J Espasandin, O Lado, C Díaz, A Bouzas, I Guler, A Baluja. These days, between the 19th and 21st of February, has taken place the learning activity titled “An Introduction to the Joint Modeling of Longitudinal and Survival Data, with Applications in R” organized by the Interdisciplinary Group of Biostatistics (ICBUSC), directed by Professor Carmen Cadarso-Suárez, from the University of Santiago de Compostela. The international nature of this scientific activity has been marked by the presence of researchers from different European countries such as Germany, Portugal, Holland, Greece or Turkey.

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Background A hospital may be defined as a place, or building, or set of facilities where patients go to receive the care they need. In ancient times, a hospital was not much more than a hotel for the poor, where they could stay and be cared for. Some centuries passed, and now we call hospitals to extremely complex structures, from both physical and functional perspectives.

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Wordclouds are one of the most visually straightforward, compelling ways of displaying text info in a graph. Of course, we have a lot of web pages (and even apps) that, given an input text, will plot you some nice tagclouds. However, when you need reproducible results, or getting done complex tasks -like combined wordclouds from several files-, a programming environment may be the best option. In R, there are (as always), several alternatives to get this done, such as tagcloud and wordcloud.

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One of the things I most like from R + Shiny is that it enables me to serve the power and flexibility of R in small “chunks” to cover different needs, allowing people not used to R to benefit from it. However, what I like most is that’s really fun and easy to program those utilities for a person without any specific programming background. Here’s a small hack done in R/Shiny: it covered an urgent need for a study involving patient randomisation to two branches of treatment, in what is commonly known as a clinical trial.

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The functions detailed inside the piece of code below (in a Gist) has been useful for me when I had to calculate many possible scenarios of statistical power and sample size. The formulae were taken from the article of Samuels et al., AJHG 2006, and the script showed even useful for making a variety of comparative plots. This is intended for estimating power/ sample size in association studies, involving mitochondrial DNA haplogroups (which are categories whose frequencies depend on each other), on a Chi-square test basis.

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In the last days I was thinking about about how researchers could collaborate efficiently with their experts in statistics. With the increasing complexity in science, interchanging information can be crucial to get the best results. But what happens when a researcher doesn’t give all the information a statistician needs?. When someone asks for help in statistics -as a clinician, I need it often-, many biostatisticians ensure that some basic points are met in a research study -prior to their analysis-, and I think they should ask for them as soon as possible:

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I like heatplots with p-values -or frequencies, or whatever-. Not very conclusive, but pretty anyway. And when talking about graphs, pretty will make our neurons to fire in more interesting ways: neurons like “pretty” graphs. Moreover, observing your data can be as important as analysing it. It’s better to observe, to listen your patients than making tests without knowing very much about them… In the heatmaps of the previous post, not a lot of information can be included.

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