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Background and objectives Post-genomic clinical trials require the participation of multiple institutions, and collecting data from several hospitals, laboratories and research facilities. This paper presents a standard-based solution to provide a uniform access endpoint to patient data involved in current clinical research. Methods The proposed approach exploits well-established standards such as HL7 v3 or SPARQL and medical vocabularies such as SNOMED CT, LOINC and HGNC. A novel mechanism to exploit semantic normalization among HL7-based data models and biomedical ontologies has been created by using Semantic Web technologies. Results Different types of queries have been used for testing the semantic interoperability solution described in this paper. The execution times obtained in the tests enable the development of end user tools within a framework that requires efficient retrieval of integrated data. Conclusions The proposed approach has been successfully tested by applications within the INTEGRATE and EURECA EU projects. These applications have been deployed and tested for: (i) patient screening, (ii) trial recruitment, and (iii) retrospective analysis; exploiting semantically interoperable access to clinical patient data from heterogeneous data sources. [ABSTRACT FROM AUTHOR]

Background and Objective Current prototypes of closed-loop systems for glucose control in type 1 diabetes mellitus, also referred to as artificial pancreas systems, require a pre-meal insulin bolus to compensate for delays in subcutaneous insulin absorption in order to avoid initial post-prandial hyperglycemia. Computing such a meal bolus is a challenging task due to the high intra-subject variability of insulin requirements. Most closed-loop systems compute this pre-meal insulin dose by a standard bolus calculation, as is commonly found in insulin pumps. However, the performance of these calculators is limited due to a lack of adaptiveness in front of dynamic changes in insulin requirements. Despite some initial attempts to include adaptation within these calculators, challenges remain. Methods In this paper we present a new technique to automatically adapt the meal-priming bolus within an artificial pancreas. The technique consists of using a novel adaptive bolus calculator based on Case-Based Reasoning and Run-To-Run control, within a closed-loop controller. Coordination between the adaptive bolus calculator and the controller was required to achieve the desired performance. For testing purposes, the clinically validated Imperial College Artificial Pancreas controller was employed. The proposed system was evaluated against itself but without bolus adaptation. The UVa-Padova T1DM v3.2 system was used to carry out a three-month in silico study on 11 adult and 11 adolescent virtual subjects taking into account inter-and intra-subject variability of insulin requirements and uncertainty on carbohydrate intake. Results Overall, the closed-loop controller enhanced by an adaptive bolus calculator improves glycemic control when compared to its non-adaptive counterpart. In particular, the following statistically significant improvements were found (non-adaptive vs. adaptive). Adults: mean glucose 142.2 ± 9.4 vs. 131.8 ± 4.2 mg/dl; percentage time in target [70, 180] mg/dl, 82.0 ± 7.0 vs. 89.5 ± 4.2; percentage time above target 17.7 ± 7.0 vs. 10.2 ± 4.1. Adolescents: mean glucose 158.2 ± 21.4 vs. 140.5 ± 13.0 mg/dl; percentage time in target, 65.9 ± 12.9 vs. 77.5 ± 12.2; percentage time above target, 31.7 ± 13.1 vs. 19.8 ± 10.2. Note that no increase in percentage time in hypoglycemia was observed. Conclusion Using an adaptive meal bolus calculator within a closed-loop control system has the potential to improve glycemic control in type 1 diabetes when compared to its non-adaptive counterpart. [ABSTRACT FROM AUTHOR]

Background and objective Cardiovascular (CV) risk stratification is a highly complex process involving an extensive set of clinical trials to support the clinical decision-making process. There are many clinical conditions (e.g. diabetes, obesity, stress, etc.) that can lead to the early diagnosis or establishment of cardiovascular disease. In order to determine all these clinical conditions, a complete set of clinical patient analyses is typically performed, including a physical examination, blood analysis, electrocardiogram, blood pressure (BP) analysis, etc. This article presents a web-based system, called Hydra, which integrates a full and detailed set of services and functionalities for clinical decision support in order to help and improve the work of clinicians in cardiovascular patient diagnosis, risk assessment, treatment and monitoring over time. Methods Hydra integrates a number of different services: a service for inputting all the information gathered by specialists (physical examination, habits, BP, blood analysis, electrocardiogram, etc.); a tool to automatically determine the CV risk stratification, including well-known standard risk stratification tables; and, finally, various tools to incorporate, analyze and graphically present the records of the ambulatory BP monitoring that provides BP analysis over a given period of time (24 or 48 hours). In addition, the platform presents a set of reports derived from all the information gathered from the patient in order to support physicians in their clinical decisions. Results Hydra was tested and validated in a real domain. In particular, internal medicine specialists at the Hypertension Unit of the Santiago de Compostela University Hospital (CHUS) validated the platform and used it in different clinical studies to demonstrate its utility. It was observed that the platform increased productivity and accuracy in the assessment of patient data yielding a cost reduction in clinical practice. Conclusions This paper proposes a complete platform that includes different services for cardiovascular clinical decision support. It was also run as a web-based application to facilitate its use by clinicians, who can access the platform from any remote computer with Internet access. Hydra also includes different automated methods to facilitate the physicians' work and avoid potential errors in the analysis of patient data. [ABSTRACT FROM AUTHOR]

Objective The analysis of treatment effects in clinical trials usually focus on efficacy and safety in separate descriptive statistical analyses. The Q-TWiST (Quality adjusted Time Without Symptoms and Toxicity) method has been proposed by Gelber in the 90s to enable a statistical comparison between two groups with a graphical representation by incorporating benefit and risk into a single analysis. Although the method has been programmed in SAS, it is rarely used. The availability of the method in the freely software environment system like R would greatly enhanced the accessibility by researchers. The objective of this paper is to present a program for Q-TWiST analyses within R software environment. Methods The qtwist function was developed in order to estimate and compare Q-TWiST for two groups. Two individual patient data files are required used for input: one for visits and one for follow-up. Q-TWiST is obtained as a sum of time spent in three health states: period in toxicity (TOX), period without relapse and toxicity (TWiST) and period in relapse (REL), weighted by associated utility scores restricted to median overall survival for example. The bootstrap method is used for testing statistical significance. Threshold analysis and gain functions allow a group comparison for different utility values. Results Input data is checked for consistency. Descriptive statistics and mean durations for each health state are provided, allowing statistical comparisons. Graphical results are presented in a PDF file. The use of the function is illustrated with data from a simulated data set and a randomized clinical trial. Conclusions qtwist is an easy to use R function, allowing a quality adjusted survival analysis with the Q-TWiST method. [ABSTRACT FROM AUTHOR]

In this paper, we present the dfcomb R package for the implementation of a single prospective clinical trial or simulation studies of phase I combination trials in oncology. The aim is to present the features of the package and to illustrate how to use it in practice though different examples. The use of combination clinical trials is growing, but the implementation of existing model-based methods is complex, so this package should promote the use of innovative adaptive designs for early phases combination trials. [ABSTRACT FROM AUTHOR]

Registration of pre-clinical images to physical space is indispensable for computer-assisted endoscopic interventions in operating rooms. Electromagnetically navigated endoscopic interventions are increasingly performed at current diagnoses and treatments. Such interventions use an electromagnetic tracker with a miniature sensor that is usually attached at an endoscope distal tip to real time track endoscope movements in a pre-clinical image space. Spatial alignment between the electromagnetic tracker (or sensor) and pre-clinical images must be performed to navigate the endoscope to target regions. This paper proposes an adaptive marker-free registration method that uses a multiple point selection strategy. This method seeks to address an assumption that the endoscope is operated along the centerline of an intraluminal organ which is easily violated during interventions. We introduce an adaptive strategy that generates multiple points in terms of sensor measurements and endoscope tip center calibration. From these generated points, we adaptively choose the optimal point, which is the closest to its assigned the centerline of the hollow organ, to perform registration. The experimental results demonstrate that our proposed adaptive strategy significantly reduced the target registration error from 5.32 to 2.59 mm in static phantoms validation, as well as from at least 7.58 mm to 4.71 mm in dynamic phantom validation compared to current available methods. [ABSTRACT FROM AUTHOR]

The domain of cancer treatment is a promising field for the implementation and evaluation of a protocol-based clinical decision support system, because of the algorithmic nature of treatment recommendations. However, many factors can limit such systems’ potential to support the decision of clinicians: technical challenges related to the interoperability with existing electronic patient records and clinical challenges related to the inherent complexity of the decisions, often collectively taken by panels of different specialists. In this paper, we evaluate the performances of an Asbru-based decision support system implementing treatment protocols for breast cancer, which accesses data from an oncological electronic patient record. Focusing on the decision on the adjuvant pharmaceutical treatment for patients affected by early invasive breast cancer, we evaluate the matching of the system's recommendations with those issued by the multidisciplinary panel held weekly in a hospital. [ABSTRACT FROM AUTHOR]

Abstract: Clinical trials aiming for regulatory approval of a therapeutic agent must be conducted according to Good Clinical Practice (GCP). Clinical Data Management Systems (CDMS) are specialized software solutions geared toward GCP-trials. They are however less suited for data management in small non-GCP research projects. For use in researcher-initiated non-GCP studies, we developed a client–server database application based on the public domain CakePHP framework. The underlying MySQL database uses a simple data model based on only five data tables. The graphical user interface can be run in any web browser inside the hospital network. Data are validated upon entry. Data contained in external database systems can be imported interactively. Data are automatically anonymized on import, and the key lists identifying the subjects being logged to a restricted part of the database. Data analysis is performed by separate statistics and analysis software connecting to the database via a generic Open Database Connectivity (ODBC) interface. Since its first pilot implementation in 2011, the solution has been applied to seven different clinical research projects covering different clinical problems in different organ systems such as cancer of the thyroid and the prostate glands. This paper shows how the adoption of a generic web application framework is a feasible, flexible, low-cost, and user-friendly way of managing multidimensional research data in researcher-initiated non-GCP clinical projects. [Copyright &y& Elsevier]

Abstract: This paper presents the R package pocrm for implementing and simulating the partial order continual reassessment method (PO-CRM; [1,2]) in Phase I trials of combinations of agents. The aim of this article is to illustrate, through examples of the pocrm package, how the PO-CRM works and how its operating characteristics can inform clinical trial investigators. This should promote the use of the PO-CRM in designing and conducting dose-finding Phase I trials of combinations of agents. [Copyright &y& Elsevier]

Abstract: Cost-Effectiveness Analysis (CEA) has become an increasingly important component of clinical trials. However, formal sample size calculations for such studies are not common. One of the reasons for this might be due to the absence of readily available computer software to perform complex calculations, particularly in a Bayesian setting. In this paper, a C++ program (using NAG library functions/subroutines) is presented to estimate the sample sizes for cost-effectiveness clinical trials in a Bayesian framework. The program can equally be used to calculate sample sizes for efficacy trials. The Bayesian approach to sample size calculation is based on that of O’Hagan and Stevens (A. O’Hagan, J.W. Stevens, Bayesian assessment of sample size for clinical trials of cost-effectiveness, Medical Decision Making 21 (2001) 219–230). With this program, the user can calculate sample sizes for various thresholds of willingness to pay and under various assumptions of the correlations between cost and effects. Under some prior, the program produces frequentist sample size as well. The program runs under windows environment and running time is very short. [Copyright &y& Elsevier]

Abstract: A major and difficult task is the design of clinical trials with a time to event endpoint. In fact, it is necessary to compute the number of events and in a second step the required number of patients. Several commercial software packages are available for computing sample size in clinical trials with sequential designs and time to event endpoints, but there are a few R functions implemented. The purpose of this paper is to describe features and use of the R function. plansurvct.func , which is an add-on function to the package gsDesign which permits in one run of the program to calculate the number of events, and required sample size but also boundaries and corresponding p-values for a group sequential design. The use of the function plansurvct.func is illustrated by several examples and validated using East software. [Copyright &y& Elsevier]

Abstract: Objectives: Efficient clinical treatment processes is considered a key factor of medical quality control. Current IT solutions are far away from this perspective since they typically have difficulty supporting the variances occurring in clinical practices, and providing adequate flexible support of clinical processes. Methods: This paper proposes a hybrid approach based on rough set theory and case-based reasoning to allow physicians to rapidly adjust patients’ treatment processes to changes of patients’ clinical states. In detail, the proposed approach recommends appropriate treatment plans in clinical process execution by adopting a similarity measure to select appropriate clinical treatment plans executed on patients who presented similar features to the current one. Such clinical treatment plans are then applied to suggest which actions to perform next in clinical treatment process execution. Results: As a motivating scenario, this study performs the experiments of type 2 diabetes patient’s treatment process. The results show that the proposed approach is feasible to recommend suitable clinical treatment plans in clinical process execution, which makes adaptive clinical treatment processes possible. [Copyright &y& Elsevier]

Abstract: This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases. [Copyright &y& Elsevier]

Abstract: In this paper, a novel P300-based concealed information test (CIT) method was proposed to improve the efficiency of differentiating deception and truth-telling. Thirty subjects including the guilty and innocent performed the paradigm based on three types of stimuli. In order to reduce the influence from the occasional variability of cognitive states on the CIT, several single-trials from Pz in probe stimuli within each subject were first averaged. Then the three groups of features were extracted from these averaged single-trials. Finally, two classes of feature samples were used to train a support vector machine (SVM) classifier. Meanwhile, the optimal number of averaged Pz waveforms and some other parameter values in the classifiers were determined by the cross validation procedures. Results show that if choosing accuracy of 90% as a detecting standard of P3 component to classify a subject''s status (guilty or innocent), our method can achieve individual diagnostic rate of 100%. The individual diagnostic rate of our method was higher than the results of the other related reports. The presented method improves efficiency of CIT, and is more practical, lower fatigue and less countermeasure behavior in comparison with previous report methods, which could extend the laboratory study to the practical application. [Copyright &y& Elsevier]

Abstract: Because of the high cost and time constraints for clinical trials, researchers often need to determine the smallest sample size that provides accurate inferences for a parameter of interest. Although most experimenters have employed frequentist sample-size determination methods, the Bayesian paradigm offers a wide variety of sample-size determination methodologies. Bayesian sample-size determination methods are becoming increasingly more popular in clinical trials because of their flexibility and easy interpretation inferences. Recently, Bayesian approaches have been used to determine the sample size of a single Poisson rate parameter in a clinical trial setting. In this paper, we extend these results to the comparison of two Poisson rates and develop methods for sample-size determination for hypothesis testing in a Bayesian context. We have created functions in R to determine the parameters for the conjugate gamma prior and calculate the sample size for the average length criterion and average power methods. We also provide two examples that implement our sample-size determination methods using clinical data. [Copyright &y& Elsevier]

Abstract: In the case of an unexpected high frequency of serious adverse events (SAE), statistical methods are needed to help in the decision making process as to continuation of accrual to the trial. This paper describes an R package, named SAE that implements a method recently developed by defining stopping rules after each observed SAE. The package function control for excessive toxicity either during the trial at the observation of each SAE (function SAE) or during the planning phase of a clinical trial (function DESIGN). This description and the package documentation are complementary to help the users to apply the method. The main difficulty in the implementation of the method is the choice of a priori parameters. Data from an ongoing clinical trial are presented as an example to improve the understanding and the use of the package. [Copyright &y& Elsevier]

Abstract: Analysis of discrete repeated outcomes is an important issue in biomedical studies. The aim of this paper is to propose a flexible and parsimonious model to account for heterogeneous variances for discrete outcomes. The proposed method is based on the use of a linear mixed model on the log of the standard deviation parameters. It is also shown how parameter estimation in this model can be performed with an exact procedure based on a Gibbs sampling algorithm implemented with the Winbugs/Openbugs software. A model comparison study is presented to illustrate the efficiency of this procedure using a well known example from the clinical trial literature. It was found that the proposed methodology considerably improved the predictive ability of the model while remaining very parsimonious. In particular, it was found that adding a random subject effect in the variance model significantly improved the posterior predictive p-value criterion of the model. [Copyright &y& Elsevier]

• In clinical trials, data collection/management together with data analysis minimises bias. • The use of NoSQL databases in clinical trials can be a user-friendly way of managing multidimensional clinical research data. • The use of relational databases introduces certain limitations when dealing with clinical data. Background and objectives : In the last decade, clinical trial management systems have become an essential support tool for data management and analysis in clinical research. However, these clinical tools have design limitations, since they are currently not able to cover the needs of adaptation to the continuous changes in the practice of the trials due to the heterogeneous and dynamic nature of the clinical research data. These systems are usually proprietary solutions provided by vendors for specific tasks. In this work, we propose FIMED, a software solution for the flexible management of clinical data from multiple trials, moving towards personalized medicine, which can contribute positively by improving clinical researchers quality and ease in clinical trials. Methods : This tool allows a dynamic and incremental design of patients' profiles in the context of clinical trials, providing a flexible user interface that hides the complexity of using databases. Clinical researchers will be able to define personalized data schemas according to their needs and clinical study specifications. Thus, FIMED allows the incorporation of separate clinical data analysis from multiple trials. Results : The efficiency of the software has been demonstrated by a real-world use case for a clinical assay in Melanoma disease, which has been indeed anonymized to provide a user demonstration. FIMED currently provides three data analysis and visualization components, guaranteeing a clinical exploration for gene expression data: heatmap visualization, clusterheatmap visualization, as well as gene regulatory network inference and visualization. An instance of this tool is freely available on the web at https://khaos.uma.es/fimed. It can be accessed with a demo user account, "researcher" , using the password "demo". Conclusion : This paper shows FIMED as a flexible and user-friendly way of managing multidimensional clinical research data. Hence, without loss of generality, FIMED is flexible enough to be used in the context of any other disease where clinical data and assays are involved. [ABSTRACT FROM AUTHOR]

Summary: Manatunga and Chen [A.K. Manatunga, S. Chen, Sample size estimation for survival outcomes in cluster-randomized studies with small cluster sizes, Biometrics 56 (2000) 616–621] proposed a method to estimate sample size and power for cluster-randomized studies where the primary outcome variable was survival time. The sample size formula was constructed by considering a bivariate marginal distribution (Clayton–Oakes model) with univariate exponential marginal distributions. In this paper, a user-friendly FORTRAN 90 program was provided to implement this method and a simple example was used to illustrate the features of the program. [Copyright &y& Elsevier]

Summary: The logistic regression model has been in use in statistical analysis for many years. The paper introduces a spline model to remove the linear restriction on logit function. By considering knot locations as free variables, spline approximation of data is improved. The number of knots and the degree of the spline functions can still be determined by using a model selection procedure. Moreover, a knot, seen as a free parameter for a piecewise linear spline, represents a break point in the logit function which may be interpreted as a threshold value. This method is applied to a clinical trial for an in vitro fertilization program. [Copyright &y& Elsevier]

The aim of dose-ranging phase I (resp. phase II) clinical trials is to rapidly identify the maximum tolerated dose (MTD) (resp., minimal effective dose (MED)) of a new drug or combination. For the conduct and analysis of such trials, Bayesian approaches such as the Continual Reassessment Method (CRM) have been proposed, based on a sequential design and analysis up to a completed fixed sample size. To optimize sample sizes, Zohar and Chevret have proposed stopping rules (Stat. Med. 20 (2001) 2827), the computation of which is not provided by available softwares. We present in this paper a user-friendly software for the design and analysis of these Bayesian Phase I (resp. phase II) dose-ranging Clinical Trials (BPCT). It allows to carry out the CRM with stopping rules or not, from the planning of the trial, with choice of model parameterization based on its operating characteristics, up to the sequential conduct and analysis of the trial, with estimation at stopping of the MTD (resp. MED) of the new drug or combination. [Copyright &y& Elsevier]

This paper presents the current features of the DIABTel telemedicine system and the evaluation outcomes of its use in clinical routine. This telemedicine system is designed to complement the daily care and intensive management of diabetic patients through telemonitoring and telecare services. The system comprises a patient unit (PU) used by patients in their day-to-day activities and a Medical Workstation used by physicians and nurses at hospitals. Both applications offer tools to collect, manage, view and interpret data and to exchange data and messages. The system was evaluated for usability, telemedical protocols, metabolic control and quality of life. This evaluation consisted in a 6-month cross-over pilot study with ten Type I diabetic patients. The results of the evaluation allowed assessment of the telemedicine protocols in terms of the number of communications/patient (21.6±7.7); days between communications (5.4±2.66); messages sent by physicians (118 text messages); and data and messages transmitted by patients (3524 blood glucose readings, 1649 day-to-day insulin adjustments, 24 exercise reports, ten diet modifications and 63 text messages). Physicians performed more therapeutic changes during the DIABTel period than in the control period. There was a trend towards HbA1c improvement during DIABTel use with no incidence in the number of hypoglycaemias. This pilot study demonstrates the feasibility of the DIABTel system in clinical routine use and its potential benefits for diabetes care: improving the availability of information necessary for therapy adjustments; offering new physician–patient communication tools; increasing patient empowerment and education; and showing a positive trend towards improving the metabolic control of patients. Further studies are needed to validate these findings and to promote telemedicine as an opportunity to better diabetes care. [Copyright &y& Elsevier]

• Personalized medicine is an important area of medical research which consists of designing therapies specifically for a patient or a group of patients. For drugs having a narrow therapeutic index or for vulnerable patients, methods such as therapeutic drug monitoring are used in a hospital setting to ensure that the blood concentration of the drug is maintained within a pre-decided range. • However, such methods can not be used for drugs which are still in the developmental phase since, generally, insufficient information is available about the pharmacokinetic behaviour of the drug. • We present a new methodology for computing individualized dose regimens for the subjects during the course of the pharmacokinetic studies such that the resultant blood concentration of the drug in each subject is maintained around a desired target concentration or within a target range. • The proposed methodology can assist in ensuring correct dosing to each subject in a clinical trial so that each subject receives only the intended exposure to the drug while simultaneously estimating the PK profile of the drug. Our methodology can also be applied in randomized concentration-controlled trials where maintenance of the target concentration in the subjects is a fundamental requirement for conducting these trials. • The proposed algorithm, therefore, has the potential to increase success rates of clinical trials while protecting the subjects from suboptimal doses. Background and objective: Personalized medicine is an important area of medical research which consists of designing therapies specifically for a patient or a group of patients. For drugs having a narrow therapeutic index or for vulnerable patients, methods such as therapeutic drug monitoring are used in a hospital setting to ensure that the blood concentration of the drug is maintained within a pre-decided range. However, such methods can not be used for drugs which are still in the developmental phase since, generally, insufficient information is available about the pharmacokinetic behaviour of the drug. Methods: In this paper, we present a new methodology for explicit optimization of dose regimens during the course of the pharmacokinetic studies such that the resultant blood concentration of the drug in each subject is maintained around a desired target concentration or within a target range. Results: We demonstrate that our algorithm is able to achieve the clinical objective of PK estimation while simultaneously individualizing the dose to every subject in the trial. Our algorithm computes dose regimens that, on average, have a relative efficiency of 97% with a standard deviation of less than 5%. The results show that the algorithm can be relied upon to ensure that the subjects in the trial are minimally over- and under-exposed to the test therapy. Conclusions: The proposed methodology can assist in ensuring correct dosing to each subject in a clinical trial so that each subject receives only the intended exposure to the drug while simultaneously estimating the PK profile of the drug. Our methodology can also be applied in randomized concentration-controlled trials where maintenance of the target concentration in the subjects is a fundamental requirement for conducting these trials. [ABSTRACT FROM AUTHOR]

• Kinetic models have been recently proposed to help in the diagnosis and characterization of von Willebrand disease (VWD). • The complexity of these kinetic models requires long and stressful clinical tests (DDAVP tests) to be carried out in-vivo to achieve a satisfactory estimation of individual haemostatic parameters. • A new kinetic model is proposed, including a set of explicit correlations linking model parameters to basal clinical trials data. • Results show the possibility to drastically reduce the duration of DDAVP tests (from 24–3 h) by exploiting complementary information from basal clinical tests through experimental design techniques. Von Willebrand disease (VWD) is one of the most severe inherited bleeding disorder in humans, and it is associated with a qualitative and/or quantitative deficiency of von Willebrand factor, a multimeric glycoprotein fundamental in the coagulation process. At present, the diagnosis of VWD is extremely challenging and mostly based on clinical experience. Kinetic models have been recently proposed and applied to help in the diagnosis and characterization of VWD, but the complexity of these models is such that they requires long and stressful clinical tests, such as the desmopressin response test (DDAVP), to achieve a satisfactory estimation of the individual haemostatic parameters. The goal of this paper is to design a minimal set of clinical tests for the identification of akinetic model to decrease the required time and effort for the characterization and diagnosis of VWD. A model proposed in the literature is used as a building block to develop a new model, where response surface methodologies have been applied to determine a set of explicit correlations linkingkinetic model parameters to basal clinical trials data. Model-based design of experiments techniques are then used to devise optimally informative tests for model validation which are shorter and easier to implement. Results show an excellent agreement between the original model for VWD and the new proposed model on representing healthy and VWD subjects. The application of experimental design techniques for model validation shows the possibility to drastically reduce the duration of DDAVP tests from 24 h–3 h by exploiting complementary information from basal clinical tests. Basal clinical tests can be used alongside a time-reduced DDAVP test to validate pharmacokinetic models for a quantitative characterisation of subjects affected by VWD and for a quicker and easier diagnosis of the disease. [ABSTRACT FROM AUTHOR]

Abstract: Correlated ordinal data are common in many areas of research. The data may arise from longitudinal studies in biology, medical, or clinical fields. The prominent characteristic of these data is that the within-subject observations are correlated, whilst between-subject observations are independent. Many methods have been proposed to analyze correlated ordinal data. One way to evaluate the performance of a proposed model or the performance of small or moderate size data sets is through simulation studies. It is thus important to provide a tool for generating correlated ordinal data to be used in simulation studies. In this paper, we describe a macro program on how to generate correlated ordinal data based on R language and SAS IML. [Copyright &y& Elsevier]