Indiana University-Purdue University Indianapolis (IUPUI)Adverse drug events (ADEs) are injuries resulting from drug-related medical
interventions. ADEs can be either induced by a single drug or a drug-drug interaction (DDI).
In order to prevent unnecessary ADEs, many regulatory agencies in public health maintain
pharmacovigilance databases for detecting novel drug-ADE associations. However,
pharmacovigilance databases usually contain a significant portion of false associations due
to their nature structure (i.e. false drug-ADE associations caused by co-medications).
Besides pharmacovigilance studies, the risks of ADEs can be minimized by understating
their mechanisms, which include abnormal pharmacokinetics/pharmacodynamics due to
genetic factors and synergistic effects between drugs. During the past decade,
pharmacogenomics studies have successfully identified several predictive markers to
reduce ADE risks. While, pharmacogenomics studies are usually limited by the sample
size and budget.
In this dissertation, we develop statistical methods for pharmacovigilance and
pharmacogenomics studies. Firstly, we propose an empirical Bayes mixture model to
identify significant drug-ADE associations. The proposed approach can be used for both
signal generation and ranking. Following this approach, the portion of false associations
from the detected signals can be well controlled. Secondly, we propose a mixture dose
response model to investigate the functional relationship between increased dimensionality
of drug combinations and the ADE risks. Moreover, this approach can be used to identify high-dimensional drug combinations that are associated with escalated ADE risks at a
significantly low local false discovery rates. Finally, we proposed a cost-efficient design
for pharmacogenomics studies. In order to pursue a further cost-efficiency, the proposed
design involves both DNA pooling and two-stage design approach. Compared to traditional
design, the cost under the proposed design will be reduced dramatically with an acceptable
compromise on statistical power. The proposed methods are examined by extensive
simulation studies. Furthermore, the proposed methods to analyze pharmacovigilance
databases are applied to the FDAโs Adverse Reporting System database and a local
electronic medical record (EMR) database. For different scenarios of pharmacogenomics
study, optimized designs to detect a functioning rare allele are given as well
