The Experts below are selected from a list of 77640 Experts worldwide ranked by ideXlab platform
K C Carriere - One of the best experts on this subject based on the ideXlab platform.
-
multiple objective response adaptive Repeated Measurement designs in clinical trials for binary responses
Statistics in Medicine, 2014Co-Authors: Yuanyuan Liang, Jing Wang, K C CarriereAbstract:A multiple-objective allocation strategy was recently proposed for constructing response-adaptive Repeated Measurement designs for continuous responses. We extend the allocation strategy to constructing response-adaptive Repeated Measurement designs for binary responses. The approach with binary responses is quite different from the continuous case, as the information matrix is a function of responses, and it involves nonlinear modeling. To deal with these problems, we first build the design on the basis of success probabilities. Then we illustrate how various models can accommodate carryover effects on the basis of logits of response profiles as well as any correlation structure. Through computer simulations, we find that the allocation strategy developed for continuous responses also works well for binary responses. As expected, design efficiency in terms of mean squared error drops sharply, as more emphasis is placed on increasing treatment benefit than estimation precision. However, we find that it can successfully allocate more patients to better treatment sequences without sacrificing much estimation precision. Copyright © 2013 John Wiley & Sons, Ltd.
-
multiple objective response adaptive Repeated Measurement designs for clinical trials
Journal of Statistical Planning and Inference, 2009Co-Authors: Yuanyuan Liang, K C CarriereAbstract:Abstract In a response-adaptive design, we review and update the trial on the basis of outcomes in order to achieve a specific goal. Response-adaptive designs for clinical trials are usually constructed to achieve a single objective. In this paper, we develop a new adaptive allocation rule to improve current strategies for building response-adaptive designs to construct multiple-objective Repeated Measurement designs. This new rule is designed to increase estimation precision and treatment benefit by assigning more patients to a better treatment sequence. We demonstrate that designs constructed under the new proposed allocation rule can be nearly as efficient as fixed optimal designs in terms of the mean squared error, while leading to improved patient care.
-
optimal two period Repeated Measurement designs with two or more treatments
Biometrika, 1993Co-Authors: K C Carriere, Gregory C ReinselAbstract:SUMMARY For two-period Repeated Measurement designs for comparing t ?> 2 treatments, we discuss universally optimal designs when the number of subjects N is a multiple of t2 and some cases when N is a multiple of t. We examine efficiencies of balanced designs and other symmetric designs relative to strongly balanced designs. In addition, efficiencies of the universally optimal designs obtained by Hedayat & Zhao (1990) are examined under both fixed subject and random subject effect model assumptions.
Philip J Schluter - One of the best experts on this subject based on the ideXlab platform.
-
a multivariate hierarchical bayesian approach to measuring agreement in Repeated Measurement method comparison studies
BMC Medical Research Methodology, 2009Co-Authors: Philip J SchluterAbstract:Assessing agreement in method comparison studies depends on two fundamentally important components; validity (the between method agreement) and reproducibility (the within method agreement). The Bland-Altman limits of agreement technique is one of the favoured approaches in medical literature for assessing between method validity. However, few researchers have adopted this approach for the assessment of both validity and reproducibility. This may be partly due to a lack of a flexible, easily implemented and readily available statistical machinery to analyse Repeated Measurement method comparison data. Adopting the Bland-Altman framework, but using Bayesian methods, we present this statistical machinery. Two multivariate hierarchical Bayesian models are advocated, one which assumes that the underlying values for subjects remain static (exchangeable replicates) and one which assumes that the underlying values can change between Repeated Measurements (non-exchangeable replicates). We illustrate the salient advantages of these models using two separate datasets that have been previously analysed and presented; (i) assuming static underlying values analysed using both multivariate hierarchical Bayesian models, and (ii) assuming each subject's underlying value is continually changing quantity and analysed using the non-exchangeable replicate multivariate hierarchical Bayesian model. These easily implemented models allow for full parameter uncertainty, simultaneous method comparison, handle unbalanced or missing data, and provide estimates and credible regions for all the parameters of interest. Computer code for the analyses in also presented, provided in the freely available and currently cost free software package WinBUGS.
Harold M Swartz - One of the best experts on this subject based on the ideXlab platform.
-
direct and Repeated Measurement of heart and brain oxygenation using in vivo epr oximetry
Methods in Enzymology, 2015Co-Authors: Nadeem Khan, Huagang Hou, Harold M Swartz, Periannan KuppusamyAbstract:Low level of oxygen (hypoxia) is a critical factor that defines the pathological consequence of several pathophysiologies, particularly ischemia, that usually occur following the blockage of a blood vessel in vital organs, such as brain and heart, or abnormalities in the microvasculature, such as peripheral vascular disease. Therefore, methods that can directly and Repeatedly quantify oxygen levels in the brain and heart will significantly improve our understanding of ischemic pathologies. Importantly, such oximetry capability will facilitate the development of strategies to counteract low levels of oxygen and thereby improve outcome following stroke or myocardial infarction. In vivo electron paramagnetic resonance (EPR) oximetry has the capability to monitor tissue oxygen levels in real time. The method has largely been tested and used in experimental animals, although some clinical Measurements have been performed. In this chapter, a brief overview of the methodology to Repeatedly quantify oxygen levels in the brain and heart of experimental animal models, ranging from mice to swine, is presented. EPR oximetry requires a one-time placement of an oxygen-sensitive probe in the tissue of interest, while the rest of the procedure for reliable, accurate, and Repeated Measurements of pO2 (partial pressure of oxygen) is noninvasive and can be Repeated as often as desired. A multisite oximetry approach can be used to monitor pO2 at many sites simultaneously. Building on significant advances in the application of EPR oximetry in experimental animal models, spectrometers have been developed for use in human subjects. Initial feasibility of pO2 Measurement in solid tumors of patients has been successfully demonstrated.
-
implantable resonators a technique for Repeated Measurement of oxygen at multiple deep sites with in vivo epr
Advances in Experimental Medicine and Biology, 2010Co-Authors: Huagang Hou, Artur Sucheta, Benjamin B Williams, Jean P Lariviere, Md Nadeem Khan, Piotr Lesniewski, Bernard Gallez, Harold M SwartzAbstract:EPR oximetry using implantable resonators allows Measurements at much deeper sites than are possible with surface resonators (> 80 vs. 10 mm) and achieves greater sensitivity at any depth. We report here the development of an improved technique that enables us to obtain the information from multiple sites and at a variety of depths. The Measurements from the various sites are resolved using a simple magnetic field gradient. In the rat brain multi-probe implanted resonators measured pO(2) at several sites simultaneously for over 6 months under normoxic, hypoxic, and hyperoxic conditions. This technique also facilitates Measurements in moving parts of the animal such as the heart, because the orientation of the paramagnetic material relative to the sensing loop is not altered by the motion. The measured response is fast, enabling Measurements in real time of physiological and pathological changes such as experimental cardiac ischemia in the mouse heart. The technique also is quite useful for following changes in tumor pO(2), including applications with simultaneous Measurements in tumors and adjacent normal tissues.
Huagang Hou - One of the best experts on this subject based on the ideXlab platform.
-
direct and Repeated Measurement of heart and brain oxygenation using in vivo epr oximetry
Methods in Enzymology, 2015Co-Authors: Nadeem Khan, Huagang Hou, Harold M Swartz, Periannan KuppusamyAbstract:Low level of oxygen (hypoxia) is a critical factor that defines the pathological consequence of several pathophysiologies, particularly ischemia, that usually occur following the blockage of a blood vessel in vital organs, such as brain and heart, or abnormalities in the microvasculature, such as peripheral vascular disease. Therefore, methods that can directly and Repeatedly quantify oxygen levels in the brain and heart will significantly improve our understanding of ischemic pathologies. Importantly, such oximetry capability will facilitate the development of strategies to counteract low levels of oxygen and thereby improve outcome following stroke or myocardial infarction. In vivo electron paramagnetic resonance (EPR) oximetry has the capability to monitor tissue oxygen levels in real time. The method has largely been tested and used in experimental animals, although some clinical Measurements have been performed. In this chapter, a brief overview of the methodology to Repeatedly quantify oxygen levels in the brain and heart of experimental animal models, ranging from mice to swine, is presented. EPR oximetry requires a one-time placement of an oxygen-sensitive probe in the tissue of interest, while the rest of the procedure for reliable, accurate, and Repeated Measurements of pO2 (partial pressure of oxygen) is noninvasive and can be Repeated as often as desired. A multisite oximetry approach can be used to monitor pO2 at many sites simultaneously. Building on significant advances in the application of EPR oximetry in experimental animal models, spectrometers have been developed for use in human subjects. Initial feasibility of pO2 Measurement in solid tumors of patients has been successfully demonstrated.
-
implantable resonators a technique for Repeated Measurement of oxygen at multiple deep sites with in vivo epr
Advances in Experimental Medicine and Biology, 2010Co-Authors: Huagang Hou, Artur Sucheta, Benjamin B Williams, Jean P Lariviere, Md Nadeem Khan, Piotr Lesniewski, Bernard Gallez, Harold M SwartzAbstract:EPR oximetry using implantable resonators allows Measurements at much deeper sites than are possible with surface resonators (> 80 vs. 10 mm) and achieves greater sensitivity at any depth. We report here the development of an improved technique that enables us to obtain the information from multiple sites and at a variety of depths. The Measurements from the various sites are resolved using a simple magnetic field gradient. In the rat brain multi-probe implanted resonators measured pO(2) at several sites simultaneously for over 6 months under normoxic, hypoxic, and hyperoxic conditions. This technique also facilitates Measurements in moving parts of the animal such as the heart, because the orientation of the paramagnetic material relative to the sensing loop is not altered by the motion. The measured response is fast, enabling Measurements in real time of physiological and pathological changes such as experimental cardiac ischemia in the mouse heart. The technique also is quite useful for following changes in tumor pO(2), including applications with simultaneous Measurements in tumors and adjacent normal tissues.
Yuanyuan Liang - One of the best experts on this subject based on the ideXlab platform.
-
multiple objective response adaptive Repeated Measurement designs in clinical trials for binary responses
Statistics in Medicine, 2014Co-Authors: Yuanyuan Liang, Jing Wang, K C CarriereAbstract:A multiple-objective allocation strategy was recently proposed for constructing response-adaptive Repeated Measurement designs for continuous responses. We extend the allocation strategy to constructing response-adaptive Repeated Measurement designs for binary responses. The approach with binary responses is quite different from the continuous case, as the information matrix is a function of responses, and it involves nonlinear modeling. To deal with these problems, we first build the design on the basis of success probabilities. Then we illustrate how various models can accommodate carryover effects on the basis of logits of response profiles as well as any correlation structure. Through computer simulations, we find that the allocation strategy developed for continuous responses also works well for binary responses. As expected, design efficiency in terms of mean squared error drops sharply, as more emphasis is placed on increasing treatment benefit than estimation precision. However, we find that it can successfully allocate more patients to better treatment sequences without sacrificing much estimation precision. Copyright © 2013 John Wiley & Sons, Ltd.
-
multiple objective response adaptive Repeated Measurement designs for clinical trials
Journal of Statistical Planning and Inference, 2009Co-Authors: Yuanyuan Liang, K C CarriereAbstract:Abstract In a response-adaptive design, we review and update the trial on the basis of outcomes in order to achieve a specific goal. Response-adaptive designs for clinical trials are usually constructed to achieve a single objective. In this paper, we develop a new adaptive allocation rule to improve current strategies for building response-adaptive designs to construct multiple-objective Repeated Measurement designs. This new rule is designed to increase estimation precision and treatment benefit by assigning more patients to a better treatment sequence. We demonstrate that designs constructed under the new proposed allocation rule can be nearly as efficient as fixed optimal designs in terms of the mean squared error, while leading to improved patient care.
