The main objective of the thesis was to compare two methods of calculating adjusted attributable fractions and deaths as applied to smoking exposure and four health outcomes, lung cancer, ischaemic heart disease, chronic obstructive pulmonary disease, and cerebrovascular disease, for Canadians 30 years or older in the year 2005. An additional objective was to calculate variance estimates for the evaluation of precision. Such estimates have not been published for Canada to date. Attributable fractions were calculated using the fully adjusted method and the partial adjustment method. This method requires confounder strata specific (stratified) estimates of relative risk, along with accompanying estimates of variance. These estimates have not previously been published, and were derived from the Cancer Prevention Study II cohort. Estimates of the prevalence of smoking in Canada were obtained from the Canadian Community Health Survey 2005. Variance estimates were calculated using a Monte Carlo simulation. The fully adjusted method produced smaller attributable fractions in each of the eight disease-sex-specific categories than the partially adjusted method. This suggests an upwards bias when using the partial adjustment method in the attributable fraction for the relationship between cigarette smoking and cause-specific mortality in Canadian men and women. Summed across both sexes and the four smoking related causes of death, the number of deaths attributable to smoking was estimated to be 25,684 using the fully adjusted method and 28,466 using the partial adjustment method, an upward bias of over ten percent, or 2,782 deaths. It is desirable, theoretically, to use methods which can fully adjust for the effect of confounding and effect modification. Given the large datasets required and access to original data, using these methods may not be feasible for some who would wish to do so.

The main objective of the thesis was to compare two methods of calculating adjusted attributable fractions and deaths as applied to smoking exposure and four health outcomes, lung cancer, ischaemic heart disease, chronic obstructive pulmonary disease, and cerebrovascular disease, for Canadians 30 years or older in the year 2005. An additional objective was to calculate variance estimates for the evaluation of precision. Such estimates have not been published for Canada to date. Attributable fractions were calculated using the fully adjusted method and the partial adjustment method. This method requires confounder strata specific (stratified) estimates of relative risk, along with accompanying estimates of variance. These estimates have not previously been published, and were derived from the Cancer Prevention Study II cohort. Estimates of the prevalence of smoking in Canada were obtained from the Canadian Community Health Survey 2005. Variance estimates were calculated using a Monte Carlo simulation. The fully adjusted method produced smaller attributable fractions in each of the eight disease-sex-specific categories than the partially adjusted method. This suggests an upwards bias when using the partial adjustment method in the attributable fraction for the relationship between cigarette smoking and cause-specific mortality in Canadian men and women. Summed across both sexes and the four smoking related causes of death, the number of deaths attributable to smoking was estimated to be 25,684 using the fully adjusted method and 28,466 using the partial adjustment method, an upward bias of over ten percent, or 2,782 deaths. It is desirable, theoretically, to use methods which can fully adjust for the effect of confounding and effect modification. Given the large datasets required and access to original data, using these methods may not be feasible for some who would wish to do so.

Le terme analyse de survie fait référence aux méthodes utilisées pour modéliser le temps d'apparition d'un ou plusieurs événements en tenant compte de la censure. L'événement d’intérêt peut être l'apparition, la récidive d'une maladie, ou le décès. Les causes de décès peuvent présenter des valeurs absentes, une situation qui peut être modélisée par des méthodes d’imputation. Dans la première partie de cette thèse nous avons passer en revue les méthodes de gestion des données manquantes. Puis nous avons détaillé les procédures qui permettent une imputation multiple des causes de décès. Nous avons développé ces méthodes dans une cohorte issue d’une étude européenne, l’ERSPC (European Randomized Study of Screening for Prostate Cancer), qui étudiait le dépistage et la mortalité par cancer de la prostate. Nous avons proposé une formulation théorique des règles de Rubin après transformation log-log complémentaire afin de combiner les estimations de survie. De plus, nous mettons à disposition le code R afférent. Dans la deuxième partie, nous présentons les méthodes d'analyse de survie, en proposant une écriture unifiée basée sur les définitions des survies brute et nette, que l’on s'intéresse à toutes les causes de décès ou à une seule cause. Cela implique la prise en compte de la censure qui peut alors être informative. Nous avons considéré les méthodes dites classiques (Kaplan-Meier, Nelson-Aalen, Cox et paramétriques), les méthodes des risques compétitifs (en considérant un modèle multi-états ou un modèle de temps latents), les méthodes dites spécifiques avec correction IPCW (Inverse Ponderation Censoring Weighting) et les méthodes de survie relative. Les méthodes dites classiques reposent sur l'hypothèse de censure non informative. Quand on s'intéresse aux décès de toutes causes, cette hypothèse est souvent valide. En revanche, pour un décès de cause particulière, les décès d'autres causes sont considérés comme une censure, et cette censure par décès d'autres causes est en général informative. Nous introduisons une approche basée sur la méthode IPCW afin de corriger cette censure informative, et nous fournissons une fonction R qui permet d’appliquer cette approche directement. Toutes les méthodes présentées dans ce chapitre sont appliquées aux bases de données complétées par imputation multiple.Enfin, dans une dernière partie nous avons cherché à déterminer le pourcentage de décès expliqué par une ou plusieurs variables en utilisant les fractions attribuables. Nous présentons les formulations théoriques des fractions attribuables, indépendantes du temps puis dépendantes du temps qui s’expriment sous la forme de survie. Nous illustrons ces concepts en utilisant toutes les méthodes de survie de la partie précédente et comparons les résultats. Les estimations obtenues avec les différentes méthodes sont très proches. / The term survival analysis refers to methods used for modeling the time of occurrence of one or more events taking censoring into account. The event of interest may be either the onset or the recurrence of a disease, or death. The causes of death may have missing values, a status that may be modeled by imputation methods.
In the first section of this thesis we made a review of the methods used to deal with these missing data. Then, we detailed the procedures that enable multiple imputation of causes of death. We have developed these methods in a subset of the ERSPC (European Randomized Study of Screening for Prostate Cancer), which studied screening and mortality for prostate cancer. We proposed a theoretical formulation of Rubin rules after a complementary log-log transformation to combine estimates of survival. In addition, we provided the related R code.
In a second section, we presented the survival analysis methods, by proposing a unified writing based on the definitions of crude and net survival, while considering either all-cause or specific cause of death. This involves consideration of censoring which can then be informative. We considered the so-called traditional methods (Kaplan-Meier, Nelson-Aalen, Cox and parametric) methods of competing risks (considering a multistate model or a latent failure time model), methods called specific that are corrected using IPCW (Inverse Ponderation Censoring Weighting) and relative survival methods. The classical methods are based on a non-informative censoring assumption. When we are interested in deaths from all causes, this assumption is often valid. However, for a particular cause of death, other causes of death are considered as a censoring. In this case, censoring by other causes of death is generally considered informative. We introduced an approach based on the IPCW method to correct this informative censoring, and we provided an R function to apply this approach directly. All methods presented in this chapter were applied to datasets completed by multiple imputation.
Finally, in a last part we sought to determine the percentage of deaths explained by one or more variables using attributable fractions. We presented the theoretical formulations of attributable fractions, time-independent and time-dependent that are expressed as survival. We illustrated these concepts using all the survival methods presented in section 2, and compared the results. Estimates obtained with the different methods were very similar.