# Example: Do Not Adjust - Lung Cancer and Matches

*Lead Author(s):* Jeff Martin, MD
## Crude and Adjusted Data

Here is an example of what can happen when you adjust for something you don’t need to adjust for.

## Comparing Results

When we look at the effect of matches on the association between smoking and lung cancer.
- There was no interaction and furthermore,
- When we looked at the measure of association in the two matching-using strata,
- we saw the same effect as the unadjusted association.

In other words, matches had no effect on the association.
## Reporting

In this case, we would report the crude estimate, only right?
- Why not report the adjusted estimate -- the average of the stratum-specific estimates?
- One answer is that it is too much work.
- The second answer is that often when you stratify, you pay a little price in terms of statistical precision.

## Loss of Precision

In other words, the confidence interval of the crude estimate will be narrower than the CI of the adjusted measure - they both will have 21 as their point estimate but the crude association will be more precise.
- As you can see, the 95% confidence interval for the crude estimate is 16.4 to 26.9 compared to 14.2 to 31.1

**Hence, this illustrates why you don’t want to adjust for things that you don’t need to.**