7.4 Confounding

Section 1.5 defined confounding as the failure of exchangeability: the exposed and unexposed would have had different risks even without the exposure. In practice confounding is attributed to particular variables.

Definition 7.4.1. A variable \(C\) is a confounder of the association between exposure \(E\) and disease \(D\) if

1.
\(C\) is a risk factor for \(D\) among the unexposed (a cause of \(D\), or a proxy for one);
2.
\(C\) is associated with \(E\) in the source population;
3.
\(C\) is not affected by \(E\): it is not on the causal path from \(E\) to \(D\).

Age is the classic confounder: it raises the risk of most diseases and is associated with most exposures. The third condition matters. A variable through which the exposure acts, a mediator, satisfies the first two conditions, but adjusting for it removes part of the effect being studied.

How large can confounding be?

Theorem 7.4.2 (Bias from an unmeasured binary confounder). Let \(C\) be a binary confounder with risk ratio \(R_C\) for disease (the same among exposed and unexposed), present in a proportion \(\pi _1\) of the exposed and \(\pi _0\) of the unexposed, and let the risk ratio for exposure be \(\theta \) within each level of \(C\). Then the crude risk ratio is \[ \RR _{\text {crude}} = \theta \times \frac {1 + \pi _1(R_C - 1)}{1 + \pi _0(R_C - 1)} . \]

Proof. Let \(r\) be the risk among the unexposed with \(C\) absent. The unexposed have risk \(r\) or \(rR_C\), so their average risk is \(r\{(1 - \pi _0) + \pi _0 R_C\} = r\{1 + \pi _0(R_C - 1)\}\). The exposed have risks \(\theta r\) and \(\theta r R_C\), averaging \(\theta r\{1 + \pi _1(R_C - 1)\}\). The ratio of the averages is the stated expression. □

The second factor is the confounding bias factor. It equals \(1\) if \(\pi _1 = \pi _0\) (no association of \(C\) with exposure) or \(R_C = 1\) (no effect of \(C\) on disease), which are conditions 2 and 1 of the definition.

Example 7.4.3. A study finds that coffee drinkers have a higher risk of a cancer. Suppose coffee has no effect (\(\theta = 1\)), but smoking multiplies the risk by \(10\), and \(60\%\) of coffee drinkers smoke against \(30\%\) of others. The crude risk ratio is \[ \frac {1 + 0.6 \times 9}{1 + 0.3 \times 9} = \frac {6.4}{3.7} = 1.73 , \] an association produced entirely by smoking.

Theorem 7.4.4 (Cornfield’s inequalities). If an observed risk ratio \(\RR _{\text {obs}} > 1\) is entirely due to a binary confounder \(C\) (so \(\theta = 1\)), then both \[ R_C \geq \RR _{\text {obs}} \qquad \text {and}\qquad \frac {\pi _1}{\pi _0} \geq \RR _{\text {obs}} . \]

Proof. With \(\theta = 1\) and \(x = R_C - 1 > 0\), \(\RR _{\text {obs}} = (1 + \pi _1 x)/(1 + \pi _0 x)\), which exceeds \(1\) only if \(\pi _1 > \pi _0\). As a function of \(x\) it is increasing (its derivative has the sign of \(\pi _1 - \pi _0\)) and tends to \(\pi _1/\pi _0\) as \(x \to \infty \), so \(\RR _{\text {obs}} \leq \pi _1/\pi _0\). For the other inequality, \begin {align*} \frac {1 + \pi _1 x}{1 + \pi _0 x} \leq 1 + x &\iff 1 + \pi _1 x \leq (1 + x)(1 + \pi _0 x) \\ &\iff \pi _1 \leq 1 + \pi _0(1 + x) , \end {align*}

which holds because \(\pi _1 \leq 1\). □

Cornfield and colleagues used this argument in 1959 against the claim that the association between smoking and lung cancer, a risk ratio of about \(9\), was due to a genetic factor that predisposed people both to smoke and to develop the cancer. Such a factor would have to multiply the risk of lung cancer at least ninefold and be at least nine times as common among smokers as among non-smokers. No such factor was known, and one so strong would hardly have gone unnoticed. The argument is the ancestor of modern sensitivity analysis for unmeasured confounding.

Confounding is not non-collapsibility

When there is no confounding, one might expect the crude measure to equal the common stratum-specific measure. This holds for the risk difference and the risk ratio, but not for the odds ratio.

Proposition 7.4.5. If \(C\) is independent of \(E\) and the risk ratio (or risk difference) is the same in every stratum of \(C\), the crude risk ratio (risk difference) equals it.

Proof. Let \(w_k = P(C = k)\), the same among exposed and unexposed by independence, and let the stratum risks be \(p_{0k}\) and \(p_{1k} = \theta p_{0k}\). The crude risks are \(\sum _k w_k p_{1k} = \theta \sum _k w_k p_{0k}\) and \(\sum _k w_k p_{0k}\), with ratio \(\theta \). For the difference, \(p_{1k} = p_{0k} + \delta \) gives crude risks differing by \(\delta \sum _k w_k = \delta \). □

Example 7.4.6. Two equal strata of \(C\) each have half their members exposed, so \(C\) is independent of \(E\) and there is no confounding. In stratum 1 the risks are \(0.8\) among the exposed and \(0.5\) among the unexposed; in stratum 2 they are \(0.5\) and \(0.2\). The odds ratios in the two strata are \[ \frac {0.8/0.2}{0.5/0.5} = 4 \qquad \text {and}\qquad \frac {0.5/0.5}{0.2/0.8} = 4 . \] The crude risks are \(0.65\) and \(0.35\), and the crude odds ratio is \[ \frac {0.65/0.35}{0.35/0.65} = 3.45 , \] not \(4\). The risk difference is \(0.3\) in both strata and \(0.3\) crude, as Proposition 7.4.5 requires. The odds ratio is non-collapsible: averaging over a risk factor that is unrelated to exposure still moves it towards \(1\). The difference between \(3.45\) and \(4\) is not confounding, and a change in the odds ratio on adjustment is therefore not by itself evidence of confounding. For a rare disease the effect is negligible.

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