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Estimators for longitudinal latent exposure models: examining measurement model assumptions
dc.contributor.authorSánchez, Brisa N.
dc.contributor.authorKim, Sehee
dc.contributor.authorSammel, Mary D.
dc.date.accessioned2017-05-10T17:48:20Z
dc.date.available2018-08-07T15:51:22Zen
dc.date.issued2017-06-15
dc.identifier.citationSánchez, Brisa N. ; Kim, Sehee; Sammel, Mary D. (2017). "Estimators for longitudinal latent exposure models: examining measurement model assumptions." Statistics in Medicine 36(13): 2048-2066.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/136711
dc.publisherWiley
dc.subject.otherestimating equations
dc.subject.othermeasurement model invariance
dc.subject.otherinstrumental variables
dc.titleEstimators for longitudinal latent exposure models: examining measurement model assumptions
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelSocial Sciences
dc.subject.hlbtoplevelHealth Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/136711/1/sim7268_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/136711/2/sim7268.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/136711/3/sim7268-sup-0001-Supplementary.pdf
dc.identifier.doi10.1002/sim.7268
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferenceLittle RJA, Rubin DB. Statistical Analysis with Missing Data (2nd edn). John Wiley & Sons, Inc: New Jersey, 2002.
dc.identifier.citedreferenceLance CE, Cornwell JM, Mulaik SA. Limited information parameter estimates for latent or mixed manifest and latent variable models. Multivariate Behavioral Research 1988; 23: 155 – 167.
dc.identifier.citedreferenceHoogland JJ, Boomsma A. Robustness studies in covariance structure modeling ‐ an overview and a meta‐analysis. Sociological Methods & Research 1998; 26 ( 3 ): 329 – 367.
dc.identifier.citedreferenceSammel MD, Ryan LM. Effects of covariance misspecification in a latent variable model for multiple outcomes. Statistica Sinica 2002; 12 ( 4 ): 1207 – 1222.
dc.identifier.citedreferenceSánchez BN, Budtz‐Jørgensen E, Ryan LM. An estimating equations approach to fitting latent exposure models with longitudinal health outcomes. Annals of Applied Statistics 2009; 3 ( 2 ): 830 – 856.
dc.identifier.citedreferenceBollen KA. 2001. Two‐stage least squares and latent variable models: simultaneous estimation and robustness to misspecification. In Structural Equation Modeling: Present and Future, Cudeck R, Du Toit S, Sorbom D  (eds). Scientific Software: Lincolnwood, IL; 119 – 138.
dc.identifier.citedreferenceBollen KA, Kirby JB, Curran PJ, Paxton PM, Chen FN. Latent variable models under misspecification ‐ two‐stage least squares (2SLS) and maximum likelihood (ml) estimators. Sociological Methods & Research 2007; 36 ( 1 ): 48 – 86.
dc.identifier.citedreferenceRoy J, Lin XH. Latent variable models for longitudinal data with multiple continuous outcomes. Biometrics 2000; 56 ( 4 ): 1047 – 1054.
dc.identifier.citedreferenceBollen KA, Bauer D. Automating the selection of model‐implied instrumental variables. Sociological Methods and Research 2004; 32 ( 4 ): 425 – 452.
dc.identifier.citedreferenceCarroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. Measurement Error in Nonlinear Models: A Modern Perspective (2nd edn), Monographs on Statistics and Applied Probability. Chapman & Hall: Boca Raton, FL, 2006.
dc.identifier.citedreferenceBayley N. Bayley Scales of Infant Development (2nd edn). The Psychological Corporation: San Antonio, TX, 1993.
dc.identifier.citedreferenceRaghunathan TE, Solenberger P, Van Hoewyk J. Iveware: Imputation and variance estimation software user guide, 2002.
dc.identifier.citedreferenceProust C, Jacqmin‐Gadda H, Taylor JMG, Ganiayre J, Commenges D. A nonlinear model with latent process for cognitive evolution using multivariate longitudinal data. Biometrics 2006; 62 ( 4 ): 1014 – 1024.
dc.identifier.citedreferenceSánchez BN, Houseman EA, Ryan LM. Residual‐based diagnostics for structural equation models. Biometrics 2009; 65 ( 1 ): 104 – 115.
dc.identifier.citedreferenceBrowne MW. Asymptotic distribution free methods in the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology 1984; 37: 127 – 141.
dc.identifier.citedreferenceBudtz‐Jørgensen E, Keiding N, Grandjean P, Weihe P, White RF. Consequences of exposure measurement error for confounder identification in environmental epidemiology. Statistics in Medicine 2003; 22 ( 19 ): 3089 – 3100.
dc.identifier.citedreferenceFarrell AD. Structural equation modeling with longitudinal data: Strategies for examining group differences and reciprocal relationships. Journal of Consulting and Clinical Psychology 1994; 62 ( 3 ): 477 – 487.
dc.identifier.citedreferenceLeiby BE, Sammel MD, Ten Have TR, Lynch KG. Identification of multivariate responders and non‐responders by using Bayesian growth curve latent class models. Journal of the Royal Statistical Society Series C‐Applied Statistics 2009; 58: 505 – 524.
dc.identifier.citedreferenceChavance M, Escolano S, Romon M, Basdevant A, de Lauzon‐Guillain B, Charles MA. Latent variables and structural equation models for longitudinal relationships: an illustration in nutritional epidemiology. BMC Medical Research Methodology 2010; 10: 37.
dc.identifier.citedreferenceBarbosa‐Leiker C, Wright BR, Burns GL, Parks CD, Strand PS. Longitudinal measurement invariance of the metabolic syndrome: is the assessment of the metabolic syndrome stable over time? Annals of Epidemiology 2011; 21 ( 2 ): 111 – 117.
dc.identifier.citedreferenceShi J‐Q, Lee SY. Latent variable models with mixed continuous and polytomous data. Journal of the Royal Statistical Society 2000; 62 ( 1 ): 77 – 87.
dc.identifier.citedreferenceNestler S. A Monte Carlo study comparing piv, uls and dwls in the estimation of dichotomous confirmatory factor analysis. British Journal of Mathematical and Statistical Psychology 2013; 66 ( 1 ): 127 – 143.
dc.identifier.citedreferenceBollen KA. Structural Equations with Latent Variables, Wiley Series in Probability and Mathematical Statisics. Wiley: New York, NY, 1989.
dc.identifier.citedreferenceMuthén B. Beyond SEM: general latent variable modeling. Behaviormetrika 2002; 29: 81 – 117.
dc.identifier.citedreferenceSkrondal A, Rabe‐Hesketh S. Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Chapman & Hall: Boca Raton, FL, 2004.
dc.identifier.citedreferenceBudtz‐Jørgensen E, Keiding N, Grandjean P, Weihe P, White RF. Statistical methods for the evaluation of health effects of prenatal mercury exposure. Environmetrics 2003; 14 ( 2 ): 105 – 120.
dc.identifier.citedreferenceNikolov MC, Coull BA, Catalano PJ, Diaz E, Godleski JJ. Statistical methods to evaluate health effects associated with major sources of air pollution: a case‐study of breathing patterns during exposure to concentrated Boston air particles. Journal of the Royal Statistical Society Series C‐Applied Statistics 2008; 57: 357 – 378.
dc.identifier.citedreferenceTao Y, Sanchez BN, B M. Latent variable models for gene environment interactions in longitudinal studies with multiple correlated exposures. Statistics in Medicine 2015; 34 ( 7 ): 227 – 1241.
dc.identifier.citedreferenceChen YH, Ferguson KK, Meeker JD, McElrath TF, B M. Statistical methods for modeling repeated measures of maternal environmental exposure biomarkers during pregnancy in association with preterm birth. Environmental Health 2015; 14: 9.
dc.identifier.citedreferenceFerguson KK, McElrath TF, Chen Y‐H, Mukherjee B, Meeker JD. Longitudinal profiling of inflammatory cytokines and c‐reactive protein during uncomplicated and preterm pregnancy. American Journal of Reproductive Immunology 2014; 72 ( 3 ): 326 – 336.
dc.identifier.citedreferenceJöreskog KG, Sörbom D. LISREL 7: A Guide to the Program and Applications. SPSS: Chicago, 1989.
dc.identifier.citedreferenceFerguson KK, Cantonwine DE, Rivera‐Gonzalez LO, Loch‐Caruso R, Mukherjee B, Anzalota Del Toro L, Jimenez‐Velez B, Calafat AM, Ye X, Alshawabkeh AN, Cordero JF, Meeker JD. Urinary phthalate metabolite associations with biomarkers of inflammation and oxidative stress across pregnancy in puerto rico. Environmental Science & Technology 2014; 48 ( 12 ): 7018 – 7025.
dc.identifier.citedreferenceStingone JA, Luben JL, Daniels TJ, Fuentes M, Richardson DB, Aylsworth AS, Herring AH, Anderka M, Botto L, Correa A, Gilboa SM, Langlois PH, Mosley B, Shaw GM, Siffel C, Olshan AF. Maternal exposure to criteria air pollutants and congenital heart defects in offspring: results from the national birth defects prevention study. Environ Health Perspect 2014; 122 ( 8 ): 863 – 872.
dc.identifier.citedreferenceHu H, Tellez‐Rojo MM, Bellinger D, Smith D, Ettinger AS, Lamadrid‐Figueroa H, Schwartz J, Schnaas L, Mercado‐Garcia A, Hernandez‐Avila M. Fetal lead exposure at each stage of pregnancy as a predictor of infant mental development. Environ Health Perspect 2006; 114 ( 11 ): 1730 – 5.
dc.identifier.citedreferenceTellez‐Rojo MM, Hernandez‐Avila M, Lamadrid‐Figueroa H, Smith D, Hernandez‐Cadena L, Mercado A, Aro A, Schwartz J, Hu H. Impact of bone lead and bone resorption on plasma and whole blood lead levels during pregnancy. Am J Epidemiol 2004; 160 ( 7 ): 668 – 78.
dc.identifier.citedreferenceKaplan D. The impact of specification error on the estimation, testing, and improvement of structural equation models. Multivariate Behavioral Research 1988; 23 ( 1 ): 69 – 86.
dc.identifier.citedreferenceMaccallum RC, Wegener DT, Uchino BN, Fabrigar LR. The problem of equivalent models in applications of covariance structure‐analysis. Psychological Bulletin 1993; 114 ( 1 ): 185 – 199.
dc.identifier.citedreferenceKirby JB, Bollen KA. Using instrumental variable (iv) tests to evaluate model specification in latent variable structural equation models. Sociological Methodology 2009; 39 ( 1 ): 327 – 355.
dc.identifier.citedreferenceCribbie RA. Evaluating the importance of individual parameters in structural equation modeling: the need for type i error control. Personality and Individual Differences 2000; 29 ( 3 ): 567 – 577.
dc.identifier.citedreferenceFaupel‐Badger JM, Hsieh CC, Troisi R, Lagiou P, Potischman N. Plasma volume expansion in pregnancy: implications for biomarkers in population studies. Cancer Epidemiol Biomarkers Prev 2007; 16 ( 9 ): 1720 – 3.
dc.identifier.citedreferenceKaplan D. Power of the likelihood ratio test in multiple group confirmatory factor‐analysis under partial measurement invariance. Educational and Psychological Measurement 1989; 49 ( 3 ): 579 – 586.
dc.identifier.citedreferenceCheung GW, Rensvold RB. Evaluating goodness‐of‐fit indexes for testing measurement invariance. Structural Equation Modeling 2002; 9 ( 2 ): 233 – 255.
dc.identifier.citedreferenceVandenberg RJ, Lance CE. A review and synthesis of the measurement invariance literature: Suggestions, practices and recommendations for organizational research. Organizational Research Methods 2000; 3 ( 1 ): 4 – 70.
dc.identifier.citedreferenceReddy SK. Effects of ignoring correlated measurement error in structural equation models. Educational and Psychological Measurement 1992; 52 ( 3 ): 549 – 570.
dc.identifier.citedreferenceMuthén LK, Muthén BO. Mplus: Statistical analysis with latent variables user’s guide. Muthén & Muthén, 2006.
dc.identifier.citedreferenceRabe‐Hesketh S, Pickles A, Skrondal A. Gllamm manual, technical report. Kings College, Institute of Psychiatry, Dept. Biostatistics and Computing: London, 2001.
dc.identifier.citedreferenceBollen KA. An alternative two stage least squares (2SLS) estimator for latent variable equations. Psychometrika 1996; 61 ( 1 ): 109 – 121.
dc.identifier.citedreferenceNestler S. How the 2SLS/IV estimator can handle equality constraints in structural equation models: a system‐of‐equations approach. British Journal of Mathematical and Statistical Psychology 2014; 67: 353 – 369.
dc.identifier.citedreferenceNestler S. Using instrumental variables to estimate the parameters in unconditional and conditional second‐order latent growth models. Structural Equation Modeling 2015; 22: 461 – 473.
dc.identifier.citedreferenceHagglund G. Factor‐analysis by instrumental variables methods. Psychometrika 1982; 47 ( 2 ): 209 – 222.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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