How to learn statistics for medical research? I'm a last year medical student, as we say "Intern doctor". In the future I want to do research on the issue that I want to get in.
Therefore I want to learn mathematics, statistics, R programming and so on, because I want to do best statistical analysis for my research. Proper statistical analysis is the most important part after the research issue as far as I'm concerned.
Consequently, for these things I watch on Youtube the Richard McElreath statistical rethinking videos and I do learning R programming on DataCamp courses, I bought annual premium plan. As far as I see the Youtube videos that I watch are somewhat hard for me to understand. Without statistical substructure I think to learn R programming on DataCamp would be on shaky ground.
What would you think about that? I want to learn the ideas, advices from more experienced people than me. What would you advice for me to learn these skills with very well?
 A: I spent the last 14 years trying to get better at statistics including earning an applied master's in it (which in honesty was disappointing). I think the answer is that there is no simple way to do this. This is made worse by the fact that much of the academic literature that uses statistics is not done by true statisticians IMHO (which of course is true of me) and they do things (lots of things) that violate various rules because they are not true experts. Simple examples include applying a method wrong, ignoring threats to external validity, using stepwise regression, failing to address omitted variable bias, dropping variables from the model because they are not statistically significant and rerunning the model with the same data and on and on.  One frightening article years ago looking at articles in the New England Journal of Medicine and Lancet found a wide range of statistical errors in articles applying logistic regression, and they are of course elite journals. And this ignores the fact that statisticians often disagree themselves.
So if you are going to learn statistics, its a lot of work. You might look at the UCLA (Idre) site or for time series the Duke University site as a starter.
A: The discussions above make fantastic points.  I recommend a parallel approach of finding a biostatistician collaborator and learning about biostatistics.  On the latter, concentrate on learning things that the biostatistician is unlikely to already know, to spur them on to a better understanding.  I've tried to cover lots of things in BBR that are in this category---things that biostatisticians and clinical researchers have to unlearn in order to make progress.  As just one example, it is commonly accepted that computing change from baseline is OK in analyzing patient outcomes.  BBR goes to great lengths to show why you should not compute change from baseline.  You'll see lots of discussions about learning medical statistics, and collaborating, at datamethods.org.
A: You're Getting The Order Wrong.
It's as though you've said "I want to learn medicine, therefor I bought a scalpel." R is a very useful tool. But it will be much harder to learn statistics from using R than it would to learn to use R after already knowing statistics.
If I were learning statistics from scratch, I would not start with Baysian. I would start really simple. Use physical objects to understand how possible outcomes work first (like dice and coins). If you flip a coin, how many outcomes are there? Can you list them out? How about two coins, etc. Getting a solid understanding of the set of possible outcomes is the best basis for thinking about how "likely" something is to fall into a given subset of those outcomes.
Conveniently, this is the way that most introductory statistics classes are taught. As such, I really recommend that you start learning statistics with an intro class.
Following that, probably the next most important skill is formal logic. That's likely to be filed under philosophy at your school or online. You will essentially never be able to actually "prove" anything with research in a strict, formal sense. Example: with the logic chain A -> B -> C, maybe you test B->C a million times, and get the desired result every time. That demonstration doesn't "prove" that B->C. But practically, we would probably accept research that asserted B->C. What you need there is the grounding and discipline to understand and clearly state your full logic chain and all your assumptions. Doing so will make the analysis easier, and will make your results more robust.
Once you have that, you can look at Baysian approaches much more usefully. I find it's easiest to think of Bayes as dealing with some epistemological problems with frequentist statistics, which means it wont make much sense unless you already understand the basics of statistics and of the logic chains. (Some people have claimed success with learning Baysian from the get-go. I have trouble even imagining how one could usefully do that. YMMV.)
Unfortunately, while you can get a solid working knowledge of applied statistics that way, it will be exceedingly difficult to get a true understanding without calculus, and fairly difficult calculus at that. So if you really want to dive deep, this is about the point where you would want to brush up on that. Then look for a calculus-based statistics. It might be called "Statistics for scientists and engineers" or similar.
A: It sounds like that you just have started with statistics and you do not have experience with it. That is totally OK.
But your goal is the other extrem that you want to be a statistic pro. That is impossible. Relax your self and do not push you to much here.
Keep one very important thing in mind. A professional (or however you want to name that) scientist never work alone. Research is a team thing. The workload itself can be done by one person. But the thinking, developing and dropping of ideas and questions and arguing with research colleagues and other experts is always a team thing.
And that will bring you back to statistics.
Even well experience research do not make there statistics alone. They make a plan how to collect and how to analyze the data. But when they finalized that plan they do not start to collect data but they contact a statistic person. They do statistic consulting (translated from German).
Because of that process you learn how to do things especially where are your borders of knowledge and expertise and when to ask other persons.
In short: You do not have to work alone. Your supervisor and your university should be able to offer you something here.
A: 
What would you think about that?

This is a good question.  I've spent a good amount of time during my Ph.D in Biostatistics consulting for academic physicians and their research.  If you (and moderators) will allow for an opinion based answer then I'm happy to give it.
Medicine for some reason has created a culture in which the physician is intended to do everything themselves.  Study design, data collection, analysis, writing, oh yea and on top of that their clinical duties and learning more about their specialty. These include responsibilities of an epidemiologist, data architect, statistician, just to name a few.  Personally, I think that is a ridiculous onus to put on a researcher.  This also might explain why medical research seems to be a copy-paste affair with bad statistics.  Statistics is hard to learn, medicine is hard to learn, so learning both tends to mean taking shortcuts on one or the other or both (and understandably, it is the statistical rigour that is sacrificed).
Rather than succumb to these expectations it might be wiser to, as whuber notes, befriend a biostatsitician. Collaboration is a good way to learn, because you get consistent advice tailored to your specific situation as opposed to a mish mash of approaches from different courses with different learning goals.  I'm not saying to defer all statistical work to a statistician, nor am I saying you should not learn about statistics independently, but I think rushing to learn all these things while also being a physician will lead to poorer work than if you were patient and collaborative.
The question is then "How do I meet/befriend a biostatsitician".  Your medical school is likely attached to a university, in which there may or may not be an epidemiology department.  Epidemiologists focus very carefully on how to do quality studies in a medical setting.  THey should be well versed enough in statistics to help you out with design, data collection, and analysis.  If you don't have an epidemiology department, there may be someone in a stats/math department, or in the sociology department (sociology is not exactly like biostatistics, but the difference between an epidemiologist and a sociologist grows smaller and smaller).
EDIT:
EdM makes a good point about the basis of fundamental probability and statistics.  I'm not prepared to give a list of topics to learn and places to learn them.  I think any undergraduate curriculum in science can give you enough to get started.
That being said, if pressed to offer one resource on a basis of prob and stats, I would recommend Introduction to Medical Statistics by Martin Bland.  The book is geared towards medical students and in the introduction states

This book is intended as an introduction to some of the statistical   ideas important to medicine.  It does not tell you all you need to know to do medical research.  Once you have understood the concepts discussed here, it is much easier to learn about the techniques of study design and statistical analysis required to answer any particular question.

The book however does not cover probability, and so you're free to pick up most introductory texts on the matter to cover that base.  I agree with Bland that this book should serve as a good basis to read academic medical literature critically, and should serve as an excellent jumping off point to learn more about statistics in medicine.
