Testing departure from additivity in Tukey's model using shrinkage: application to a longitudinal setting
dc.contributor.author | Ko, Yi‐an | en_US |
dc.contributor.author | Mukherjee, Bhramar | en_US |
dc.contributor.author | Smith, Jennifer A. | en_US |
dc.contributor.author | Park, Sung Kyun | en_US |
dc.contributor.author | Kardia, Sharon L. r. | en_US |
dc.contributor.author | Allison, Matthew A. | en_US |
dc.contributor.author | Vokonas, Pantel S. | en_US |
dc.contributor.author | Chen, Jinbo | en_US |
dc.contributor.author | Diez‐roux, Ana V. | en_US |
dc.date.accessioned | 2014-12-09T16:53:24Z | |
dc.date.available | WITHHELD_13_MONTHS | en_US |
dc.date.available | 2014-12-09T16:53:24Z | |
dc.date.issued | 2014-12-20 | en_US |
dc.identifier.citation | Ko, Yi‐an ; Mukherjee, Bhramar; Smith, Jennifer A.; Park, Sung Kyun; Kardia, Sharon L. r. ; Allison, Matthew A.; Vokonas, Pantel S.; Chen, Jinbo; Diez‐roux, Ana V. (2014). "Testing departure from additivity in Tukey's model using shrinkage: application to a longitudinal setting." Statistics in Medicine 33(29): 5177-5191. | en_US |
dc.identifier.issn | 0277-6715 | en_US |
dc.identifier.issn | 1097-0258 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/109558 | |
dc.publisher | Wiley | en_US |
dc.subject.other | Tukey's One‐DF Test for Non‐Additivity | en_US |
dc.subject.other | Longitudinal Data | en_US |
dc.subject.other | Gene–Environment Interaction | en_US |
dc.subject.other | Adaptive Shrinkage Estimation | en_US |
dc.title | Testing departure from additivity in Tukey's model using shrinkage: application to a longitudinal setting | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Medicine (General) | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbsecondlevel | Public Health | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/109558/1/sim6281-sup-0001-WebBased.pdf | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/109558/2/sim6281.pdf | |
dc.identifier.doi | 10.1002/sim.6281 | en_US |
dc.identifier.source | Statistics in Medicine | en_US |
dc.identifier.citedreference | Perlstein T, Weuve J, Schwartz J, Sparrow D, Wright R, Litonjua A, Nie H, Hu H. Cumulative community‐level lead exposure and pulse pressure: the Normative Aging Study. Environmental Health Perspectives 2007; 115 ( 12 ): 1696. | en_US |
dc.identifier.citedreference | Moreno‐Macias H, Romieu I, London SJ, Laird NM. Gene‐environment interaction tests for family studies with quantitative phenotypes: a review and extension to longitudinal measures. Human Genomics 2010; 4 ( 5 ): 302 – 326. | en_US |
dc.identifier.citedreference | Tukey JW. One degree of freedom for non‐additivity. Biometrics 1949; 5 ( 3 ): 232 – 242. | en_US |
dc.identifier.citedreference | Maity A, Carroll RJ, Mammen E, Chatterjee N. Testing in semiparametric models with interaction, with applications to gene–environment interactions. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2009; 71 ( 1 ): 75 – 96. | en_US |
dc.identifier.citedreference | Kooperberg C, LeBlanc M. Increasing the power of identifying gene × gene interactions in genome‐wide association studies. Genetic Epidemiology 2008; 32 ( 3 ): 255 – 263. | en_US |
dc.identifier.citedreference | Murcray CE, Lewinger JP, Gauderman WJ. Gene‐environment interaction in genome‐wide association studies. American Journal of Epidemiology 2009; 169 ( 2 ): 219 – 226. | en_US |
dc.identifier.citedreference | Chatterjee N, Kalaylioglu Z, Moslehi R, Peters U, Wacholder S. Powerful multilocus tests of genetic association in the presence of gene‐gene and gene‐environment interactions. The American Journal of Human Genetics 2006; 79 ( 6 ): 1002 – 1016. | en_US |
dc.identifier.citedreference | Barhdadi A, Dubé MP. Testing for gene‐gene interaction with AMMI models. Statistical Applications in Genetics and Molecular Biology 2010; 9 ( Article 2 ): 1 – 27. | en_US |
dc.identifier.citedreference | Chen YH, Chatterjee N, Carroll RJ. Shrinkage estimators for robust and efficient inference in haplotype‐based case‐control studies. Journal of the American Statistical Association 2009; 104 ( 485 ): 220 – 233. | en_US |
dc.identifier.citedreference | Bates DM, Watts DG. Nonlinear Regression Analysis and Its Applications. Wiley: New York, 1988. | en_US |
dc.identifier.citedreference | Lindstrom MJ, Bates DM. Nonlinear mixed effects models for repeated measures data. Biometrics 1990; 46: 673 – 687. | en_US |
dc.identifier.citedreference | Vonesh EF, Carter RL. Mixed‐effects nonlinear regression for unbalanced repeated measures. Biometrics 1992; 48 ( 1 ): 1 – 17. | en_US |
dc.identifier.citedreference | Crainiceanu CM, Ruppert D. Likelihood ratio tests for goodness‐of‐fit of a nonlinear regression model. Journal of Multivariate Analysis 2004; 91 ( 1 ): 35 – 52. | en_US |
dc.identifier.citedreference | Gallant AR. Nonlinear Statistical Models. Wiley: New York, 2009. | en_US |
dc.identifier.citedreference | Vonesh EF, Wang H, Majumdar D. Generalized least squares, Taylor series linearization and Fisher's scoring in multivariate nonlinear regression. Journal of the American Statistical Association 2001; 96 ( 453 ): 282 – 291. | en_US |
dc.identifier.citedreference | Gollob HF. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 1968; 33 ( 1 ): 73 – 115. | en_US |
dc.identifier.citedreference | Mandel J. A new analysis of variance model for non‐additive data. Technometrics 1971; 13 ( 1 ): 1 – 18. | en_US |
dc.identifier.citedreference | Bell B, Rose CL, Damon A. The veterans administration longitudinal study of healthy aging. The Gerontologist 1966; 6 ( 4 ): 179 – 184. | en_US |
dc.identifier.citedreference | Franklin SS, Khan SA, Wong ND, Larson MG, Levy D. Is pulse pressure useful in predicting risk for coronary heart disease? The Framingham Heart Study. Circulation 1999; 100 ( 4 ): 354 – 360. | en_US |
dc.identifier.citedreference | Kwong WT, Friello P, Semba RD. Interactions between iron deficiency and lead poisoning: epidemiology and pathogenesis. Science of The Total Environment 2004; 330 ( 1 ): 21 – 37. | en_US |
dc.identifier.citedreference | Bradman A, Eskenazi B, Sutton P, Athanasoulis M, Goldman LR. Iron deficiency associated with higher blood lead in children living in contaminated environments. Environmental Health Perspectives 2001; 109 ( 10 ): 1079 – 1084. | en_US |
dc.identifier.citedreference | Zhang A, Park SK, Wright RO, Weisskopf MG, Mukherjee B, Nie H, Sparrow D, Hu H. HFE H63D polymorphism as a modifier of the effect of cumulative lead exposure on pulse pressure: the Normative Aging Study. Environmental Health Perspectives 2010; 118 ( 9 ): 1261 – 1266. | en_US |
dc.identifier.citedreference | Knutson M, Wessling‐Resnick M. Iron metabolism in the reticuloendothelial system. Critical Reviews in Biochemistry and Molecular Biology 2003; 38 ( 1 ): 61 – 88. | en_US |
dc.identifier.citedreference | Chung J, Wessling‐Resnick M. Molecular mechanisms and regulation of iron transport. Critical Reviews in Clinical Laboratory Sciences 2003; 40 ( 2 ): 151 – 182. | en_US |
dc.identifier.citedreference | Gao X, Starmer J, Martin ER. A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. Genetic Epidemiology 2008; 32 ( 4 ): 361 – 369. | en_US |
dc.identifier.citedreference | Bild DE, Bluemke DA, Burke GL, Detrano R, Roux AVD, Folsom AR, Greenland P, Jacobs, Jr DR, Kronmal R, Liu K, Nelson JC, O'Leary D, Saad MF, Shea S, Szklo M, Tracy RP. Multi‐ethnic Study of Atherosclerosis: objectives and design. American Journal of Epidemiology 2002; 156 ( 9 ): 871 – 881. | en_US |
dc.identifier.citedreference | Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Allen HL, Lindgren CM, Luan J, Mägi R, Randall JC, Vedantam S, Winkler TW, Qi L, Workalemahu T, Heid IM, Steinthorsdottir V, Stringham HM, Weedon MN, Wheeler E, Wood AR, Ferreira T, Weyant RJ, Segrè AV, Estrada K, Liang L, Nemesh J, Park JH, Gustafsson S, Kilpeläinen TO, Yang J, Bouatia‐Naji N, Esko T, Feitosa MF, Kutalik Z, Mangino M, Raychaudhuri S, Scherag A, Smith AV, Welch R, Zhao JH, Aben KK, Absher DM, Amin N, Dixon AL, Fisher E, Glazer NL, Goddard ME, Heard‐Costa NL, Hoesel V, Hottenga JJ, Johansson A, Johnson T, Ketkar S, Lamina C, Li S, Moffatt MF, Myers RH, Narisu N, Perry JR, Peters MJ, Preuss M, Ripatti S, Rivadeneira F, Sandholt C, Scott LJ, Timpson NJ, Tyrer JP, van Wingerden S, Watanabe RM, White CC, Wiklund F, Barlassina C, Chasman DI, Cooper MN, Jansson JO, Lawrence RW, Pellikka N, Prokopenko I, Shi J, Thiering E, Alavere H, Alibrandi MT, Almgren P, Arnold AM, Aspelund T, Atwood LD, Balkau B, Balmforth AJ, Bennett AJ, Ben‐Shlomo Y, Bergman RN, Bergmann S, Biebermann H, Blakemore AI, Boes T, Bonnycastle LL, Bornstein SR, Brown MJ, Buchanan TA, Busonero F, Campbell H, Cappuccio FP, Cavalcanti‐Proença C, Chen YD, Chen CM, Chines PS, Clarke R, Coin L, Connell J, Day IN, den Heijer M, Duan J, Ebrahim S, Elliott P, Elosua R, Eiriksdottir G, Erdos MR, Eriksson JG, Facheris MF, Felix SB, Fischer‐Posovszky P, Folsom AR, Friedrich N, Freimer NB, Fu M, Gaget S, Gejman PV, Geus EJ, Gieger C, Gjesing AP, Goel A, Goyette P, Grallert H, Grässler J, Greenawalt DM, Groves CJ, Gudnason V, Guiducci C, Hartikainen AL, Hassanali N, Hall AS, Havulinna AS, Hayward C, Heath AC, Hengstenberg C, Hicks AA, Hinney A, Hofman A, Homuth G, Hui J, Igl W, Iribarren C, Isomaa B, Jacobs KB, Jarick I, Jewell E, John U, Jørgensen T, Jousilahti P, Jula A, Kaakinen M, Kajantie E, Kaplan LM, Kathiresan S, Kettunen J, Kinnunen L, Knowles JW, Kolcic I, König IR, Koskinen S, Kovacs P, Kuusisto J, Kraft P, Kvaløy K, Laitinen J, Lantieri O, Lanzani C, Launer LJ, Lecoeur C, Lehtimäki T, Lettre G, Liu J, Lokki ML, Lorentzon M, Luben RN, Ludwig B; MAGIC, Manunta P, Marek D, Marre M, Martin NG, McArdle WL, McCarthy A, McKnight B, Meitinger T, Melander O, Meyre D, Midthjell K, Montgomery GW, Morken MA, Morris AP, Mulic R, Ngwa JS, Nelis M, Neville MJ, Nyholt DR, O'Donnell CJ, O'Rahilly S, Ong KK, Oostra B, Paré G, Parker AN, Perola M, Pichler I, Pietiläinen KH, Platou CG, Polasek O, Pouta A, Rafelt S, Raitakari O, Rayner NW, Ridderstråle M, Rief W, Ruokonen A, Robertson NR, Rzehak P, Salomaa V, Sanders AR, Sandhu MS, Sanna S, Saramies J, Savolainen MJ, Scherag S, Schipf S, Schreiber S, Schunkert H, Silander K, Sinisalo J, Siscovick DS, Smit JH, Soranzo N, Sovio U, Stephens J, Surakka I, Swift AJ, Tammesoo ML, Tardif JC, Teder‐Laving M, Teslovich TM, Thompson JR, Thomson B, Tönjes A, Tuomi T, van Meurs JB, van Ommen GJ, Vatin V, Viikari J, Visvikis‐Siest S, Vitart V, Vogel CI, Voight BF, Waite LL, Wallaschofski H, Walters GB, Widen E, Wiegand S, Wild SH, Willemsen G, Witte DR, Witteman JC, Xu J, Zhang Q, Zgaga L, Ziegler A, Zitting P, Beilby JP, Farooqi IS, Hebebrand J, Huikuri HV, James AL, Kähönen M, Levinson DF, Macciardi F, Nieminen MS, Ohlsson C, Palmer LJ, Ridker PM, Stumvoll M, Beckmann JS, Boeing H, Boerwinkle E, Boomsma DI, Caulfield MJ, Chanock SJ, Collins FS, Cupples LA, Smith GD, Erdmann J, Froguel P, Grönberg H, Gyllensten U, Hall P, Hansen T, Harris TB, Hattersley AT, Hayes RB, Heinrich J, Hu FB, Hveem K, Illig T, Jarvelin MR, Kaprio J, Karpe F, Khaw KT, Kiemeney LA, Krude H, Laakso M, Lawlor DA, Metspalu A, Munroe PB, Ouwehand WH, Pedersen O, Penninx BW, Peters A, Pramstaller PP, Quertermous T, Reinehr T, Rissanen A, Rudan I, Samani NJ, Schwarz PE, Shuldiner AR, Spector TD, Tuomilehto J, Uda M, Uitterlinden A, Valle TT, Wabitsch M, Waeber G, Wareham NJ, Watkins H, Procardis Consortium, Wilson JF, Wright AF, Zillikens MC, Chatterjee N, McCarroll SA, Purcell S, Schadt EE, Visscher PM, Assimes TL, Borecki IB, Deloukas P, Fox CS, Groop LC, Haritunians T, Hunter DJ, Kaplan RC, Mohlke KL, O'Connell JR, Peltonen L, Schlessinger D, Strachan DP, van Duijn CM, Wichmann HE, Frayling TM, Thorsteinsdottir U, Abecasis GR, Barroso I, Boehnke M, Stefansson K, North KE, McCarthy MI, Hirschhorn JN, Ingelsson E, Loos RJ. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nature Genetics 2010; 42 ( 11 ): 937 – 948. | en_US |
dc.identifier.citedreference | Fisher RA. Statistical Methods for Research Workers. Oliver & Boyd: Edinburgh, 1925. | en_US |
dc.identifier.citedreference | Hoover DR, Rice JA, Wu CO, Yang LP. Nonparametric smoothing estimates of time‐varying coefficient models with longitudinal data. Biometrika 1998; 85 ( 4 ): 809 – 822. | en_US |
dc.identifier.citedreference | Kraft P, Yen YC, Stram DO, Morrison J, Gauderman WJ. Exploiting gene‐environment interaction to detect genetic associations. Human Heredity 2007; 63 ( 2 ): 111 – 119. | en_US |
dc.identifier.citedreference | Mukherjee B, Chatterjee N. Exploiting gene‐environment independence for analysis of case–control studies: an empirical Bayes‐type shrinkage estimator to trade‐off between bias and efficiency. Biometrics 2008; 64 ( 3 ): 685 – 694. | en_US |
dc.identifier.citedreference | Mukherjee B, Ahn J, Gruber SB, Chatterjee N. Testing gene‐environment interaction in large‐scale case‐control association studies: possible choices and comparisons. American Journal of Epidemiology 2012; 175 ( 3 ): 177 – 190. | en_US |
dc.identifier.citedreference | Wang Y, Huang C, Fang Y, Yang Q, Li R. Flexible semiparametric analysis of longitudinal genetic studies by reduced rank smoothing. Journal of the Royal Statistical Society: Series C (Applied Statistics) 2012; 61 ( 1 ): 1 – 24. | en_US |
dc.identifier.citedreference | Fan R, Zhang Y, Albert PS, Liu A, Wang Y, Xiong M. Longitudinal association analysis of quantitative traits. Genetic Epidemiology 2012; 36 ( 8 ): 856 – 869. | en_US |
dc.identifier.citedreference | Zhang H. Multivariate adaptive splines for analysis of longitudinal data. Journal of Computational and Graphical Statistics 1997; 6 ( 1 ): 74 – 91. | en_US |
dc.identifier.citedreference | Zhang H. Mixed effects multivariate adaptive splines model for the analysis of longitudinal and growth curve data. Statistical Methods in Medical Research 2004; 13 ( 1 ): 63 – 82. | en_US |
dc.identifier.citedreference | Zhu W, Cho K, Chen X, Zhang M, Wang M, Zhang H. Agenome‐wide association analysis of Framingham Heart Study longitudinal data using multivariate adaptive splines. BMC proceedings, Vol. 3, BioMed Central Ltd, 2009, S119. | en_US |
dc.identifier.citedreference | Xu S. An empirical Bayes method for estimating epistatic effects of quantitative trait loci. Biometrics 2007; 63 ( 2 ): 513 – 521. | en_US |
dc.identifier.citedreference | Malzahn D, Schillert A, Müller M, Bickeböller H. The longitudinal nonparametric test as a new tool to explore gene‐gene and gene‐time effects in cohorts. Genetic Epidemiology 2010; 34 ( 5 ): 469 – 478. | en_US |
dc.identifier.citedreference | Mukherjee B, Ko YA, VanderWeele T, Roy A, Park SK, Chen J. Principal interactions analysis for repeated measures data: application to gene–gene and gene–environment interactions. Statistics in Medicine 2012; 31 ( 22 ): 2531 – 2551. | en_US |
dc.identifier.citedreference | Ko YA, Chudhuri PS, Park SK, Vokonas PS, Mukherjee B. Novel likelihood ratio tests for screening gene‐gene and gene‐environment interactions with unbalanced repeated‐measures data. Genetic Epidemiology 2013; 37: 581 – 591. | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.