Example: Backwards Strategy - MRSA

Lead Author(s): Jeff Martin, MD

Kleinbaum provides an example of using backwards strategy to adjust for potential confounders.

Research Question

Research question: Is prior hospitalization associated with the presence of methicillin-resistant S. aureus (MRSA)? (from Kleinbaum)

Outcome variable: MRSA (present or absent)

Primary predictor: prior hospitalization (yes/no)

Potential confounders: age (<55, >55), gender, prior antibiotic use (atbxuse; yes/no) The potential confounders are age, dichotomized as less than or greater than 55 years old, gender, and prior antibiotic use.

The table below shows the various odds ratios.


Crude Odds Ratio

In the first line is the crude measure of association between prior hospitalization and presence of MRSA.

Adjust for All Three Confounders

In the second line is the odds ratio when we adjust jointly for all three potential confounders.

Drop Age

What happens if we then drop age?

Drop Gender

What if we drop gender?

Drop Antibiotic Use

What if we drop prior antibiotic use? What if you just adjusted for antibiotic use alone? The resulting odds ratio is 5.0, again not very different from the gold standard.

Reporting Odds Ratio

Now it is time to report your final result? Which odds ratio would you use?

From a validity perspective, you could choose the gold standard or any of the lines where the odds ratio is within 10% of 4.66.

Examine what has happened to the confidence interval. A good compromise to the bias-variance tradeoff is to choose the estimate that adjusts for antibiotics only.


Kleinbaum, D. G., Sullivan, K. M., & Barker, N. D. (2003). ActiveEpi Companion Text: Springer Publishers.