Welcome to the StatsClinic RU-vid channel. We are using this channel to archive and share videos from our courses. In addition to the videos, there are slide decks, homeworks and datasets that are distributed during our live courses. Our current completed courses are 1) Introduction to Medical Statistics 2) EPI101 - Introduction to the design of clinical and population health studies. 3) SRMA - Systematic Reviews and Meta-analysis. If you are interested in the next iteration of our course and would like to get access to our content, please email courses@stats.clinic.
Hi, dear, I am sapna. I hope you are well. I saw that your RU-vid videos are very lovely and very helpful. You have enough of the videos but your video is not getting views because there is some problem with your videos. Your SEO score is very poor (0% out of 100)and you need optimization for better results. If you update the videos then the views of the video will increase. If you want you can discuss it with me.Thanks
In my Understanding, Input: as Independent Variables meaning, these data have a way of affecting the outcome of your research. Example: Height is input, because it can affect BMI calculations in a research related to BMI analysis. Target: as Dependent Variables, are datas that are the Outcomes themselves of the research being done. They don't affect anything, because they are the Result of the Research or the answer to a question, hence their dependency of on other datas to determine them. None: these data do not determine research results and are not Research results themselves. Example: Names, Everyone has random names, if has nothing to do with a research. Another is ID which can just be random numbers given to participants in a Research. The idea is they don't really affect the Research in any way, they are present, so their role is None. These are the ones I can hack with my own understanding of what roles could be.
Though it is represented as some numbers, it is an identification used in categorizing people, So it is a categorical Variable. Categorical Variables are Either Nominal or Ordinal, since ID has no order or level (random number assigned to people//No one ID is greater than the Another ID), then it has to be nominal.
It is well presented and simple , you can create your data for meta--analysis in Excel, important the data into R studio, and then you only need to provide the corresponding variable names for n=, event=, studlab= for metaprop to work, the rest of the codes can be copied from the video at 5:02
thanks for your briefing the meta-proportion analysis , but the video is not clear and words are also not visible. i have also a question last year i had conducted meta proportion analysis using R version 3.5.3 and Rstudio version 1.2.....i got the result and now i had again conducted meta-proportion analysis using R version 4-3.1 and R studio 1.5....for that data but the results are significantly different with the same data and the same commands but different versions of R , so how could this happen ?
Thank you very much this. I have been searching for an opportunity like this. But please I have a question, how often will the videos be uploaded cause from this it’s like 11videos were uploaded at ones, that’s a lot to handle especially for low level medical statisticians myself
