Net Efficacy Adjusted for Risk (NEAR): A Simple Procedure for Measuring Risk:Benefit Balance

BACKGROUND: Although several mathematical models have been proposed to assess the risk:benefit of drugs in one measure, their use in practice has been rather limited. Our objective was to design a simple, easily applicable model. In this respect, measuring the proportion of patients who respond favorably to treatment without being affected by adverse drug reactions (ADR) could be a suitable endpoint. However, remarkably few published clinical trials report the data required to calculate this proportion. As an approach to the problem, we calculated the expected proportion of this type of patients. METHODOLOGY/PRINCIPAL FINDINGS: Theoretically, responders without ADR may be obtained by multiplying the total number of responders by the total number of subjects that did not suffer ADR, and dividing the product by the total number of subjects studied. When two drugs are studied, the same calculation may be repeated for the second drug. Then, by constructing a 2 x 2 table with the expected frequencies of responders with and without ADR, and non-responders with and without ADR, the odds ratio and relative risk with their confidence intervals may be easily calculated and graphically represented on a logarithmic scale. Such measures represent "net efficacy adjusted for risk" (NEAR). We assayed the model with results extracted from several published clinical trials or meta-analyses. On comparing our results with those originally reported by the authors, marked differences were found in some cases, with ADR arising as a relevant factor to balance the clinical benefit obtained. The particular features of the adverse reaction that must be weighed against benefit is discussed in the paper. CONCLUSION: NEAR representing overall risk-benefit may contribute to improving knowledge of drug clinical usefulness. As most published clinical trials tend to overestimate benefits and underestimate toxicity, our measure represents an effort to change this trend

Systems medicine and infection

By using a systems based approach, mathematical and computational techniques can be used to develop models that describe the important mechanisms involved in infectious diseases. An iterative approach to model development allows new discoveries to continually improve the model, and ultimately increase the accuracy of predictions. SIR models are used to describe epi demics, predicting the extent and spread of disease. Genome-wide genotyping and sequencing technologies can be used to identify the biological mechanisms behind diseases. These tools help to build strategies for disease prevention and treatment, an example being the recent outbreak of Ebola in West Africa where these techniques were deployed. HIV is a complex disease where much is still to be learnt about the virus and the best effective treatment. With basic mathematical modelling techniques, significant discoveries have been made over the last 20 years. With recent technological advances, the computation al resources now available and interdisciplinary cooperation, further breakthroughs are inevitable. In TB, modelling has traditionally been empirical in nature, with clinical data providing the fuel for this top-down approach. Recently, projects have begun to use data derived from laboratory experiments and clinical trials to create mathematical models that describe the mechanisms responsible for the disease. A systems medicine approach to infection modelling helps identify important biological questions that then direct future experiments , the results of which improve the model in an iterative cycle . This means that data from several model systems can be integrated and synthesised to explore complex biological systems .Postprin

A tool to balance benefit and harm when deciding about adjuvant therapy

Adjuvant therapy aims to prevent outgrowth of residual disease but can induce serious side effects. Weighing conflicting treatment effects and communicating this information with patients is not elementary. This study presents a scheme balancing benefit and harm of adjuvant therapy vs no adjuvant therapy. It is illustrated by the available evidence on adjuvant pelvic external beam radiotherapy (RT) for intermediate-risk stage I endometrial carcinoma patients. The scheme comprises five outcome possibilities of adjuvant therapy: patients who benefit from adjuvant therapy (some at the cost of complications) vs those who neither benefit nor contract complications, those who do not benefit but contract severe complications, or those who die. Using absolute risk differences, a fictive cohort of 1000 patients receiving adjuvant RT is categorised. Three large randomised clinical trials were included. Recurrences will be prevented by adjuvant RT in 60 patients, a majority of 908 patients will neither benefit nor suffer severe radiation-induced harm but 28 patients will suffer severe complications due to adjuvant RT and an expected four patients will die. This scheme readily summarises the different possible treatment outcomes and can be of practical value for clinicians and patients in decision making about adjuvant therapies

Modelling the molecular mechanisms of ageing

This document is the Accepted Manuscript version of a published work that appeared in final form in Bioscience reports. To access the final edited and published work see http://www.bioscirep.org/content/37/1/BSR20160177.The ageing process is driven at the cellular level by random molecular damage which slowly accumulates with age. Although cells possess mechanisms to repair or remove damage, they are not 100% efficient and their efficiency declines with age. There are many molecular mechanisms involved and exogenous factors such as stress also contribute to the ageing process. The complexity of the ageing process has stimulated the use of computational modelling in order to increase our understanding of the system, test hypotheses and make testable predictions. As many different mechanisms are involved, a wide range of models have been developed. This paper gives an overview of the types of models that have been developed, the range of tools used, modelling standards, and discusses many specific examples of models which have been grouped according to the main mechanisms that they address. We conclude by discussing the opportunities and challenges for future modelling in this field

The effectiveness of intraocular pressure reduction in the treatment of normal-tension glaucoma

PURPOSE: In a companion paper, we determined that intraocular pressure is part of the pathogenesis of normal-tension glaucoma by analyzing the effect of a 30% intraocular pressure reduction on the subsequent course of the disease. We report an intent-to-treat analysis of the study data to determine the effectiveness of pressure reduction. METHODS: One eligible eye of 145 subjects with normal-tension glaucoma was randomized either to no treatment (control) or to a 30% intraocular pressure reduction from baseline. To be eligible for randomization, the normal-tension glaucoma eyes had to show documented progression of field defects or a new disk hemorrhage or had to have field defects that threatened fixation when first presented for the study. Survival analysis compared time to progression of all randomly assigned patients during the course of follow-up from the initial baseline at randomization. In a separate analysis, data of patients developing cataracts were censored at the time that cataract produced 2 lines of Snellen visual acuity loss. RESULTS: Visual field progression occurred at indistinguishable rates in the pressure-lowered (22/66) and the untreated control (31/79) arms of the study ( P = .21). In an analysis with data censored when cataract affected visual acuity, visual field progression was significantly more common in the untreated group (21/79) compared with the treated group (8/66). An overall survival analysis showed a survival of 80% in the treated arm and of 60% in the control arm at 3 years, and 80% in the treated arm and 40% in the controls at 5 years. The Kaplan-Meier curves were significantly different ( P = .0018). The analyses gave different results because of a higher incidence of cataract in the group that underwent filtration surgery. CONCLUSIONS: The favorable effect of intraocular pressure reduction on progression of visual change in normal-tension glaucoma was only found when the impact of cataracts on visual field progression, produced largely by surgery, was removed. Lowering intraocular pressure without producing cataracts is beneficial. Because not all untreated patients progressed, the natural history of normal-tension glaucoma must be considered before embarking on intraocular pressure reduction with therapy apt to exacerbate cataract formation unless normal-tension glaucoma threatens serious visual loss