Misclassification of Outcome
Misclassification of outcomes can also introduce bias into a study, but it usually has much less of an impact than misclassification of exposure. First, most of the problems with misclassification occur with respect to exposure status since exposures are frequently more difficult to assess and categorize. We glibly talk about smokers and non-smokers, but what do these terms really mean? One needs to consider how heavily the individual smoked, the duration, how long ago they started, whether and when they stopped, and even whether they inhaled or whether they were exposed to environmental smoke. In addition, as illustrated above, there are a number of mechanisms by which misclassification of exposure can be introduced. In contrast, most outcomes are more definitive and there are few mechanisms that introduce errors in outcome classification.
Another important consideration is that most of the outcomes that one studies are relatively uncommon; even when an association does exist, the majority of exposed and non-exposed subjects do not experience the outcome. As a result, there is much less potential for errors to have a major effect in distorting the measure of association.
Certainly there may be clerical and diagnostic errors in classification of outcome, but compared to the frequency of exposure misclassification, errors in outcome classification tend to be less common and have much less impact on the estimate of association. In addition to having little impact on the estimate of effect, misclassification of outcome will generally bias toward the null, so if an association is demonstrated, if anything the true effect might be slightly greater.
Example:
Consider the case-control conducted by Doll and Hill in 1947. This was one of the first analytic studies that examined the association between smoking and lung cancer. The study gathered data from more than twenty hospitals in the London area. Cases with a recent diagnosis of lung cancer were identified and interviewed about their past exposures, including a detailed history of smoking tobacco. Non-cancer control patients in the same hospitals were also interviewed. The study was quite extensive, but the bottom line was that statistically significant associations between smoking and lung cancer were found in both males and females (although the association was not as strong in females.
The investigators took steps to verify the diagnoses whenever possible by checking operative findings, pathology reports, and autopsy findings. Given the nature of the disease and the efforts to verify the diagnosis, it is likely that the diagnosis was correct in the vast majority of subjects. However, far more problematic was the classification of the degree of exposure to tobacco. The assessment of exposure could be influenced not only by misclassification as a result of trying to remember the details of smoking exposure over a lifetime, but the potential problems with recall bias and interviewer bias.
Differential Misclassification of Outcome
To illustrate differential misclassification of outcome Rothman
uses the following example"
"Suppose a follow-up study were undertaken to compare incidence rates of emphysema among smokers and nonsmokers. Emphysema is a disease that may go undiagnosed without unusual medical attention. If smokers, because of concern about health effects of smoking (such as bronchitis), seek medical attention to a greater degree than nonsmokers, then emphysema might be diagnosed more frequently among smokers than among nonsmokers simply as a consequence of the greater medical attention. Unless steps were taken to ensure comparable follow-up, an information bias would result. An 'excess' of emphysema incidence would be found among smokers compared with nonsmokers that is unrelated to any biologic effect of smoking. This is an example of differential misclassification, since the underdiagnosis of emphysema, a misclassification error, occurs more frequently for nonsmokers than for smokers."
Non-differential Misclassification of Outcome
Non-differential misclassification of a dichotomous outcome will generally bias toward the null, but there are situations in which it will not bias the risk ratio. Bias in the risk difference depends upon the sensitivity (probability that someone who truly has the outcome will be identified as such) and specificity (probability that someone who does not have the outcome will be identified as such).
OPTIONAL
This is additional detail on the effects of non-differential misclassification of outcome that is not required in the introductory course, although it is required in Intermediate Epidemiology.
Effect of Decreased Sensitivity of Detecting Diseased Subjects
From "Modern Epidemiology" (3rd edition, page 142):
"Consider a cohort study in which 40 cases actually occur among 100 exposed subjects and 20 cases actually occur among 200 unexposed subjects. Then, the actual risk ratio is (40/100)/(20/200) = 4, and the actual risk difference is 40/100-20/200 = 0.30. Suppose that specificity of disease detection is perfect (there are no false positives), but sensitivity is only 70% in both exposure groups. (that is sensitivity of disease detection is nondifferential and does not depend on errors in classification of exposure). The expected numbers detected will then be 0.70(40) = 28 exposed cases and 0.70(20) = 14 unexposed cases, which yield an expected risk-ratio estimate of (28/100)/(14/200) = 4 and expected risk-difference estimate of 28/100 - 14/200 = 0.21. Thus, the disease misclassification produced no bias in the risk ratio, but the expected risk-difference estimate is only 0.21/0.30 = 70% of the actual risk difference.
"This example illustrates how independent nondifferential disease misclassification with perfect specificity will not bias the risk-ratio estimate, but will downwardly bias the absolute magnitude of the risk-difference estimate by a factor equal to the false-negative probability (Rogers and MacMahon, 1995). With this type of misclassification, the odds ratio and the rate ratio will remain biased toward the null, although the bias will be small when the risk of disease is low (<10%) in both exposure groups. This approximation is a consequence of the relation of the odds ratio and the rate ratio to the risk ratio when the disease risk is low in all exposure groups."
The scenario described above could be summarized with the following contingency tables.
First, consider the true relationship:
Table - True Relationship A:
|
|
Diseased |
Non-diseased |
Total |
|---|---|---|---|
|
Exposed |
40 |
60 |
100 |
|
Unexposed |
20 |
180 |
200 |
Sensitivity = 100% (all disease cases were detected)
Specificity = 100% (all non-cases correctly classified)
Risk Ratio = (40/100)/(20/200) = 4
Risk Difference = 40/100-20/200 = 0.30
Then consider:
Table - Misclassification of Outcome #1
|
|
Diseased |
Non-diseased |
Total |
|---|---|---|---|
|
Exposed |
28 |
72 |
100 |
|
Unexposed |
14 |
186 |
200 |
Sensitivity = 70% (30% false negative rate)
Specificity = 100% (all non-cases correctly classified)
Risk Ratio = (28/100)/(14/200) = 4
Risk Difference = 28/100-14/200 = 0.21
This illustrates the effect when all of the non-diseased subjects are correctly classified, but some of the diseased subjects are misclassified as non-diseased. As you can see the risk ratio is not biased under these circumstances, but the risk difference is. The reason for this is that decreased sensitivity results in a proportionate decrease in the cumulative incidence in both groups, so the ratio of the two (the risk ratio) is unchanged. However, the groups are of unequal size, so the absolute difference between the groups (the risk difference) does change.
Effect of Decreased Specificity of Detecting Diseased Subjects
It is also possible for non-diseased subjects to be incorrectly classified as diseased, i.e., specificity <100%. For the scenario above, suppose that sensitivity had been 100% (all of the truly diseased subjects were identified), but the specificity was only 70%, i.e., 70% of the non-diseased people were correctly categorized as non-diseased, but 30% of them were incorrectly identified as diseased. In that case the scenario would give a contingency table as illustrated below.
Table - Misclassification of Outcome #2
|
|
Diseased |
Non-diseased |
Total |
|---|---|---|---|
|
Exposed |
58 |
42 |
100 |
|
Unexposed |
74 |
126 |
200 |
Sensitivity = 100% (all disease cases were detected)
Specificity = 70% (30% of non-cases incorrectly classified)
Risk Ratio = (58/100)/(74/200) = 1.57
Risk Difference = 58/100-74/200 = 0.58-0.37= 0.21
Here, the specificity is 70% in both groups, but there are more non-diseased subjects in the unexposed, so the result is a disproportionate increase in the apparent number of diseased subjects in the unexposed group, and both the risk ratio and the risk difference are underestimated.
This is also true when the number of subjects in the exposed group is larger as illustrated in the example below.
First, consider true relationship B:
Table - True Relationship B
|
|
Diseased |
Non-diseased |
Total |
|---|---|---|---|
|
Exposed |
40 |
160 |
200 |
|
Unexposed |
10 |
90 |
100 |
Sensitivity = 100% (all disease cases were detected)
Specificity = 100% (all non-cases correctly classified)
Risk Ratio = (40/200)/(10/100) = 0.2/0.1 = 2
Risk Difference = 40/200-10/100 = 0.20-0.10 = 0.21
In contrast, consider the next table with misclassification of outcome, but a larger number of exposed subjects..
Table - Misclassification of Outcome #3
|
|
Diseased |
Non-diseased |
Total |
|---|---|---|---|
|
Exposed |
88 |
112 |
200 |
|
Unexposed |
37 |
63 |
100 |
Sensitivity = 100% (all disease cases were detected)
Specificity = 70% (30% of non-cases incorrectly classified)
Risk Ratio = (88/200)/(40/100) = 0.44/0.40 = 1.1
Risk Difference = 88/200-40/100 = 0.44-0.40= 0.04
In this example, sensitivity is again 100% and specificity is 70%. As a result, 0.30*160 = 48 diseased subjects in the exposed group are incorrectly classified as diseased and move from cell B to cell A. Similarly, in the unexposed group, 0.30*90 = 27 non-diseased people are incorrectly classified as diseased and move from cell D to cell C. Again, the risk ratio and the risk difference are biased toward the null.