3 resultados para size effect

em DigitalCommons@The Texas Medical Center


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An interim analysis is usually applied in later phase II or phase III trials to find convincing evidence of a significant treatment difference that may lead to trial termination at an earlier point than planned at the beginning. This can result in the saving of patient resources and shortening of drug development and approval time. In addition, ethics and economics are also the reasons to stop a trial earlier. In clinical trials of eyes, ears, knees, arms, kidneys, lungs, and other clustered treatments, data may include distribution-free random variables with matched and unmatched subjects in one study. It is important to properly include both subjects in the interim and the final analyses so that the maximum efficiency of statistical and clinical inferences can be obtained at different stages of the trials. So far, no publication has applied a statistical method for distribution-free data with matched and unmatched subjects in the interim analysis of clinical trials. In this simulation study, the hybrid statistic was used to estimate the empirical powers and the empirical type I errors among the simulated datasets with different sample sizes, different effect sizes, different correlation coefficients for matched pairs, and different data distributions, respectively, in the interim and final analysis with 4 different group sequential methods. Empirical powers and empirical type I errors were also compared to those estimated by using the meta-analysis t-test among the same simulated datasets. Results from this simulation study show that, compared to the meta-analysis t-test commonly used for data with normally distributed observations, the hybrid statistic has a greater power for data observed from normally, log-normally, and multinomially distributed random variables with matched and unmatched subjects and with outliers. Powers rose with the increase in sample size, effect size, and correlation coefficient for the matched pairs. In addition, lower type I errors were observed estimated by using the hybrid statistic, which indicates that this test is also conservative for data with outliers in the interim analysis of clinical trials.^

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BACKGROUND: General anesthesia in adult humans is associated with narrowing or complete closure of the pharyngeal airway. The purpose of this study was to determine the effect of progressive mandibular advancement on pharyngeal airway size in normal adults during intravenous infusion of propofol for anesthesia. METHODS: Magnetic resonance imaging was performed in nine normal adults during wakefulness and during propofol anesthesia. A commercially available intraoral appliance was used to manually advance the mandible. Images were obtained during wakefulness without the appliance and during anesthesia with the participants wearing the appliance under three conditions: without mandibular advancement, advancement to 50% maximum voluntary advancement, and maximum advancement. Using computer software, airway area and maximum anteroposterior and lateral airway diameters were measured on the axial images at the level of the soft palate, uvula, tip of the epiglottis, and base of the epiglottis. RESULTS: Airway area across all four airway levels decreased during anesthesia without mandibular advancement compared with airway area during wakefulness (P < 0.007). Across all levels, airway area at 50% advancement during anesthesia was less than that at centric occlusion during wakefulness (P = 0.06), but airway area with maximum advancement during anesthesia was similar to that during wakefulness (P = 0.64). In general, anteroposterior and lateral airway diameters during anesthesia without mandibular advancement were decreased compared with wakefulness and were restored to their wakefulness values with 50% and/or maximal advancement. CONCLUSIONS: Maximum mandibular advancement during propofol anesthesia is required to restore the pharyngeal airway to its size during wakefulness in normal adults.

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The determination of size as well as power of a test is a vital part of a Clinical Trial Design. This research focuses on the simulation of clinical trial data with time-to-event as the primary outcome. It investigates the impact of different recruitment patterns, and time dependent hazard structures on size and power of the log-rank test. A non-homogeneous Poisson process is used to simulate entry times according to the different accrual patterns. A Weibull distribution is employed to simulate survival times according to the different hazard structures. The current study utilizes simulation methods to evaluate the effect of different recruitment patterns on size and power estimates of the log-rank test. The size of the log-rank test is estimated by simulating survival times with identical hazard rates between the treatment and the control arm of the study resulting in a hazard ratio of one. Powers of the log-rank test at specific values of hazard ratio (≠1) are estimated by simulating survival times with different, but proportional hazard rates for the two arms of the study. Different shapes (constant, decreasing, or increasing) of the hazard function of the Weibull distribution are also considered to assess the effect of hazard structure on the size and power of the log-rank test. ^