Working with a Statistician
How and Why
Karen Pieper
Associate Director of Clinical Trials Statistical
Operations
Duke Clinical Trials Institute
Why?
Pre-Lecture Examination: A key reason to
know a statistician is: (True or False)
Question 1
They will be the life of your next party.
Question 2:
Statisticians display the latest in fashion.
Therefore interacting with one helps keep
you up-to-date in what to wear.
True
Question 3:
Statistical support can make your grant
proposal or manuscript stronger.
TRUE

Estimating the value of an internal
biostatistical consulting service –Statist Med
2000; 19:2131-2145:

“our biostatistics group returns more than
$6 for each dollar spent in institutional
support in 1998”
8
Question 4:
Statistical Support can help insure that your
results are correct and valid.
TRUE
Some of the Key Steps in Performing Research
1.
Develop Hypotheses
2.
Write Grant / Protocol
3.
Develop Study Materials
4.
Perform the Experiment
5.
Collect the data
6.
Analyze the data
7.
Share results
1. Develop Hypotheses
Drug A will be better at decreasing myocardial
infarctions than Drug B
1. Develop Hypotheses
Drug A will be better at decreasing myocardial
infarctions (MIs) than Drug B
1. How do you define a myocardial infarction?
2. How do you define better; at least a 20% decrease, an absolute
difference of at least 5%,… ?
3. What is the time frame for developing an MI?– 30 days, 5
years…?
4. What in what sort of patients do you expect to see Drug A work
better than Drug B?
5. What about patients who die? Will you count these as MIs?
2. Write Grant / Protocol
1. Sample Size Calculations- Is it ethical to
expose subjects (animals, patients, healthy
humans…) to an experimental treatment if you
have little to no possibility of answering the
question of interest.
2. Statistical methods section
3. Definitions of key factors
4. Review
3. Develop Study Materials
1.
Randomization Scheme
2.
Data Collection Tools
1. (True, TRUE, TRU, T, t, 1)
3.
Instructions
4.
Database creation
What should a statistician have picked up?
What should a statistician have picked up?
Appears to be w.r.t. CABG
6. Analyze the data
Example:

A clinical trial evaluated the treatment
effect of a new drug (A) versus placebo
(P) in ACS patients. The primary
endpoint of the trial was 30-day death or
MI. Of special interest was the
effectiveness of the new drug in
patients who had received a PCI versus
those who had not.
Sample Patient 1
Randomization
PCI
Death or MI
30-day
Assessment
Sample Patient 2
Randomization
Death or MI
30-day
Assessment
Original Table
Incidence of 1 Endpoint
Eptifibatide Placebo
PCI < 72 hours
P
Odds Ratio
(95% CI)
(N = 606)
(N = 622)
96 hours
57 (9.4)
95 (15.3)
0.002 0.576 (0.406, 0.817)
7 days
62 (10.2)
100 (16.1)
0.003 0.595 (0.424, 0.835)
30 days
70 (11.6)
104 (16.7)
0.010 0.650 (0.469, 0.901)
No PCI < 72 hrs (N = 4116) (N = 4117)
96 hours
302 (7.3)
334 (8.1)
0.188 0.897 (0.763, 1.055)
7 days
415 (10.1)
452 (11.0)
0.185 0.909 (0.790, 1.047)
30 days
602 (14.6)
641 (15.6)
0.232 0.929 (0.823, 1.048)
PCI = percutaneous coronary intervention; CI = confidence interval
Pieper, KS. Tsiatis AA. Davidian M et.al., Circ. 109 641-646, 2004
Sample Patient 3
Randomization
MI
PCI
30-day
Assessment
New Table
Time Interval
Eptifibatide
(N = 606)
Placebo
(N = 622)
Absolute
Reduction
P-value
Before PTCA
Death/MI
1.7%
5.5%
3.8%
< 0.001
96 hours
Death/MI*
8.1%
10.9%
2.9%
0.090
7 days
Death/MI*
8.9%
11.7%
2.8%
0.105
30 days
Death/MI*
10.2%
12.4%
2.2%
0.235
*Composite only includes myocardial infarctions (MI) occurring after the percutaneous intevention
Pieper, KS. Tsiatis AA. Davidian M et.al., Circ. 109 641-646, 2004
Logistic Regression Example:
The Linearity Assumption
0.0
Logit
-1.0
-2.0
-3.0
-4.0
32
48
64
80
96
112
128
Weight (kg)
144
160
176
192
Logistic Regression Example:
The Linearity Assumption
0.0
Logit
-1.0
-2.0
-3.0
-4.0
32
48
64
80
96
112
128
Weight (kg)
144
160
176
192
7. Share results
TMQF Committee Formation and Charge

Duke Chancellor for Health Affairs Victor Dzau, MD,
convened an internal Duke committee to review
institutional approaches to assure the quality of
discovery science destined for clinical application.

A panel of external experts was also convened to
review the recommendations of the TMQF
Committee.

The report is now available at
http://medschool.duke.edu/modules/som_research/in
dex.php?id=22.

Faculty and staff comments are welcome at
tmqffeedback@duke.edu
http://medschool.duke.edu/modules/som_research/index.php?id=22
How-
Timing

When do I consult a statistician?
 It’s never too early

Have realistic time expectations
 It takes more than a couple of days
to prepare an abstract or grant.
Statistician may not be free to help
on the day you call for information.
Be Involved in all Aspects of the Research
Choice of Methods
Don’t be wedded to the method that you just
learned about or that is usually used in the
literature.
Remember the Question

Fishing expeditions are
expensive.

The more time you
take, the fewer papers
the statistician can
work on.

Remembering the
hypothesis makes the
paper manageable
Timeliness
Every time a project is put away, extra
time is needed to refresh one’s
memory about the project and find all
relevant documents / programs.
When Statisticians love working with investigators
Enthusiastic about Research
Appreciative
Inquisitive
Take time to share knowledge
B&B Organization

Most faculty affiliated with specific Center, Institute,
Department
 17 with DCRI
 13 with Cancer Center

3 with VA/DGIM

1 with IGSP (about to expand)

2 with Aging Center

2 with Radiology Department

Many co-located with clinical colleagues
 Good mix of disease specific and methodological
research
 Expertise in medical subject area leads to statistical
issues to be resolved
Some areas of expertise

Clinical trials design and analysis




Early phase studies
Adaptive designs
Sequential analysis
Surrogate endpoints

Pharmacokinetics/pharmacodynamics

Bioavailability/bioequivalence
Some areas of expertise (cont)

Health Services Research




Cost-effectiveness analysis
Decision modeling
Causal inference
Predictive modeling/Provider performance

Genetics, genomics, proteomics,
metabolomics

Biomarker development
CTSA Biostatistics Core

Established with funding from DTMI/CTSA

Partially funds several B&B faculty members and some masters
statisticians to work with those needing statistical support
(currently)

Original Goal:
 Investigators paired with statisticians develop relationships
 Partnership breaks off from the Core with other funding

Slightly revised and expanded incubator concept:
 Direct more of the Core funding toward education
 Continue to allocate faculty and staff effort to the Core
 “Contract” with SBRs/Depts/Centers/Institutes for percent effort
and associated expenses
 Goal is still to develop productive relationships
Contacts
 Diana.abbott@duke.edu
 Yuliya.lokhnygina@duke.edu
 Elizabeth.delong@duke.edu
Points to Remember

Research is rewarding and fun, and requires
statistics

Statistics is fun (contrary to preconceived
biases)

Clinical research is a “contact sport”—it
requires contact and collaboration with
many people, including statisticians.

All types of research also needs statistical
support for all phases of the research

Make friends with a statistician. You’ll find it
will be helpful in the future