Background Fractures have serious health consequences in older adults. While some
medications are individually associated with increased risk of falls and fractures, it is not …

Variable selection in regression models is a particularly important issue in epidemiology,
where one usually encounters observational studies. In contrast to randomized trials or …

Background Causal feature selection is essential for estimating effects from observational
data. Identifying confounders is a crucial step in this process. Traditionally, researchers …

In observational studies, identifying and adjusting for a sufficient set of confounders is crucial
for accurately estimating the causal effect of the exposure on the outcome. Even in studies …

Background: Non-adherence to antipsychotics in schizophrenia is associated with an
increased risk of psychotic relapse and hospitalization, a risk that is reduced with the use of …

We demonstrate that a change in effect could be due to confounding, noncollapsibility, or a
combination of both and that noncollapsibility can be a powerful tool for data analysis and …

Objectives This study aimed to examine the effectiveness of the best response rate (BRR) as
a surrogate for overall survival (OS), using the modified Response Evaluation Criteria in …

Little is known about skipping breakfast and breakfast patterns (BP) and their evaluation
according to sociodemographic, clinical, lifestyle, cardiometabolic and nutritional data in …

Background Motor control patterns are altered when women with pregnancy-related pelvic
girdle pain (PGP) experience pain. In low back pain, these adaptations can persist after …

Background In causal analyses, some third factor may distort the relationship between the
exposure and the outcome variables under study, which gives spurious results. In this case …