Cystic fibrosis (CF) is the most common life-restricting, genetically inherited disease among Caucasians affecting approximately 30,000 people in the United States. Lung disease is the major cause of morbidity and mortality in CF. A number of oral, inhaled, and intravenous therapies are available to combat CF lung disease. Of these, this research project focused on inhaled dornase alfa, oral azithromycin, inhaled tobramycin, and inhaled aztreonam. Data to address three research aims were requested and obtained from the Cystic Fibrosis Foundation Patient Registry (CFFPR). The first aim examined the use of inhaled dornase alfa in younger children with CF. With no clinical efficacy data of dornase alfa in children ≤ 6 years of age, the study utilized subsequent forced expiratory volume in 1 second (FEV₁) measured between 6 - 7 years of age, to assess the effectiveness of long-term dornase alfa use ≤ 6 years of age. Propensity score methods were used to reduce the likelihood of treatment indication bias. The results suggested that receiving treatment with dornase alfa before 6 years of age did not improve FEV₁ between 6 - 7 years. Unmeasured covariates leading to treatment indication bias were likely one of the key explanations for these results. Additionally, lack of a more sensitive outcome than FEV₁ to assess lung function in young patients with early lung damage was thought to be another reason for the failure to reject the null hypothesis. The second aim assessed the long-term clinical effectiveness of chronic azithromycin use on the rate of FEV₁ decline in CF patients between 6 - 20 years of age. This study was novel in that the rate of FEV₁ decline, rather than change in FEV₁ from baseline, was the primary outcome, which was characterized using propensity score matching followed by a linear mixed model analysis. The results of the analysis suggested that the rate of FEV₁ decline was slower in patients who did not receive chronic treatment with azithromycin. Treatment indication bias was thought to play an important role in the direction of the association between treatment and outcome. Associations between FEV₁ % predicted and many of the other study variables included in the analysis were consistent with previous studies. The final aim compared the clinical effectiveness of a combination of inhaled tobramycin and aztreonam with inhaled tobramycin alone on the rate of FEV₁ decline in CF patients between 6 - 20 years of age. This aim was novel in that the effect of this combination treatment on rate of decline in FEV₁ has never been assessed. A linear mixed model analysis was used after matching patients in the two treatment groups on their propensity scores. Once again, the results were contrary to the alternative hypothesis with the combination group having a steeper rate of FEV₁ decline than the group that was treated with tobramycin alone. An important reason for this result was thought to be unresolved treatment indication bias that could not be eliminated even with the use of the propensity score methods used to test the associated hypothesis. The use of validated methods of analysis, i.e., propensity scores, to counter treatment indication bias using the largest available observational dataset for CF, was one of the key strengths of this study. Moreover, this study highlighted important weaknesses in the CFFPR with regards to lack of data on patient and physician-level variables - an area of active interest for the Cystic Fibrosis Foundation.

Les scores de propension (PS) sont fréquemment utilisés dans l’ajustement pour des facteurs confondants liés au biais d’indication. Cependant, ils sont limités par le fait qu’ils permettent uniquement l’ajustement pour des facteurs confondants connus et mesurés. Les scores de propension à hautes dimensions (hdPS), une variante des PS, utilisent un algorithme standardisé afin de sélectionner les covariables pour lesquelles ils vont ajuster. L’utilisation de cet algorithme pourrait permettre l’ajustement de tous les types de facteurs confondants. Cette thèse a pour but d’évaluer la performance de l’hdPS vis-à-vis le biais d’indication dans le contexte d’une étude observationnelle examinant l’effet diabétogénique potentiel des statines. Dans un premier temps, nous avons examiné si l’exposition aux statines était associée au risque de diabète. Les résultats de ce premier article suggèrent que l’exposition aux statines est associée avec une augmentation du risque de diabète et que cette relation est dose-dépendante et réversible dans le temps. Suite à l’identification de cette association, nous avons examiné dans un deuxième article si l’hdPS permettait un meilleur ajustement pour le biais d’indication que le PS; cette évaluation fut entreprise grâce à deux approches: 1) en fonction des mesures d’association ajustées et 2) en fonction de la capacité du PS et de l’hdPS à sélectionner des sous-cohortes appariées de patients présentant des caractéristiques similaires vis-à-vis 19 caractéristiques lorsqu’ils sont utilisés comme critère d’appariement. Selon les résultats présentés dans le cadre du deuxième article, nous avons démontré que l’évaluation de la performance en fonction de la première approche était non concluante, mais que l’évaluation en fonction de la deuxième approche favorisait l’hdPS dans son ajustement pour le biais d’indication. Le dernier article de cette thèse a cherché à examiner la performance de l’hdPS lorsque des facteurs confondants connus et mesurés sont masqués à l’algorithme de sélection. Les résultats de ce dernier article indiquent que l’hdPS pourrait, au moins partiellement, ajuster pour des facteurs confondants masqués et qu’il pourrait donc potentiellement ajuster pour des facteurs confondants non mesurés. Ensemble ces résultats indiquent que l’hdPS serait supérieur au PS dans l’ajustement pour le biais d’indication et supportent son utilisation lors de futures études observationnelles basées sur des données médico-administratives. / Propensity scores (PS) are frequently used to adjust for confounders leading to indication bias. However, PS are limited by the fact that they can only adjust for measured and known confounders. High-dimensional propensity scores (hdPS), a specific type of PS, select which variables they adjust for by means of a standardized selection algorithm. Thanks to the use of this selection algorithm, hdPS could potentially adjust for all type of confounders. This thesis aims to evaluate the hdPS’s performance in the adjustment for indication bias in the context of an observational study focussing on the potential diabetogenic effect of statins. The first article’s aim was to identify if the exposure to statins was associated with the risk of diabetes. Results of this article suggest that exposure to statins is associated with an increase in the risk of diabetes and that this association is dose-dependent and reversible in nature. After having identified this association, we examined if the hdPS outperforms the PS in the adjustment for indication bias. Both methods’ performance were compared by means of the obtained adjusted measures of associations and by means of the standardized differences regarding 19 characteristics following the creation of two matched sub-cohorts (each matched on either patients’ PS or patients’ hdPS). Results of this second article identify that the performance of either method could not be differentiated by means of the first approach but that, based on the second approach, the hdPS outperforms the PS in its adjustment for indication bias. The last article aimed to evaluate if the hdPS could adjust for known confounders which were hidden to the selection algorithm. Results of this third article suggest that the hdPS method can adjust for at least some hidden confounders and that it could potentially adjust for some unmeasured confounders. As a whole, this thesis suggests that the hdPS method could be superior to the PS method in its ability to adjust for indication bias and supports its use in future observational studies using medico-administrative databases.