8000013 Selection bias for observational studies in hazard identification

<h3>Objective</h3> Present methods to detect and evaluate selection bias in epidemiologic studies. <h3>Methods</h3> In epidemiological studies, there is usually a target population to which we wish to draw inferences. The study is then conducted in a source population followed over a clearly defined period of time (the risk period). There is also a (perhaps smaller) study population, i.e. the group of people who actually take part in the study. Selection bias refers to differences between the source population and the study population. In cohort studies, important mechanisms for selection bias include non-response at baseline, loss-to-follow-up, left truncation, right truncation, and immortal person-time bias. In case-control studies, all of these biases are possible, but bias can also occur due to inappropriate selection of controls. <h3>Results</h3> Qualitative methods for assessing the existence, direction, and strength of selection bias, include the use of negative control exposures, negative control outcomes, ad hoc re-analyses of published data, comparisons with external data, and the use of multiple control groups. Quantitative methods also exist for sensitivity analyses in which ‘adjustments’ are made for hypothesized selection bias. <h3>Conclusions</h3> One of the primary questions posed to reviewers involved in an IARC Monograph is, ‘Can we reasonably rule out selection bias as an explanation for an observed exposure-cancer association?’ This can be particularly difficult to assess, since most published studies have little or no discussion of the potential for selection bias. It is therefore important that authors, and editors, are encouraged to report the information that is required for a valid assessment of the potential, direction and strength of possible selection bias.

Authors

Institutions

Publication Details

Published
2025-10-01
DOI
https://doi.org/10.1136/oemed-2025-epicohabstracts.187
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

8000013 Selection bias for observational studies in hazard identification

Neil Pearce
Fault Detection and Control Systems
article

8000013 Selection bias for observational studies in hazard identification

Neil Pearce
article en

Abstract

<h3>Objective</h3> Present methods to detect and evaluate selection bias in epidemiologic studies. <h3>Methods</h3> In epidemiological studies, there is usually a target population to which we wish to draw inferences. The study is then conducted in a source population followed over a clearly defined period of time (the risk period). There is also a (perhaps smaller) study population, i.e. the group of people who actually take part in the study. Selection bias refers to differences between the source population and the study population. In cohort studies, important mechanisms for selection bias include non-response at baseline, loss-to-follow-up, left truncation, right truncation, and immortal person-time bias. In case-control studies, all of these biases are possible, but bias can also occur due to inappropriate selection of controls. <h3>Results</h3> Qualitative methods for assessing the existence, direction, and strength of selection bias, include the use of negative control exposures, negative control outcomes, ad hoc re-analyses of published data, comparisons with external data, and the use of multiple control groups. Quantitative methods also exist for sensitivity analyses in which ‘adjustments’ are made for hypothesized selection bias. <h3>Conclusions</h3> One of the primary questions posed to reviewers involved in an IARC Monograph is, ‘Can we reasonably rule out selection bias as an explanation for an observed exposure-cancer association?’ This can be particularly difficult to assess, since most published studies have little or no discussion of the potential for selection bias. It is therefore important that authors, and editors, are encouraged to report the information that is required for a valid assessment of the potential, direction and strength of possible selection bias.

London School of Hygiene & Tropical Medicine (GB)
Openalex Percentile: Top 65%
Fault Detection and Control Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

8000013 Selection bias for observational studies in hazard identification — Neil Pearce · (2025) | TGRS Research Map | TGRS