Time introduces complexity in causal inference for observational studies. This thesis investigates two sources of bias for studies involving time-varying data. One is the missing …
Provider profiling is used to gauge the level of care that is being offered across providers. The extensive repercussions of provider profiling on the health …
This thesis work is motivated by the specific challenges in analyzing data from clinical trials in behavioral medicine. Specifically, smoking cessation and physical activity trials. …
Abstract of Analysis of temporal trends in opioid prescribing: Assessing the Effectiveness of an opioid prescribing policy using segmented regression, difference in difference versus propensity …
Prediction and causality are of significant importance in public health and epidemiology. Questions of interest related to predictive modeling include the screening for high risk …
Individual-Level Data (IPD) approach has become increasingly popular as it does not depend on published results. Combining IPD across multiple studies usually adopts a multilevel …
We present an analysis of hospitalization rates following opioid and non-pharmacologic treatment among Medicare beneficiaries suffering from chronic back pain. Treatment for chronic back pain …
The gold standard to estimate the causal effect of an exposure on an outcome is a completely randomized design. Randomized designs are attractive for estimating …
Causal mediation analysis is widely applied in the social, economic and biological sciences to assess the causal mechanism between three variables: a treatment, an intermediate …
In South Africa, the epicentre of the HIV pandemic, there is a high prevalence of circular migration: within-country rural-to-urban cyclical movement, an artifact of apartheid-era …
Background. Ukraine has one of the highest burdens of rifampicin-resistant tuberculosis (RR-TB) in the world, and since 2014, the country has been involved in an …
There exist many non-oral treatments for osteoporosis such as teriparatide and denosumab. The purpose of this study is to evaluate the causal effect of teriparatide …
Background: The treatment effect observed in randomized controlled trials may not reflect the actual effect in the target population if the trial and target populations …
Causal inference in observational studies relies on the fundamental assumption that treatment assignments are ignorable conditional on observed covariates. For this reason, it has been …
Cluster randomization trials (CRTs) have been increasingly used to evaluate public health and public policy interventions in which individual units are clustered by social organizations …
The objective of this dissertation is to develop methods for statistical record linkage that are more robust when identifying information is limited or recorded with …
Across studies that use household-based survey samples, clinical diagnostic interviews, and medical records, estimates of the life-time prevalence of schizophrenia and related psychotic disorders in …
Estimating the average treatment effect (ATE) in the target population is often a main interest of epidemiologic studies. I consider three different ways to estimate …
Heterogeneous treatment effect estimation is commonly performed by exploring treatment covariate interactions in regression models or by performing pre-specified subgroup analyses using data from randomized …