Reddit mentions: The best biostatistics books

if you're doing this to help prepare to switch careers, look at industries and companies you might be interested in. Every vertical has different tech stack choices that are common. Medicine has a lot of SAS, pharmaceutical researchers I've met all use R, main industry and research at this point is mostly Python. Python gets you the most bang for your buck. If you need to step outside ML and throw together a back end DB, a REST API and a front end to glue the whole thing together or whatever, Python's just as useful there as it will be with ML. I don't use R, but from what I hear it's much less versatile. The Stats libraries for R are a lot more mature though apparently, so if you want to get into doing some more intense statistical stuff, I've heard Python is a little less friendly. I haven't run into any of those limitations, but I've been more playing around with RL and stuff, and doing less intense statistical analysis with rigorous confidence bounds or whatever.

For forecasting from historical data, you're looking at time series. Unfortunately I don't know a ton about time series modeling yet. It's much more complicated than a situation where you're assuming N iid draws from a stationary distribution (the 'typical' entry point for classification and such that you see in supervised machine learning).

Keeping in mind that I have no business giving you advice where to start because I haven't made the trek yet myself, I've heard good things about Time Series Analysis and Its Applications. It's a grad level stats book though, so I hope you aren't joking about your math background, haha. The examples in that book are all in R too, as a head's up.

For a slightly easier (but still standard) introduction to the topic, I've also heard Wei's Time Series Analysis is decent. If you look around for a good introduction to multivariable time series analysis though, I'm sure you could find a lot of resources and judge for yourself what would most fit your needs. If you did pick one of those two books to pound out, I suspect you'll have a radically better idea how to go the rest of the way and get into practical application. As you're getting into the theory (whatever resource you use), I'd highly recommend picking a few datasets you're interested in (Kaggle might be a good source, to go with whatever you care to get into for your own reasons) and as you go, try applying the various methods you're learning on those few different datasets to get some sense of how it works and why. Pro-tip: one or two of your go-to toy datasets should be generated yourself with some simple to understand function to help give a really easily understandable case to play with, where your intuition can still hold up. y(t) = sin(t) +kt + N(0,b) maybe, or some simple dynamic process of the form y^t+1 = f(y^t ).

But either way, make sure you're rolling up your sleeves and cracking your assumptions against actual data in code to make sure you get the idea. All theory and no practical makes Jack a dull boy.

Edit: if you want a more broad introduction without necessarily having the rigorous focus on time series forecasting, 'applied predictive modeling' and 'introduction to statistical learning' are both good big picture intros. The new hands on machine learning book is good too, but more narrow and less comprehensive. Elements of Statistical Learning is kind of the defacto standard reference text going over all the common algorithms from a mathematical perspective. If you have the mathematical maturity to tackle ELS, that'd be a great way to start to get a deep foundation in the theoretical ideas across ML as a whole, though obviously none of that is going to be time series specific.

I've posted a similar answer before, but can't find the comment anymore.

If you are interested in doing your own statistics and modeling (like regression modeling), learn R. It pays amazing dividends for anyone who does any sort of data analysis, even basic biostats. Excel is for accountants and is terrible for biological data. It screws up your datasets when you open them, has no version control/tracking, has only rudimentary visualization capabilities, and cannot do the kind of stats you need to use the most (like right-censored data for Cox proportional hazards models or Kaplan-Meier curves). I've used SAS, Stata, SPSS, Excel, and a whole bunch of other junk in various classes and various projects over the years, and now use only R, Python, and Unix/Shell with nearly all the statistical work being in R. I'm definitely a biased recommender, because what started off as just a way to make a quick survival curve that I couldn't do in Excel as a medical student led me down a rabbit hole and now my whole career is based on data analysis. That said, my entire fellowship cohort now at least dabbles in R for making figures and doing basic statistics, so it's not just me.

R is free, has an amazing online community, and is in heavy use by biostatisticians. The biggest downsides are

• R is actually a strange and unpopular general programming language (Python is far superior for writing actual programs)
  
• It has a steep initial learning curve (though once you get the basics it is very easy to learn advanced techniques).
  
  Unfortunately learning R won't teach you actual statistics.... for that I've had the best luck with brick-and-mortar classes throughout med school and later fellowship but many, many MOOCs, textbooks, and online workshops exist to teach you the basics.
  
  If I were doing it all over again from the start, I would take a course or use a textbook that integrated R from the very beginning such as this.
  
  Some other great statistical textbooks:
  
  
• Introduction to Statistical Learning -- free legal PDF here -- I can't recommend this book enough
  
• Elements of Statistical Learning -- A masterpiece of machine learning and modeling. I can't pretend to understand this whole book, but it is a frequent reference and aspirational read.
  
  Online classes:
  So many to choose from, but I am partial to DataCamp
  
  Want to get started?
  
  
• Download R directly from its host, CRAN
  
• Download RStudio (an integrated development environment for R that makes life infinitely easier) from its website (also free)
  
• Fire up RStudio and type the following commands after the > prompt in the console:
  
  install.packages("swirl")

library("swirl")

swirl()
  
  And you'll be off an running in a built-in tutorial that starts with the basics (how do I add two numbers) and ends (last I checked) with linear regression models.
  
  ALL OF THAT SAID ------
  
  You don't need to do any of that to be a good doctor, or even a good researcher. All academic institutions have dedicated statisticians (I still work with them all the time -- I know enough to know I don't really know what I am doing). If you can do your own data analysis though, you can work much faster and do many more interesting things than if you have to pay by the hour for someone to make basic figures for you.

Honestly I think she selected qualitative by defaut of fear. She is courageous and smart, but this class has her a bit shook.

> "it's the university's job to teach her what she needs to succeed!"

Agreed! I appreciate this point and will help her see it that way. I think there should certainly be a early conversation with the Instructor on the concepts and expectations in this class. That would certainly focus the conversation. Setting sights on only passing the class isn't really ideal, but it is an important step. Obviously learning the material is the goal.

Thank you for the text recommendation. I found PDQ stat on Amazon.
To anyone reading along, here is the LINK

> Pretty Darn Quick Amazon Review:


By An epidemiologist
Format:Paperback

This book is the only statistics book of its type. For each section covering a specific statistical method (from simple methods to those you may not even cover in your PhD training), a concise 2-5 page summary is presented. The goal is not to enable the reader to calculate any of these statistics, but to understand conceptually what each statistic means. This is where it can fill in information other statistics texts never get to. A student (or researcher!) who can churn out factorial ANOVA results, but doesn't truly understand what they mean can turn to this book for clarity. It's simple (for statistics), it's short, it's clear, and you have to love a book that is dedicated "To the many people who have made this book both possible and necessary -- authors of other statistics books"!

Given your background, you could read Ken Binmore's Game Theory: A Very Short Introduction (2007). It's really short, but it assumes the reader is familiar with probability theory and a fair amount of mathematics. Binmore has another textbook Playing for Real (2007) which is goes much more in depth. It assumes the reader is familiar with linear algebra.

One of the central results of von Neumann and Morgenstern's Theory of Games and Economic Behavior (1928) is the minimax theorem. This was John von Neumann's favorite theorem from that book. John Nash generalized this in his PhD thesis in 1950. The minimax theorem establishes the existence of Nash equilibrium for zero-sum games with finite players and strategies. Nash's extended this and showed that any normal form game with finite players and strategies has an equilibrium. You might have seen the movie A Beautiful Mind which depicted John Nash working on this. If you are interested, you can read about Nash's proof in Luce and Raiffa's Games and Decisions: Introduction and Critical Survey (1957). The proof does assumes the reader is familiar with point set topology.

Outside of economics, game theory is also applied to evolutionary biology. One of the best books on evolutionary game theory is Martin Nowak's Evolutionary Dynamics: Exploring the Equations of Life (2006). You might also like John Maynard Smith's Evolution and the Theory of Games (1982). Maynard Smith assumes the reader is familiar with homogenous differential equations.

Hope this helps!

You need Intuitive Biostatistics. It's written specifically for scientists and medical professionals without a math background to learn how to interpret data in scientific papers. I'm a PhD student in cell bio and it is invaluable. The only thing it might not cover super in-depth is probability, but it pretty much covers the gamut of everything else without delving into the mathematics behind everything. The guy who wrote it also makes the Graphpad Prism software, which a lot of bioscientists use for data analysis.

For the next step, I highly recommend JB Statistics videos. They are the best moderate math level explanations for the common concepts I have yet come across. Especially watch the sampling distribution playlist several times to fully comprehend the CLT.

Some other advice I wish I was told before I started learning statistics: 1) Statistics is the inverse of probability. 2) Statistics is unintuitive and hard to understand. You will have to read some things dozens of times and from different people's wording to fully understand a concept (looking at you, p-values). Best of luck.

I've never really found a probability book that I love. Here is the one I had for undergrad:

Pleasures of Probability

It's verbose, but provides excellent coverage of the major stuff. Here's three free ones, but it looks like they jump in a little too quickly:

http://www.math.uiuc.edu/~r-ash/BPT.html

https://web.math.princeton.edu/~nelson/books/rept.pdf

Probability Theory, the logic of science

And an MIT course:

http://ocw.mit.edu/courses/mathematics/18-440-probability-and-random-variables-spring-2011/index.htm

You basically want a strong understanding of:

Conditional Probability, Baye's Thm and it's use, Expectation. And to be familiar with the Law of Large Numbers, Central Limit Thm and Moment Generating functions, and the use of all three.

Freund's Mathematical Statistics is the go-to book for mathematical statistics. It requires a strong grasp of integration techniques (including changing coordinate systems through substitution) and probability.

The graduate level econometrics texts most commonly used are Greene's Econometric Analysis and Hayashi's Econometrics (I have a slight preference for Hayashi)

Someone over on r/rlanguage posted this link to a list of R help resources. As we don't know quite what level you're at, you may want to look through there to see what's applicable to you.
If you are a total novice, one site I've had recommended (and is also linked on the above blog) is datacamp. Personally I found this useful as a start to learning some of R's commands, but the first chunk of the course left me unable to actually make or run a program as it didn't fill in the basics (eg what a working directory is, how to actually download R). So I used that website in conjunction with the book getting started with R - whilst it is targeted at biologists, the first half is certainly applicable to anyone getting to grips with R.
You'll have to decide yourself whether it's worth spending money on books if you'll only be using them for this one class or whether it would be better trying out some of the free online resources (or seeing if you find free ebook versions!).
As u/fang_xianfu said, a specific question will probably give you more targeted help and advice, so ask away!

First Idea: Language. Some-one might invent a Constructed Language or conlang, that helps thinking and communicating. Life is more complicated than language. We should reject both mistake theory and conflict theory. Politics is nasty due to linguistic poverty. Our few words muddle together different things leading to quarrels.

Examples avoiding culture war: 1)The opening paragraph here 2)the word error as used by journalists reporting on medical tests. We are really interested in positive predictive value and negative predictive value. We could work them out for ourselves if we knew the false positive rate and the false negative rate and the prevalence. Here our language is rich enough that there are words for the concepts that I claim we don't have words for. That is good, because it allows me to write down an example using words, and I can fall back on pointing out that natural language only has error. The other words and phrase belong to unnatural language :-)

Second Ideal: Quantitative Dynamic Sociology. Think about Quantitative Ecological Theory All those foxes eating hares until hares are rare and the foxes starve and die, and the hare population revives and the few remaining foxes put on weight and eventually start breeding again. Like that, but for ideas, like marriage, divorce, income tax, minimal wages,... Why do they wax and wane?

Peter Turchin is on to this and calls it Cliodynamics. Great, but I fear premature. I'm expecting a book like Evolution and the Theory of Games which gets criticized for unrealistic models. First we need some-one to come up with a compendium of toy models for Quantitative Dynamic Sociology to show what it would even look like. Then, in 2119, the great mind can revolutionize the new field with models that actually work.

Here's my favorite general, theoretical intro to Bayesian stats, by the author of the logic of science book above. Interesting to read and not too long-
http://bayes.wustl.edu/etj/articles/general.background.pdf

More...This one tries to re-teach stats from square one. It's alright, but stops short of Markov Chain Monte Carlo, which is where things get fun.
http://www.amazon.co.uk/Introduction-Bayesian-Statistics-William-Bolstad/dp/0470141158/ref=sr_1_1?ie=UTF8&s=books&qid=1280142090&sr=1-1

This is the one I'm reading now, which explains bayes for people in the social sciences, and makes an effort to break down the cool stuff into simple terms. I really like the writer and its good so far-
http://www.amazon.co.uk/Bayesian-Methods-Behavioral-Sciences-Statistics/dp/1584885629/ref=sr_1_2?ie=UTF8&s=books&qid=1280142179&sr=1-2

>"Gödel, Escher, Bach" by Douglas R. Hofstadter is the most awesome book that I have ever read. If there is one book that emphasizes the tragedy of Death, it is this book, because it's terrible that so many people have died without reading it."

Apparently I never got remotely far-enough into the book for this statement to make sense.

(I got tired of carrying that huge paperback around in my backpack.)

Lemme go get a Kindle copy.

I've heard good things about Good and Real.

>Artificial Intelligence: A Modern Approach[2] , also recommended by Yudkowsky, is the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. It's the leading textbook in the field of artificial intelligence, used in over 1100 universities worldwide. I think it's obvious why a community read-through of this would be beneficial.

Russel and Norvig is the standard textbook for "Good Old-Fashioned AI", ie: the kind that's not at all worthy of being called "AI". It's used as a textbook in the first course in GOFAI for undergrads. It teaches fairly little programming, very little mathematics, and covers nothing of the kind of modern machine-learning techniques that actually get results these days, let alone the increasingly elegant and advanced learning techniques that are yielding good models of what cognition is.

On the textbook front, though, I can recommend that anyone with basic Calc 1+2 under their belt can go ahead and read Introduction to Bayesian Statistics to get a first taste of how "Bayesianism" actually works, and also why it hasn't taken over the world already (hint: computational concerns).

Not OP, but I'm gonna check this one out, thanks. FWIW, there is a newer (4th) edition that came out this year here.

Great question, OP. I'm in a similar boat currently trying to learn more and at the minimum understand how to look at studies and papers critically. Hopefully more people will chime in with advice.

It's been a few years since I read it, but I really enjoyed Harvey Motulsky's Intuitive Biostatistics. He has a very good writing style and explains concepts clearly and broadly, rather than going into unnecessary details.

Depends on what you're looking for.
If you mean population genetics:
https://www.bookdepository.com/Genetics-of-Populations-Philip-W-Hedrick/9780763747725?ref=bd_ser_1_1

If you mean genetic epidemiology:
https://www.amazon.com/Fundamentals-Genetic-Epidemiology-Muin-Khoury/dp/0195052889

Plus, read this paper:
https://www.ncbi.nlm.nih.gov/pubmed/17554300

The problem is that everything gets outdated so quickly, but if you understand the pitfalls of a GWAS using 100K Affy SNP arrays, it's not a big leap to understand the related pitfalls using whole-exome sequencing as your genotyping method.

As a third option, I was going to list a preferred bioinformatics textbook, but it just seems silly to be honest. The best course of action is generally to learn to code (R or Python, plus BASH tools) and then to start using whatever software you're going to want to use to process genetic/epigenetic data.

I created a "Health Statistics" Libguide for the students at KUMC. It may be of some help to you! It was specifically designed for Kansas students, but it has helpful health and medical statistics links/resources.

[Health Statistics!] (http://guides.library.kumc.edu/healthstats)

The book [Medical Statistics at a Glance] (http://www.amazon.com/Medical-Statistics-Glance-Aviva-Petrie/dp/140518051X) may also be a good resource for you.

I read an earlier edition of Primer of Biostatistics around the time that I was an M1, and really enjoyed it. It's a very general, high-level view of biostatistics. It will help you understand why we use the different statistical tests that we use, in simple language. It also might help you decide what more specific, technical resources you should read or learn next. Highly recommended!

I've thought about this a lot, and from both sides of the coin. Here's kind of where my mindset is at the moment...

Some regulation is absolutely necessary in certain segments of the industry.

There is a very good (but very hard to read) book called Risk Society written by Ulrich Beck that caused something of a paradigm shift in the engineering mindset in the 90s.

To oversimplify, society (and the world it exists in) has become complex to the point that you can not engineer risk out of the equation.

This idea is supported by the findings of people like Sidney Dekker in The Field Guide to Understanding Human Failure, who performs what could be considered root cause analysis of surgical and aeronautical accidents. The systems that he deals with are now complex to the point where there is no single root cause, because failure is an inherent operational condition of the environment. In other words, asking why something failed is exactly like asking why something didn't fail - it was the end result of an impossibly complex web of interrelationships, all of which culminated in the eventual success (or failure) of the system.

There are a lot of scenarios where the tasks undertaken by system administrators do have life or death consequences, and in order to architect those infrastructures with adequate resiliency, a lot of education is necessary.

The path of a lot of system administrators from amateur to professional resembles that of a child who is exceptionally gifted at building erector sets being hired to construct a pedestrian bridge. Then, if the bridge doesn't fall, the kid gets to build bridges designed to handle interstate traffic.

I don't write this to disparage the upwardly mobile system administrator who has learned on the job, acquired a high skill level, and is successful in the systems that they engineer. Someone who does that should be justly proud.

When you start considering the potential loss of human life in such a system, however, you start to realize that "best effort" learning isn't enough, particularly when there is no test to establish a safe knowledge level.

Why should you require a degree in civil engineering to design and implement a traffic control system, then not require the slightest test of the people who administer the IT infrastructure that it runs on?

No, I anticipate that in the future, "critical infrastructure" administrators will have certain requirements laid on them for the benefit of everyone who uses the system. The difficult decision will be where to draw the line.

What are your thoughts?

I love [this] (https://www.amazon.co.uk/Biostatistics-Essentials-Geoffrey-R-Norman/dp/1550093479)

It's even fun ;o)

In some sense you have a form of rare-events happening and thus your "outliers" could probably be modelled using standard Extreme Value Theory - you fit your overall trend in some standard way, and then the extreme residuals have some distribution (that you can e.g. fit parameters to). In some sense what you want to do depends o what you want to do though. If you have a library handy you might consider skimming through a book such as this: http://www.amazon.com/Introduction-Statistical-Modeling-Extreme-Values/dp/1852334592 .

Epidemiology is more science oriented whereas PH is more policy oriented. Both are very interesting and there's a substantial overlap. Epidemiology it self has very many subtypes such as environmental, clinical, social etc - again you can pick the one that interest's you the most.

A good introduction would either be to buy a book or do the LSHTM online course

I'm not asking you to trust anything i say.

Assumption 1:Flu is ubiquitous.. it is. (is it truly necessary to pull up everyone who has had a flu?) http://www.cdc.gov/mmWR/preview/mmwrhtml/mm5749a3.htm

Assumption 2: Pigs are good mixing vessels for horizontal transfer http://www.pnas.org/content/104/52/20949.abstract

Nothing here is fallacious...

The rest is taken from your article, which summarizes the actual Science article. Unfortunately a full text is not available to the public.

Those two are the basis for the hypothesis that this new strain did originate from Mexico. The data fits this hypothesis. The rest is from a biology text. You did take some biology in high school or college, right? Further information is in here: http://www.amazon.com/Epidemiology-STUDENT-CONSULT-Online-Access/dp/1416040021/ref=sr_1_1?ie=UTF8&s=books&qid=1240956369&sr=8-1


And... your argument changed from, "I just gave you evidence, you didn't look at it" to "APPEAL TO AUTHORITY!!!!!!"

This book is fantastic. There is a PDF floating around somewhere for free you can probably find with a quick Google search.

https://www.amazon.com/Handbook-Functional-MRI-Data-Analysis/dp/0521517664

The analysis of biological data is fantastic for foundation stuff. Really recommend it.

https://www.amazon.com/Analysis-Biological-Data-Michael-Whitlock/dp/0981519407

It sounds like you're looking for time series material, but Applied Predictive Modeling may be of interest to you. For time series and R specifically, this text seems well-reviewed.

If you're just starting out I might suggest the following websites for an intuitive understanding of statistics. I think they're better than most books for visualizing and explaining the fundamentals.

https://www.youtube.com/channel/UCFrjdcImgcQVyFbK04MBEhA

https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw

https://statquest.org/video-index/

http://www.bcfoltz.com/blog/stats-101/

As far as books go for intuitively understanding the basics: there's PDQ Statistics, I also like the stats for dummies books.

This is bogus download link. It is just a free offer mill and will not legally let you download this book. This is a $65 book sold on Amazon, and appears to be a college text.

http://www.amazon.com/Intuitive-Biostatistics-Nonmathematical-Statistical-Thinking/dp/0199946647

Think you'll just need to buy it or find someone to borrow it from.

I have used the following two books:

1. Good introduction, with a discussion of frequentist vs Bayesian statistics:
   
   www.amazon.com/gp/aw/d/0470141158?pc_redir=1411138170&robot_redir=1
   
   
2. PDF available online, more machine learning oriented:
   
   http://web4.cs.ucl.ac.uk/staff/d.barber/pmwiki/pmwiki.php?n=Brml.HomePage?from=Main.Textbook

Well if it is stats you are looking for then the standard in my department is Gotelli's A primer of ecological statistics. For more general biological stats look at Whitlock & Schluter and Quinn & Keough. Also don't forget the classic Biostatistical Analysis.

Sounds like a solid book for someone with an undergraduate math background,
https://amzn.com/038794415X
http://www.springer.com/us/book/9780387944159

A good one is A Handbook of Functional MRI Data Analysis by Russell Poldrack.

Biostatistics, The Bare Essentials is excellent. Very practical statistics explained in fairly simple terms. Obviously this is skewed toward statistical problems from biology, but it's fairly easy to extrapolate to other data science problems.

Evolution and the Theory of Games

Intuitive Biostatistics by Harvey Motulsky

I have this edition: https://www.amazon.es/Analysis-Biological-Data-Michael-Whitlock/dp/0981519407

We're not here to help you break the law.

Buy the book

> This is a false idea, unless you know the standard deviation of the dataset.

Bayesian statistics bro. Here's a good book

Also, to extrapolate solely from the standard deviation, I would have to believe the distribution is normal. I have no reason to believe such a thing, and neither do you. A distribution would be an interesting measurement to cite, but seeing as you couldn't cite either the percentage you claimed in Boston (15%) nor the nationwide standard deviation, I doubt you have any information on the distribution.

Last semester my pharmacy school program made us self learn/review stats with this book.

Worst stats book ever. I also hate seeing math scribbled on powerpoints,

I think I've done enough complaining for one day...