%0 Journal Article %1 Janes2008 %A Janes, Holly %A Sheppard, Lianne %A Shepherd, Kristen %D 2008 %J Annals of epidemiology %K Adolescent AirPollutants AirPollutants:adverseeffects AirPollution AirPollution:adverseeffects AirPollution:statistics&numericaldata Child ConfoundingFactors(Epidemiology) DataInterpretation EnvironmentalExposure EnvironmentalExposure:adverseeffects Female Humans LongitudinalStudies Male Models Statistical Theoretical TimeFactors Washington Washington:epidemiology %N 10 %P 792-802 %R 10.1016/j.annepidem.2008.06.004 %T Statistical analysis of air pollution panel studies: an illustration. %U http://www.ncbi.nlm.nih.gov/pubmed/18922395 %V 18 %X PURPOSE: The panel study design is commonly used to evaluate the short-term health effects of air pollution. Standard statistical methods are available for analyzing longitudinal data, but the literature reveals that these methods are poorly understood by practitioners. METHODS: We review standard statistical methods for modeling longitudinal data. Marginal, conditional, and transitional approaches are reviewed and contrasted with respect to their parameter interpretation and methods for accounting for correlation and dealing with missing data. We also discuss techniques for controlling for time-dependent and time-independent confounding and for exploring and summarizing panel study data. Notes on available software are provided. RESULTS: These methods are illustrated by using data from the 1999 to 2002 Seattle Panel Study. CONCLUSIONS: The quality of statistical analyses and presentation of results of panel studies could be improved if the methods we present were followed. %@ 1873-2585

@article{Janes2008, abstract = {PURPOSE: The panel study design is commonly used to evaluate the short-term health effects of air pollution. Standard statistical methods are available for analyzing longitudinal data, but the literature reveals that these methods are poorly understood by practitioners. METHODS: We review standard statistical methods for modeling longitudinal data. Marginal, conditional, and transitional approaches are reviewed and contrasted with respect to their parameter interpretation and methods for accounting for correlation and dealing with missing data. We also discuss techniques for controlling for time-dependent and time-independent confounding and for exploring and summarizing panel study data. Notes on available software are provided. RESULTS: These methods are illustrated by using data from the 1999 to 2002 Seattle Panel Study. CONCLUSIONS: The quality of statistical analyses and presentation of results of panel studies could be improved if the methods we present were followed.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Janes, Holly and Sheppard, Lianne and Shepherd, Kristen}, biburl = {https://www.bibsonomy.org/bibtex/297a2db281e117200d292c7ec2554037e/jepcastel}, city = {Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, University of Washington, Seattle, USA. hjanes@scharp.org}, doi = {10.1016/j.annepidem.2008.06.004}, interhash = {34d10bba3aa625d5ed7c4f60a44d02be}, intrahash = {97a2db281e117200d292c7ec2554037e}, isbn = {1873-2585}, issn = {1873-2585}, journal = {Annals of epidemiology}, keywords = {Adolescent AirPollutants AirPollutants:adverseeffects AirPollution AirPollution:adverseeffects AirPollution:statistics&numericaldata Child ConfoundingFactors(Epidemiology) DataInterpretation EnvironmentalExposure EnvironmentalExposure:adverseeffects Female Humans LongitudinalStudies Male Models Statistical Theoretical TimeFactors Washington Washington:epidemiology}, month = {10}, note = {4973<m:linebreak></m:linebreak>JID: 9100013; 0 (Air Pollutants); 2008/01/09 [received]; 2008/05/17 [revised]; 2008/06/18 [accepted]; ppublish;}, number = 10, pages = {792-802}, pmid = {18922395}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Statistical analysis of air pollution panel studies: an illustration.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18922395}, volume = 18, year = 2008 }