Appendix

The Conditional Logistic Regression model (Breslow and Day, 1980)

In matched case-control studies, conditional logistic regression for matched sets is used to investigate the relationship between an outcome of being a case (patient) or a control and a set of prognostic factors, as described by Breslow and Day46. (Breslow NE. and Day NE Statistical Methods in Cancer Research. Volume I The analysis of case-controls studies. Lyon: IARC Scientific Publications, numero 32, 1980). The model was constructed to respond to the following question: Do genotypes/or alleles have different risks of developing affective disorder? By constructing a Conditional Logistic Regression model for matched sets, with diagnosis (patient /control) as dependant variable and genotypes/or alleles entered as covariates, we tested the effects genotypes/alleles on disorder.

Stratification, Mantel-Haenszel and Breslow Day tests (Breslow and Day, 1980)

Both confounding and effect modification are evaluated through the process of stratification. Stratification is a technique to control confounding factors in the analysis of a study that involves the evaluation of the association within strata of the confounding variable.

cases controls exposed a b Non c d exposed

Suppose that cases and controls are stratified on the basis of one or more variables into k subgroups. The Mantel-Haenszel estimate of the odds ratio, adjusted for the effects of the stratification variables (OR MH), is calculated as:

ORMH= i=1 to k (aidi/T)/ (bici/T), (T=a+b+c+d). The quantities in the numerator and denominator are summed separately over each of the individual strata. The crude OR is calculated as: ORcrude= ad/bc The magnitude of confounding can be evaluated by observing the degree of discrepancy between the crude (ORcrude) and adjusted (ORMH) estimates. (Tolerated difference is not more than 15%). If ORcrude and ORMH are identical, there is no confounding effect by the stratification variable (gender in our study).

To evaluate the likelihood that the association between exposure and disease, after adjustment for a potential 2 confounding factor (gender in our study) is due to chance, the Mantel-Haenszel chi-square statistic c MH can be calculated as follows:

  a-  (a+b) (a+c)  2 T

 (a+b)(c+d)(a+c)(b+d) T2(T-1)

When the association between the exposure and disease under study varies by level of a third factor (gender /center in our study), there is a modifier effect. To evaluate the uniformity of the stratum-specific estimates, a chi- square test for homogeneity was proposed by Breslow and Day that test the null hypothesis that the degree of variability in the series of stratum-specific estimates is consistent with random variation. 2 (i=1 to k) ai-Ai(ORg) /var(ai;ORg) k=number of strata, ai=number of observed exposed cases in stratum i; Ai(ORg)=number of attended exposed cases, according to the ORg (global Odd ratio).