Present: Marianne Burke, Nancy Gell, Kairn Kelley, Ayodelle LeBruin, Ben Littenberg, Connie
van Eeghen
Start Up: Family drama is everywhere.
1.
Discussion: Nancy
Gell: Cancer Survivorship Exercise Adherence
a. Nancy
provided preliminary findings from analysis of “steps to wellness” data base
b. The
underlying issue: how would you intervene during Oncology Rehab to improve
later adherence?
i.
What are the characteristics of the program that result
in adherence 3 months after discharge?
1. Age:
not a predictor of interest; may be a confounder. May indicate a need for a different
intervention.
2. Attendance:
a variable that measures interaction of both patient and program
3. Program:
doesn’t vary
c. Research
question: what are the characteristics of patients that predict long term
exercise
d. Analytical
plan:
i.
Use data at the start of STW as potential predictors
ii.
Consider data at the end of STW as potential predictors
as well
iii.
Outcome data are limited to the 79 patients that
completed the 3 months post data collection process
1. Missing
~100 people’s data; that’s a lot to assign a “zero” score to
a. The
missing data are not missing randomly
b. They
did not finish the program (Baseline 288 – 162 = 126)
i.
Many are likely to be non-exercisers
ii.
Did not complete yet? These were taken out.
c. They
finished the program but could not be found (154 – 79 = 85)
i.
Did they die?
2. 54%
of the 79 were exercisers: 43; 43/288 = 15% long term exercisers (Meets the PA
recommendation); or 1 in 7 patients is a success
3. If
the program costs $100/person, then a success costs $700
iv.
But we do know that whether the 288 met PA
recommendations; for those that don’t have data afterwards, they are assigned
their baseline status
v.
With the 10:1 rule, there is room for about 5
predictors
1. 1
variable for every 10 positive patient outcomes
vi.
Build a descriptive model and find the most strongly
associated variables
1. Age,
sex, timing BMI, CA type (Table 1 characteristics), also including fatigue,
PHQ9
a. Eliminate
those for which no action is possible from a programmatic perspective
b. Outcomes:
meets PA recommendations or GLTEK
2. Identify
most likely predictors
3. Sub-analyses:
sub groups
4. Univariate
analysis (loss of interactions)
e. Come
back with next step!
a.
August 14: Kairn: Update on manuscript draft
b.
August 21: Marianne: Update
c. August 28: Nancy: Preliminary findings from analysis
of “steps to wellness” data base
d.
Future: Connie’s Pfizer application and reviewer
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