Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

This doctoral thesis is devoted to the study of the effects of late fertility on health of the newborn and to the implications of late fertility in the fertility decision process. Late fertility is defined as the reproduction process after 30 years old. The interest lies as well from the maternal age point of view as from the paternal age point of view. The first part is devoted to the study of the determinants of infant morbidity and mortality with a particular attention to the parental age, without neglecting the other determinants. The originality of this part is located from the methodological point of view. We construct a structural model of infant morbidity/mortality in order to isolate the causal effect of late fertility. By a structural model we mean a model which represents a set of causal relationships represented mathematically by a multi-equation model and graphically by directed acyclic graphs. As a complementary approach, a chapter of the thesis is devoted to an exploratory model highlighting the role of the extreme values rather than average values traditionally of interest in most statistical analyses. Both methods are tested with Hungarian data: individual registration forms of livebirths and infant deaths (1984-1984 and 1994-1998), and the Hungarian case-control surveillance of congenital abnormalities (1997-2002). The second part analyses the effect of parental ageing in the fertility decision process. We try to determine whether the detrimental effect of late fertility on health of the child and on fecundity of the couples intervene in the preferences for a child. We assume that parental age influences the preferences for a child through effects on the desire for a child and on the beliefs in the capacity of reproduction of a healthy child. This hypothesis is tested using the data from the National Survey of Family Growth (United States, 2002) and from the Fertility and Family Survey (Hungary, Czech Republic and Belgium).

Les analyses effectuées dans le cadre de ce mémoire ont été réalisées à l'aide du module MatchIt disponible sous l’environnent d'analyse statistique R. / Statistical analyzes of this thesis were performed using the MatchIt package available in the statistical analysis environment R. / L’estimation sans biais de l’effet causal d’une intervention nécessite la comparaison de deux groupes homogènes. Il est rare qu’une étude observationnelle dispose de groupes comparables et même une étude expérimentale peut se retrouver avec des groupes non comparables. Les chercheurs ont alors recours à des techniques de correction afin de rendre les deux groupes aussi semblables que possible. Le problème consiste alors à choisir la méthode de correction appropriée. En ce qui nous concerne, nous limiterons nos recherches à une famille de méthodes dites d’appariement. Il est reconnu que ce qui importe lors d’un appariement est l’équilibre des deux groupes sur les caractéristiques retenues. Autrement dit, il faut que les variables soient distribuées de façon similaire dans les deux groupes. Avant même de considérer la distribution des variables entre les deux groupes, il est nécessaire de savoir si les données en question permettent une inférence causale. Afin de présenter le problème de façon rigoureuse, le modèle causal contrefactuel sera exposé. Par la suite, les propriétés formelles de trois méthodes d’appariement seront présentées. Ces méthodes sont l’appariement par la distance de Mahalanobis, de l’appariement par le score de propension et de l’appariement génétique. Le choix de la technique d’appariement appropriée reposera sur quatre critères empiriques dont le plus important est la différence des moyennes standardisées. Les résultats obtenus à l’aide des données de l’Enquête longitudinale et expérimentale de Montréal (ÉLEM) indiquent que des trois techniques d’appariement, l’appariement génétique est celui qui équilibre mieux les variables entre les groupes sur tous les critères retenus. L’estimation de l’effet de l’intervention varie sensiblement d’une technique à l’autre, bien que dans tous les cas cet effet est non significatif. Ainsi, le choix d’une technique d’appariement influence l’estimation de l’effet d’une intervention. Il est donc impérieux de choisir la technique qui permet d’obtenir un équilibre optimal des variables selon les données à la disposition du chercheur. / The unbiased estimate of the causal effect of an intervention requires the comparison of two homogeneous groups. It is rare that an observational study has comparable groups and even an experiment may end up with non-comparable groups. The researchers then used correction techniques to make the two groups as similar as possible. The problem then is to choose the appropriate correction method. In our case, we will restrict our research to a family of so-called matching methods. It is recognized that what matters in a match is the balance between the two groups on selected characteristics. In other words, it is necessary that the variables are distributed similarly in both groups. Even before considering the distribution of variables between the two groups, it is necessary to know whether the data in question allow for causal inference. To present the problem rigorously, the counterfactual causal model will be exposed. Thereafter, the formal properties of three matching methods will be presented. Those methods are the Mahalanobis matching, the propensity score matching and genetic matching. The choice of the appropriate matching technique is based on four empirical criteria which the most important is the standardized mean difference. Results obtained using data from the Montréal Longitudinal and Experimental Study indicate that of the three matching techniques, genetic matching is the one that better balance the variables between groups on all criteria. The estimate of the effect of intervention varies substantially from one technique to another, although in all cases this effect is non significant. Thus, the selection of a matching technique influences the estimation of the effect of an intervention. Therefore, it is imperative to choose the technique that provides an optimal balance of the variables based on data available to the researcher.