose by prescribing clinicians within clinics. The study’s primary aim is the comparison of EM/AF + PF + PPC versus EM/AF alone on change in MME from month 3 to month 21. The secondary aim is to derive cost estimates for each of the four sequences and compare them. The exploratory aim is to examine four tailoring variables that can be used to construct an adaptive implementation strategy to meet the needs of different primary care clinics. Systems consultation is a practical blend of implementation strategies used in this case to improve opioid prescribing practices in primary care. The blend offers a range of strategies in sequences from minimally to substantially intensive. The results of this study promise to help us understand how to cost effectively improve the implementation of evidence-based practices. NCT 04044521 (ClinicalTrials.gov). Registered 05 August 2019.

  • The Balanced Opioid Initiative: Protocol for a clustered, sequential, Multiple-Assignment randomized trial to construct an adaptive implementation strategy to improve guideline-concordant opioid prescribing in primary care
    2020
    Co-Authors: Andrew Quanbeck, Daniel Almirall, Nora Jacobson, Jillian K. Landeck, Brienna Deyo, James Robinson, Randall T. Brown, Lynn Madden, Andrew Cohen, Roberta A. Johnson
    Abstract:

    Abstract Background Rates of opioid prescribing tripled in the United States between 1999 and 2015 and were associated with significant increases in opioid misuse and overdose death. Roughly half of all opioids are prescribed in primary care. Although clinical guidelines describe recommended opioid prescribing practices, implementing these guidelines in a way that balances safety and effectiveness vs. risk remains a challenge. The literature offers little help about which implementation strategies work best in different clinical settings or how strategies could be tailored to optimize their effectiveness in different contexts. Systems consultation consists of: (1) educational/engagement meetings with audit and feedback reports, (2) practice facilitation, and (3) prescriber peer consulting. The study is designed to discover the most cost-effective sequence and combination of strategies for improving opioid prescribing practices in diverse primary care clinics.Methods/Design The study is a hybrid type 3 clustered, sequential, Multiple-Assignment randomized trial (SMART) that randomizes clinics from two health systems at two points, months 3 and 9, of a 21-month intervention. Clinics are provided one of four sequences of implementation strategies: a condition consisting of educational/engagement meetings and audit and feedback alone (EM/AF), EM/AF plus practice facilitation (PF), EM/AF + prescriber peer consulting (PPC), and EM/AF + PF + PPC. The study’s primary outcome is morphine-milligram equivalent (MME) dose by prescribing clinicians within clinics. The study’s primary aim is the comparison of EM/AF + PF + PPC versus EM/AF alone on change in MME from month 3 to month 21. The secondary aim is to derive cost estimates for each of the four sequences and compare them. The exploratory aim is to examine four tailoring variables that can be used to construct an adaptive implementation strategy to meet the needs of different primary care clinics.Discussion Systems consultation is a practical blend of implementation strategies used in this case to improve opioid prescribing practices in primary care. The blend offers a range of strategies in sequences from minimally to substantially intensive. The results of this study promise to help us understand how to cost effectively improve the implementation of evidence-based practices.Trial registration NCT 04044521 (ClinicalTrial.gov). Registered 05 August 2019.

  • SMM877520 Supplemental Material - Supplemental material for Sample size considerations for comparing dynamic treatment regimens in a sequential Multiple-Assignment randomized trial with a continuous longitudinal outcome
    2019
    Co-Authors: Nicholas J. Seewald, Inbal Nahum-shani, Kelley M Kidwell, James R. Mckay, Daniel Almirall
    Abstract:

    Supplemental material, SMM877520 Supplemental Material for Sample size considerations for comparing dynamic treatment regimens in a sequential Multiple-Assignment randomized trial with a continuous longitudinal outcome by Nicholas J Seewald, Kelley M Kidwell, Inbal Nahum-Shani, Tianshuang Wu, James R McKay and Daniel Almirall in Statistical Methods in Medical Research

  • sample size considerations for comparing dynamic treatment regimens in a sequential Multiple Assignment randomized trial with a continuous longitudinal outcome
    arXiv: Methodology, 2018
    Co-Authors: Nicholas J. Seewald, Kelley M Kidwell, James R. Mckay, Inbal Nahumshani, Daniel Almirall
    Abstract:

    Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen (DTR) is a sequence of pre-specified decision rules which can be used to guide the delivery of a sequence of treatments or interventions that are tailored to the changing needs of the individual. The sequential Multiple-Assignment randomized trial (SMART) is a research tool which allows for the construction of effective DTRs. We derive easy-to-use formulae for computing the total sample size for three common two-stage SMART designs in which the primary aim is to compare mean end-of-study outcomes for two embedded DTRs which recommend different first-stage treatments. The formulae are derived in the context of a regression model which leverages information from a longitudinal outcome collected over the entire study. We show that the sample size formula for a SMART can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a longitudinal analysis, and an inflation factor that accounts for the design of a SMART. The SMART design inflation factor is typically a function of the anticipated probability of response to first-stage treatment. We review modeling and estimation for DTR effect analyses using a longitudinal outcome from a SMART, as well as the estimation of standard errors. We also present estimators for the covariance matrix for a variety of common working correlation structures. Methods are motivated using the ENGAGE study, a SMART aimed at developing a DTR for increasing motivation to attend treatments among alcohol- and cocaine-dependent patients.

  • Design and analysis considerations for comparing dynamic treatment regimens with binary outcomes from sequential Multiple Assignment randomized trials
    Journal of applied statistics, 2017
    Co-Authors: Kelley M Kidwell, Nicholas J. Seewald, Qui Tran, Connie Kasari, Daniel Almirall
    Abstract:

    ABSTRACTIn behavioral, educational and medical practice, interventions are often personalized over time using strategies that are based on individual behaviors and characteristics and changes in symptoms, severity, or adherence that are a result of one's treatment. Such strategies that more closely mimic real practice, are known as dynamic treatment regimens (DTRs). A sequential Multiple Assignment randomized trial (SMART) is a multi-stage trial design that can be used to construct effective DTRs. This article reviews a simple to use ‘weighted and replicated’ estimation technique for comparing DTRs embedded in a SMART design using logistic regression for a binary, end-of-study outcome variable. Based on a Wald test that compares two embedded DTRs of interest from the ‘weighted and replicated’ regression model, a sample size calculation is presented with a corresponding user-friendly applet to aid in the process of designing a SMART. The analytic models and sample size calculations are presented for three ...

Kelley M Kidwell - One of the best experts on this subject based on the ideXlab platform.

  • power prior models for treatment effect estimation in a small n sequential Multiple Assignment randomized trial
    arXiv: Methodology, 2020
    Co-Authors: Yancheng Chao, Roy N Tamura, Thomas M Braun, Kelley M Kidwell
    Abstract:

    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 treatment effect of interest in an snSMART is the first-stage response rate, 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 applies power prior models, in which first stage outcomes from an snSMART are regarded as the primary data and second stage outcomes are regarded as supplemental. 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 treatment effect 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.

  • Bayesian methods to compare dose levels with placebo in a small n, sequential, Multiple Assignment, randomized trial.
    Statistics in medicine, 2020
    Co-Authors: Fang Fang, Roy N Tamura, Thomas M Braun, K Hochstedler, Kelley M Kidwell
    Abstract:

    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.

  • design and analysis considerations for utilizing a mapping function in a small sample sequential Multiple Assignment randomized trials with continuous outcomes
    Statistics in Medicine, 2020
    Co-Authors: Holly E Hartman, Roy N Tamura, Matthew J Schipper, Kelley M Kidwell
    Abstract:

    Small sample, sequential, Multiple Assignment, randomized trials (snSMARTs) are multistage trials with the overall goal of determining the best treatment after a fixed amount of time. In snSMART trials, patients are first randomized to one of three treatments and a binary (e.g. response/nonresponse) outcome is measured at the end of the first stage. Responders to first stage treatment continue their treatment. Nonresponders to first stage treatment are rerandomized to one of the remaining treatments. The same binary outcome is measured at the end of the first and second stages, and data from both stages are pooled together to find the best first stage treatment. However, in many settings the primary endpoint may be continuous, and dichotomizing this continuous variable may reduce statistical efficiency. In this article, we extend the snSMART design and methods to allow for continuous outcomes. Instead of requiring a binary outcome at the first stage for rerandomization, the probability of staying on the same treatment or switching treatment is a function of the first stage outcome. Rerandomization based on a mapping function of a continuous outcome allows for snSMART designs without requiring a binary outcome. We perform simulation studies to compare the proposed design with continuous outcomes to standard snSMART designs with binary outcomes. The proposed design results in more efficient treatment effect estimates and similar outcomes for trial patients.

  • sample size determination for bayesian analysis of small n sequential Multiple Assignment randomized trials snsmarts with three agents
    Journal of Biopharmaceutical Statistics, 2020
    Co-Authors: Boxian Wei, Thomas M Braun, Roy N Tamura, Kelley M Kidwell
    Abstract:

    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 s...

  • Dynamic treatment regimens in small n, sequential, Multiple Assignment, randomized trials: An application in focal segmental glomerulosclerosis.
    Contemporary clinical trials, 2020
    Co-Authors: Yancheng Chao, Thomas M Braun, Howard Trachtman, Debbie S. Gipson, Cathie Spino, Kelley M Kidwell
    Abstract:

    Focal segmental glomerulosclerosis (FSGS) is a rare kidney disease with an annual incidence of 0.2-1.8 cases per 100,000 individuals. Most rare diseases like FSGS lack effective treatments, and it is difficult to implement clinical trials to study rare diseases because of the small sample sizes and difficulty in recruitment. A novel clinical trial design, a small sample, sequential, Multiple Assignment, randomized trial (snSMART) has been proposed to efficiently identify effective treatments for rare diseases. In this work, we review and expand the snSMART design applied to studying treatments for FSGS. The snSMART is a multistage trial that randomizes participants to one of three active treatments in the first stage and then re-randomizes those who do not respond to the initial treatment to one of the other two treatments in the second stage. A Bayesian joint stage model efficiently shares information across the stages to find the best first stage treatment. In this setting, we modify the previously presented design and methods (Wei et al. 2018) such that the proposed design includes a standard of care as opposed to three active treatments. We present Bayesian and frequentist models to compare the two novel therapies to the standard of care. Additionally, we show for the first time how we should estimate and compare tailored sequences of treatments or dynamic treatment regimens (DTRs) and contrast the results from our methods to existing methods for analyzing DTRs from a SMART. We also propose a sample size calculation method for our snSMART design when implementing the frequentist model with Dunnett's correction.

Inbal Nahum-shani - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive intervention for prevention of adolescent suicidal behavior after hospitalization: a pilot sequential Multiple Assignment randomized trial
    Journal of child psychology and psychiatry and allied disciplines, 2021
    Co-Authors: Ewa K. Czyz, Cheryl A. King, David Prouty, Valerie J. Micol, Maureen A. Walton, Inbal Nahum-shani
    Abstract:

    BACKGROUND The need for effective interventions for psychiatrically hospitalized adolescents who have varying levels of postdischarge suicide risk calls for personalized approaches, such as adaptive interventions (AIs). We conducted a nonrestricted pilot Sequential, Multiple Assignment, Randomized Trial (SMART) to guide the development of an AI targeting suicide risk after hospitalization. METHODS Adolescent inpatients (N = 80; ages 13-17; 67.5% female) were randomized in Phase 1 to a Motivational Interview-Enhanced Safety Plan (MI-SP), delivered during hospitalization, alone or in combination with postdischarge text-based support (Texts). Two weeks after discharge, participants were re-randomized in Phase 2 to added telephone booster calls or to no calls. Mechanisms of change were assessed with daily diaries for four weeks and over a 1- and 3-month follow-up. This trial is registered with clinicaltrials.gov (identifier: NCT03838198). RESULTS Procedures were feasible and acceptable. Mixed effects models indicate that adolescents randomized to MI-SP + Texts (Phase 1) and those randomized to booster calls (Phase 2) experienced significant improvement in daily-level mechanisms, including safety plan use, self-efficacy to refrain from suicidal action, and coping by support seeking. Those randomized to MI-SP + Texts also reported significantly higher coping self-efficacy at 1 and 3 months. Although exploratory, results were in the expected direction for MI-SP + Texts, versus MI-SP alone, in terms of lower risk of suicide attempts (Hazard ratio = 0.30; 95% CI = 0.06, 1.48) and suicidal behavior (Hazard ratio = 0.36; 95% CI = 0.10, 1.37) three months after discharge. Moreover, augmentation with booster calls did not have an overall meaningful impact on suicide attempts (Hazard ratio = 0.65; 95% CI = 0.17, 3.05) or suicidal behavior (Hazard ratio = 0.78; 95% CI = 0.23, 2.67); however, boosters benefited most those initially assigned to MI-SP + Texts. CONCLUSIONS The current SMART was feasible and acceptable for the purpose of informing an AI for suicidal adolescents, warranting additional study. Findings also indicate that postdischarge text-based support offers a promising augmentation to safety planning delivered during hospitalization.

  • SMM877520 Supplemental Material - Supplemental material for Sample size considerations for comparing dynamic treatment regimens in a sequential Multiple-Assignment randomized trial with a continuous longitudinal outcome
    2019
    Co-Authors: Nicholas J. Seewald, Inbal Nahum-shani, Kelley M Kidwell, James R. Mckay, Daniel Almirall
    Abstract:

    Supplemental material, SMM877520 Supplemental Material for Sample size considerations for comparing dynamic treatment regimens in a sequential Multiple-Assignment randomized trial with a continuous longitudinal outcome by Nicholas J Seewald, Kelley M Kidwell, Inbal Nahum-Shani, Tianshuang Wu, James R McKay and Daniel Almirall in Statistical Methods in Medical Research

  • Noninferiority and equivalence tests in sequential, Multiple Assignment, randomized trials (SMARTs)
    Psychological methods, 2019
    Co-Authors: Palash Ghosh, Inbal Nahum-shani, Bonnie Spring, Bibhas Chakraborty
    Abstract:

    Adaptive interventions (AIs) are increasingly popular in the behavioral sciences. An AI is a sequence of decision rules that specify for whom and under what conditions different intervention options should be offered, in order to address the changing needs of individuals as they progress over time. The sequential, Multiple Assignment, randomized trial (SMART) is a novel trial design that was developed to aid in empirically constructing effective AIs. The sequential randomizations in a SMART often yield Multiple AIs that are embedded in the trial by design. Many SMARTs are motivated by scientific questions pertaining to the comparison of such embedded AIs. Existing data analytic methods and sample size planning resources for SMARTs are suitable only for superiority testing, namely for testing whether one embedded AI yields better primary outcomes on average than another. This calls for noninferiority/equivalence testing methods, because AIs are often motivated by the need to deliver support/care in a less costly or less burdensome manner, while still yielding benefits that are equivalent or noninferior to those produced by a more costly/burdensome standard of care. Here, we develop data-analytic methods and sample-size formulas for SMARTs testing the noninferiority or equivalence of one AI over another. Sample size and power considerations are discussed with supporting simulations, and online resources for sample size planning are provided. A simulated data analysis shows how to test noninferiority and equivalence hypotheses with SMART data. For illustration, we use an example from a SMART in the area of health psychology aiming to develop an AI for promoting weight loss among overweight/obese adults. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

  • SMART: Study protocol for a sequential Multiple Assignment randomized controlled trial to optimize weight loss management.
    Contemporary clinical trials, 2019
    Co-Authors: Angela Fidler Pfammatter, Inbal Nahum-shani, Margaret Dezelar, Laura Scanlan, H. Gene Mcfadden, Juned Siddique, Donald Hedeker, Bonnie Spring
    Abstract:

    BACKGROUND Stepped care is a rational resource allocation approach to reduce population obesity. Evidence is lacking to guide decisions on use of low cost treatment components such as mobile health (mHealth) tools without compromising weight loss of those needing more expensive traditional treatment components (e.g., coaching, meal replacement). A sequential Multiple Assignment randomization trial (SMART) will be conducted to inform the development of an empirically based stepped care intervention that incorporates mHealth and traditional treatment components. OBJECTIVE The primary aim tests the non-inferiority of app alone, compared to app plus coaching, as first line obesity treatment, measured by weight change from baseline to 6 months. Secondary aims are to identify the best tactic to address early treatment non-response and the optimal treatment sequence for resource efficient weight loss. STUDY DESIGN Four hundred participants, 18-60 years old with Body Mass Index between 27 and 45 kg/m2 will be randomized to receive a weight loss smartphone app (APP) or the app plus weekly coaching (APP + C) for a 12 week period. Those achieving

  • Non-Inferiority and Equivalence Tests in A Sequential Multiple-Assignment Randomized Trial (SMART)
    arXiv: Applications, 2017
    Co-Authors: Palash Ghosh, Inbal Nahum-shani, Bonnie Spring, Bibhas Chakraborty
    Abstract:

    Adaptive interventions (AIs) are increasingly becoming popular in medical and behavioral sciences. An AI is a sequence of individualized intervention options that specify for whom and under what conditions different intervention options should be offered, in order to address the changing needs of individuals as they progress over time. The sequential, Multiple Assignment, randomized trial (SMART) is a novel trial design that was developed to aid in empirically constructing effective AIs. The sequential randomizations in a SMART often yield Multiple AIs that are embedded in the trial by design. Many SMARTs are motivated by scientific questions pertaining to the comparison of such embedded AIs. Existing data analytic methods and sample size planning resources for SMARTs are suitable for superiority testing, namely for testing whether one embedded AI yields better primary outcomes on average than another. This represents a major scientific gap since AIs are often motivated by the need to deliver support/care in a less costly or less burdensome manner, while still yielding benefits that are equivalent or non-inferior to those produced by a more costly/burdensome standard of care. Here, we develop data analytic methods and sample size formulas for SMART studies aiming to test the non-inferiority or equivalence of one AI over another. Sample size and power considerations are discussed with supporting simulations, and online sample size planning resources are provided. For illustration, we use an example from a SMART study aiming to develop an AI for promoting weight loss among overweight/obese adults.

Martha Shumway - One of the best experts on this subject based on the ideXlab platform.

  • M21. THE STEP TRIAL: A SEQUENTIAL Multiple Assignment RANDOMISED TRIAL (SMART) OF INTERVENTIONS FOR PATIENTS AT ULTRA-HIGH RISK OF PSYCHOSIS - STUDY RATIONALE, DESIGN AND BASELINE DATA
    Schizophrenia Bulletin, 2020
    Co-Authors: Barnaby Nelson, Hok Pan Yuen, Nicky Wallis, Melissa Kerr, Cameron S Carter, Rachel Loewy, Tara A Niendam, G. Paul Amminger, Jessica Spark, Martha Shumway
    Abstract:

    Abstract Background Although approximately twenty randomised controlled trials have now been conducted with young people identified as being at high clinical risk of psychotic disorder, it remains unclear what the optimal type and sequence of treatments are for this clinical population. There has also been increased focus on clinical outcomes other than transition to psychotic disorder, such as psychosocial functioning, persistent attenuated psychotic symptoms and non-psychotic disorders. At Orygen, we are currently conducting a trial of a sequence of interventions consisting of two psychosocial therapies (support and problem solving [SPS] and cognitive-behavioural case management [CBCM]) and antidepressant medication. The primary outcome of the study is functional outcome after 6 months. This presentation will outline the background, rationale, design, recruitment and retention data and preliminary baseline results. Methods STEP is a sequential Multiple Assignment randomised trial (SMART) of treatments for young people (12–25 year olds) who meet ultra high risk for psychosis (UHR) criteria. Participants were recruited from primary (headspace) and secondary/tertiary (Orygen Youth Health) mental health services in Melbourne, Australia. The trial consists of three steps: Step 1: SPS (1.5 months); Step 2: SPS vs Cognitive Behavioural Case Management (4.5 months); Step 3: Cognitive Behavioural Case Management + Antidepressant Medication vs Cognitive Behavioural Case Management + Placebo (6 months). Patients who do not respond by the end of each step graduate to the next step in treatment. Responders are randomised to SPS or monitoring. Treatment response is based a combination of reduced attenuated psychotic symptoms, rated using the Comprehensive Assessment of At-Risk Mental States (CAARMS), and functional improvement (Social and Occupational Functioning Assessment Scale [SOFAS]) at the end of the treatment step. A ‘fast fail’ option is built into Step 3, whereby patients who deteriorate or have not responded 3 months into Step 3 are offered a choice of continuing existing treatment or commencing omega-3 fatty acids or low-dose antipsychotic medication. The intervention is for 12 months, with follow up at 18 and 24 months. A pilot study using the same design is currently being conducted at The University of California Davis. Results Recruitment has recently completed, with 342 patients recruited over a 2.4 year period, representing the largest UHR treatment study conducted to date. Preliminary results indicate an 8% response rate to Step 1 and a 23% response rate to Step 2. Discontinuation rates are 15% (step 1), 43% (step 2), 32% (step 3), primarily due to participants being lost to follow up or not wanting to start medication. The current transition to psychosis rate is 10.2%. Baseline clinical data are currently being analysed and will be presented at the conference. Discussion Preliminary results indicate high non-response rates following SPS and moderate non-response rates following extended SPS or CBCM, possibly partly due to the stringent definition of response, which required substantial and persistent improvement in both attenuated psychotic symptoms and functioning. Discontinuation rates are low to moderate, reflecting the complexity and severity of this clinical population. The recruitment and retention data show that it is possible to conduct large-scale and complex stepped care trials with this high risk population in a primary mental health care setting (headspace services). Outcomes will inform the most effective type and sequence of treatments for improving psychosocial functioning, symptoms and reducing risk of developing psychotic disorder in this group, as well as identify predictors of treatment response.

  • staged treatment in early psychosis a sequential Multiple Assignment randomised trial of interventions for ultra high risk of psychosis patients
    Early Intervention in Psychiatry, 2018
    Co-Authors: Barnaby Nelson, Paul G Amminger, Hok Pan Yuen, Nicky Wallis, Melissa Kerr, Lisa B Dixon, Cameron S Carter, Rachel Loewy, Tara A Niendam, Martha Shumway
    Abstract:

    Aim Previous research indicates that preventive intervention is likely to benefit patients “at risk” of psychosis, in terms of functional improvement, symptom reduction and delay or prevention of onset of threshold psychotic disorder. The primary aim of the current study is to test outcomes of ultra high risk (UHR) patients, primarily functional outcome, in response to a sequential intervention strategy consisting of support and problem solving (SPS), cognitive-behavioural case management and antidepressant medication. A secondary aim is to test biological and psychological variables that moderate and mediate response to this sequential treatment strategy. Methods This is a sequential Multiple Assignment randomised trial (SMART) consisting of three steps: Step 1: SPS (1.5 months); Step 2: SPS vs Cognitive Behavioural Case Management (4.5 months); Step 3: Cognitive Behavioural Case Management + Antidepressant Medication vs Cognitive Behavioural Case Management + Placebo (6 months). The intervention is of 12 months duration in total and participants will be followed up at 18 months and 24 months post baseline. Conclusion This paper reports on the rationale and protocol of the Staged Treatment in Early Psychosis (STEP) study. With a large sample of 500 UHR participants this study will investigate the most effective type and sequence of treatments for improving functioning and reducing the risk of developing psychotic disorder in this clinical population.

Roy N Tamura - One of the best experts on this subject based on the ideXlab platform.

  • power prior models for treatment effect estimation in a small n sequential Multiple Assignment randomized trial
    arXiv: Methodology, 2020
    Co-Authors: Yancheng Chao, Roy N Tamura, Thomas M Braun, Kelley M Kidwell
    Abstract:

    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 treatment effect of interest in an snSMART is the first-stage response rate, 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 applies power prior models, in which first stage outcomes from an snSMART are regarded as the primary data and second stage outcomes are regarded as supplemental. 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 treatment effect 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.

  • Bayesian methods to compare dose levels with placebo in a small n, sequential, Multiple Assignment, randomized trial.
    Statistics in medicine, 2020
    Co-Authors: Fang Fang, Roy N Tamura, Thomas M Braun, K Hochstedler, Kelley M Kidwell
    Abstract:

    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.

  • design and analysis considerations for utilizing a mapping function in a small sample sequential Multiple Assignment randomized trials with continuous outcomes
    Statistics in Medicine, 2020
    Co-Authors: Holly E Hartman, Roy N Tamura, Matthew J Schipper, Kelley M Kidwell
    Abstract:

    Small sample, sequential, Multiple Assignment, randomized trials (snSMARTs) are multistage trials with the overall goal of determining the best treatment after a fixed amount of time. In snSMART trials, patients are first randomized to one of three treatments and a binary (e.g. response/nonresponse) outcome is measured at the end of the first stage. Responders to first stage treatment continue their treatment. Nonresponders to first stage treatment are rerandomized to one of the remaining treatments. The same binary outcome is measured at the end of the first and second stages, and data from both stages are pooled together to find the best first stage treatment. However, in many settings the primary endpoint may be continuous, and dichotomizing this continuous variable may reduce statistical efficiency. In this article, we extend the snSMART design and methods to allow for continuous outcomes. Instead of requiring a binary outcome at the first stage for rerandomization, the probability of staying on the same treatment or switching treatment is a function of the first stage outcome. Rerandomization based on a mapping function of a continuous outcome allows for snSMART designs without requiring a binary outcome. We perform simulation studies to compare the proposed design with continuous outcomes to standard snSMART designs with binary outcomes. The proposed design results in more efficient treatment effect estimates and similar outcomes for trial patients.

  • sample size determination for bayesian analysis of small n sequential Multiple Assignment randomized trials snsmarts with three agents
    Journal of Biopharmaceutical Statistics, 2020
    Co-Authors: Boxian Wei, Thomas M Braun, Roy N Tamura, Kelley M Kidwell
    Abstract:

    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 s...

  • A Bayesian analysis of small n sequential Multiple Assignment randomized trials (snSMARTs)
    Statistics in medicine, 2018
    Co-Authors: Boxian Wei, Thomas M Braun, Roy N Tamura, Kelley M Kidwell
    Abstract:

    Designing clinical trials to study treatments for rare diseases is challenging because of the limited number of available patients. A suggested design is known as the small n sequential Multiple Assignment randomized trial (snSMART), in which patients are first randomized to one of Multiple treatments (stage 1). Patients who respond to their initial treatment continue the same treatment for another stage, while those who fail to respond are rerandomized to one of the remaining treatments (stage 2). The data from both stages are used to compare the efficacy between treatments. Analysis approaches for snSMARTs are limited, and we propose a Bayesian approach that allows for borrowing of information across both stages. Through simulation, we compare the bias, root-mean-square error, width, and coverage rate of 95% confidence/credible interval of estimators from of our approach to estimators produced from (i) standard approaches that only use the data from stage 1, and (ii) a log-Poisson model using data from both stages whose parameters are estimated via generalized estimating equations. We demonstrate the root-mean-square error and width of 95% confidence/credible intervals of our estimators are smaller than the other approaches in realistic settings, so that the collection and use of stage 2 data in snSMARTs provide improved inference for treatments of rare diseases.