Acute respiratory infections (ARI) caused by influenza and other respiratory viruses affect millions of people annually. Although usually self-limiting a more complicated or severe course may occur in previously healthy people but are more likely in individuals with underlying illnesses. The most common viral agent is rhinovirus whereas influenza is less frequent but is well known to cause winter epidemics. In primary care, rapid diagnosis of influenza virus infections is essential in order to provide treatment. Clinical presentations vary among the different pathogens but may overlap and may also depend on host factors. Predictive models have been developed for influenza but study results may be biased because only individuals presenting with fever were included. Most of these models have not been adequately validated and their predictive power, therefore, is likely overestimated. The main objective of this thesis was to compare different mathematical models for the derivation of clinical prediction rules in individuals presenting with symptoms of ARI to better distinguish between influenza, influenza A subtypes and entero-/rhinovirus-related illness in children and adults and to evaluate model performance by using data-splitting for internal validation. Data from a completed prospective cluster-randomized trial for the indirect effect of influenza vaccination in children of Hutterite communities served as a basis of my thesis. There were a total of 3288 first episodes per season of ARI in 2202 individuals and 321 (9.8%) influenza positive events over three influenza seasons (2008-2011). The data set was divided into children under 18 years and adults. Both data sets were randomly split by subjects into a derivation (2/3 of the dataset) and a validation population (1/3 of the dataset). All predictive models were developed in the derivation sets. Demographic factors and the classical symptoms of ARI were evaluated with logistic regression and Cox proportional hazard models using forward stepwise selection applying robust estimators to account for non-independent data and by means of recursive partitioning. The beta coefficients of the independent predictors were used to develop different point scores. These scores were then tested in the validation groups and performance between validation and derivation set was compared using receiver operating characteristics (ROC) curves. We determined sensitivities and specificities, positive and negative predictive values, and likelihood ratios at different cut-points which could reflect test and treatment thresholds. Fever, chills, and cough were the most important predictors in children whereas chills and cough but not fever were most predictive of influenza virus infection in adults. Performance of the individual models was moderate with areas under the receiver operating characteristic curves between 0.75 and 0.80 for the main outcome influenza A or B virus infection. There was no statistically significant difference in performance between the derivation and validation sets for the main outcome. The results have shown, that various mathematical models have similar discriminative ability to distinguish influenza from other respiratory viruses. The scores could assist clinicians in their decision-making. However, performance of the models was slightly overestimated due to potential clustering of data and the results would first needed to be validated in a different population before application in clinical practice. / Thesis / Master of Science (MSc) / Every year, millions of people are attacked by "the flu" or the common cold. Certain signs and symptoms apparently are more discriminative between the common cold and the flu. However, the decision between starting a simple symptom orientated treatment, treating empirically for influenza or ordering a rapid diagnostic test that has only moderate sensitivity and specificity can be challenging. This thesis, therefore, aims to help physicians in their decision-making process by developing simple scores and decision trees for the diagnosis of influenza versus non-influenza respiratory infections. Data from a completed trial for the indirect effect of influenza vaccination in children of Hutterite communities served as a basis of my thesis. There were a total of 3288 first seasonal episodes of ARI in 2202 individuals and 321 (9.8%) influenza positive events over three influenza seasons (2008-2011). The data set was divided into children under 18 years and adults. Both data sets were split into a derivation and a validation set (=holdout group). Different mathematical models were applied to the derivation set and demographic factors as well as the classical symptoms of ARI were evaluated. The scores generated from the most important factors that remained in the model were then tested in the validation group and performance between validation and derivation set was compared. Accuracy was determined at different cut-points which could reflect test and treatment thresholds. Fever, chills, and cough were the most important predictors in children whereas chills and cough but not fever were most predictive of influenza virus infection in adults. Performance of the individual models was moderate for the main outcome influenza A or B virus infection. There was no statistically significant difference in performance between the derivation and validation sets for the main outcome. The results have shown, that various mathematical models have similar discriminative ability to distinguish influenza from other respiratory viruses. The scores could assist clinicians in their decision-making. However, the results would first needed to be validated in a different population before application in clinical practice.

Mémoire en recherche-création / L'arthrose (OA) est la maladie musculosquelettique la plus fréquente au monde et peut affecter toutes les articulations. L’arthrose est caractérisée par la dégradation progressive du cartilage, l’inflammation de la synoviale et le remodelage de l’os sous-chondral. Ces changements sont dus à une augmentation d’expression des médiateurs pro-inflammatoires tels que la cyclooxygénase 2 (COX-2) et de facteurs cataboliques, notamment les métalloprotéinases 1 et 13 (MMP-1 et 13). Les métabolites de la 12-lipoxygénase jouent un rôle important dans de nombreux processus physiologiques et pathologiques. À la suite des réactions d'oxygénation / réduction de la 12 LOX, un facteur eicosanoïde particulier est produit: le 12-HydroxyEicosaTétraÉnoique (12-HETE), dont le rôle dans la pathogenèse de l’OA n’est pas caractérisé. L’objectif de ce travail est de définir le rôle de la 12-HETE dans la régulation des réponses inflammatoires et cataboliques dans les tissus articulaires, chondrocytes et synoviocytes, humains. Nous avons démontré que la 12-HETE n’affecte pas la prolifération (test MTT) des chondrocytes et des synoviocytes et n’a aucun effet sur leur migration (test de rayure). Un traitement avec la 12-HETE augmente l’induction de l’expression de la COX-2 dans les deux types cellulaires. La 12-HETE n’avait aucun effet sur l’expression de la MMP-1 et la MMP-13. La 12-HETE induit ses effets à l’aide d’un récepteur couplé à la protéine G : la GRP31. Nous avons décrit l’arthrose sur des coupes histologiques humaines, puis nous avons observé que l’expression de GPR31 était similaire dans le cartilage dégradé et le cartilage non dégradé. Finalement, nous avons montré que les chondrocytes et les synoviocytes expriment la GPR31 et son niveau est diminué en présence de l’interleukine-1 béta (IL-1β). En conclusion, nous avons démontré que la 12-HETE a des effets divers sur les réponses inflammatoires et cataboliques dans les tissus articulaires : elle augmente l’expression de la cyclooxygénase 2 (COX- 2) et n’a pas d’effet sur l’expression de la MMP-1 et la MMP-13. / Osteoarthritis (OA) is the world's most common musculoskeletal disease and can affect all joints. Osteoarthritis is characterized by progressive degradation of cartilage, inflammation of the synovium and remodelling of the subchondral bone. These changes are due to increased expression of pro-inflammatory mediators such as cyclooxygenase 2 (COX-2) and catabolic factors including metalloproteinases 1 and 13 (MMP-1 and 13). 12-lipoxygenase metabolites play an important role in many physiological and pathological processes. Following the oxygenation/reduction reactions of 12 LOX, a particular eicosanoid factor is produced: 12-HydroxyEicosaTetraenoic (12-HETE), whose role in the pathogenesis of AO is uncharacterized. This work aims to define the role of 12-HydroxyEicosaTetraenoic (12-HETE) in regulating inflammatory and catabolic responses in human joint tissues, chondrocytes and synoviocytes. We have demonstrated that 12-HETE does not affect the proliferation (MTT assay) of chondrocytes and synoviocytes and has no effect on their migration (scratch assay). Treatment with 12-HETE increased the induction of COX-2 expression in both cell types. 12-HETE had no effects on MMP-1 and MMP-13 expression. 12-HETE induces its effects using a G protein-coupled receptor: GRP31. We described osteoarthritis on human histological sections and then observed that GPR31 expression was the same in degraded and non-degraded cartilage. Finally, we showed that chondrocytes and synoviocytes express GPR31 and its level is decreased in the presence of Interleukin-1 beta (IL-1β). In conclusion, we demonstrated that 12-HETE has different effects on inflammatory and catabolic responses in joint tissues by increasing COX-2 expression and has no effect on MMP-1 and MMP-13 expression.

Indiana University-Purdue University Indianapolis (IUPUI) / Time-to-event outcomes are widely utilized in medical research. Assessing the cumulative effects of time-varying exposures on time-to-event outcomes poses challenges in statistical modeling. First, exposure status, intensity, or duration may vary over time. Second, exposure effects may be delayed over a latent period, a situation that is not considered in traditional survival models. Third, exposures that occur within a time window may cumulatively in uence an outcome. Fourth, such cumulative exposure effects may be non-linear over exposure latent period. Lastly, exposure-outcome dynamics may differ among groups defined by individuals' characteristics. These challenges have not been adequately addressed in current statistical models. The objective of this dissertation is to provide a novel approach to modeling group-specific dynamics between cumulative timevarying exposures and a time-to-event outcome. A framework of group-specific dynamic models is introduced utilizing functional time-dependent cumulative exposures within an etiologically relevant time window. Penalizedspline time-dependent Cox models are proposed to evaluate group-specific outcome-exposure dynamics through the associations of a time-to-event outcome with functional cumulative exposures and group-by-exposure interactions. Model parameter estimation is achieved by penalized partial likelihood. Hypothesis testing for comparison of group-specific exposure effects is performed by Wald type tests. These models are extended to group-specific non-linear exposure intensity-latency-outcome relationship and group-specific interaction effect from multiple exposures. Extensive simulation studies are conducted and demonstrate satisfactory model performances. The proposed methods are applied to the analyses of group-specific associations between antidepressant use and time to coronary artery disease in a depression-screening cohort using data extracted from electronic medical records.

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Accurate prediction of mortality is an important component of forest growth and yield prediction systems, yet mortality remains one of the least understood components of the system. Whole-stand and individual-tree mortality models were developed for loblolly pine plantations throughout its geographic range in the United States. The model for predicting stand mortality were developed using stand characteristics and biophysical variables. The models were constructed using two modeling approaches. In the first approach, mortality functions for directly predicting tree number reduction were developed using algebraic difference equation method. In the second approach, a two-step modeling strategy was used where a model predicting the probability of tree death occurring over a period was developed in the first step and a function that estimates the reduction in tree number was developed in the second step. Individual-tree mortality models were developed using multilevel logistic regression and survival analysis techniques. Multilevel data structure inherent in permanent sample plots data i.e. measurement occasions nested within trees (e.g., repeated measurements) and trees nested within plots, is often ignored in modeling tree mortality in forestry applications. Multilevel mixed-effects logistic regression takes into account the full hierarchical structure of the data. Multilevel mixed-effects models gave better predictions than the fixed effects model; however, the model fits and predictions were further improved by taking into account the full hierarchical structure of the data. Semiparametric proportional hazards regression was also used to develop model for individual-tree mortality. Shared frailty model, mixed model extension of Cox proportional hazards model, was used to account for unobserved heterogeneity not explained by the observed covariates in the Cox model. / Ph. D.

Maintenance of medical equipment plays an important role in ensuring the healthcare quality so that the care can be conducted with minimal risk. Preventive maintenance is performed to maintain the equipment in satisfactory operating condition, while corrective maintenance is made when there is an unpredicted maintenance requirement. This study aims to determine what effect preventive maintenance has on corrective maintenance. A correlation analysis, regression analysis and survival analysis are performed on work-order data from 2000-2021. The results obtained indicate that increasing the number of preventive maintenances made to medical equipment will decrease the number of corrective maintenances required for the medical equipment.