Abstract
Background: Biases may exist in the limited longitudinal data focusing on work-related injuries among the ageing workforce. Standard statistical techniques may not provide valid estimates when the data are time-varying and when prior exposures and outcomes may influence future outcomes. This research effort uses marginal structural models (MSMs), a class of causal models rarely applied for injury epidemiology research to analyse work-related injuries. Methods: 7212 working US adults aged ≥50 years, obtained from the Health and Retirement Study sample in the year 2004 formed the study cohort that was followed until 2014. The analyses compared estimates measuring the associations between physical work requirements and work-related injuries using MSMs and a traditional regression model. The weights used in the MSMs, besides accounting for time-varying exposures, also accounted for the recurrent nature of injuries. Results: The results were consistent with regard to directionality between the two models. However, the effect estimate was greater when the same data were analysed using MSMs, built without the restriction for complete case analyses. Conclusions: MSMs can be particularly useful for observational data, especially with the inclusion of recurrent outcomes as these can be incorporated in the weights themselves.
| Original language | English (US) |
|---|---|
| Journal | Injury Prevention |
| DOIs | |
| State | Published - Jan 1 2019 |
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Keywords
- inverse probability weighting
- marginal structural models
- time-varying data
- work-related injuries
PubMed: MeSH publication types
- Journal Article
Cite this
A marginal structural model approach to analyse work-related injuries : An example using data from the health and retirement study. / Baidwan, Navneet Kaur; Gerberich, Susan G; Kim, Hyun; Ryan, Andrew; Church, Timothy R; Capistrant, Benjamin.
In: Injury Prevention, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A marginal structural model approach to analyse work-related injuries
T2 - An example using data from the health and retirement study
AU - Baidwan, Navneet Kaur
AU - Gerberich, Susan G
AU - Kim, Hyun
AU - Ryan, Andrew
AU - Church, Timothy R
AU - Capistrant, Benjamin
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Background: Biases may exist in the limited longitudinal data focusing on work-related injuries among the ageing workforce. Standard statistical techniques may not provide valid estimates when the data are time-varying and when prior exposures and outcomes may influence future outcomes. This research effort uses marginal structural models (MSMs), a class of causal models rarely applied for injury epidemiology research to analyse work-related injuries. Methods: 7212 working US adults aged ≥50 years, obtained from the Health and Retirement Study sample in the year 2004 formed the study cohort that was followed until 2014. The analyses compared estimates measuring the associations between physical work requirements and work-related injuries using MSMs and a traditional regression model. The weights used in the MSMs, besides accounting for time-varying exposures, also accounted for the recurrent nature of injuries. Results: The results were consistent with regard to directionality between the two models. However, the effect estimate was greater when the same data were analysed using MSMs, built without the restriction for complete case analyses. Conclusions: MSMs can be particularly useful for observational data, especially with the inclusion of recurrent outcomes as these can be incorporated in the weights themselves.
AB - Background: Biases may exist in the limited longitudinal data focusing on work-related injuries among the ageing workforce. Standard statistical techniques may not provide valid estimates when the data are time-varying and when prior exposures and outcomes may influence future outcomes. This research effort uses marginal structural models (MSMs), a class of causal models rarely applied for injury epidemiology research to analyse work-related injuries. Methods: 7212 working US adults aged ≥50 years, obtained from the Health and Retirement Study sample in the year 2004 formed the study cohort that was followed until 2014. The analyses compared estimates measuring the associations between physical work requirements and work-related injuries using MSMs and a traditional regression model. The weights used in the MSMs, besides accounting for time-varying exposures, also accounted for the recurrent nature of injuries. Results: The results were consistent with regard to directionality between the two models. However, the effect estimate was greater when the same data were analysed using MSMs, built without the restriction for complete case analyses. Conclusions: MSMs can be particularly useful for observational data, especially with the inclusion of recurrent outcomes as these can be incorporated in the weights themselves.
KW - inverse probability weighting
KW - marginal structural models
KW - time-varying data
KW - work-related injuries
UR - http://www.scopus.com/inward/record.url?scp=85065322898&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065322898&partnerID=8YFLogxK
U2 - 10.1136/injuryprev-2018-043124
DO - 10.1136/injuryprev-2018-043124
M3 - Article
C2 - 31018941
AN - SCOPUS:85065322898
JO - Injury Prevention
JF - Injury Prevention
SN - 1353-8047
ER -