cyprusprior

I would also recommend University of Washington http://www.stat.washington.edu/statgen/ and University of Michigan http://csg.sph.umich.edu/ Pretty much any top-ten biostats PhD program is likely to have at least a few people working on applications to statistical genetics and/or cancer. You could also browse the CVs of researchers at major cancer research centers and see where they did their PhDs: http://www.fhcrc.org/en.html http://www.dana-farber.org/ http://www.mdanderson.org/

I don't know anything about the programs at these schools, but here are a couple of other places that I believe have biostats PhD programs: - Vanderbilt - Virginia Commonwealth University - Rutgers - Rochester - Duke (brand new program, hence unranked) - University of Texas Health Science Center (Houston- I think this is affiliated with MD Anderson Cancer Center, a strong place for Bayesian stats) Another possibility to expore- some higher ranked biostats departments have a separate, less mathematically rigorous track to a "Doctor of Public Health" instead of PhD. I think UNC and Emory might be in this category. Final thought- What are your long-term career goals? If you're sure you want to do biostats research, maybe it's worth it to take some more advance math classes and go for a top program. If you're not sure, maybe it would provide clarity to work for a few years doing applied data analysis in a health or biology setting and find out if you like the statistics more or the biology more. And in any case, it's definitely worth learning some basic programming skills because that will be useful in almost any career track. If you have a chance to take computer science 101 or a MOOC along those lines, you may end up becoming fascinated by comp-sci and could orient more towards "bioinformatics" or "computational biology". There are so many diverse opportunities out there, good luck!

I would recommend contacting UW and finding out whether you can get a position as a research assistant as a masters student. I think that there are many projects beyond just the stats department that might be seeking RAs to help with basic applied statistics that could provide some funding.

wrong forum. Use this one instead: http://stats.stackexchange.com/

I'm familiar with the Georgetown program, less so for the other two. I agree that Georgetown's MS in math/stat is more "applied". You can tell this is true because they don't even require real analysis. It is not supposed to be preparation for a PhD in math/stat, but rather, a way for working professionals to gain some quantitative skills that they can take back and use in their jobs at financial regulatory agencies, advertising companies, consulting firms, etc. I do think however that it is a decent preparation for someone wanting to pursue a PhD in a quantitative, but not proof-based theoretical field (for example, econometrics, biostats, maybe operations research...), especially if their undergraduate major was in something like econ or political science (ie, not math/stat/ comp sci). If your main goal is to get a good job in the DC area, especially in the financial or government/ regulatory space, Georgetown's program can be a great way to go since they have a lot of connections to local employers. On the other hand, if you think there's a chance you would eventually want to pursue a PhD in applied math or stats, I would probably suggest Maryland instead, since it's likely to be cheaper and much more rigorous. I have also heard GW's masters program is very theoretical, but it is also quite expensive and I don't think it provides much of an advantage over Maryland in terms of placement, though I'm not very sure about this last point.

I'm sure many of us on the forum are wrestling with choosing the right program. I've compiled a list of factors I am using to weigh the pros and cons of different departments. I'll just share them here, in no particular priority order, in the hopes that if there's a big factor I'm forgetting to consider, someone might point it out. Also, I hope that this could be a useful checklist for future applicants when they go to visit various department open houses. Academic Factors: * time to degree * course requirements- how many core vs electives, are they in biostat or stat department, are they relevant to what I want to research? * prelim difficulty * TA workload * RA workload * stipend, is it guaranteed? how much? * academic job placement * industry job placement * ranking * bayesian versus frequentist * research opportunities depth/diversity * faculty working in particular research areas I find interesting? (computational/ genomics/ clinical trials/ survival analysis/ spatial/ etc) * dissertation- 3 papers or 100 pg manuscript? How much theory vs applied is acceptable? How long does it take for most students? * how stressed are the current students * are students working together or are they competing with each other? * student offices * strong theoretical training? * dropout rate * friendly faculty? * age structure of the faculty * friendly students? * what programming languages do I need to learn (SAS vs R, do faculty use C/C++, python, etc) Personal Factors: * cost of living * Can spouse/ significant other find a job in that city? * ease of using public transportation * ease of using car * ease of accessing major airport * climate/weather (at its worst in winter) * distance from home, family * running/ biking trails nearby * hiking/ proximity to mountains * exercise facilities (basketball court, swimming pools, etc)

I think MD Anderson might have a PhD program. They are well known for Bayesian Adaptive trials. I agree that Michigan is not considered strong for clinical trials, even though they are #1 in statistical genetics. University of Washington is an extremely strong place for clinical trials. Other places to consider off the top of my head: Minnesota, Penn, UNC, and Columbia. I also think NC State's statistics department would have a number of faculty doing research in clinical trials that you could work with.

After visiting a few biostats departments, and reading about a couple of others online, I've started to notice some interesting differences in the kinds of classes students need to take to be able to pass the qualifying exams. Since this is a prerequisite in most places to starting on research, the amount of time and energy one spends on this coursework is an important factor to consider in weighing different programs. Here's my totally subjective ranking of "coursework/ qual difficulty" from hardest to easiest. 1. tie between UW and Hopkins 2. Michigan 3. UNC 4. Minnesota 5. Harvard Feel free to chime in if you think I got something wrong, or if you know about programs I haven't listed (Columbia, Wisconsin, UCLA). I definitely don't think that harder coursework means a program is necessarily "better" or "worse" than another, but it's just something I think future applicants might want to keep in mind, especially relative to their experience with writing proofs. If a student's ultimate goal is to do more theoretical research, hard coursework is probably a big pro. But if their goal is to do applied work, it could be a con, since they might become frustrated studying hard on stuff that may not be relevant to their dissertation. I'm sure that faculty spend a lot of time trying to figure out the optimal dosage of deeper theory to help their students succeed, so it's nice to know that there is some variety to choose from.

There are several examples of recent biostat PhD graduates who have gotten jobs at places like Google and Etsy as "data scientists". However, I don't think this is the typical path for biostat PhD folks.

I don't know anything specifically about the programs you are describing, but I did talk to one of the faculty at Harvard during the interviews who is from Europe. His comments led me to believe that there is not as much funding to support biostats faculty/ students in the UK and continental Europe, in comparison to the NIH here in the US. This made it seem like many talented researchers from Europe end up coming to the US. Also, I feel like if your ultimate goal was to work in the US, it might be advantageous to do a PhD in the US since you could more easily travel to conferences and network with your future colleagues. On the other hand, if you are from Europe or the UK, it might be nice to be a bit closer to home.

Got a rejection from Emory biostats.

Undergrad Institution: Top 20 small liberal arts college Major(s): BS Biology GPA: 3.74 Type of Student: Domestic Male Masters Institution: 2nd-tier medium sized research university Concentration: MS Stats/ Applied Math (terminal masters, very applied, spread out over several years via night classes) GPA: 4.0 GRE General Test: I took the Kaplan class and contrary to my expectations it helped a lot. Q: 170(98%) V: 166 (96%) W: 5.0 (93%) GRE Subject Test in Mathematics: Did not take Programs Applying: Biostatistics, Statistics, Computational Biology Research Experience: plenty of bio research experience including 1 publication, none relevant to PhD programs. Awards/Honors/Recognitions: Fulbright, various departmental awards at undergrad institution, no awards at grad institution. Pertinent Activities or Jobs: worked as a software tester for 5 years. Learned a ton about programming and databases, forgot a ton of linear algebra and real analysis. Letters of Recommendation: one stat professor, one biostat professor (both from grad inst.), one undergrad bio professor who was my research advisor. Good relationships with all three. Applying to Where: Biostats: UW (Seattle)- admitted January Harvard- invited to interview January, accepted Feb Hopkins- invited to interview January, withdrew application UNC- admitted Jan/Feb Minnesota- admitted Jan/Feb Berkeley- admitted Feb, turned down offer Michigan- admitted Feb Emory- ??? UCLA- ??? Stats: NC State- admitted January, turned down offer Comp. Bio: Duke- invited to interview Jan/Feb Yale- invited to interview, withdrew application Carnegie-Mellon/ Pitt- invited to interview, withdrew application Stanford- rejected via postal mail, Jan/Feb UCSD- ???

Having gotten into several different biostats PhD programs, I'm now trying to decide where to go. One of the main factors I find myself contemplating is the importance of measure theory as a "required" part of the PhD curriculum. From what I can tell, there is an active debate going on in many biostats, and even pure stats, departments about whether measure theory should be required (ie, included on the qualifying exams) or whether it should be an elective. I can see good arguments on either side. For example: http://andrewgelman.com/2008/01/14/what_to_learn_i/ http://simplystatistics.org/2012/08/06/in-which-brian-debates-abstraction-with-t-bone/ http://simplystatistics.org/2012/08/08/on-the-relative-importance-of-mathematical-abstraction/ Even if I end up attending a PhD program where measure theory is not required, I do actually plan to take it just from my own curiosity. My main question is, does the presence/absence of measure theory in one's PhD curriculum affect academic job placement upon graduation? I can see a scenario where a department is considered less "rigorous" because they don't require measure theory. On the other hand, if the department produces students with, say, a stronger computational skill, maybe it would compensate? In a similar vein, can anyone give some concrete examples of how learning measure theory helped them in their later research, other than "developing abstract thinking skills"?

Just got an acceptance from Michigan Biostats! It was a really nice phone call from one of the professors there. I'm looking forward to attending their open house.

Congratulations! I applied to Michigan as well, but haven't heard anything and don't know too much about the department. Out of curiosity, what would make you choose Michigan over Harvard (or vice-versa)?

Just got an acceptance via postal mail for Harvard Biostats (PhD). I am going to withdraw my application from Hopkins, Berkeley, and UCLA.

I haven't lived in Pittsburgh personally, but I visited there spring of last year. I was very pleasantly surprised by the city. It is far from the industrial wasteland I expected. I really admire the pride the local people take in the improvements in their surroundings. I was able to rent a bike and explore along one of the three rivers (I think it was the Monongahela?). I also am a big outdoors enthusiast, and while it's not necessarily my ideal place to live, I think it compares favorably with most other similar-sized cities. Actually, while there may be more outdoorsy people in places like Madison Wisconsin, I think Pittsburgh has the advantage of being close to the awesome mountains and wilderness areas of West Virginia. Bottom line, I would not rule it out as "unlivable" until you can visit in person.

I got an email from them last week (acceptance). There was an invitation to their open house, but no offer of reimbursement for travel costs, so I am doubtful I will go (I live on the East coast).

From what I can tell, SAS is most popular with government or "blue chip" companies (ones that are large and have been around a long time). R is more popular with up and coming start-up types, and academia. So if your goal is definitely to work for the federal government, SAS would probably be worth learning. Even if it is ultimately eclipsed by R, the government will take a long time to catch up to market trends (for example, I have read that despite the abundance of alternative "modern" languages out there, many government web sites run on old versions of Coldfusion). Keep in mind it is very expensive to buy SAS on your own, so try to get a copy through your employer or some academic institution (or maybe they have discounts for veterans?). When I was trying to learn SAS, I found this book to be a good reference: Delwiche and Slaughter: The Little SAS Book. As for SQL, I would put it in a totally different category from R or SAS. SQL is typically used upstream from the analysis stage of working with data. You might for example write SQL queries to generate a report containing the data you need by drawing it from various tables in a database. Then, you could export that report and process it using R or SAS. I think SQL is absolutely worth learning, since relational databases are everywhere and it's almost certain that your data will have problems requiring some knowledge of databases to solve. One place to start with learning SQL would be to get SQLite (https://sqlite.org/) on your local machine and play around with it on the command line, or try using this handy firefox extension: https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/. Here is a useful SQL reference for learning: http://www.w3schools.com/SQl/default.asp Good Luck!

I think the notification they sent out from the department is just an informal one. I expect within the next week we should be getting a formal letter from the graduate school dean which would have more detailed information about things like funding. I think the department was just trying to get the news out as quickly as possible so they could start collecting RSVPs for the visit day. I'm not too worried.

I imagine a common interview question would be "What do you want to do once you finish your PhD?". My honest answer is, I think I would enjoy being a professor, but I could also see myself working for a start-up, a pharmaceutical company, or a government agency such as the NIH or CDC. I'm wondering if in this case honesty is the best policy. Do you think it is OK to tell the interviewer that you are open to careers outside of academia (pharmaceutical company, finance, government such as CDC/ NIH)? Or will this reduce one's chances of acceptance into the program? I have read elsewhere that one should pretend, not only at interviews but throughout the PhD program that academia is the only goal in order to appease faculty who frown on non-academic career paths. I've never met any faculty in my personal experience, so I was just wondering how common this would be in statistics/biostats departments.

Institution: University of Minnesota Program: Biostatistics PhD Decision: Admitted Funding: not specified Notification date: Jan.16th Notified through: phone call from professor

Institution: University of North Carolina - Chapel Hill Program: Biostatistics PhD Decision: Admitted Funding: not specified Notification date: Jan.13th Notified through: Email

Institution: University of Washington Seattle Program: Biostatistics PhD Decision: Admitted Funding: ~$2100/month for RA or TA position (12 month appointment) Notification date: Jan.10th Notified through: Email

Institution: NC State Program: Statistics PhD Decision: Admitted Funding: $1875/month for RA or TA position (9 month appointment) Notification date: Jan.10th Notified through: Email