The most popular method used to compute a weighted risk ratio or odds ratio is the Mantel Haenszel method, which can be used for risk ratios or rate ratios.

From the following table

 

 

Cases

Total

Stratum 1

Exposed

a1

Te1

Unexposed

c1

Tu1

Total

 

T1

Stratum 2

Exposed

a2

Te2

Unexposed

c2

Tu2

Total

 

T2

The Mantel Haenszel risk ratio (RRMH) can be computed as follows:

In which:
a and c are the number of cases exposed and unexposed in a stratum
Te and Tu are the total number exposed and unexposed in a stratum
T is the total of a stratum
The sums ∑ are calculated for the i strata.

Returning to the example of the cohort study with vaccinated girls and boys:

Crude RR

Gender

Cases

Total

Attack Rate

RR

Boys

819

1000

82%

4.52

Girls

181

1000

18%

ref

Stratified RRs

 

Gender

Cases

Total

Attack Rate

RR

Unvaccinated

Boys

814

950

86%

1.00

Girls

86

100

86%

ref

 

 

1050

 

 

Vaccinated

Boys

5

50

10%

0.95

Girls

95

900

11%

ref

 

 

950

 

 

In our example the crude measure of effect (the risk ratio) was 4.5. The weighted measure of effect calculated with the Mantel Haenszel method is close to 1. It is obtained as follows:

RRMH =

 (aiTui/Ti)

=

[(814*100)/1050)] + [(5*900)/950]

=

82.2

=

0.99

 

 

∑ (ciTei/Ti)

[(86*950)/1050)] + [(95*50) / 950)]

82.8

 

The relative difference between the weighted and the crude measures of effect is more than 15% (4.5/0.99 *100 = 450%) therefore suggesting that, in our hypothetical study, vaccination is confounding (is a confounding factor for) the association between gender and disease. Had a stratified analysis been omitted, the data may lead to the conclusion that being a boy was a risk factor for the disease.

The adjusted RR 0.99 is presented, which concludes that this is the measure of association between gender and disease. This is different from effect modification, where two RRs would be presented.

Mathematically, the adjusted estimate is a weighted average of the stratum specific measures of the risk ratio. It will therefore always lie within the range of the stratum specific measures of the effect. (i.e. in the example above;  0.99 is between the range 0.95 and 1.00 - the stratum specific RRs).

For a case control study the Mantel Haenszel odds ratio (ORMH) can be computed as follows:

 Stratified

Risk Factor

Cases

Controls

Totals

Stratum 1

Exposed

a1

b1

 

Unexposed

c1

d1

 

 

 

 

T1

Stratum 2

Exposed

a2

b2

 

Unexposed

c2

d2

 

 

 

 

T2

 

In which:
a and c are the number of cases exposed and unexposed in a stratum,
b and d are the number of controls exposed and unexposed in a stratum.
T is the total for a stratum
The sums ∑ are calculated for the i strata.

It can become customary to 'eyeball' the data: comparing the crude measure to the range of the stratum-specific measures. If the crude measure is not included in the range between stratum-specific measures, confounding may exist.

A watertight method for identifying confounding variables exists. It requires the construction of a causal diagram summarizing the knowledge and assumptions between all exposures, confounders and disease outcome; which is then analysed using graphical algorithms [1].  

 

References

 

1. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology 1999 Jan;10(1):37-48.