Writing a Biostatistics Doctoral Dissertation Proposal

You have finished all your courses. You have passed your written comprehensive exams. Congratulations! What’s next? If you haven’t already (and you should have), you pick an advisor and start to work on your doctoral dissertation. Writing a dissertation and finishing your doctoral degree involves several steps. Two important steps in finishing your PhD are partially bureaucratic in nature: the preliminary oral and the final oral. These may well be the last two exams of your academic career. This blog post is about the preliminary oral exam and the dissertation proposal.

Depending on your university rules and department traditions, the specific steps in preparing a thesis will vary. Here I talk about my university, which is UCLA, and my department, which is biostatistics and this moment in time, which is late 2018. But please realize: the rules, procedures, and customs surrounding prelim oral exams and dissertation proposals have evolved over the decades. They are not fixed in stone, except by university and department written rules which can and do change. You can expect procedures to evolve over time and to vary by committee and most especially by dissertation advisor.

At UCLA, you have an advisor (you can have two, but one is most common) and you will pick a dissertation committee in conjunction with your advisor. You will prepare a dissertation proposal and hand (or email) it out to your dissertation committee members in advance of the exam. Two weeks in advance is a courtesy (we used to say 3 weeks). The committee meets, you present a talk on your proposal. The committee members ask you questions about your proposal and talk which you answer to the best of your ability. The committee supplies feedback to you and your advisor about your proposed dissertation and makes a decision about you passing or failing the exam.

The preliminary oral exam in our department usually is scheduled to take 2 hours. We’re allowed 3 hours by University policy, but 3 hours is a lot of time for faculty these days, and 3 hours is exhausting for students to present and answer questions. And we haven’t noticed any benefit to the extra hour. So we try to restrict the exam to 2 hours. It is mostly your advisor’s responsibility to make sure the exam runs on time, but you may need to help your advisor out with this. If there are a lot of questions, and usually there are, then you won’t finish all your slides. Not finishing all your slides is possibly the norm, not the exception. You should prepare ahead of time various cuts that you can take without harming the narrative of your talk.

Talk to the student affairs officer to schedule a room for the exam. Often the exam takes place in our biostatistics library, but occasionally it can be an outside room depending on scheduling conflicts. Typically the room is scheduled for half an hour before the exam so that you can set up your computer and display equipment. The exam is for 2 hours, then you have the room for a half an hour to dismantle the equipment, though usually it shouldn’t take that long.

The preliminary oral exam is closed, meaning only the student and the dissertation committee members are allowed in the room during the exam. One committee member who is not the chair or co-chair may connect remotely – I really don’t recommend that – but it is allowed.

Once the committee is fully assembled, the exam starts by you stepping out of the room while the committee meets without you. During this time, the chair will provide an assessment of how it has been working with you. The committee will discuss your academic background, your academic progress, what they can expect from you in terms of progress and development. They may provide feedback on your proposal to your chair. The committee may also discuss the weather. This initial meeting may take from 5 to 20 minutes. When the committee is done, someone will call you back into the room. You will now start presenting. After the presentation and questions are finished, you will step out of the room once again while the committee discusses your presentation and provides your advisor with guidance. After the exam you will typically meet with your advisor to discuss the results of the exam and any comments from the committee.

During your presentation, the committee members will typically interrupt with questions. There are many purposes for these questions:

  • To clarify meaning, as in a short clarification or asking for a definition;
  • To see if you understand what you are saying;
  • To see if you can think on your feet and respond to something different;
  • To see if you understand or are aware of a particular reference;
  • To see if you can extend your work in response to a new idea;
  • To see if you can explain what you just said in a different way;
  • To see if you can answer a question in the middle of a talk.

Students often make assumptions about what the questions mean or imply, but this is usually a mistake and these assumptions are usually incorrect. Do not assume that a question means that the committee member disagrees with something you said. Nor should you assume that the committee member doesn’t understand you, even if the question starts with “I don’t understand …”. An angry question doesn’t actually mean the faculty member is actually angry with you – it more likely means they didn’t get enough sleep the night before or that that is their style. Far and away best is to take the question at face value and answer as best possible.

It used to be that the committee had 5 members, but in these busy times, it has become impossible to get 5 faculty members into a single room at the same time, so the University has reduced the committee size to 4 faculty members. The rules on who can be on a committee are surprisingly complicated. For UCLA’s rules, see https://grad.ucla.edu/academics/doctoral-studies/minimum-standards-for-…. The general intent is to get enough faculty on the committee to supply additional expertise should the student need it, to provide faculty expertise to be able to confirm that your dissertation will be a new contribution to knowledge, and to insure that the committee members have sufficient seniority to provide sensible guidance. At the same time, there is flexibility to find additional expertise if needed, including potentially going outside the university to find a committee member with needed expertise.

The biostatistics department has placed an additional constraint on dissertation committees: one of the faculty members must have a primary appointment from outside the department. This is intended to mean that someone with subject matter scientific expertise is included in the committee, not that someone from mathematics, statistics or computer science is placed on your committee. Our intent is that you explain to someone who is a scientist, and who is specifically not a statistician, what the tools you will develop in your dissertation are, and why they might potentially benefit the scientist. Explaining to a scientist what your tools are teaches you to explain your statistical tools non-technically and requires that you think about the scientist while you work on your dissertation. It’s not enough to say that you’ve improved the root mean square of some estimator – what good will you do for the scientist, and by extension, society with your dissertation work?

The dissertation proposal is a document that you write that tells the committee where you plan to go with your dissertation. This document can take many forms, and it may range in length from arbitrarily short to arbitrarily long, with 40 to 100 double-spaced pages being pretty common. The dissertation proposal has several purposes, though an individual document may not serve all purposes:

  • To show what you know. What you know might be demonstrated by a literature review for example. A long literature review is definitely not required, and is becoming rarer.
  • To show that you can handle the thesis topic. For example, by illustrating the results of example calculations that are similar to what needs to be done in the topic.
  • To show that you can do research.
    • The easiest way to do this is to actually show some novel model or novel results in the proposal.
    • For example, your advisor might have you start working on writing a first paper and that material would then show up in the dissertation proposal.
    • However, you might demonstrate research competence by demonstrating your awareness of past research and knowledge of currently unanswered questions.
  • To show the committee an outline of the future research you intend to undertake in your dissertation. This is the proposal part of the dissertation proposal.
    • This includes any novel research already undertaken that might be given in the proposal.
    • But typically this is unfinished work and is in outline form and shows the committee that you have an idea of where you are going and what you will do.
    • Outline form may mean a paragraph or two on each idea that you propose to execute during your dissertation research.

A very important part of the proposal is where you indicate what is old and what is novel. That is, what old material has already occurred in the literature, and what new material is your own novel work. You will be receiving a PhD because of your novel contributions to biostatistics. If you don’t indicate what is novel and what is old, you cannot expect your committee to understand this distinction. If you don’t indicate what is new, then it becomes up to the committee to figure out what is new, and they might err on the side that everything you said is review. Much easier if you tell them what is new.

In your proposal you should have a section that outlines the planned future research: a “proposal” section. The proposal section will sketch projects that you plan to tackle in your dissertation. I consider it important to have a worked numerical example that shows that you are able to compute with the sort of data and models and methods that you intend to develop in the dissertation. If you’ve submitted your first paper prior to the preliminary oral, you can add an introduction, a non-technical discussion of your paper and proposed additional work, and a proposal section and your proposal is ready for the committee.

Your committee members are expected to read your proposal but might not. If they read the proposal, you can assume they will read or skim the proposal the night before. Thus there is little value in checking in with faculty about issues or comments prior to your talk. Your talk needs to be self-contained, and should not depend on the committee having read the proposal.

The member who is not from biostatistics may well have difficulty reading the mathematical statistical portion of your proposal. So why did the department require you to have a non-statistician scientist on your committee? The reason is that we want you to be able to communicate the value of your statistical research to a non-statistician. Biostatistics has a substantial collaborative aspect to it. Thus, as part of your proposal, you should have a section that explains the value of your work in layman’s terms. This is a courtesy to the outside member of your committee, as well as being important in its own right. Similar, as part of your dissertation, you should have a section or chapter that describes your contributions to biostatistics and science in non-technical language. This section should be completely accurate but not rely on mathematical notation or technical statistical jargon to make its points.

A dissertation can take many forms. A common form that is increasingly popular is to write three separate research papers and then bind them in dissertation format and submit these as the final dissertation. This is not required, and is decided upon primarily by the advisor, with input from the student and possibly the committee. The dissertation is supposed to be publishable, but if one merely writes a dissertation that contains three publishable ideas then it can take a long while to turn the dissertation into the three papers. In contrast, if one writes three papers, then it is quite quick to turn three papers into a dissertation, taking perhaps a few weeks at most, with time mostly spent on formatting your papers into the UCLA dissertation style. For students interested in academia these days, substantial ability to publish must necessarily be demonstrated, so having a good CV out of grad school with a number of publications published or in submission is necessary. The three papers model of a dissertation is required for those students. Similarly, many faculty require the three papers model. I assume this model for the dissertation in the remainder of this discussion.

When writing your proposal, there are a number of technical issues. You may be learning LaTeX, or even if you know LaTeX, you will need to learn new features to format your proposal properly. Similarly, you need to learn bibtex to format your bibliography.

You may not be used to reading technical papers in the statistics literature and you need to start doing this immediately. Those papers can be models for what your dissertation papers will be like. Further, these papers illustrate how to write technical material. Some papers are better written than others, so learn to be critical so that you can learn to write well. Well written technical prose is a signal to the reader that you take your job seriously as an author and it signals that your underlying work may well be worth their time to read. Similarly, formatting your text properly flags to the reader that you take your job of presenting your work seriously and that the underlying work is worth the reader’s time to read. Not formatting your proposal properly signals to your committee that either (a) you don’t take your work seriously, or (b) you don’t understand your tools (LaTeX and English) very well. Or both. And either of those highly correlates with weak or bad statistics. [Bad = wrong, weak = very little new.]

I have read a large number of papers submitted for publication in my lifetime. Poorly written usually (not always, but usually) translates to uninteresting work and it certainly can mean unintelligible. Similarly, in submitting a paper for publication, sloppy formatting is a strong indicator to me that the underlying material is not publishable. Editors of journals have choices of many papers to publish. They don’t mind if they don’t publish the next great paper from you, because they can publish many other people’s next great paper. If you don’t take your work seriously, why should they take your paper seriously? Also, refereeing a statistics paper is hard and if they can take a short cut by recognizing that the paper is poorly written and formatted, they may reject a paper without making a serious determination as to the quality of the underlying work.

The goal of a paper is to communicate new methodology. Similarly the goal of your proposal is to communicate to your committee that you can write a dissertation. The skills needed to write a good proposal will translate to writing good papers and to writing a good dissertation. So take the formatting seriously and take the writing seriously. At the same time, once the preliminary oral exam is over, and assuming you passed, then the proposal is of little interest to anybody. The amount of work in the proposal that you can re-use in the dissertation and in your papers translates to time saved. Hence the advantage of the form where most of the proposal is a start on your first submitted paper. But any time spent on learning to format the proposal is time well spent. And time spent on learning to write technical prose is time well spent. You will spend your life writing technical prose. The better you write, the more useful you will be to your employer, whether you end up self-employed, a professor or go into industry or government.

The preliminary oral exam is a pass-fail exam. The purpose is to confirm that you can do research, that the research topic you have chosen is worth researching, and that you can do the project. The committee will advise your advisor or you on whether you are proposing to do too little or too much, or that the project is too hard for you.

There are many resources on the web about preliminary exams and proposals. The statistics department at UCLA has a nice discussion of the oral exam at http://answers.stat.ucla.edu/groups/answers/wiki/abdb2/Taking_the_Oral_… and a quick check of google finds many resources at UCLA and around the United States.

Good luck!

How to Prepare for a PhD in Biostatistics

How would you advise an undergraduate interested in a PhD to prepare for studying biostats?

More math. You can't be too rich or know too much math. In terms of courses, take more mathematics, take as much as you can. When you get into our biostatistics graduate program, we teach you statistics, so taking more statistics now won't help you in the long run, plus we may have to un-teach something if you learn statistics badly.

In terms of what math courses to take, try to take real analysis. Advanced linear algebra is very helpful. Every part of math is useful somewhere in statistics, though connections may be obscure, or more likely, just not part of some current fad. Numerical analysis and combinatorics are also helpful, and everything else is helpful too. First though is Real Analysis and Advanced linear algebra.

What might I read outside of my courses?

Start picking up books and articles that relate to statistics, math, science, and public health and read them. Lately there have been a number of excellent popular science books that relate to science, statistics and statistical thinking. Anything and everything by Malcolm Gladwell I highly recommend. Other books that come to mind are things like Stephen Jay Gould's essays; The Signal and the Noise by Nate Silver; The Theory That Would Not Die by McGrayne; any of the books by Jared Diamond. A very important book for most anyone: Thinking, Fast and Slow by Kahneman. The Black Swan by Nassim Taleb; How to Lie With Statistics by Darrell Huff.

For someone not yet expert in statistics, books on statistical graphics are directly statistical and will be much more accessible than a technical book. Books on statistical graphics will directly make you a better statistician, now. These teach you both to look at data and how to look at data. There's a set of 4 books by Edward Tufte. See http://www.edwardtufte.com/tufte/ . Get all four, I recommend hardcover over paperback, and definitely I wouldn't get the e-books. Read all four! These are not always practical books, but they inspire us to do our best and to be creative in our statistical and graphical analyses.

Read Visualizing Data by William Cleveland (A+ wonderful book). Additional graphics books include Graphical Methods for Data Analysis by Chambers Cleveland Kleiner and Tukey if you can find a copy. A more statistical book that will help instill the proper attitude about data is Exploratory Data Analysis by Tukey.

Read anything else you can find to read. Read widely and diversely. As you get stronger in math and statistics, change the level of the books. Start exploring the literature. Dive into one area and read as much as you can. Then find another area and check it out.

Can I depend on the department to teach me everything I need to be a good statistician?

Of course not. Active learning is paramount. No graduate department will teach you everything. All departments teach a core set of material, and it is up to students to supplement that core with additional material. How you supplement that core determines what kind of statistician you can be, how far you can go. Some people might supplement their core material with an in depth study of non-parametrics; others with Bayesian methods, statistical computing, spatial data analysis or clinical trials. I supplemented my graduate education with statistical graphics, Bayesian methods, statistical computing, regression methods, hierarchical models, semi-parametric modeling, foundations and longitudinal data analysis. The semi-parametric modeling, graphics and computing mostly came from books. The longitudinal data analysis came from a mix of books and journal articles. Bayesian methods and hierarchical models I learned mostly from journal articles. Foundations came from talking to people and listening to seminars, as well as from journal articles and books. I also tried to learn additional mathematical statistics using various texts, but wasn't very successful; similarly with optimal design.

How you supplement your education depends on your interests and may help you refine your interests. I found I wasn't that interested in time series, survey sampling, stochastic processes or optimal design. If you're interested in working with a particular professor, you're going to need to supplement with books in her/his area, and you're going to need to read that professor's research papers to see what you're going to be getting into.

What programming language(s) should I learn?

R is growing fast and may take over, sort of like kudzu, so it is well worth your time to become expert in it. Definitely learn/use R Studio. Some folks make a living just off their R expertise. A lot more make a living off their SAS expertise. But I bet the R people are having a lot more fun. The rest of this is what I garner from others, not from direct knowledge. If you want less of a statistics specialty language and to be closer to the computer end of things, C++ or JAVA are extremely popular (you should figure out why). Python seems to be coming on very strong. So maybe R and Python? Depends on what you like. Learn something about algorithms and something about modern computer programming interfaces. And a little HTML.

Go learn latex now. Become at least a partial expert. Knowing latex before you come in to grad school is very helpful.

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