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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.
This article was published in the following journal.
Name: Contemporary clinical trials
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Work consisting of a clinical trial involving one or more test treatments, at least one control treatment, specified outcome measures for evaluating the studied intervention, and a bias-free method for assigning patients to the test treatment. The treatment may be drugs, devices, or procedures studied for diagnostic, therapeutic, or prophylactic effectiveness. Control measures include placebos, active medicine, no-treatment, dosage forms and regimens, historical comparisons, etc. When randomization using mathematical techniques, such as the use of a random numbers table, is employed to assign patients to test or control treatments, the trial is characterized as a RANDOMIZED CONTROLLED TRIAL.
Clinical trials involving one or more test treatments, at least one control treatment, specified outcome measures for evaluating the studied intervention, and a bias-free method for assigning patients to the test treatment. The treatment may be drugs, devices, or procedures studied for diagnostic, therapeutic, or prophylactic effectiveness. Control measures include placebos, active medicines, no-treatment, dosage forms and regimens, historical comparisons, etc. When randomization using mathematical techniques, such as the use of a random numbers table, is employed to assign patients to test or control treatments, the trials are characterized as RANDOMIZED CONTROLLED TRIALS AS TOPIC.
Works about a study where participants are assigned to a treatment, procedure, or intervention by methods that are not random. Non-randomized clinical trials are sometimes referred to as quasi-experimental clinical trials or non-equivalent control group designs.
Drug regimens, for patients with HIV INFECTIONS, that aggressively suppress HIV replication. The regimens usually involve administration of three or more different drugs including a protease inhibitor.
A thiosemicarbazone that is used in association with other antimycobacterial agents in the initial and continuation phases of antituberculosis regimens. Thiacetazone containing regimens are less effective than the short-course regimen recommended by the International Union Against Tuberculosis and are used in some developing countries to reduce drug costs. (From Martindale, The Extra Pharmacopoeia, 30th ed, p217)
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