Writing a ten page article looks simple … in theory! You have collected data for 2 or 3 years, and now you "only" have to analyze it and write the article. Yet, many pitfalls remain between you and publishing.
The first step is to clearly define what you are looking for, and then think about how to show it. Doctors came into my office (when I was a statistician) on numerous occasions to present me with data, but unfortunately absolutely nothing could be done because some key variables had not been measured or had been badly measured. "But it took us 3 years to collect everything ?!?!?" This does not change the fact that a poorly crafted study at the beginning will not be analyzable at the end.
Journals and reviewers are becoming more and more demanding. And the higher the level of the journal, the cleaner and better the statistics will have to be. At this stage, it is therefore necessary to distinguish two types of article:
10-20 pages doesn't seem long. But it's very complicated to write. Great authors write an average of two pages a day. It's one thing to have clear ideas, it's another to make them accessible and understandable to colleagues. Another difficulty is integrating the relevant statistics and graphs into the article. First, you need to sort them (because you probably have more stats and graphs than you need for the article) and then export them from your software to your article. In general, it is also at this stage that you need to draw up the time-consuming Table 1, the table of comparison of groups.
In R++, everything is oriented towards "article publication". R++ gives you the tools to efficiently address the three problems above.
R++ cannot do the study design for you. But to help you, we have integrated a module for "calculating the number of subjects needed". Before you even collect your data, choose the test that suits you, fill in the expected information and the number of subjects needed is calculated automatically. This will give you a good idea of the feasibility of the study, or the need to restart the design if the number of subjects is not realistic.
Statistical analysis is the heart of R++. Everything is done to give you access to the statistics you need (those found in medical articles, no more, no less) in a simple and intuitive way. Many testimonials mention impressive time savings thanks to R++. Isabelle Sourrouille (video testimonial below) felt R++ cut the time spent by a factor of 10. And even more, R++ is so simple that making stats, generally considered the most painful step in the production of an article, becomes enjoyable. Again, it's not from us; it's our customers who say it (video below).
Finally, when you have your results, it is not always easy to integrate them into the article. With R++, a simple copy and paste or drag and drop is enough. Your Table 1 is generated automatically: select the group variable, then click on all the variables you want online. Your Table 1 is finished, ready to be integrated.
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I am bad at stats, I hate stats. R++, it (almost) made me love stats.
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