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

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

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

Upstream causal factor: exposure disease confounder

Upstream causal factor: stress preterm delivery exercise

Upstream causal factor: preterm delivery exercise income

Upstream causal factor: preterm delivery prenatal care income

Upstream causal factor: preterm delivery prenatal care anxiety

Upstream causal factor: preterm delivery prenatal care vigilance

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

Example: confounding by indication: preterm delivery bed rest PROM

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

Example: confounding by severity: heart attack exercise subclinical CVD

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

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)

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

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

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

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”

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

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)

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

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