Improving analysis practice of continuous adverse event outcomes in randomised controlled trials – a distributional approach

Background Randomised controlled trials (RCTs) provide valuable information for developing harm profiles but current analysis practices to detect between-group differences are suboptimal. Drug trials routinely screen continuous clinical and biological data to monitor participant harm. These outcomes are regularly dichotomised into abnormal/normal values for analysis. Despite the simplicity gained for clinical interpretation, it is well established that dichotomising outcomes results in a considerable reduction in information and thus statistical power. We propose an automated procedure for the routine implementation of the distributional method for the dichotomisation of continuous outcomes proposed by Peacock and Sauzet, which retains the precision of the comparison of means. Methods We explored the use of a distributional approach to compare differences in proportions based on the comparison of means which retains the power of the latter. We applied this approach to the screening of clinical and biological data as a means to detect ‘signals’ for potential adverse drug reactions (ADRs). Signals can then be followed-up in further confirmatory studies. Three distributional methods suitable for different types of distributions are described. We propose the use of an automated approach using the observed data to select the most appropriate distribution as an analysis strategy in a RCT setting for multiple continuous outcomes. We illustrate this approach using data from three RCTs assessing the efficacy of mepolizumab in asthma or COPD. Published reference ranges were used to define the proportions of participants with abnormal values for a subset of 10 blood tests. The between-group distributional and empirical differences in proportions were estimated for each blood test and compared. Results Within trials, the distributions varied across the 10 outcomes demonstrating value in a practical approach to selecting the distributional method in the context of multiple adverse event outcomes. Across trials, there were three outcomes where the method chosen by the automated procedure varied for the same outcome. The distributional approach identified three signals (eosinophils, haematocrit, and haemoglobin) compared to only one when using the Fisher’s exact test (eosinophils) and two identified by use of the 95% confidence interval for the difference in proportions (eosinophils and potassium). Conclusion When dichotomisation of continuous adverse event outcomes aids clinical interpretation, we advocate use of a distributional approach to retain statistical power. Methods are now easy to implement. Retaining information is especially valuable in the context of the analysis of adverse events in RCTs. The routine implementation of this automated approach requires further evaluation

On the relationship between sloppiness and identifiability

25 páginas, 11 figuras, 2 tablasDynamic models of biochemical networks are often formulated as sets of non-linear ordinary differential equations, whose states are the concentrations or abundances of the network components. They typically have a large number of kinetic parameters, which must be determined by calibrating the model with experimental data. In recent years it has been suggested that dynamic systems biology models are universally sloppy, meaning that the values of some parameters can be perturbed by several orders of magnitude without causing significant changes in the model output. This observation has prompted calls for focusing on model predictions rather than on parameters. In this work we examine the concept of sloppiness, investigating its links with the long-established notions of structural and practical identifiability. By analysing a set of case studies we show that sloppiness is not equivalent to lack of identifiability, and that sloppy models can be identifiable. Thus, using sloppiness to draw conclusions about the possibility of estimating parameter values can be misleading. Instead, structural and practical identifiability analyses are better tools for assessing the confidence in parameter estimates. Furthermore, we show that, when designing new experiments to decrease parametric uncertainty, designs that optimize practical identifiability criteria are more informative than those that minimize sloppinessThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 686282 (“CANPATHPRO”) and from the Spanish government (MINECO) and the European Regional Development Fund (ERDF) through the projects “SYNBIOFACTORY” (grant number DPI2014-55276-C5-2-R), and “IMPROWINE” (grant number AGL2015-67504-C3-2-R)N

In search of stool donors: a multicenter study of prior knowledge, perceptions, motivators, and deterrents among potential donors for fecal microbiota transplantation

Introduction: Fecal microbiota transplantation (FMT) is a highly effective therapy for recurrent Clostridium difficile infection. Stool donors are essential, but difficult to recruit and retain. We identified factors influencing willingness to donate stool.Methods: A 32-item questionnaire targeted young adults and health care workers via social media and university email lists in Edmonton and Kingston, Canada; London and Nottingham, England; and Indianapolis and Boston, USA. Items included baseline demographics and FMT knowledge and perception. Investigated motivators and deterrents included economic compensation, screening process, time commitment, and stool donation logistics. Logistic regression and linear regression models estimated associations of study variables with self-assessed willingness to donate stool.Results: 802 respondents completed our questionnaire: 387 (48.3%) age 21–30 years, 573 (71.4%) female, 323 (40%) health care workers. Country of residence, age and occupation were not associated with willingness to donate stool. Factors increasing willingness to donate were: already a blood donor (OR 1.64), male, altruism, economic benefit, knowledge of how FMT can help patients (OR 1.32), and positive attitudes towards FMT (OR 1.39). Factors decreasing willingness to donate were: stool collection unpleasant (OR 0.92), screening process invasive (OR 0.92), higher donation frequency, negative social perceptions of stool, and logistics of collecting/transporting feces.Discussion: Blood donors and males are more willing to consider stool donation. Altruism, economic compensation, and positive feedback are motivators. Screening process, high donation frequency, logistics of collecting/transporting feces, lack of public awareness, and negative social perception are deterrents. Considering these variables could maximize donor recruitment and retention

Generalized fractional hybrid hamilton pontryagin equations

In this paper we present a new approach on the study of dynamical systems. Combining the two ways of expressing the uncertainty, using probabilistic theory and credibility theory, we have investigated the generalized fractional hybrid equations. We have introduced the concepts of generalized fractional Wiener process, generalized fractional Liu process and the combination between them, generalized fractional hybrid process. Corresponding generalized fractional stochastic, respectively fuzzy, respectively hybrid dynamical systems were defined. We have applied the theory for generalized fractional hybrid HamiltonPontryagin (HP) equation and generalized fractional Hamiltonian equations. We have found fractional Langevin equations from the general fractional hybrid Hamiltonian equations. For these cases and specific parameters, numerical simulations were done.Peer reviewe

Review of stochastic stability and analysis tumor-immune systems

11 páginasIn this paper we review and at the same time investigate some stochastic models for tumor-immune systems. To describe these models, we used a Wiener process, as the noise has a stabilization effect. Their dynamics are studied in terms of stochastic stability around the equilibrium points, by constructing the Lyapunov exponent, depending on the parameters that describe the model. Stochastic stability was also proved by constructing a Lyapunov function and the second order moments. We have studied and analyzed a Kuznetsov-Taylor like stochastic model and a Bell stochastic model for tumor-immune systems. These stochastic models are studied from stability point of view and they were graphically represented using the second order Euler scheme and Maple 12 software.Peer reviewe

GenSSI: a new toolbox for testing structural identifiability insystems biology

1 poster presented at the 5th Annual Workshop on the Business-Government Interface, Braga, 6 June 2011Spanish MICINN project ”MultiSysBio” (ref. DPI2008-06880-C03-02; CSIC intramural project ”BioREDES” (ref. PIE-201170E018)N