Lung cancer mortality and exposure to synthetic metalworking fluid and biocides: controlling for the healthy worker survivor effect

Garcia, Erika; Picciotto, Sally; Neophytou, Andreas M.; Bradshaw, Patrick T.; Balmes, John R.; Eisen, Ellen A.

doi:10.1136/oemed-2017-104812

Cited by 10 publications

(3 citation statements)

References 31 publications

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“…A series of results from exposure-response analyses have also been reported based on the extensive historical exposure assessment for straight, soluble and synthetic MWF. Results based on Cox proportional hazard models for digestive and respiratory cancer mortality in relation to MWF exposures have been largely null (11)(12)(13)(14)(15). However, results based on cancer incidence in this cohort have been more mixed.…”

mentioning

confidence: 78%

Metalworking fluids and cancer mortality in a US autoworker cohort (1941–2015)

Costello¹,

Chen²,

Picciotto³

et al. 2020

Scand J Work Environ Health

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We present an extended cancer mortality follow-up of an autoworkers cohort potentially exposed to metalworking fluids. Patterns are consistent with previous reports, providing further evidence of increased risk of skin and female breast cancer with straight fluids, as well as new evidence for stomach cancer. Overall, there continues to be enough evidence to support a federal standard for metalworking fluid.

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mentioning

confidence: 78%

Metalworking fluids and cancer mortality in a US autoworker cohort (1941–2015)

Costello¹,

Chen²,

Picciotto³

et al. 2020

Scand J Work Environ Health

Self Cite

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“…Investigators dismiss the g-null paradox when they find nonnull effect estimates if substantive knowledge or prior studies suggested that the sharp causal null (condition 3) does not hold (e.g., see Refs. [13][14][15] ) or when they find null effect estimates precisely because, despite the potential for the existence of the g-null paradox, they do find a null result (e.g., see Ref. 16 ).…”

Section: Beyond the Sharp Causal Null Hypothesismentioning

confidence: 99%

Revisiting the g-null Paradox

2021

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The (noniterative conditional expectation) parametric g-formula is an approach to estimating causal effects of sustained treatment strategies from observational data. An often-cited limitation of the parametric g-formula is the g-null paradox: a phenomenon in which model misspecification in the parametric g-formula is guaranteed in some settings consistent with the conditions that motivate its use (i.e., when identifiability conditions hold and measured timevarying confounders are affected by past treatment). Many users of the parametric g-formula acknowledge the g-null paradox as a limitation when reporting results but still require clarity on its meaning and implications. Here, we revisit the g-null paradox to clarify its role in causal inference studies. In doing so, we present analytic examples and a simulation-based illustration of the bias of parametric g-formula estimates under the conditions associated with this paradox. Our results highlight the importance of avoiding overly parsimonious models for the components of the g-formula when using this method.

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“…The possibility of the g-null paradox is often handled informally in practice. Investigators dismiss the g-null paradox when they find non-null effect estimates if substantive knowledge or prior studies suggested that the sharp causal null (condition 3) does not hold (e.g., see [10,11,12]) or when they find null effect estimates precisely because, despite the potential for the existence of the g-null paradox, they do find a null result (e.g., see [13]).…”

Section: Beyond the Sharp Causal Null Hypothesismentioning

confidence: 99%

Revisiting the g-null paradox

McGrath,

Young,

Hernán

2021

Preprint

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The parametric g-formula is an approach to estimating causal effects of sustained treatment strategies from observational data. An often cited limitation of the parametric g-formula is the g-null paradox: a phenomenon in which model misspecification in the parametric g-formula is guaranteed under the conditions that motivate its use (i.e., when identifiability conditions hold and measured time-varying confounders are affected by past treatment). Many users of the parametric g-formula know they must acknowledge the g-null paradox as a limitation when reporting results but still require clarity on its meaning and implications. Here we revisit the g-null paradox to clarify its role in causal inference studies. In doing so, we present analytic examples and a simulation-based illustration of the bias of parametric g-formula estimates under the conditions associated with this paradox. Our results highlight the importance of avoiding overly parsimonious models for the components of the g-formula when using this method.

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Lung cancer mortality and exposure to synthetic metalworking fluid and biocides: controlling for the healthy worker survivor effect

Abstract: Findings suggest a modest positive association for synthetic MWF with lung cancer mortality, contrary to the negative associations reported in earlier studies. Biocide exposure, however, was inversely associated with risk of lung cancer mortality.

Cited by 10 publications

References 31 publications

Metalworking fluids and cancer mortality in a US autoworker cohort (1941–2015)

Metalworking fluids and cancer mortality in a US autoworker cohort (1941–2015)

Revisiting the g-null Paradox

Revisiting the g-null paradox

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