Browsing by Subject "Multi-level"

In this dissertation, I present three essays from three different research projects and they involve different usages of propensity scores in drawing causal inferences in observational studies.

Chapter 1 talks about the general idea of causal inference as well as the concept of randomized experiments and observational studies. It introduces the three different projects and their contributions to the literature.

Chapter 2 gives a critical review and an extensive discussion of several commonly-used propensity score methods when the data have a multilevel structure, including matching, weighting, stratification, and methods that combine these with regression. The usage of these methods is illustrated using a data set about endoscopic vein-graft harvesting in coronary artery bypass graft (CABG) surgeries. We discuss important aspects of the implementation of these methods such as model specification and standard error calculations. Based on the comparison, we provide general guidelines for using propensity score methods with multilevel data in practice. We also provide the relevant code in the form of an \textsf{R} package, available on GitHub.

In observational studies, subjects are no longer assigned to treatment at random as in randomized experiments, and thus the association between the treatment and outcome can be due to some unmeasured variable that affects both the treatment and the outcome. Chapter 3 focuses on conducting sensitivity analysis to assess the robustness of the estimated quantity when the unconfoundedness assumption is violated. Two approaches to sensitivity analysis are presented, both are extensions from previous works to accommodate for a count outcome. One method is based on the subclassification estimator and it relies on maximum likelihood estimation. The second method is more flexible on the estimation method and is based on simulations. We illustrate both methods using a data set from a traffic safety research study about the safety effectiveness (measured in crash counts reduction) of the combined application of center line rumble strips and shoulder rumble strips on two-lane rural roads in Pennsylvania.

Chapter 4 proposes a method for estimating heterogeneous causal effects in observational studies by augmenting additive-interactive Gaussian process regression using the propensity scores, yielding a flexible yet robust way to predict the potential outcome surface from which the conditional treatment effects can be calculated. We show that our method works well even in presence of strong confounding and illustrate this by comparing with commonly-used methods in different settings using simulated data.

Finally, chapter 5 concludes this dissertation and discusses possible future works for each of the projects.

A multilevel analysis of malaria risk factors was conducted using data gathered from community-wide surveillance along the Iquitos-Mazan Road and Napo River in Loreto, Peru. In total, 1650 individuals nested within 338 households nested within 18 communities were included in the study. Personal travel (Odds Ratios [OR] 2.48; 95% Confidence Interval [CI] = 1.46, 4.21) and other house member's malaria statuses (OR = 2.54; 95% CI = 1.49, 4.32) were all associated with increased odds in having a malaria episode. Having a large household (>5 individuals) (OR = 0.33; 95% CI = 0.12, 0.93), presence of a community health post / secondary school (OR =0.26; 95% CI = 0.08, 0.80) and church (OR = 0.33; 95% CI = 0.30, 0.78) were associated with lower odds of having a malaria episode. Malaria clustering was evident as 54% of the malaria burden occurred in only 6% of the households surveyed.

The need for effective multi-level governance arrangements is becoming increasingly apparent because of the high functional interdependencies between biophysical and socioeconomic factors in the realm of natural resource governance. Such arrangements provide a basis for the exchange, discussion, and deliberation of information, knowledge, and data across diverse user groups and entities. Multi-level governance is operationalized by using a microinstitutional analysis that links decision-making arenas across three distinct levels: operational, collective-choice, and constitutional. Within this context, I argue that the effectiveness and success of actors' participatory processes across these three levels depend on the amount of social capital among actors within the governance system. I assessed the concept of social capital using two different models: (1) a structural approach focused on resources embedded within an individual's network, and (2) a combined structural-cultural approach that incorporates various aspects of group membership with relations of trust, rules, and norms. To explore the effects of social capital on participatory processes related to the implementation and management of natural resources, I analyzed different small-scale fisheries governance regimes from the Gulf of California, Mexico. I collected data using surveys (n=371), interviews (n=82), and participant observation techniques conducted among the residents of four small-scale fishing communities that live adjacent to marine protected areas along the Baja California, Mexico, peninsula. Data analysis included both quantitative (logit regression model), and qualitative (narrative analysis) approaches. Overall, my results suggest that both social capital models reveal the multidimensional nature of social capital where none of its individual types form a consistent and statistically significant relationship with the six outcomes that I measured. However, these types are related in different ways to fishers engagement in participatory processess across the three levels. The extent of fishers' engagement in participatory processess across different levels was not high. Qualitative analysis revealed that participatory processes related to fisheries conservation and management, although present do not reach their full potential and are stymied by a historical context and a lack of general participatory culture.