In the July 2015 issue of EpidEmiology in the article by Davies, “An Even Clearer Portrait of Bias in Observational Studies?”, the Creative Commons License was cited incorrectly. The correct license is: This is an open access distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original ...

The Surveillance, Epidemiology, and End Results (SEER) cancer registry contributed to technology assessment by providing population-based samples for detailed case-control studies, by serving as the control group in comparisons with various experimental groups, by allowing an assessment of selection bias in clinical trials, and by facilitating evaluations of classification and coding systems.

Reality can be distorted in many ways when seen through a questionnaire or an interview. Such distortions may be systematic, introducing bias. Bias can spoil research by indicating false associations or failing to detect true relationships. It is practically impossible to eliminate measurement errors totally, but estimating the extent of disagreement and assessing whether the errors are systema...

While observational epidemiology is considered to be the scientific foundation of public health2 it is often viewed as a ‘soft science’ limited by an inherent inability to fully control for confounding, misclassification, and selection bias.3 Enter the ‘genomics era’.4,5 With the completion of the Human Genome Project, and the eventual identification of thousands of human genetic variants, scie...

Sample selection bias has long been recognized in many fields including clinical trials, epidemiology studies, genome-wide association studies, and wildlife management. This paper investigates the maximum likelihood estimation for censored survival data with selection bias under the Cox regression models where the selection process is modeled parametrically. A novel expectation-maximization alg...

The recent revolution in genomics is already having a profound impact on the practice of epidemiology. The purpose of this commentary is to demonstrate how genomics and epidemiology will continue to rely heavily on each other, now and in the future, by illustrating a number of interaction points between these 2 disciplines: (1) the use of genomics to estimate disease heritability; (2) the impac...

This thesis addresses the intersection of two important areas in epidemiology and statistics: genetic linkage analysis and missing data methods, respectively. Genetic linkage analysis is a promising method in viral epidemiology which involves learning about transmission patterns by studying clusters of similar gene sequences. For example, similar sequences found in a pair of geographically dist...

Bicyclists and transportation professionals would do better to decline advice drawn from characteristically epidemiological studies. The faults of epidemiology are both accidental (unpreparedness for the task) and essential (unsuitability of the methods). Characteristically epidemiological methods are known to be error-prone, and when applied to bicycle transportation suffer from diversion bias...

In occupational epidemiology, group-based exposure assessment entails estimating the average exposure level in a group of workers and assigning the average to all members of the group. The assigned exposure values can be used in epidemiological analyses and have been shown to produce virtually unbiased relative-risk estimates in many situations. Although the group-based exposure assessment cont...

This chapter provides an introduction to epidemiology. It covers the key epidemiological concepts such as bias and confounding, as well as providing an overview of the nature, history and types of epidemiology. The main epidemiological study designs are described, including case series, ecological, cross-sectional, case–control, cohort, randomised controlled trial and systematic review. The adv...