Experience sampling methodology (ESM) is a real-time data capture method that can be used to monitor symptoms and behaviours as they occur during everyday life. With measures completed multiple times a day, over several days, this intensive longitudinal data collection method results in multilevel data with observations nested within days, nested within subjects. The aim of this thesis was to investigate the optimal use of multilevel models for ESM in the design, reporting and analysis of ESM data, and apply these models to a study in people with psychosis. A methodological systematic review was conducted to identify design, analysis and statistical reporting practices in current ESM studies. Seventy four studies from 2012 were reviewed, and together with the analysis of a motivating example, four significant areas of interest were identified: power and sample size, missing data, momentary variation and predicting momentary change. Appropriate multilevel methods were sought for each of these areas, and were evaluated in the three-level context of ESM.Missing data was found to be both underreported and rarely considered when choosing analysis methods in practice. This work has introduced a more detailed understanding of nonresponse in ESM studies and has discussed appropriate statistical methods in the presence of missing data. This thesis has extended two-level statistical methodology for data analysis to accommodate the three-level structure of ESM. Novel applications of time trends have been developed, were time can be measured at two separate levels. The suitability of predicting momentary change in ESM data has been questioned; it is argued that the first-difference and joint modelling methods that are claimed in the literature to remove bias possibly induce more in this context. Finally, Monte Carlo simulations were shown to be a flexible option for estimating empirical power under varying sample sizes at levels 3, 2 and 1, with recommendations made for conservative power estimates when a priori parameter estimates are unknown. In summary, this work demonstrates how multilevel models can be used to examine the rich data structure of ESM and fully utilize the variation in measures captured at all levels.

The majority of current smokers in the United States want to quit smoking; however, long-term abstinence rates do not improve beyond 30%, despite the availability of effective pharmaco-behavioral treatments and increased outreach of awareness programs on quitting benefits. One of the reasons is non-adherence to pharmacological treatment. Pharmacological treatments are developed to alleviate withdrawal symptoms experienced during a quit attempt. However, without continued treatment adherence, especially in the first few weeks of a quit attempt (when withdrawal symptoms fluctuate the most), the chances of relapse peak. Thus, adherence to pharmacological treatments must be improved to sustain long-term smoking abstinence. Moreover, smoking cessation is a complex and time-varying process. Therefore, the time-varying causal structure of adherence and smoking cessation must be studied carefully.The time-varying mechanisms underlying the smoking cessation process can be captured efficiently through intensive longitudinal data and quantified through appropriate methods. Mediation analysis is an efficient tool for studying such mechanisms. However, despite the time-varying nature of the data, existing approaches for assessing mediation provide overall average (in)direct effects over time and omit describing the temporal characteristic of the dynamic effect. This dissertation research aims to develop a new approach to estimating time-varying causal (in)direct effects of pharmacological treatments on daily smoking cessation outcome(s) mediated via daily treatment adherence. Additionally, it is hypothesized that adherence is influenced by daily stress events related to social contextual factors, not treatment-induced. The purpose of this research is to derive time-varying causal (in)direct effects. A local polynomial regression-based approach integrated with the mediational g-formula was proposed as a possible solution. Furthermore, since no other studies have studied time-specific mediation effects using a potential outcomes framework-based method, the performance of the proposed method was tested using two simulation studies. Finally, the optimum analytical approach (based on the findings from the simulation studies) was applied to answer the substantive research questions on smoking cessation using empirical data from a smoking cessation clinical trial. This dissertation is divided into six chapters. A brief overview of the chapters is as follows: Chapter 1 provides a comprehensive background and rationale for the methodological and substantive research that motivated this work. The chapter concludes with the three specific aims addressed in this research and a summary of the next steps. In Chapter 2, the longitudinal causal frameworks and the assumptions required to interpret the estimated time-varying (in)direct effects as causal are described in detail. These frameworks were further used in Chapters 3 and 4 for the two simulation studies that evaluated the performance of the proposed new approach. The simulation study in Chapter 3 evaluates the time-varying (in)direct effects in a longitudinal study in the absence of exposure-induced time-varying confounding of a mediator-outcome pathway. Four outcome scenarios with a binary exposure, a binary mediator, and a time-varying binary confounder of the mediator-outcome pathway were examined: 1) continuous outcome, 2) rare binary outcome, 3) common binary outcome, and 4) count outcome that is not zero-inflated. Two types of path-specific causal estimands are identifiable for these scenarios. The findings suggest good performance of the proposed analytical approach in producing accurate effect estimates (reduced bias and reasonable coverage) of these estimands for all the outcome scenarios. The simulation study in Chapter 4 evaluates the time-varying (in)direct effects in a longitudinal study in the presence of exposure-induced time-varying confounding of a mediator-outcome pathway. A zero-inflated count outcome scenario with a binary exposure, a binary mediator, and a time-varying binary confounder of the mediator-outcome pathway was examined. Four types of path-specific causal estimands are identifiable for this scenario, and the findings suggest good performance of the proposed analytical approach in producing accurate effect estimates. Chapter 5 uses the Wisconsin Smokers Health Study II data to assess the mechanisms via which pharmacological smoking cessation treatments affect the cessation-related outcome(s) in the presence of time-varying confounding that is not exposure induced. We found that individuals randomized to Nicotine Patch only group have better smoking cessation outcome(s) compared to individuals on Varenicline or combination Nicotine Replacement Therapy. This is due to better adherence among Nicotine Patch-only users. Finally, Chapter 6 presents the concluding remarks, including key findings from the three studies, limitations, and recommendations for future research. / Epidemiology

The dynamic structural equation modeling (DSEM) framework incorporates hierarchical latent modeling (HLM), structural equation modeling (SEM), time series analysis (TSA), and time-varying effects modeling (TVEM) to model the dynamic relationship between latent and observed variables. To model the functional relationships between variables, a Gaussian process (GP), by definition of its covariance function(s), allows researchers to define Gaussian distributions over functions of input variables. Therefore, by incorporating GPs to model the presence of significant trend in either latent or observed variables, this dissertation explores the adequacy and performance of GPs in manipulated conditions of sample size using the flexible Bayesian analysis approach. The overall results of these Monte Carlo simulation studies showcase the ability of the multi-output GPs to properly explore the presence of trends. Also, in modeling intensive longitudinal data, GPs can be specified to properly account for trends, without generating significantly biased and imprecise estimates.

Cette thèse vise à expliquer les fluctuations du bien-être momentané tout au long de la journée et de la semaine, en tenant compte de l'influence de l'environnement construit et social sur ces variations. Cette étude a évalué l'impact du moment de la journée, des interactions sociales, de la météo et des environnements bâtis et sociaux, sur le bien-être momentané à l’aide d’un devis longitudinal par évaluation écologique momentanée géographique (GEMA). Un total de 899 participants résident dans le Grand Montréal, âgés de 18 à 80 ans (Âge : M = 41,71, md = 39 ; femmes = 55,7%) ont rempli une échelle brève de l'humeur trois fois par jour pendant sept jours consécutifs sur leurs téléphones intelligents (application EthicaData). Lors des réponses, la coordonnée GPS de leur localisation a également été captée, et a servi à mesurer diverses expositions environnementales dans un système d’information géographique. Un modèle à effets mixtes à trois niveaux avec des effets aléatoires a montré une corrélation positive entre le bien-être et l'âge, les après-midis, les week-ends et les interactions sociales impliquant la famille et les amis. En revanche, le bien-être était négativement associé aux soirées. Quelques variables de l’environnement bâti et social étaient significativement associées au bien-être. Ces liens ont persisté après contrôle des facteurs de confusion potentiels. De plus, un effet d'interaction a révélé que l'influence des interactions sociales momentanées différait entre les hommes et les femmes. Cette étude met en lumière le rôle des facteurs environnementaux et sociaux dans la compréhension du bien-être momentané. L'intégration de la technologie géospatiale et des évaluations écologiques momentanées offre des perspectives précieuses pour l'urbanisme et la santé publique dans l’exploration des liens entre contexte et santé. / This thesis aims to explain the fluctuations of momentary well-being throughout the day and week, taking into consideration how the built and social environment affects these variations. In this seven-day longitudinal study using GPS-enabled smartphones and EthicaData software with a geographic ecological momentary assessment (GEMA) approach, the real-time impact of built and social environments on self-reported momentary well-being of residents of Greater Montreal between 2018 and 2021 was investigated. A total of 889 participants aged 18–80 years (Age: M=41.71, md=39; females = 55.7%) completed the Short Mood Scale three times daily for seven consecutive days. A three-level mixed-effects model with random effects showed a positive correlation between well-being and age, afternoons, weekends, and social interactions involving family and friends. On the other hand, well-being was negatively associated with evenings. Only a few built and social environmental variables were found to be significantly associated with well-being. These links remained after controlling for potential confounding factors. Moreover, an interaction effect revealed that the influence of momentary social interactions differed for men and women. This study highlights the significance of environmental and social factors in comprehending momentary well-being, which has important implications for urban planning and public health initiatives. Integrating geospatial technology and EMA provides valuable insights into this intricate relationship.