Propose a potential case of confounding or effect modification. Please do not use examples from SPH courses or published studies straight from literature/internet. Consider instead examples from your pasts experience or present research.
● State the research question clearly using words either for an EM situation or a confounding situation. Define the outcome, independent variable of interest, and the potential effect modifier(s) for EM or confounder(s) for confounding.
o For the effect modifier(s) (EM)–Qualify the consequences introduced when EM is not accounting for, using figures to illustrate as needed. Explain clearly how to investigate effect modification using appropriate regression methods.
o For the confounding variable(s) (C)–Qualify the consequences introduced when C is not adjusted, using a causal diagram, and figure to illustrate as needed. Explain clearly how you would examine the confounding using appropriate regression methods.
Group members are to come up with one common example together through discussion as a team. However, the project is to be submitted individually with a cover page that has your name and your group’s name. While the example is the same within a group, I do expect that presentation and description are different among group members. It is not a data analysis project rather an exercise of abstract thinking and conceptualization.
Use a cover page. Print your and your group’s name on the cover page. For the project report, there is a maximum two-pages limit including figures/tables. Please use 12pt font and double line space.
Clarity and creativity in the presentation are part of grading considerations. Exceptional leadership and/or noncontributing of the group members should be noted in your report. The best team(s) may have the opportunity to present their project in class (10 minutes with Q and A).
(4% of total)
Group 7:
Qualify the consequences introduced when C is not adjusted, using a causal diagram, and figure to illustrate as needed:
How to examine confounding effects using regression method:
Each group should create a name for your group for identification purposes.
Project description:
● P: adolescents who experienced knee pain for one month or more
● E: knee pain for more than one month (pain levels/categories, low, moderate, high)
● C: adolescents who did not experience knee pain
● O: physical activity (minutes/week) at 6-month and 12-month
Cohort study: followed up for 1-year
Research question: What are the effects of knee pain on physical activity?
Outcome: physical activity (minutes) -continuous; Multiple Linear Regression
- Main exposure of interest: levels of pain (low/moderate/high) (Alex: Perhaps we should give the pain scale that we are going to use, and a sample of how we are going to subdivide the scores into the categorical variable proposed here - Brief Pain Index, Pain and Disability Index, Visual Analog Scale - VAS is a popular one with kids. Or did you guys have another one in mind)
- Independent/explanatory variables: sex, age (continuous), bmi (continuous), kinesiophobia/stress, seasonality of physical activity (summer/winter)
Potential EM(s)/confounders: seasonality of physical activity[1] : winter vs. summer (categorical) (effect modifier); summer as the reference group (Alex: Confounders - depression/anxiety, pain catastrophization behaviors, enrollment in a formal recreational exercise/sports program. Potential EM - the presence of structural MSK issues (genu valgum, external tibial torsion, etc...)
Alternative
Population: Adolescents with knee-pain
Exposure: physical Activity
Outcome: Knee pain
Figure : DAG system
Analysis: Multiple Linear Regression STATA commands: xi regress
How to assess effect modifier:
● Test the effect of the interaction between seasonality of and knee pain on physical activity
● Summer vs. winter as the two strata (outcome in summer vs. outcome in winter) rationale on the association between seasons and physical activity using existing research studies
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