The term effect modification is applied to situations in which the magnitude of the effect of an exposure of interest differs depending on the level of a third variable. Reye's syndrome is a rare, but severe condition characterized by the sudden development of brain damage and liver dysfunction after a viral illness. The syndrome is most commonly seen in children between the ages of 4-14 who have been treated with aspirin while recovering from a viral illness, most commonly chickenpox or influenza. Fortunately, Reye's syndrome has become very uncommon since aspirin is no longer recommended for routine use in children. While Reye's syndrome can occur in adults, it is distinctly more common in children. Thus, the effect of aspirin treatment for a viral illness is very clearly modified by age.
In this situation, computing an overall estimate of association is misleading. One common way of dealing with effect modification is examine the association separately for each level of the third variable. For example, if one were to calculate the odds ratio for the association between aspirin treatment during a viral infection and development of Reye's syndrome, the odds ratio would be substantially greater in children than in adults. As another example, suppose a clinical trial is conducted and the drug is shown to result in a statistically significant reduction in total cholesterol. However, suppose that with closer scrutiny of the data, the investigators find that the drug is only effective in subjects with a specific genetic marker and that there is no effect in persons who do not possess the marker. The effect of the treatment is different depending on the presence or absence of the genetic marker. This is an example of effect modification or "statistical interaction".
Evaluation of a Drug to Increase HDL Cholesterol
Consider the following clinical trial conducted to evaluate the efficacy of a new drug to increase HDL cholesterol (the "good" cholesterol). One hundred patients are enrolled in the trial and randomized to receive either the new drug or a placebo. Background characteristics (e.g., age, sex, educational level, income) and clinical characteristics (e.g., height, weight, blood pressure, total and HDL cholesterol levels) are measured at baseline, and they are found to be comparable in the two comparison groups. Subjects are instructed to take the assigned medication for 8 weeks, at which time their HDL cholesterol is measured again. The results are shown in the table below.
Sample Size
Mean HDL
Standard Deviation of HDL
New Drug
Placebo
On average, the mean HDL levels are 0.95 units higher in patients treated with the new medication. A two sample test to compare mean HDL levels between treatments has a test statistic of Z = -1.13 which is not statistically significant at α=0.05.
Based on their preliminary studies, the investigators had expected a statistically significant increase in HDL cholesterol in the group treated with the new drug, and they wondered whether another variable might be masking the effect of the treatment. Other studies had, if fact, suggested that the effectiveness of a similar drug was différèrent in men and women. In this study, there are 19 men and 81 women. The table below shows the number and percent of men assigned to each treatment.
Sample Size
Number (%) of Men
New Drug
Placebo
There is no meaningful difference in the proportions of men assigned to receive the new drug or the placebo, so sex cannot be a confounder here, since it does not differed in the treatment groups. However, when the data are stratified by sex, they find the following:
Sample Size
Mean HDL
Standard Deviation of HDL
New Drug
Placebo
MEN
Sample Size
Mean HDL
Standard Deviation of HDL
New Drug
Placebo
On average, the mean HDL levels are very similar in treated and untreated women, but the mean HDL levels are 6.19 units higher in men treated with the new drug. This is an example of effect modification by sex, i.e., the effect of the drug on HDL cholesterol is different for men and women. In this case there is no apparent effect in women, but there appears to be a moderately large effect in men. (Note, however, that the comparison in men is based on a very small sample size, so this difference should be interpreted cautiously, since it could be the result of random error or confounding.
When there is effect modification, analysis of the pooled data can be misleading. In this example, the pooled data (men and women combined), shows no effect of treatment. Because there is effect modification by sex, it is important to look at the differences in HDL levels among men and women, considered separately. In stratified analyses, however, investigators must be careful to ensure that the sample size is adequate to provide a meaningful analysis.
Consider the following hypothetical study comparing hospitalization after a motor vehicle collision for male and female drivers.
Crude risk ratio=1.44
Stratum-specific risk ratio=1.80
Stratum-specific risk ratio=0.93
In this case, the crude analysis suggests an association between male gender and frequency of hospitalization for motor vehicle collisions. However, if we stratify this by age, we see a strong association with male gender in subjects
Another good example of effect modification is seen with skin cancers. It is well established that excessive exposure to UV irradiation increases one's risk of skin cancer. However, the risk of UV-induced skin cancer is 1,000 times greater in people with xeroderma pigmentosum. This is a rate hereditary defect (autosomal recessive) in the enzyme system that repairs UV-induced damage to DNA. It is characterized by photosensitivity, pigmentary changes, premature skin aging, and greatly increased susceptibility to malignant tumor development.
If effect modification is present, it is NOT appropriate to use Mantel-Haenszel methods to combine the stratum-specific measures of association into a single pooled measurement. Effect modification is a biological phenomenon that should be described, so the stratum-specific estimates should be reported separately. In contrast, confounding is a distortion of the true association caused by an imbalance of some other risk factor.
1) If the stratum-specific estimates differ from one another, and they are both less than the crude estimate or if they are both greater than the crude estimate, then there is both confounding and effect modification.
2) If the stratum-specific estimates differ from one another, and the crude estimate is between the two stratum-specific estimates, then you need to pool the stratum-specific estimates (with a Mantel-Haenszel equation) to determine whether the pooled estimate is more than 10% different from the crude estimate.
Note that in this situation you are only pooling the stratum-specific estimates in order to make a decision about whether confounding is present; you should not report the pooled estimate as an "adjusted" measure of association if there is effect modification
While the discussion above provides a standard description of effect modification, but on closer scrutiny the concept of effect modification is more complicated than this. Consider the figure below (adapted from KJ Rothman: Epidemiology - An Introduction, Oxford University Press, 2002.) We see two scenarios in which incidence rates in exposed and unexposed individuals are assessed at different ages. Rate ratio and rate difference are both measures of effect, but depending on which we use, our conclusions about effect modification differ.
In the first scenario the rate difference remains constant across the spectrum of age, suggesting no effective modification. However, the rate ratio decreases with increasing age (RR=3 at age 15; RR=1.5 at age 75). In the second scenario the rate ratio remains relatively constant, but the rate difference increases with age. Our conclusion regarding whether or not there is effect modification will depend on which measure of effect we use.
Consider also the hypothetical data on the risk of lung cancer in smokers and non-smokers, both with and without exposure to asbestos (also adapted from Rothman).
Table - Hypothetical 1-Year Risk of Lung Cancer per 100,000
Without Asbestos
With Asbestos Exposure
Smokers
