Optimising the case-crossover design for use in shared exposure settings.

Abstract:

:With a case-crossover design, a case's exposure during a risk period is compared to the case's exposures at referent periods. The selection of referents for this self-controlled design is determined by the referent selection strategy (RSS). Previous research mainly focused on systematic bias associated with the RSS. We additionally focused on how RSS determines the number of referents per risk, sensitivity to overdispersion and time-varying confounding.We illustrated the consequences of different RSS using a simulation study informed by data on meteorological variables and Legionnaires' disease. By randomising the events and exposure time series, we explored statistical power associated with time-stratified and fixed bidirectional RSS and their susceptibility to systematic bias and confounding bias. In addition, we investigated how a high number of events on the same date (e.g. outbreaks) affected coefficient estimation. As illustrated by our work, referent selection alone can be insufficient to control for a time-varying confounding bias. In contrast to systematic bias, confounding bias can be hard to detect. We studied potential solutions: varying the model parameters and link-function, outlier-removal and aggregating the input-data over smaller areas. Our simulation study offers a framework for researchers looking to detect and to avoid bias in case-crossover studies.

journal_name

Epidemiol Infect

authors

Braeye T,Hens N

doi

10.1017/S0950268820000916

subject

Has Abstract

pub_date

2020-05-04 00:00:00

pages

e151

eissn

0950-2688

issn

1469-4409

pii

S0950268820000916

journal_volume

148

pub_type

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