1. Lecture 4: Introduction to confounding (part 2)
Jeffrey E. Korte, PhD
BMTRY 747: Foundations of Epidemiology II
Department of Public Health Sciences
Medical University of South Carolina
Spring 2015

2. Causal inference
(go through Rothman examples of sufficient causes, necessary causes, component causes)

3. Different reasons for confounding
Upstream causal factor independently related to outcome of interest
Confounding by severity (“indication”)
Education, income, exercise
Non-causally associated factor independently related to outcome
Smoking confounds alcohol-lung cancer
Cohort effects; confounding by age

4. Upstream causal factor:
exposure
disease
confounder

5. Upstream causal factor:
stress
preterm delivery
exercise

6. Upstream causal factor:
preterm delivery
exercise
income

7. Upstream causal factor:
preterm delivery
prenatal care
income

8. Upstream causal factor:
preterm delivery
prenatal care
anxiety

9. Upstream causal factor:
preterm delivery
prenatal care
vigilance

10. Upstream causal factor:
Example: confounding by indication
Putative exposure is delivered by health care system in response to some other risk factor for the outcome of interest
In this case, putative exposure may not be in the causal pathway at all

11. Example: confounding by indication:
preterm delivery
bed rest
PROM

12. Confounding by severity
Exposure might not be something received from doctor; might be something influenced by upstream health status
Example: evaluating association between exercise and MI, or weight loss and colon cancer
Exposure of interest may be in causal pathway, but association is partially due to upstream causal relationships

13. Example: confounding by severity:
heart attack
exercise
subclinical CVD

14. Example: confounding by severity:
incident cancer
exercise
subclinical pre-cancer

15. Non-causal associated factor:
Example: Cohort effects (an interaction between age and calendar time)
Cross-sectional studies are vulnerable
If study is really longitudinal (decades), it may be crucial to separate cohort effects (birth cohort) from confounding by age
Examples: Szklo section 1.2
(Spend time going through examples)

16. Non-causal associated factor:
Note: confounding by age per se is separate from cohort effects and period effects
Age is predictive of many diseases
But age does not causally determine many exposures

17. Residual confounding
Uncontrolled confounding of observed association, despite an attempt to control for the confounder
Occurs when:
Your variable is a poor proxy for the “true” confounder
Confounder is misclassified or miscategorized

18. Residual confounding example 1
Variable is poor proxy for confounder: this is a lack of “construct validity”
e.g. analysis of race and CVD, adjusting for education in an attempt to control “social class”
Residual racial differences in wealth, income, access to goods, access to social networks, etc. may exist within educational categories
The differences produce residual confounding

19. Residual confounding example 1
For this example: assume there is no independent relationship between ethnic group and CVD risk
Assume any observed ethnic disparities are due to differences in income
Income data not available
Try to adjust for income, using proxy variable “education”

20. Residual confounding example 1
<HS HS
>HS
Race W B
Income
1.5 1
1.7
1.4
1.9
1.8

21. Residual confounding example 1
Result: observed rate of CVD will be a little higher for blacks than whites, within each education category
Weighted average of stratum-specific risk ratios will produce an “adjusted” risk ratio showing an association between race and CVD (erroneous, produced by residual confounding)

22. Residual confounding example 2
Confounder categories are too wide (when categorizing a continuous variable)
e.g. adjusting for “smoker/nonsmoker” when risk is dependent on age at starting, duration of smoking, smoking intensity, and pattern of smoking (increase, decrease, periods of quitting, etc.)
e.g. adjusting for “non”, “<10/day”, “10+/day”
Missing informative exposure data results in residual confounding

23. Residual confounding example 3
An important confounder is missing from the analysis
This is “uncontrolled confounding” by one variable, when other variables are controlled
Missing informative exposure data results in outcome risk appearing higher or lower in main exposure group