Readability of the effect measures on health interventions

The measure most used to report treatment effects in survival studies is the Hazard RateRatio (HRR). Patients should be able to make decisions about interventions based on information provided by a health expert. However, the medical literature is replete with erroneous interpretations of the HRR which threatens the decision-making process. When confronted with a treatment which may affect a patient's longevity, the most fundamental question that both patients and doctors must face is: Which option would allow me to live longer? But HRR evaluates differences based on the proportions of survivors. Instead, the ratio of two survival medians (MR: Median Ratio) allows the comparison of times to event. The ultimate goal is to improve the readability of the survival studies. Mainly, we focus on determining the relationship of the HRR with the MR. The specific objective is to empirically quantify the concordance between the inverse of HRR and MR in survival studies published in the New England Journal of Medicine(NEJM)

Estudi aleatoritzat d'una intervenció per a la promoció de la salut reproductiva

L’objectiu d’aquest estudi és avaluar si una intervenció educativa d’apropament i visita domiciliaria sobre salut reproductiva augmenta l’ús de serveis de salut reproductiva, així com l’ús i el coneixement de mètodes anticonceptius. L’estudi es realitza a través d’un assaig clínic amb assignació aleatòria i minimització segons la comunitat d’origen. S’han inclòs dones d’edat compresa entre 15 i 49 anys residents als barris de Sant Antoni o El Poble Sec (Barcelona). En ambdós grups, s’avalua en el mes 1 i en el mes 3. El grup intervingut rep la intervenció desprès de l’avaluació del mes 1, mentre que el grup control la rep desprès de l’avaluació del mes 3. En aquest anàlisi intermedi, han participat 183 dones (96 tractades i 87 controls) amb una edat mitjana de 32.2 anys ( ± 8.1). D’aquestes participants un 59.6% són immigrades, un 48.6% tenen estudis secundaris i un 70.0% tenen parella

Decision tool and Sample Size Calculator for composite endpoints

This article considers the combination of two binary or two time-to-event endpoints to form the primary composite endpoint for leading a trial. • It discusses the relative efficiency of choosing a composite endpoint over one of its components in terms of: the frequencies of observing each component; the relative treatment effect of the tested therapy; and the association between both components. • We highlight the very important role of the association between components in choosing the most efficient endpoint to use as primary. • For better grounded future trials, we recommend trialists to always reporting the association between components of the composite endpoint • Common fallacies to note when using composite endpoints: i) composite endpoints always imply higher power; ii) treatment effect on the composite endpoint is similar to the average effects of its components; and iii) the probability of observing the primary endpoint increases significantly.Peer ReviewedPreprin

MSMpred: interactive modelling and prediction of individual evolution via multistate models

Background: Modelling the course of a disease regarding severe events and identifying prognostic factors is of great clinical relevance. Multistate models (MSM) can be used to describe diseases or processes that change over time using different states and the transitions between them. Specifically, they are useful to analyse a disease with an increasing degree of severity, that may precede death. The complexity of these models changes depending on the number of states and transitions taken into account. Due to that, a web tool has been developed making it easier to work with those models. Results: MSMpred is a web tool created with the shiny R package that has two main features: 1) to allow fitting an MSM from specific data; 2) to predict the clinical evolution for a given subject. To fit the model, the data to be analysed must be uploaded in a prespecified format. Then, the user has to define the states and transitions as well as the covariates (e.g., age or gender) involved in each transition. From this information, the app returns histograms or barplots, as appropriate, to represent the distributions of the selected covariates and boxplots to show the patient’s length of stay (for uncensored data) in each state. To make predictions, the values of selected covariates from a new subject at baseline has to be provided. From these inputs, the app provides some indicators of the subject’s evolution, such as the probability of 30-day death or the most likely state at a fixed time. Furthermore, visual representations (e.g., the stacked transition probabilities plot) are given to make predictions more understandable. Conclusions: MSMpred is an intuitive and visual app that eases the work of biostatisticians and facilitates to the medical personnel the interpretation of MSMs.This research was funded by the Ministerio de Ciencia e Innovación (Spain) [PID2019-104830RB-I00/ DOI (AEI): 10.13039/501100011033] and by Generalitat de Catalunya (2020PANDE00148).Peer ReviewedPostprint (published version

Design of trials with composite endpoints with the R package compAREdesign

Composite endpoints are widely used as primary endpoints in clinical trials. Designing trials with time-to-event endpoints can be particularly challenging because the proportional hazard assumption usually does not hold when using a composite endpoint, even when the premise remains true for their components. Consequently, the conventional formulae for sample size calculation do not longer apply. We present the R package CompAREdesign by means of which the key elements of trial designs, such as the sample size and effect sizes, can be computed based on the information on the composite endpoint components. CompAREdesign provides the functions to assess the sensitivity and robustness of design calculations to variations in initial values and assumptions. Furthermore, we describe other features of the package, such as functions for the design of trials with binary composite endpoints, and functions to simulate trials with composite endpoints under a wide range of scenarios.This work was supported by the Ministerio de Economía y Competitividad (Spain) under Grant PID2019- 104830RB-I00 and the Departament d’Empresa i Coneixement de la Generalitat de Catalunya (Spain) under Grant 2017 SGR 622 (GRBIO). Marta Bofill Roig is a member of the EU Patient Centric Clinical Trial Platforms (EU-PEARL). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA and Children’s Tumor Foundation, Global Alliance for TB Drug Development nonprofit organization, Springworks Therapeutics Inc. This publication reflects the authors’ views. Neither IMI nor the European Union, EFPIA, nor any Associated Partners are responsible for any use that may be made of the information contained herein.Peer ReviewedPostprint (author's final draft

Using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints

Background: Sample size calculation is a key point in the design of a randomized controlled trial. With time-to-event outcomes, it’s often based on the logrank test. We provide a sample size calculation method for a composite endpoint (CE) based on the geometric average hazard ratio (gAHR) in case the proportional hazards assumption can be assumed to hold for the components, but not for the CE. Methods: The required number of events, sample size and power formulae are based on the non-centrality parameter of the logrank test under the alternative hypothesis which is a function of the gAHR. We use the web platform, CompARE, for the sample size computations. A simulation study evaluates the empirical power of the logrank test for the CE based on the sample size in terms of the gAHR. We consider different values of the component hazard ratios, the probabilities of observing the events in the control group and the degrees of association between the components. We illustrate the sample size computations using two published randomized controlled trials. Their primary CEs are, respectively, progression-free survival (time to progression of disease or death) and the composite of bacteriologically confirmed treatment failure or Staphylococcus aureus related death by 12 weeks. Results: For a target power of 0.80, the simulation study provided mean (± SE) empirical powers equal to 0.799 (±0.004) and 0.798 (±0.004) in the exponential and non-exponential settings, respectively. The power was attained in more than 95% of the simulated scenarios and was always above 0.78, regardless of compliance with the proportional-hazard assumption. Conclusions: The geometric average hazard ratio as an effect measure for a composite endpoint has a meaningful interpretation in the case of non-proportional hazards. Furthermore it is the natural effect measure when using the logrank test to compare the hazard rates of two groups and should be used instead of the standard hazard ratio.G. G´omez and J. Cort´es were partially supported by the Ministerio de Econom´ıa y Competitividad (Spain) [MTM2015-64465-C2-1-R (MINECO/FEDER)], the Ministerio de Ciencia, innovaci´on y Universidades [PID2019-104830RB-I00] and the Departament d’Economia i Coneixement de la Generalitat de Catalunya (Spain)[2017 SGR 622 (GRBIO)]. Ronald B. Geskus was supported by the Wellcome Trust (grant number 106680/Z/14/Z).Peer ReviewedPostprint (author's final draft

Assessing Shiny apps through student feedback: recommendations from a qualitative study

This is the accepted version of the following article: Gonzalez, J., Lopez, M., Cobo, E., Cortes, J. Assessing Shiny apps through student feedback: recommendations from a qualitative study. "Computer applications in engineering education", Setembre 2018, vol. 26, núm. 5, p. 1813-1824., which has been published in final form at https://onlinelibrary.wiley.com/doi/abs/10.1002/cae.21932Teaching statistics has benefited from Java applets, the successful technology that appeared in the late 90s and which allowed real interactivity on an Internet browser. Combining dynamic functionality with the web provides an inspirational complement to the contents of many subjects in undergraduate statistics courses, especially for active learning activities. Since Java applets are becoming obsolete, we explore a different technology based on R (currently a popular statistical language) and Shiny, which is a web framework for developing interactive applications inside the R environment. Although the pedagogical value of these tools has been implicitly accepted so far, our aim is to consider the students' perspective while investigating more suitable means to accompany the use of apps in statistics. We conducted a qualitative study in which we tested 10 of our applications and collected student opinions through questionnaires and regular meetings. Our conclusions indicate that the students view these resources positively, although they demand more support, just enough to facilitate both getting started and using the tools effectively. In addition, programming in R is surely more accessible and satisfying for statistics lecturers than other languages and, consequently, implementing instructional activities can be specially tailored by the teacher.Peer ReviewedPostprint (author's final draft

La Influencia del orden de las preguntas en los exámenes de primer curso

El orden de las preguntas en un examen no debería tener influencia en sus resultados. Sin embargo, los autores tenemos la sensación de que los estudiantes de primero suelen ser secuenciales a la hora de resolver los exámenes. ¿Lo son realmente?, y si lo son ¿afecta esta manera de contestar los exámenes a los resultados finales? En este artículo analizamos estas cuestiones con un experimento realizado en la asignatura Estructura de Computadores, de primer curso del grado en Ingeniería Informática.SUMMARY -- The order of the questions on a test should have no influence on the final results. However, the authors had the feeling that students often solve the exam sequentially. Is this assumption true? If so, how does it affect the final results? In this paper we analyze the results of an experiment we designed to answer these questions. The experiment took place in the Computer Organization subject, a first-year course in the Computer Science Degree

(Bio)Functionalisation of Metal-Organic Polyhedra by Using Click Chemistry

The surface chemistry of Metal-Organic Polyhedra (MOPs) is crucial to their physicochemical properties because it governs how they interact with external substances such as solvents, synthetic organic molecules, metal ions, and even biomolecules. Consequently, the advancement of synthetic methods that facilitate the incorporation of diverse functional groups onto MOP surfaces will significantly broaden the range of properties and potential applications for MOPs. This study describes the use of copper(I)-catalysed, azide-alkyne cycloaddition (CuAAC) click reactions to post-synthetically modify the surface of alkyne-functionalised cuboctahedral MOPs. To this end, a novel Rh(II)-based MOP with 24 available surface alkyne groups was synthesised. Each of the 24 alkyne groups on the surface of the "clickable" Rh-MOP can react with azide-containing molecules at room temperature, without compromising the integrity of the MOP. The wide substrate catalogue and orthogonal nature of CuAAC click chemistry was exploited to densely functionalise MOPs with diverse functional groups, including polymers, carboxylic and phosphonic acids, and even biotin moieties, which retained their recognition capabilities once anchored onto the surface of the MOP