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American College of Surgeons Oncology Group
 

Outcomes Research Course

2008 Preliminary Course Schedule

 

Day 1 – Friday, November 14, 2008

Overview of Outcomes Research

7:00 – 8:00 am Breakfast
8:00 – 8:15 am

John Birkmeyer
Lecture #1: Introduction

  • Methodologies, tools, and subcultures
  • Course overview
8:15 – 8:40 am

Nancy Birkmeyer
Lecture #2: Observational studies: Principles

  • Study design
  • Chance, bias, confounding, generalizability
8:40 – 9:30 am

David Flum
Lecture #3: Observational studies: Practice

9:30 – 9:45 am Q & A and Discussion
9:45 – 10:15 am Coffee Break
10:15 – 12:00 pm

Breakout: Critiquing surgical outcomes studies

12:00 – 1:00 pm

Lunch

1:00 – 1:30 pm

David Urbach
Lecture #4: Administrative databases

  • Medicare, HCUP, VA, and other data sources
  • Use and misuses of administrative data
1:30 – 1:50 pm

Nancy Birkmeyer
Lecture #5: Building your own clinical registries

  • Designing, implementing, and maintaining registries
1:50 – 2:05 pm

Clifford Ko
Lecture #6: Using ACS Clinical Registries for research

  • National Trauma Database and National Cancer Database
  • Getting access and things you should know
2:05 – 2:20 pm

Matthew Hutter
Lecture #7: ACS NSQIP

  • Getting access, sampling and data elements, pitfalls
  • Future changes in ACS NSQIP
2:20 – 2:40 pm Q & A and Discussion
2:40 – 3:00 pm Break
3:00 – 5:00 pm

Justin Dimick
Lecture and lab: Intro to data analysis I

  • Biostatistics review
  • Creating analysis datasets
  • Data analysis with STATA
6:00 – 7:00 pm Group Reception

Day 2 – Saturday, November 15, 2008

7:00 – 8:00 am Breakfast
8:00 – 8:30 am

Nancy Baxter
Lecture #8: Health-related quality of life: Theory and practice

8:30 – 9:00 am

Samuel Finlayson
Lecture #9: Decision analysis/Cost-effectiveness analysis

9:00 – 9:30 am

David Urbach
Lecture #10: Meta-analysis

9:30 – 9:45 am

Q & A and Discussion

9:45 – 10:15 am

Coffee Break

10:15 – 12:00 pm

Justin Dimick
Lecture and lab: Intro to data analysis II

12:00 – 1:00 pm Lunch
1:00 – 3:00 pm

Samuel Finlayson and Janice Cormier
Skills lab: Decision analysis I

[Building on key concepts in the Overview lecture, this skills lab will introduce participants to TreeAge decision analytic software and demonstrate basic techniques for building and analyzing a decision tree.]

1:00 – 3:00 pm

David Urbach
Skills lab: Meta-analysis

[Building on key concepts in the Overview lecture, this skills lab will introduce participants to RevMan software and practical aspects of conducting a meta-analysis.]

1:00 – 3:00 pm

Module: Participant Research in Progress I

3:00 – 3:30 pm

Break

3:30 – 5:30 pm

Samuel Finlayson and Janice Cormier
Lecture and skills lab: Advanced decision analysis

[Recommended for participants already familiar with basic decision analysis, this module will introduce more advanced decision analytic tools, including Markov models.]

3:30 – 5:30 pm

Nancy Baxter
Module: Health-related quality of life assessment

[In this session, course faculty will focus on practical aspects of instrument selection and development, as well as issues related to analysis of HRQOL data.]

3:30 – 5:30 pm Module: Participant Research in Progress II
6:00 – 7:00 pm Dinner

Day 3 – Sunday, November 16, 2008

7:00 – 8:00 am

Breakfast

[Interactive sessions with Course Faculty presenting their own ongoing research as a platform for reinforcing concepts presented earlier and helping participants “get under the hood” of mature, NIH-funded research projects.]

8:00 – 8:40 am

Justin Dimick
Faculty research in progress:
“Developing composite measures of surgical quality”

8:40 – 9:20 am

David Flum
Faculty research in progress
Title TBA

9:20 – 10:00 am

Martin Makary
Faculty research in progress
Title TBA

10:00 – 10:30 am

Coffee Break

10:30 – 11:10 am

Janice Cormier
Faculty research in progress
Title TBA

11:10 – 11:50 am

John Birkmeyer
Faculty research in progress
“Measuring and improving surgical efficiency”

Adjourn

 

Outcomes Research Course

Outcomes Research Course Faculty

 

Revised June 25, 2008

 

Continuous Quality Improvement


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