STATISTICS 536B (201415, Second half of Term 2)
Statistical Theory for the Design and Analysis of Clinical Studies
(1.5 Credits)
Lectures:
Tuesday and Thursday, 9:3011:00, ESB 4192, starting Feb. 24th.
Instructor:
Paul Gustafson, ESB 3128, gustaf at stat dot ubc dot ca.
Course Website (this page):
www.stat.ubc.ca/~gustaf/stat536.html
Prererequisites:
Open to any interested graduate students in the Department of Statistics.
Note that 536A (taught by Lang Wu in the firsthalf of the term) is
not a prerequisite. (But Lang and I are coordinating to avoid
excess repitition across the two courses.)
Graduate students from other departments
are welcome,
provided they have sufficient statistical and mathematical
backgrounds. Such students should consult the instructor about
suitability.
To be clear, this course is primarily aimed at training statisticians or
biostatisticians, so understanding the math and computing
behind the methods is a key part of the course. It isn't an appropriate
course for someone who just wants to use statistical methods;
it's for
students seeking conceptual and theoretical understanding of methods and models.
Against that, though, note that the Plan B evaluation scheme described below is offered to make
the course more accessible to students from other units.
Description:
While the official calendar title of this course is "Statistical theory for the design and analysis of clinical studies,"
a more apt title might simply be "Topics in Biostatistics."
The planned topics cleave nicely into three groups. So the plan
is to use our 13 lecture sessions to cover:

Slicing and dicing exposuredisease data (4 lectures): issues of retrospective versus prospective study data, dichotomization, stratification, and
matching.

Metaanalysis (3 lectures): key concepts of statistically combining results from multiple studies.

"Modern" causal inference (6 lectures): propensity scores, instrumental variables, models based on counterfactuals, timedependent confounding.
In each of these three units we will try to survey a few key statistical ideas that
seem most exciting and relevant.
Lecture Format:
For a typical lecture:

There will be some reading assigned beforehand.

By 6pm the day before the lecture, each student will email me a very brief comment or question indicating something they found surprising or confusing or interesting in the reading.

The lecture will start with review and emphasis of selected
ideas from the reading
(perhaps 30 minutes).
Note that for each lecture I will post
skeletal slides
in advance, and we will ``flesh these out'' in the lecture.

Then we will break into small groups (freshly randomized each time),
to work on a discussion or brainstorming or problemsolving activity
(perhaps 20 minutes).

Then I'll present some further material (perhaps some Rbased empirical
work) pertaining to one of the topics at hand
(perhaps 25 minutes).
Textbooks:
Nothing to buy!
I will try to keep things relatively simple by assigning readings
only from titles that the UBC library has in its ebook
collection. The following list
may be ammended as we progress:

Vittingoff, Glidden, Shiboski, McCulloch.
Regression methods in biostatistics: Linear, logistic, survival, and repeated
measures models.
(2nd Edition)
Springer, 2012.

Steyerberg.
Clinical prediction models: A practical approach to development, validation,
and updating.
Springer, 2009.

Lachin.
Biostatistical methods: the assessment of relative risks (2nd. Ed.).
Wiley, 2011.

Holford.
Multivariate methods in epidemiology.
Oxford, 2002.

Borenstein, Hedges, Higgins, Rothstein.
Introduction to metaanalysis.
Wiley, 2009.

Chen, Peace.
Applied metaanalysis with R.
Chapman and Hall, 2013.

Hernan, Robins.
Causal inference (prepublication version).
Evaluation: Plan A (mandatory for STAT students)

Participation  20%: This includes the prelecture emails, participation in the inclass activities, and general participation in the lecture discussion. You will get credit here for reasonable participation  you don't need to
try to outparticipate your classmates, and it would be a mistake to replace quality of participation with quantity.

Assignment Questions  40%:
Problems will be set as we go along,
likely to be collected and graded in two batches.

Final assignment in lieu of exam  40%:
This will be due near the end of the exam period.
It will involve preparing a short report on a topic that we didn't
have time to cover in the lectures.
Evaluation: Plan B NonSTAT students may elect to replace the
Assignment Questions with a (second) short report on a topic we didn't have
time to explore fully in the lectures. This is in recognition that students from other units may not have the same level of math/stat/computing backgrounds.
Prelecture versions of slides
Evolving schedule of readings for lectures:

Tuesday Feb. 24: Vitingoff, Sections 5.1 through 5.3.
(The earlier parts of this may well be review  so
feel free to just start reading at whatever point some of the ideas
become new to you.)

Thursday Feb. 26: Steyerberg, Ch. 9 (quite a short chapter).

Tuesday Mar. 3: Holford, section on stratified analysis (bottom of p. 63
through to middle of p. 73)  but focus on what goals are trying to be achieved,
not on the formulae.

Thursday Mar. 5:
Holford, first part of Ch. 10 (pp. 253261).

Tuesday Mar. 10:
Borenstein et. al., Ch. 13 (don't worry  these are *very*
short and nontechnical chapters).

Thursday Mar. 12:
Algra and Rothwell (2012, Lancet Oncology)
(access via UBC library,
we are reading this to see metaanalysis ``in action'').

Tuesday Mar. 17: NOCLASS (PG outoftown)

Thursday Mar. 19:
Dias et al (2013, Medical Decision Making)
(access via UBC library,
just skim, looking for a flavour of what `indirect comparisons
and network metaanalysis' are about).

Tuesday, Mar. 24.
No advance reading, as I'd like to introduce propensity scores
without any preconceptions.

Thursday Mar. 26.
To get some flavour for propensity scores in practice, take a quick look at
Mongelluzzo et. al. (2008, JAMA)
(access via UBC library).

Tuesday, Mar. 31.
No advance reading,
as I'd like to introduce instrumental variables
without any preconceptions.

Thursday, Apr. 2.
Hernan and Robins, Ch. 1.

Tuesday, April 7.
No advance reading  the plan is to introduce the notion of "mediation,"
whereby an exposure can have both "direct" and "indirect" influence on the outcome.

Thursday, April 10. No *formal* advance reading, i.e., no need to send an
email. But if you get a chance to skim
this short discussion piece, or at least can bring a copy to the lecture,
then you may get more out of the lecture.