2 2. Material and Methods Most data discussed in the paper are based on published material. Some new data are obtained by analyses of Swedish Health Registers and notably of the Swedish Medical Birth Register. Such analyses were made with Mantel-Haenszel methodology with adjustment for relevant covariables. Details of this register are available in [2]. 3. Results and Discussion 3.1. Methods for the Control of Confounding. Diﬀerent meth- ods exist for the control of confounders. This can be done by matching. If the study is a case-control study, controls to cases are then selected with, for instance, the same maternal age and other characteristics one wants to adjust for. If it is a cohort study, the unexposed subjects are selected with the same characteristics as the exposed subjects. Either matching is made by selection of pairs or triplets (or more) of case and control(s) with similar characteristics, or a group of controls is chosen with a composition similar to the whole group of cases (“frequency matching”). More common, notably when large data sets are ana- lyzed, is to adjust for the confounder(s) in the statistical analysis. The most common way to do this is by using a logistic regression model. This is a regression method to predict outcome (e.g., rate of congenital malformations) as inﬂuenced by one or more confounding factors. In the standard analysis, such predictions are based on linear regressions which may be inadequate, but it is obviously possible to replace them with other mathematical functions which more adequately describe the relationship between a certain variable and the outcome. (The basic formula for a logistic regression is: ln(p/(1 − p)) = α + β1 ∗ X1 + β2 ∗ X2 + · · · + βn ∗ Xn, where ln(p/(1 − p)) is the logarithm for the odds of the occurrence of the outcome, α is a constant, and the various β are regression coeﬃcients for X, where, for instance, β1 and X1 represent the exposure under study and β2 to βn and X2 to Xn the various studied confounders. The regression coeﬃcients can be transformed to odds ratios.) When the relationship is U-formed, a linear regression may not reveal the relationship properly but the ﬁtted straight line may be nearly horizontal, indicating no relationship. There are many advantages with this method, for instance, that quantitative data do not have to be grouped and that estimates can be made by interpolations when control data are missing. It should be remembered that the eﬀect of each one of the studied variables represents the eﬀect after adjustment for the other included factors. If, for instance, maternal age and parity are both added in the formula, the odds ratio for maternal age will represent the eﬀect of age adjusted for parity and vice versa. Another method, especially useful when analyses are based on very large materials, is the Mantel-Haenszel technique. In this technique, cases and controls are compared within a number of strata, deﬁned by chosen confounders, for example, maternal age group, parity group, and smoking group. Within each stratum, the occurrence of exposure among cases is compared with the occurrence of exposures Obstetrics and Gynecology International Exposure Exposure Exposure Confounder (a) Intermediary (b) (c) Preterm birth Outcome Outcome Outcome Figure 1: Diagrams showing relationships between exposure, outcome, and a third factor. among controls and a summary chi-square is calculated (based on one degree of freedom) which gives the average association between exposure and outcome after adjustment for the confounders. When this method is applied to materials of limited size, controls may be missing in some strata which are then rejected, reducing the power of the study. When large control materials are present (like in register studies) this risk is small. The methods can of course also be used to characterize the importance of the selected putative confounders for exposure and for outcome separately and in the ﬁnal analysis only true confounders (aﬀecting both exposure and outcome) can be included. In most instances, the two methods give rather similar results. Logistic regression is used in analyses aiming at risk determinations in a dichotomous situation, for instance, presence or absence of a malformation. When outcome can show a number of quantitative eﬀects (e.g., IQ) a multiple regression model will be used instead. The adequacy of the statistical model is equally important in both methods. The decision on the inclusion of confounders in the ﬁnal analysis can be made in diﬀerent ways. It is obvious that factors which in the analysis are identiﬁed as confounders (i.e., aﬀect both exposure and outcome) should be included. Sometimes other factors, known from previous studies or the literature as confounders, are included. It is rather common that one includes also factors which only aﬀect exposure or outcome but not both. Such factors are thus not confounders and should not be included but if they are, it does not matter much. 3.2. Confounding and Stratiﬁcation: The Example of Infant Sex. Confounding should be kept apart from the possibility that the eﬀect of the exposure diﬀers, for instance, in male and female fetuses, that infant sex modiﬁes the eﬀect of exposure. If this is the case, the odds ratio obtained with or without sex adjustment will be somewhere between that for male and that for female infants. This can be seen from a study of meclozine and subluxation of the hip. The odds ratio for this condition after maternal use of meclozine in male