We aim to estimate parameters in a generalized linear model (GLM) for a binary outcome
when, in addition to the raw data from the internal study, more than 1 external study provides …

In Data Science, we are concerned with the integration of relevant sciences in observed and
empirical contexts. This results in the unification of analytical methodologies, and of …

There are now a growing number of applications of multilevel regression and
poststratification (MRP) in population health and epidemiological studies. MRP uses …

Background: When population-based databases are unavailable, nationwide assessments
of the disease burden of multiple sclerosis (MS) resort to clinical, administrative or …

Epidemiologists study associations but they are usually interested in causation that could
lead to disease prevention. Experience show, however, that many of the associations we …

Collecting experimental cognitive data with young children usually requires undertaking one-
on-one assessments, which can be both expensive and time-consuming. In addition, there is …

Most human subjects research involving contact with participants has been halted in the US
due to the COVID-19 crisis. We have been testing an online method to recruit and survey …

Abstract In the United States (US), three in 10 cannabis users develop cannabis use
disorder (CUD). Usage patterns in line with CUD may be associated with socio-economic …

Multiple data sources are becoming increasingly available for statistical analyses in the era
of big data. As an important example in finite-population inference, we consider an …

Background Crowding in the emergency department (ED) is associated with increased
mortality, increased treatment cost, and reduced quality of care. Crowding arises when …