Harvard School of Public Health
Methods for Comparative Effectiveness Research
Spring 2012
Thursday 3:30-5:30PM
Course Description:
Comparative
effectiveness research (CER) is designed to inform health-care decisions by
providing evidence on the effectiveness, benefits, and harms of different treatment
options. The evidence is generated from research studies that compare drugs,
medical devices, tests, surgeries, or ways to deliver health care. This course
will introduce students to statistical issues in the design and analysis of
comparative effectiveness studies. Topics will range broadly and will include
causal inference, decision analysis, multilevel models, chronic disease
modeling and more. The format will be that of a reading group. After a few
initial overview lectures, the group will identify a set of papers of interest,
drawing both from the statistical and medical literature. Students will take
turns being the primary reviewer for a paper, though in each session all
students are expected to independently and proactively engage in a critical
evaluation of current approaches and methodologies.
Instructors:
Giovanni
Parmigiani
Professor
of Biostatistics, Harvard School of Public Health
Chair, Department of Biostatistics and Computational Biology, Dana-Farber
Cancer Institute
CLSB
11042, 3 Blackfan Circle
James
Robins
Mitchell
L. and Robin LaFoley Dong Professor of Epidemiology
Department
of Epidemiology and Biostatistics
Kresge Building Room 823
Course Materials:
The course will be a
combination of instructor, student, and guest lecturers. Each speaker will
provide reading materials relevant to their particular topic in advance of each
class. Students will be expected to have completed the reading prior to the class.
Class Participation
Active learning through class participation and discussion are an
important component of the course. Students are expected to attend and participate
in all classes.
Written Assignments
Homework:
Prepare brief written assignments that critically evaluate select epidemiologic
studies.
There will be six (6) homework assignments. Each assignment will be
graded on a scale of up to 10 points each. Required format: All homework
assignments must be 12 point font or larger and 2 pages or less (single spaced
and 1Ō margins).
The assignment is due at the start of class on the date due. If you will
miss a class, the assignment is due before the class. No assignment may be
handed in late.
You must individually write your own answers to the homework
assignments. You may, and are encouraged, to work together in groups to discuss
the homework readings.
Project
:
You will be asked to work in small groups on the synthesis of
epidemiologic studies in a defined topic area. The project includes an in-class
presentation. In addition, it is expected that after the presentation, there
will be in-depth in-class discussion on key aspects of the papers within the defined
topic area.
Please see detailed description on next page. The class project is worth
20 points. The grade is based on the group presentation, the post-presentation
discussion (i.e., your groupÕs ability to ask and respond to relevant questions
on epidemiologic methods), and a 2 page fact sheet
that is handed out at the start of class on the day your group presents.
Examination:
There is an in-class examination worth 10 points. 50% of the questions
will be on concepts discussed during the in-class project presentations and 50%
of the questions will be on concepts discussed during the in-class
modules/lectures.
Grading Criteria
Grades are based on a total of 90 points.
There
are a total of the six (6) homework assignments (up to 60 points total) plus
the grade on the class project (up to 20 points) and the in-class examination
(up to 10 points).
Course Evaluations:
At
the end of the course, remember to complete a course evaluation in order to
receive your grade.
Class Schedule Spring 2012
|
January 26 |
First Class |
Introduction |
|
February 2 |
Jamie Robins |
Analyzing Observational Studies Like Randomized Trials |
February 9 |
Cory Zigler |
Causal analyses with surrogate outcomes, and related issues |
|
February 16 |
Giovanni Parmigiani |
Modeling in Medical decision Making: A Bayesian Approach |
|
February 23 |
Deborah Schrag |
Overview of
CER in the Context of Health Care Reform; A CER Example; and Observational
vs. Experimental CER Designs |
|
March 1 |
Jamie Robins |
Conflict between the randomized trials and observational
studies of the effect of postmenopausal hormone therapy with combined
estrogen progestin on heart disease |
|
March 8 |
Giovanni Parmigiani |
A network meta analysis case study |
|
March 15 |
Spring Break |
No class |
|
March 22 |
Xabier Garcia-De-Albeniz |
Examples of how to address relevant clinical questions
using observational data |
|
March 29 |
Sandra Lee |
CISNET and the mammography controversy |
|
April 5 |
Sebastien Haneuse:
|
On the use of EMR data when addressing questions of
comparative effectiveness |
|
April 12 |
Sharon_Lise Normand |
Methodological Needs for Conducting CER Research |
|
April 19 |
Lauren Kunz |
Network meta-analyses: An application to resynchronisation and implantable defibrillator therapy |
|
April 26 |
Sonia
Hernandez-Dias |
Case study in pharmoco
epidemiology and comparative effectiveness |
|
May 3 |
Alan Zaslavsky |
Using cancer registry
data with underreporting to assess quality of care |