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Sequential Designs (sequential + design)
Selected AbstractsOptimal Spending Functions for Asymmetric Group Sequential DesignsBIOMETRICAL JOURNAL, Issue 3 2007Keaven M. Anderson Abstract We present optimized group sequential designs where testing of a single parameter , is of interest. We require specification of a loss function and of a prior distribution for ,. For the examples presented, we pre-specify Type I and II error rates and minimize the expected sample size over the prior distribution for ,. Minimizing the square of sample size rather than the sample size is found to produce designs with slightly less aggressive interim stopping rules and smaller maximum sample sizes with essentially identical expected sample size. We compare optimal designs using Hwang-Shih-DeCani and Kim-DeMets spending functions to fully optimized designs not restricted by a spending function family. In the examples selected, we also examine when there might be substantial benefit gained by adding an interim analysis. Finally, we provide specific optimal asymmetric spending function designs that should be generally useful and simply applied when a design with minimal expected sample size is desired. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] Sequential design in quality control and validation of land cover databasesAPPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 2 2009Elisabetta Carfagna Abstract We have faced the problem of evaluating the quality of land cover databases produced through photo-interpretation of remote-sensing data according to a legend of land cover types. First, we have considered the quality control, that is, the comparison of a land cover database with the result of the photo-interpretation made by a more expert photo-interpreter, on a sample of the polygons. Then we have analysed the problem of validation, that is, the check of the photo-interpretation through a ground survey. We have used the percentage of area correctly photo-interpreted as a quality measure. Since the kind of land cover type and the size of the polygons affect the probability of making mistakes in the photo-interpretation, we stratify the polygons according to two variables: the land cover type of the photo-interpretation and the size of the polygons. We have proposed an adaptive sequential procedure with permanent random numbers in which the sample size per stratum is dependent on the previously selected units but the sample selection is not, and the stopping rule is not based on the estimates of the quality parameter. We have proved that this quality control and validation procedure allows unbiased and efficient estimates of the quality parameters and allows reaching high precision of estimates with the smallest sample size. Copyright © 2009 John Wiley & Sons, Ltd. [source] Sequential designs for ordinal phase I clinical trialsBIOMETRICAL JOURNAL, Issue 2 2009Guohui Liu Abstract Sequential designs for phase I clinical trials which incorporate maximum likelihood estimates (MLE) as data accrue are inherently problematic because of limited data for estimation early on. We address this problem for small phase I clinical trials with ordinal responses. In particular, we explore the problem of the nonexistence of the MLE of the logistic parameters under a proportional odds model with one predictor. We incorporate the probability of an undetermined MLE as a restriction, as well as ethical considerations, into a proposed sequential optimal approach, which consists of a start-up design, a follow-on design and a sequential dose-finding design. Comparisons with nonparametric sequential designs are also performed based on simulation studies with parameters drawn from a real data set. [source] Real-time adaptive sequential design for optimal acquisition of arterial spin labeling MRI dataMAGNETIC RESONANCE IN MEDICINE, Issue 1 2010Jingyi Xie Abstract An optimal sampling schedule strategy based on the Fisher information matrix and the D-optimality criterion has previously been proposed as a formal framework for optimizing inversion time scheduling for multi-inversion-time arterial spin labeling experiments. Optimal sampling schedule possesses the primary advantage of improving parameter estimation precision but requires a priori estimation of plausible parameter distributions that may not be available in all situations. An adaptive sequential design approach addresses this issue by incorporating the optimal sampling schedule strategy into an adaptive process that iteratively updates the parameter estimates and adjusts the optimal sampling schedule accordingly as data are acquired. In this study, the adaptive sequential design method was experimentally implemented with a real-time feedback scheme on a clinical MRI scanner and was tested in six normal volunteers. Adapted schedules were found to accommodate the intrinsically prolonged arterial transit times in the occipital lobe of the brain. Simulation of applying the adaptive sequential design approach on subjects with pathologically reduced perfusion was also implemented. Simulation results show that the adaptive sequential design approach is capable of incorporating pathologic parameter information into an optimal arterial spin labeling scheduling design within a clinically useful experimental time. Magn Reson Med, 2010. © 2010 Wiley-Liss, Inc. [source] Sequential methods and group sequential designs for comparative clinical trialsFUNDAMENTAL & CLINICAL PHARMACOLOGY, Issue 5 2003Véronique Sébille Abstract Comparative clinical trials are performed to assess whether a new treatment has superior efficacy than a placebo or a standard treatment (one-sided formulation) or whether two active treatments have different efficacies (two-sided formulation) in a given population. The reference approach is the single-stage design and the statistical test is performed after inclusion and evaluation of a predetermined sample size. In practice, the single-stage design is sometimes difficult to implement because of ethical concerns and/or economic reasons. Thus, specific early termination procedures have been developed to allow repeated statistical analyses to be performed on accumulating data and stop the trial as soon as the information is sufficient to conclude. Two main different approaches can be used. The first one is derived from strictly sequential methods and includes the sequential probability ratio test and the triangular test. The second one is derived from group sequential designs and includes Peto, Pocock, and O'Brien and Fleming methods, , and , spending functions, and one-parameter boundaries. We review all these methods and describe the bases on which they rely as well as their statistical properties. We also compare these methods and comment on their advantages and drawbacks. We present software packages which are available for the planning, monitoring and analysis of comparative clinical trials with these methods and discuss the practical problems encountered when using them. The latest versions of all these methods can offer substantial sample size reductions when compared with the single-stage design not only in the case of clear efficacy but also in the case of complete lack of efficacy of the new treatment. The software packages make their use quite simple. However, it has to be stressed that using these methods requires efficient logistics with real-time data monitoring and, apart from survival studies or long-term clinical trials with censored endpoints, is most appropriate when the endpoint is obtained quickly when compared with the recruitment rate. [source] Robust sequential designs for nonlinear regressionTHE CANADIAN JOURNAL OF STATISTICS, Issue 4 2002Sanjoy Sinha Abstract The authors introduce the formal notion of an approximately specified nonlinear regression model and investigate sequential design methodologies when the fitted model is possibly of an incorrect parametric form. They present small-sample simulation studies which indicate that their new designs can be very successful, relative to some common competitors, in reducing mean squared error due to model misspecifi-cation and to heteroscedastic variation. Their simulations also suggest that standard normal-theory inference procedures remain approximately valid under the sequential sampling schemes. The methods are illustrated both by simulation and in an example using data from an experiment described in the chemical engineering literature. Les auteurs définissent formellement le concept de modéle de régression non linéaire approxima-tif et proposentdes plans d'expérience séquentiels pour les situations o4uG la forme paramétrique du modéle ajusté est inexacte. Ils présentent une étude de simulation qui montre que, pour de petits échantillons, leurs nouveaux plans sont largement préférables aux plans usuels en terme de réduction de I'erreur quadratique moyenne associée à rinadéquation du modéle et à l'hétéroscédasticité. Leurs simulations montrent aussi que les procédures d'inférence classiques associées au paradigme normal restent valables, à peu de choses prés, pour ces plans expéimentaux se'quentiels. La methodologie proposde est illustrée par voie de simulation et au moyen d'une application concréte tirée de la pratique du génie chimique. [source] Sequential designs for ordinal phase I clinical trialsBIOMETRICAL JOURNAL, Issue 2 2009Guohui Liu Abstract Sequential designs for phase I clinical trials which incorporate maximum likelihood estimates (MLE) as data accrue are inherently problematic because of limited data for estimation early on. We address this problem for small phase I clinical trials with ordinal responses. In particular, we explore the problem of the nonexistence of the MLE of the logistic parameters under a proportional odds model with one predictor. We incorporate the probability of an undetermined MLE as a restriction, as well as ethical considerations, into a proposed sequential optimal approach, which consists of a start-up design, a follow-on design and a sequential dose-finding design. Comparisons with nonparametric sequential designs are also performed based on simulation studies with parameters drawn from a real data set. [source] Optimal Spending Functions for Asymmetric Group Sequential DesignsBIOMETRICAL JOURNAL, Issue 3 2007Keaven M. Anderson Abstract We present optimized group sequential designs where testing of a single parameter , is of interest. We require specification of a loss function and of a prior distribution for ,. For the examples presented, we pre-specify Type I and II error rates and minimize the expected sample size over the prior distribution for ,. Minimizing the square of sample size rather than the sample size is found to produce designs with slightly less aggressive interim stopping rules and smaller maximum sample sizes with essentially identical expected sample size. We compare optimal designs using Hwang-Shih-DeCani and Kim-DeMets spending functions to fully optimized designs not restricted by a spending function family. In the examples selected, we also examine when there might be substantial benefit gained by adding an interim analysis. Finally, we provide specific optimal asymmetric spending function designs that should be generally useful and simply applied when a design with minimal expected sample size is desired. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] | |||||||||||||