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Comment
. 2021 Mar;129(3):38002.
doi: 10.1289/EHP8820. Epub 2021 Mar 10.

Response to "Comment on 'A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures'"

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Comment

Response to "Comment on 'A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures'"

Alexander P Keil et al. Environ Health Perspect. 2021 Mar.
No abstract available

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References

    1. Gennings C. 2021. Comment on “a quantile-based g-computation approach to addressing the effects of exposure mixtures.” Environ Health Perspect 129:xxx, 10.1289/EHP8739. - DOI - PMC - PubMed
    1. Keil AP, Buckley JP, Kalkbrenner AK. 2020a. Bayesian g-computation to estimate impacts of interventions on exposure mixtures: Demonstration with metals from coal-fired power plants and birthweight. Am J Epidemiol, 10.1093/aje/kwab053 [in press]. - DOI - PMC - PubMed
    1. Keil AP, Buckley JP, O’Brien KM, Ferguson KK, Zhao S, White AJ. 2020b. A quantile-based g-computation approach to addressing the effects of exposure mixtures. Environ Health Perspect 128(4):47004, PMID: 32255670, 10.1289/EHP5838. - DOI - PMC - PubMed
    1. Keil AP, Daza EJ, Engel SM, Buckley JP, Edwards JK. 2018a. A Bayesian approach to the g-formula. Stat Methods Med Res 27(10):3183–3204, PMID: 29298607, 10.1177/0962280217694665. - DOI - PMC - PubMed
    1. Keil AP, Edwards JK, Richardson DR, Naimi AI, Cole SR. 2014. The parametric g-formula for time-to-event data: intuition with a worked example. Epidemiology 25(6):889–897, PMID: 25140837, 10.1097/EDE.0000000000000160. - DOI - PMC - PubMed

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