Population-based health care databases are a valuable tool for observational studies as
they reflect daily medical practice for large and representative populations. A constant …

Confounding is an important source of bias, but it is often misunderstood. We consider how
confounding occurs and how to address confounding using examples. Study results are …

Loss to follow-up is problematic in most cohort studies and often leads to bias. Although
guidelines suggest acceptable follow-up rates, the authors are unaware of studies that test …

Methods to assess uncertainty in the estimated population attributable risk (PAR) by
calculating 95 per cent confidence intervals (CIs) are not readily available in software for …

Odds ratios are a necessary evil in medical research; although used as a measure of effect
size from logistic regressions and case-control studies, they are poorly understood. This …

What should be the role of P-values and confidence intervals in the interpretation of scientific
results? This question is not new1 and our field of epidemiology is far from alone in …

Background Retaining participants in cohort studies with multiple follow-up waves is difficult.
Commonly, researchers are faced with the problem of missing data, which may introduce …

The Norwegian Mother and Child Cohort Study (MoBa) was planned in the 1990s partly by
researchers at the Medical Birth Registry of Norway (MBRN) 1 and partly by researchers at …

Background In epidemiological studies researchers use logistic regression as an analytical
tool to study the association of a binary outcome to a set of possible exposures. Methods …

Non-participation in population studies is likely to be a source of bias in many types of
epidemiologic studies, including those describing social disparities in health. The objective …