Browsing by Subject "Multicenter"

Now showing 1 - 3 of 3
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    Nonparametric analysis of nonhomogeneous multistate processes with clustered observations
    (Biometrics, 2020-06-24) Bakoyannis, Giorgos
    Frequently, clinical trials and observational studies involve complex event history data with multiple events. When the observations are independent, the analysis of such studies can be based on standard methods for multistate models. However, the independence assumption is often violated, such as in multicenter studies, which makes standard methods improper. This work addresses the issue of nonparametric estimation and two-sample testing for the population-averaged transition and state occupation probabilities under general multistate models with cluster-correlated, right-censored, and/or left-truncated observations. The proposed methods do not impose assumptions regarding the within-cluster dependence, allow for informative cluster size, and are applicable to both Markov and non-Markov processes. Using empirical process theory, the estimators are shown to be uniformly consistent and to converge weakly to tight Gaussian processes. Closed-form variance estimators are derived, rigorous methodology for the calculation of simultaneous confidence bands is proposed, and the asymptotic properties of the nonparametric tests are established. Furthermore, I provide theoretical arguments for the validity of the nonparametric cluster bootstrap, which can be readily implemented in practice regardless of how complex the underlying multistate model is. Simulation studies show that the performance of the proposed methods is good, and that methods that ignore the within-cluster dependence can lead to invalid inferences. Finally, the methods are illustrated using data from a multicenter randomized controlled trial.
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    Nonparametric tests for multistate processes with clustered data
    (Springer, 2022) Bakoyannis, Giorgos; Bandyopadhyay, Dipankar; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    In this work, we propose nonparametric two-sample tests for population-averaged transition and state occupation probabilities for continuous-time and finite state space processes with clustered, right-censored, and/or left-truncated data. We consider settings where the two groups under comparison are independent or dependent, with or without complete cluster structure. The proposed tests do not impose assumptions regarding the structure of the within-cluster dependence and are applicable to settings with informative cluster size and/or non-Markov processes. The asymptotic properties of the tests are rigorously established using empirical process theory. Simulation studies show that the proposed tests work well even with a small number of clusters, and that they can be substantially more powerful compared to the only, to the best of our knowledge, previously proposed test for this problem. The tests are illustrated using data from a multicenter randomized controlled trial on metastatic squamous-cell carcinoma of the head and neck.
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    Precision and accuracy of hyperglycemic clamps in a multicenter study
    (American Physiological Society, 2021) Mather, Kieren J.; Tjaden, Ashley H.; Hoehn, Adam; Nadeau, Kristen J.; Buchanan, Thomas A.; Kahn, Steven E.; Arslanian, Silva A.; Caprio, Sonia; Atkinson, Karen M.; Cree-Green, Melanie; Utzschneider, Kristina M.; Edelstein, Sharon L.; RISE Consortium; Medicine, School of Medicine
    Application of glucose clamp methodologies in multicenter studies brings challenges for standardization. The Restoring Insulin Secretion (RISE) Consortium implemented a hyperglycemic clamp protocol across seven centers using a combination of technical and management approaches to achieve standardization. Two-stage hyperglycemic clamps with glucose targets of 200 mg/dL and >450 mg/dL were performed utilizing a centralized spreadsheet-based algorithm that guided dextrose infusion rates using bedside plasma glucose measurements. Clamp operators received initial and repeated training with ongoing feedback based on surveillance of clamp performance. The precision and accuracy of the achieved stage-specific glucose targets were evaluated, including differences by study center. We also evaluated robustness of the method to baseline physiologic differences and on-study treatment effects. The RISE approach produced high overall precision (3%–9% variance in achieved plasma glucose from target at various times across the procedure) and accuracy (SD < 10% overall). Statistically significant but numerically small differences in achieved target glucose concentrations were observed across study centers, within the magnitude of the observed technical variability. Variation of the achieved target glucose over time in placebo-treated individuals was low (<3% variation), and the method was robust to differences in baseline physiology (youth vs. adult, IGT vs. diabetes status) and differences in physiology induced by study treatments. The RISE approach to standardization of the hyperglycemic clamp methodology across multiple study centers produced technically excellent standardization of achieved glucose concentrations. This approach provides a reliable method for implementing glucose clamp methodology across multiple study centers. NEW & NOTEWORTHY: The Restoring Insulin Secretion (RISE) study centers undertook hyperglycemic clamps using a simplified methodology and a decision guidance algorithm implemented in an easy-to-use spreadsheet. This approach, combined with active management including ongoing central data surveillance and routine feedback to study centers, produced technically excellent standardization of achieved glucose concentrations on repeat studies within and across study centers.