%0 Journal Article %1 Zhao2017 %A Zhao, Wei %A Chen, Ying Qing %A Hsu, Li %D 2017 %J Biometrics %K Case-controlstudy Kernelsmoother Populationattributablefraction Time-varying %N 3 %P 866-875 %R 10.1111/biom.12648 %T On estimation of time-dependent attributable fraction from population-based case-control studies. %U http://doi.wiley.com/10.1111/biom.12648 http://www.ncbi.nlm.nih.gov/pubmed/28099992 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5515699 %V 73 %X Population attributable fraction (PAF) is widely used to quantify the disease burden associated with a modifiable exposure in a population. It has been extended to a time-varying measure that provides additional information on when and how the exposure's impact varies over time for cohort studies. However, there is no estimation procedure for PAF using data that are collected from population-based case-control studies, which, because of time and cost efficiency, are commonly used for studying genetic and environmental risk factors of disease incidences. In this article, we show that time-varying PAF is identifiable from a case-control study and develop a novel estimator of PAF. Our estimator combines odds ratio estimates from logistic regression models and density estimates of the risk factor distribution conditional on failure times in cases from a kernel smoother. The proposed estimator is shown to be consistent and asymptotically normal with asymptotic variance that can be estimated empirically from the data. Simulation studies demonstrate that the proposed estimator performs well in finite sample sizes. Finally, the method is illustrated by a population-based case-control study of colorectal cancer.

@article{Zhao2017, abstract = {Population attributable fraction (PAF) is widely used to quantify the disease burden associated with a modifiable exposure in a population. It has been extended to a time-varying measure that provides additional information on when and how the exposure's impact varies over time for cohort studies. However, there is no estimation procedure for PAF using data that are collected from population-based case-control studies, which, because of time and cost efficiency, are commonly used for studying genetic and environmental risk factors of disease incidences. In this article, we show that time-varying PAF is identifiable from a case-control study and develop a novel estimator of PAF. Our estimator combines odds ratio estimates from logistic regression models and density estimates of the risk factor distribution conditional on failure times in cases from a kernel smoother. The proposed estimator is shown to be consistent and asymptotically normal with asymptotic variance that can be estimated empirically from the data. Simulation studies demonstrate that the proposed estimator performs well in finite sample sizes. Finally, the method is illustrated by a population-based case-control study of colorectal cancer.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Zhao, Wei and Chen, Ying Qing and Hsu, Li}, biburl = {https://www.bibsonomy.org/bibtex/26a58a43dd42936bcea55bb933836f800/jepcastel}, doi = {10.1111/biom.12648}, interhash = {cc574e9ad9197b842432a90ef305031a}, intrahash = {6a58a43dd42936bcea55bb933836f800}, issn = {1541-0420}, journal = {Biometrics}, keywords = {Case-controlstudy Kernelsmoother Populationattributablefraction Time-varying}, month = {9}, note = {Risc atribuïble; Cas-control; Online}, number = 3, pages = {866-875}, pmid = {28099992}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {On estimation of time-dependent attributable fraction from population-based case-control studies.}, url = {http://doi.wiley.com/10.1111/biom.12648 http://www.ncbi.nlm.nih.gov/pubmed/28099992 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC5515699}, volume = 73, year = 2017 }