EPID 623-88 Introduction to Analysis and

Interpretation of HIV/STD Data

Confounding

Manya Magnus, Ph.D.Summer 2001

adapted from M. O’Brien and P. Kissinger

Definition of Confounding

• A non-causal association between a given exposure and an outcome is observed as a result of the influence of a third variable (or group of variables) designated as confounding variable(s).

Rules of Confounding

• The confounding variable is:– Causally associated with the outcome– Non-causally or causally associated with

the exposure– Not an intermediate variable in the causal

pathway between exposure and outcome

Types of

• Positive – overestimation of the true strength of association

• Negative – underestimation of the true strength of association

• Qualitative – inverse in the direction of the association

Different strategies to assess confounding

• Examine crude and adjusted estimates of the association

• Stratification and examination of measures of association by strata

Crude Associations

More ideas about confounding

• Partial confounding can occur (not an all or nothing thing)

• Residual confounding (occurs when categories of confounders controlled for are too broad or when confounding variables remain unaccounted for)

Collinearity

Effect Modifiers

Interaction

• Two or more risk factors modify the effect of each other with regard to the occurrence or level of a given outcome

• Also known as effect modification• Synergistic (positive interaction) – potentiates

the effect of the exposure of interest• Antagonistic (negative interaction) –

diminishes or eliminates the effect of the exposure of interest

Confounding versus Interaction

• Sometimes the same variable may be both a confounder and an effect modifier

• Confounding makes it difficult to evaluate whether a statistical association is also causal

• Interaction is part of the web of causation• Do not adjusted for a variable that is both a

confounder and an effect modifer (reporting an average odds may be meaningless)

Risk factors for sinusitis among HIV-infected

persons in Multivariate logistic regression Sinusitis

(n = 521) No

Sinusitis (n = 3645)

Unadjusted O.R. Adjusted O.R. (95% C.I.)

Race White Non-white

42.6 57.4

32.5 67.5

1.54 (1.28 – 1.86)** 1.00

1.67 (1.37 – 2.04)** 1.00

Age <35 35

49.3 50.7

44.9 55.1

1.19 ( .99 – 1.43) 1.00

1.19 ( .99 – 1.44) 1.00

Sex Female Male

29.6 70.4

26.9 73.1

1.13 ( .93 – 1.39) 1.00

1.30 (1.04 – 1.62)* 1.00

CD4 at entry 200 < 200

64.3 35.7

59.1 40.9

1.24 ( 1.03 – 1.51)* 1.00

1.33 (1.07 – 1.65)** 1.00

Opportunistic Infection Present Absent

29.2 70.8

25.9 74.1

1.18 ( .96 – 1.44) 1.00

1.43 (1.34 – 1.80)** 1.00