Agresti, A., Booth, J.G., Hobart, J.P., and Caffo, B. (2000). Random-effects modelling of categorical response data. Sociological Methodology, 30, 27–80.CrossRefGoogle Scholar

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–23.CrossRefGoogle Scholar

Albert, P.S. (1999). Longitudinal data analysis (repeated measures) in clinical trials. Statistics in Medicine, 18, 1707–32.3.0.CO;2-H>CrossRefGoogle ScholarPubMed

Altman, D.G. (1991). Practical Statistics for Medical Research. London, UK: Chapman and Hall.Google Scholar

Baecke, J.A.H., Burema, J., and Frijters, J.E.R. (1982). A short questionnaire for the measurement of habitual physical activity in epidemiological studies. American Journal of Clinical Nutrition, 36, 936–42.CrossRefGoogle Scholar

Barbosa, M.F. and Goldstein, H. (2000). Discrete response multilevel models for repeated measures: an application to intentions data. Quality and Quantity, 34, 323–30.CrossRefGoogle Scholar

Barnard, J. and Meng, X-L. (1999). Applications of multiple imputation in medical studies: from AIDS to NHANES. Statistical Methods in Medical Research, 8, 17–36.CrossRefGoogle ScholarPubMed

Berkhof, J., Knol, D.J., Rijmen, F., et al. (2009). Relapse–remission and remission–relapse switches in rheumatiod arthritis patients were modeled by random effects. Journal of Clinical Epidemiology, 62, 1085–94.CrossRefGoogle Scholar

Bernaards, C.A., Belin, T.R., and Schafer, J.L. (2007). Robustness of a multivariate normal approximation for imputation of incomplete binary data. Statistics in Medicine, 26, 1368–82.CrossRefGoogle ScholarPubMed

Blomquist, N. (1977). On the relation between change and initial value. Journal of the American Statistical Association, 72, 746–9.Google Scholar

Boshuizen, H. (2005). Re: Twisk and Proper: evaluation of the results of a randomized controlled trial: how to define changes between baseline and follow-up. Journal of Clinical Epidemiology, 58, 209–10.CrossRefGoogle ScholarPubMed

Box-Steffensmeier, J.M and De Boef, S. (2006). Repeated events survival models: the conditional frailty model. Statistics in Medicine, 25, 3518–33.CrossRefGoogle ScholarPubMed

Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika, 52, 345–70.CrossRefGoogle Scholar

Breslow, N.E. and Clayton, D.G. (1993). Approximate inference in generalised linear models. Journal of the American Statistical Association, 88, 9–25.Google Scholar

Burton, A., Altman, D.G., Royston, P., and Holder, R.L. (2006). The design of simulation studies in medical statistics. Statistics in Medicine, 25, 4279–92.CrossRefGoogle ScholarPubMed

Burton, P., Gurrin, L., and Sly, P. (1998). Extending the simple linear regression model to account for correlated responses: an introduction to generalized estimating equations and multi-level mixed modelling. Statistics in Medicine, 17, 1261–91.3.0.CO;2-Z>CrossRefGoogle ScholarPubMed

Carey, V., Zeger, S.L., and Diggle, P.J. (1993). Modeling multivariate binary data with alternating logistic regression. Biometrika, 80, 517–26.CrossRefGoogle Scholar

Chen, P-L., Wong, E., Dominik, R., and Steiner, M.J. (2000). A transitional model of barrier methods compliance with unbalanced loss to follow-up. Statistics in Medicine, 19, 71–82.3.0.CO;2-O>CrossRefGoogle ScholarPubMed

Connell, A. and Frye, A. (2006a). Growth mixture modelling in developmental psychology: overview and demonstration of heterogeneity in developmental trajectories of adolescent antisocial behavior. Infant and Child Development, 15, 609–21.CrossRefGoogle Scholar

Connell, A. and Frye, A. (2006b). Response to commentaries on target paper, “Growth Mixture Modelling in Developmental Psychology.” Infant and Child Development, 15, 639–42.CrossRefGoogle Scholar

Conway, M.R. (1990). A random effects model for binary data. Biometrics, 46, 317–28.CrossRefGoogle Scholar

Crowder, M.J. and Hand, D.J. (1990). Analysis of Repeated Measures. London, UK: Chapman and Hall.Google Scholar

Dalgaard, P. (2002). Introductory Statistics with R. New York, NY: Springer.Google Scholar

Deeg, D.J.H. and Westendorp-de Serière, M. (eds) (1994). Autonomy and Well-Being in the Aging Population I: Report from the Longitudinal Aging Study Amsterdam 1992–1993. Amsterdam, the Netherlands: Vrije University Press.Google Scholar

Demirtas, H. and Schafer, J.L. (2003). On the performance of random-coefficient pattern-mixture models for non-ignorable drop-out. Statistics in Medicine, 22, 2553–75.CrossRefGoogle ScholarPubMed

Demirtas, H., Freels, S.A., and Yucel, R.M. (2008). Plausibility of multivariate normality assumption when multiple imputing non-Gaussian continuous outcomes: a simulation assessment. Journal of Statistical Computation and Simulation, 78, 69–84.CrossRefGoogle Scholar

Diggle, P.J. (1989). Testing for random dropouts in repeated measurement data. Biometrics, 45, 1255–8.CrossRefGoogle Scholar

Diggle, P.J., Liang, K.-Y., and Zeger, S.L. (1994). Analysis of Longitudinal Data. New York, NY: Oxford University Press.Google Scholar

Dik, M.G., Jonker, C., Comijs, H.C., et al. (2001). Memory complaints and Apo E ε4 accelerate cognitive decline in cognitively normal elderly. Neurology, 57, 2217–22.CrossRefGoogle ScholarPubMed

Duncan, T., Duncan, S., Stryker, L., Li, F., and Alpert, A. (1999). An Introduction to Latent Variable Modelling. Concepts, Issues and Applications. Mahwah, NJ, USA: Lawrence Erlbaum.Google Scholar

Enders, C.K. (2010). Applied Missing Data Analysis. New York, NY, USA: The Guilford Press.Google Scholar

Fairclough, D.L., Thijs, H., Huang, I.-C., Finnern, H.W., and Wu, A.W. (2008). Handling missing quality of life data in HIV clinical trials: what is practical? Quality of Life Research, 17, 61–73.CrossRefGoogle ScholarPubMed

Feldman, B., Masyn, K., and Conger, R. (2009). New approaches to studying problem behaviors: a comparison of methods for modeling longitudinal, categorical adolescent drinking data. Developmental Psychology, 45, 652–76.CrossRefGoogle ScholarPubMed

Fitzmaurice, G.M., Laird, N.M., and Lipsitz, S.R. (1994). Analysing incomplete longitudinal binary responses: a likelihood-based approach. Biometrics, 50, 601–12.CrossRefGoogle ScholarPubMed

Fitzmaurice, G.M., Laird, N.M., and Ware, J.H. (2004) Applied Longitudinal Data Analysis. Hoboken, NJ, USA: Wiley.Google Scholar

Fleiss, J.L. (1981). Statistical Methods for Rates and Proportions. New York, NY, USA: Wiley.Google Scholar

Forbes, A.B. and Carlin, J.B. (2005). “Residual change” analysis is not equivalent to analysis of covariance. Journal of Clinical Epidemiology, 58, 540–1.CrossRefGoogle Scholar

Fox, J. (2002). An R and S-Plus Comparison to Applied Regression. New York, NY, USA: Sage Publications.Google Scholar

Gandar, W. and Gautschi, W. (2000). Adaptive quadrature – revisited. BIT Numerical Mathematics, 40, 84–101.CrossRefGoogle Scholar

Gibbons, R.D. and Hedeker, D. (1997). Random effects probit and logistic regression models for three level data. Biometrics, 53, 1527–37.CrossRefGoogle ScholarPubMed

Glynn, R.J., Stukel, T.A., Sharp, S.M, et al. (1993). Estimating the variance of standardized rates of recurrent events, with application to hospitalizations among the elderly in New England. American Journal of Epidemiology, 137, 776–86.CrossRefGoogle ScholarPubMed

Goldstein, H. (1986). Multilevel mixed linear model analysis using iterative generalised least squares. Biometrika, 73, 43–56.CrossRefGoogle Scholar

Goldstein, H. (1989). Restricted unbiased iterative generalised least squares estimation. Biometrika, 76, 622–3.CrossRefGoogle Scholar

Goldstein, H. (1991). Nonlinear multilevel models with an application to discrete response data. Biometrika, 78, 45–51.CrossRefGoogle Scholar

Goldstein, H. (1995). Multilevel Statistical Models. London, UK: Edward Arnold.Google Scholar

Goldstein, H. and Rasbash, J. (1996). Improved approximation for multilevel models with binary responses. Journal of the Royal Statistical Society, 159, 505–13.CrossRefGoogle Scholar

Goldstein, H., Rasbash, J., Plewis, I., et al. (1998). A User's Guide to MLwiN. London, UK: Institute of Education.Google Scholar

Graham, J.W. (2009). Missing data analysis: making it work in the real world. Annual Review of Psychology, 60, 549–76.CrossRefGoogle ScholarPubMed

Greenland, S. and Finkle, D. (1995). A critical look at methods for handling missing covariates in epidemiologic regression analysis. American Journal of Epidemiology, 142, 1255–64.CrossRefGoogle Scholar

Haan, M.N., Shemanski, L., Jagust, W.J., Manolio, T.A., and Kuller, L. (1999). The role of APOE ε4 in modulating effects of other risk factors for cognitive decline in elderly persons. Journal of the American Medical Association, 282, 40–6.CrossRefGoogle ScholarPubMed

Hajos, T.R.S., Pouwer, F., de Grooth, R., et al. (2011). Initiation of insulin glargine in patients with type 2 diabetes in suboptimal glycaemic control positively impacts health-related quality of life. A prospective cohort study in primary care. Diabetic Medicine, 28, 1096–102.CrossRefGoogle ScholarPubMed

Hand, D.J. and Crowder, M.J. (1996). Practical Longitudinal Data Analysis. London, UK: Chapman and Hall.CrossRefGoogle Scholar

Harville, D.A. (1977). Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association, 72, 320–40.CrossRefGoogle Scholar

Hedeker, D., Gibbons, R.D., and Waternaux, C. (1999). Sample size estimation for longitudinal designs with attrition: comparing time-related contrasts between groups. Journal of Education and Behavioral Statistics, 24, 70–93.CrossRefGoogle Scholar

Hoeksma, J. and Kelderman, H. (2006). On growth curves and mixture models. Infant and Child Development, 15, 627–634.CrossRefGoogle Scholar

Hogan, J.W. and Laird, N.M. (1997). Mixture models for the joint distribution of repeated measures and event times. Statistics in Medicine, 16, 239–57.3.0.CO;2-X>CrossRefGoogle ScholarPubMed

Hogan, J.W., Roy, J., and Korkontzelou, C. (2004). Handling drop-out in longitudinal studies. Statistics in Medicine, 23, 1455–1497.CrossRefGoogle ScholarPubMed

Holford, T.R. (1992). Analysing the temporal effects of age, period and cohort. Statistical Methods in Medical Research, 1, 317–37.CrossRefGoogle Scholar

Holford, T.R., Armitage, P., and Colton, T. (2005). Age–Period–Cohort Analysis Encyclopedia of Biostatistics, Vol. 2. New York, NY, USA: John Wiley and Sons, pp. 82–99.Google Scholar

Hosmer, D.W. and Lemeshow, S. (1989). Applied Logistic Regression. New York, NY, USA: Wiley.Google Scholar

Hu, F.B., Goldberg, J., Hedeker, D., Flay, B.R., and Pentz, M.A. (1998). Comparison of population-averaged and subject specific approaches for analyzing repeated measures binary outcomes. American Journal of Epidemiology, 147, 694–703.CrossRefGoogle Scholar

Hurvich, C.M. and Tsai, C.-L. (1989). Regression and time series model selection in small samples. Biometrika, 76, 297–307.CrossRefGoogle Scholar

Jennrich, R.I. and Schluchter, M.D. (1986). Unbalanced repeated measures models with structured covariance matrices. Biometrics, 42, 805–20.CrossRefGoogle ScholarPubMed

Jones, B., Nagin, D., and Roeder, K. (2001). A SAS procedure based on mixed models for estimating developmental trajectories. Social Methods Research, 229, 374–93.CrossRefGoogle Scholar

Judd, C.M., Smith, E.R., and Kidder, L.H. (1991). Research Methods in Social Relations. Fort Worth, TX, USA: Harcourt Brace Jovanovich College Publishers.Google Scholar

Jung, T. and Wickrama, K.A.S. (2008). Introduction to latent class growth analysis and growth mixture modelling. Social and Personality Psychology Compass, 2, 302–17.CrossRefGoogle Scholar

Kelly, P.J. and Lim, L.-Y. (2003). Survival analysis for recurrent event data: an application to childhood infectious diseases. Statistics in Medicine, 19, 13–33.3.0.CO;2-5>CrossRefGoogle Scholar

Kemper, H.C.G. (ed.) (1995). The Amsterdam Growth Study: A Longitudinal Analysis of Health, Fitness and Lifestyle. HK Sport Science Monograph Series, Vol. 6. Champaign, IL, USA: Human Kinetics Publishers.Google Scholar

Kenward, M.G. (1998). Selection models for repeated measurements with non-random dropout: an illustration of sensitivity. Statistics in Medicine, 17, 2723–32.3.0.CO;2-5>CrossRefGoogle Scholar

Kenward, M.G. and Carpenter, J. (2007). Multiple imputation: current perspectives. Statistical Methods in Medical Research, 16, 199–218.CrossRefGoogle ScholarPubMed

Kenward, M.G. and Molenberghs, G. (1999). Parametric models for incomplete continuous and categorical longitudinal data. Statistical Methods in Medical Research, 8, 51–84.CrossRefGoogle ScholarPubMed

Kleinbaum, D.G. (1994). Logistic Regression. A Self-Learning Text. New York, NY, USA: Springer-Verlag.CrossRefGoogle Scholar

Kristman, V.L., Manno, M., and Côté, P. (2005). Methods to account for attrition in longitudinal data: do they work? A simulation study. European Journal of Epidemiology, 20, 657–62.CrossRefGoogle Scholar

Kupper, L.L., Janis, J.M., Karmous, A., and Greenberg, B.G. (1985). Statistical age–period–cohort analysis: a review and critique. Journal of Chronic Diseases, 38, 811–30.CrossRefGoogle ScholarPubMed

Kwakkel, G., Wagenaar, R.C., Twisk, J.W.R., Lankhorst, G.J., and Koetsier, J.C. (1999). Intensity of leg and arm training after primary middle-cerebral artery stroke: a randomised trial. Lancet, 354, 191–6.CrossRefGoogle ScholarPubMed

Laird, N.M. and Ware, J.H. (1982). Random effects models for longitudinal data. Biometrics, 38, 963–74.CrossRefGoogle ScholarPubMed

Lebowitz, M.D. (1996). Age, period, and cohort effects. Influences on differences between cross-sectional and longitudinal pulmonary function results. American Journal of Respiratory and Critical Care Medicine, 154, S273–7.CrossRefGoogle ScholarPubMed

Lee, E.W. and Durbin, N. (1994). Estimation and sample size considerations for clustered binary responses. Statistics in Medicine, 13, 1241–52.CrossRefGoogle ScholarPubMed

Lesaffre, E. and Spiessens, B. (2001). On the effect of the number of quadrature points in a logistic random-effects model: an example. Applied Statistics, 50, 325–35.Google Scholar

Liang, K.-Y. and Zeger, S.L. (1986). Longitudinal data analysis using generalised linear models. Biometrica, 73, 45–51.CrossRefGoogle Scholar

Liang, K.-Y. and Zeger, S.L. (1993). Regression analysis for correlated data. Annual Review of Public Health, 14, 43–68.CrossRefGoogle ScholarPubMed

Liang, K.-Y., Zeger, S.L., and Qaqish, B. (1992). Multivariate regression analysis for categorical data. Journal of the Royal Statistical Society, 54, 3–40.Google Scholar

Lindsey, J.K. (1993). Models for Repeated Measurements. Oxford, UK: Oxford University Press.Google Scholar

Lingsma, H. (2010). Covariate adjustment increases statistical power in randomised controlled trials. Journal of Clinical Epidemiology, 63, 1391.CrossRefGoogle Scholar

Lipsitz, S.R. and Fitzmaurice, G.M. (1994). Sample size for repeated measures studies with binary repsonses. Statistics in Medicine, 13, 1233–9.CrossRefGoogle Scholar

Lipsitz, S.R. and Fitzmaurice, G.M. (1996). Estimating equations for measures of association between repeated binary responses. Biometrics, 52, 903–12.CrossRefGoogle ScholarPubMed

Lipsitz, S.R., Laird, N.M., and Harrington, D.P. (1991). Generalized estimating equations for correlated binary data: using the odds ratio as a measure of association. Biometrika, 78, 153–60.CrossRefGoogle Scholar

Lipsitz, S.R., Fitzmaurice, G.M., Orav, E.J., and Laird, N.M. (1994a). Performance of generalised estimating equations in practical situations. Biometrics, 50, 270–8.CrossRefGoogle Scholar

Lipsitz, S.R., Kim, K., and Zhao, L. (1994b). Analysis of repeated categorical data using generalised estimating equations. Statistics in Medicine, 13, 1149–63.CrossRefGoogle Scholar

Littel, R.C., Freund, R.J., and Spector, P.C. (1991). SAS System for Linear Models, 3rd edn. Cary, NC, USA: SASInstitute, Inc.Google Scholar

Littel, R.C., Milliken, G.A., Stroup, W.W., and Wolfinger, R.D. (1996). SAS System for Mixed Models. Cary, NC, USA: SAS Institute, Inc.

Littel, R.C., Pendergast, J., and Natarajan, R. (2000). Modelling covariance structures in the analysis of repeated measures data. Statistics in Medicine, 19, 1793–819.3.0.CO;2-Q>CrossRefGoogle Scholar

Little, R.J.A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association, 88, 125–34.Google Scholar

Little, R.J.A. (1994). A class of pattern-mixture models for normal incomplete data. Biometrika, 81, 471–83.CrossRefGoogle Scholar

Little, R.J.A. (1995). Modelling the drop-out mechanism repeated measures studies. Journal of the American Statistical Association, 90, 1112–21.CrossRefGoogle Scholar

Little, R.J.A. and Rubin, D.B. (2003). Statistical Analysis with Missing Data, 2nd edn. New York, NY, USA: Wiley.Google Scholar

Liu, G. and Liang, K.-Y. (1997). Sample size calculations for studies with correlated observations. Biometrics, 53, 937–47.CrossRefGoogle ScholarPubMed

Liu, Q. and Pierce, D.A. (1994). A note on Gauss–Hermite quadrature. Biometrika, 81, 624–9.Google Scholar

Longford, N.T. (1993). Random Coefficient Models. Oxford, UK:Oxford University Press.Google Scholar

Lui, K.-J. and Cumberland, W.G. (1992). Sample size requirement for repeated measurements in continuous data. Statistics in Medicine, 11, 633–41.CrossRefGoogle ScholarPubMed

Maindonald, J. and Braun, J. (2003). Data Analysis and Graphics Using R: An Example-Based Approach. Cambridge, UK: Cambridge University Press.Google Scholar

Mayer, K.U. and Huinink, J. (1990). Age, period, and cohort in the study of the life course: a comparison of classical A–P–C-analysis with event history analysis, or farewell to Lexis? In Data Quality in Longitudinal Research, eds Magnusson, D. and Bergman, L. R.. Cambridge, UK: Cambridge University Press, pp. 211–32.Google Scholar

Mazumdar, S., Tang, G., Houck, P.R., et al. (2007). Statistical analysis of longitudinal psychiatric data with dropouts. Journal of Psychological Research, 41, 1032–41.CrossRefGoogle ScholarPubMed

McCullagh, P. (1983). Quasi-likelihood functions. Annals of Statistics, 11, 59–67.CrossRefGoogle Scholar

McNally, R.J., Alexander, F.E., Strains, A., and Cartwright, R.A. (1997). A comparison of three methods of analysis age–period–cohort models with application to incidence data on non-Hodgkin's lymphoma. International Journal of Epidemiology, 26, 32–46.CrossRefGoogle ScholarPubMed

Miller, M.E., Davis, C.S., and Landis, J.R. (1993). The analysis of longitudinal polytomous data: generalized estimating equations and connections with weighted least squares. Biometrics, 49, 1033–44.CrossRefGoogle ScholarPubMed

Molenberghs, G., Michiels, B., Kenward, M.G., and Diggle, P.J. (1998). Monotone missing data and pattern-mixture models. Statistica Neerlandica, 52, 153–61.CrossRefGoogle Scholar

Muthén, B. (2004). Latent variable analysis: growth mixture modeling and related techniques for longitudinal data. In Handbook of Quantitative Methodology for the Social Sciences, ed. Kaplan, D.Newbury Park, CA, USA: Sage Publications, pp. 345–68.Google Scholar

Muthén, B. (2006). The potential of growth mixture modelling. Infant and Child Development, 15. 623–5.CrossRefGoogle Scholar

Muthén, B. and Asparouhov, B. (2008). Growth mixture modeling: analysis with non-Gaussian random effects. In Longitudinal Data Analysis, eds Fitmaurice, G, Davidian, M, Vebeke, G, and Molenberghs, G.. Raton, Boca, FL, USA: Chapman and Hall/CRC Press, pp. 143–65.Google Scholar

Muthén, B. and Muthén, L. (2000). Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes. Alcoholism. Clinical and Experimental Research, 24, 882–91.CrossRefGoogle ScholarPubMed

Muthén, B. and Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463–9.CrossRefGoogle ScholarPubMed

Nagin, D. (1999). Analyzing developmental trajectories. A semi-parametric group based approach. Psychological Methods, 4, 139–57.CrossRefGoogle Scholar

Nagin, D. and Tremblay, R. (2001). Analyzing developmental trajectories of distinct but related behaviors: a group-based method. Psychological Methods, 6, 18–34.CrossRefGoogle ScholarPubMed

Nelder, J.A. and Lee, Y. (1992). Likelihood, quasi-likelihood and psuedo-likelihood: some comparisons. Journal of the Royal Statistical Society Series B, 54, 273–84.Google Scholar

Nelder, J.A. and Pregibon, D. (1987). An extended quasi-likelihood function. Biometrika, 74, 221–32.CrossRefGoogle Scholar

Neuhaus, J.M., Kalbfleisch, J.D., and Hauck, W.W. (1991). A comparison of cluster-specific and population-averaged approaches for analyzing correlated binary data. International Statistical Reviews, 59, 25–36.CrossRefGoogle Scholar

Omar, R.Z., Wright, E.M., Turner, R.M., and Thompson, S.G. (1999). Analysing repeated measurements data: a practical comparison of methods. Statistics in Medicine, 18, 1587–603.3.0.CO;2-Z>CrossRefGoogle ScholarPubMed

Pinheiro, J.C. and Bates, D.M. (1995). Approximations to the log-likelihood function in the non-linear mixed-effects model. Journal of Computational and Graphical Statistics, 4, 12–35.Google Scholar

Pinheiro, J.C. and Bates, D.M. (2000). Mixed-Effects Models in S and S-PLUS. New York, NY, USA: Springer-Verlag.CrossRefGoogle Scholar

Pockok, S.J. (1983). Clinical Trials: A Practical Approach. Chichester, UK: Wiley.Google Scholar

Potthoff, R.F., Tudor, G.E., Pieper, K.S., and Hasselblad, V. (2006). Can one assess whether missing data are missing at random in medical studies? Statistical Methods in Medical Research, 15, 213–34.CrossRefGoogle ScholarPubMed

Prentice, R.L. (1988). Correlated binary regression with covariates specific to each binary observation. Biometrics, 44, 1033–48.CrossRefGoogle ScholarPubMed

Proper, K.I., Hildebrandt, V.H., Beek van de, A.J., Twisk, J.W.R., and Mechelen van, W. (2003). Individual counseling and physical activity, fitness and health: a randomised controlled trial in a worksite setting. American Journal of Preventive Medicine, 24, 218–26.CrossRefGoogle Scholar

Rabe-Hesketh, S. and Pickles, A. (1999). Generalised linear latent and mixed models. In Proceedings of the 14th International Workshop on Statistical Modelling, eds Friedl, H, Berghold, A., and Kauermann, G.. Graz, Austria, pp. 332–9.Google Scholar

Rabe-Hesketh, S. and Skrondal, A. (2001). Parameterisation of multivariate random effects models for categorical data. Biometrics, 57, 1256–64.CrossRefGoogle Scholar

Rabe-Hesketh, S., Pickles, A., and Taylor, C. (2000). Sg129: generalized linear latent and mixed models. Stata Technical Bulletin, 53, 47–57.Google Scholar

Rabe-Hesketh, S., Pickles, A., and Skrondal, A. (2001a).GLLAMM Manual Technical Report 2001. London, UK: Department of Biostatistics and Computing, Institute of Psychiatry, King's College, University of London.Google Scholar

Rabe-Hesketh, S., Pickles, A., and Skrondal, A. (2001b). GLLAMM: a class of models and a STATA program. Multilevel Modelling Newsletter, 13(1), 17–23.Google Scholar

Rasbash, J., Browne, W., Goldstein, H., et al. (1999). A User's Guide to MLwiN, 2nd edn. London, UK: Institute of Education.Google Scholar

Rice, J.C. (1975). A metalgorithm for adaptive quadrature. Journal of the Association for Computing Machinery, 22, 61–82.CrossRefGoogle Scholar

Ridout, M.S. (1991). Testing for random dropouts in repeated measurement data. Reader reaction. Biometrics, 47, 1617–21.CrossRefGoogle Scholar

Robertson, C. and Boyle, P. (1998). Age–period–cohort analysis of chronic disease rates; I modelling approach. Statistics in Medicine, 17, 1302–23.Google Scholar

Robertson, C., Gandini, S., and Boyle, P. (1999). Age–period–cohort models: a comparative study of available methodologies, Journal of Clinical Epidemiology, 52, 569–83.CrossRefGoogle ScholarPubMed

Robins, J. and Wang, N. (2000). Inference for imputation estimators. Biometrika, 87, 113–24.CrossRefGoogle Scholar

Rodriguez, G. and Goldman, N. (1995). An assessment of estimation procedures for multilevel models with binary responses. Journal of the Royal Statistical Association, 158, 73–89.CrossRefGoogle Scholar

Rodriguez, G. and Goldman, N. (2001). Improved estimation procedures for multilevel models with binary responses: a case study. Journal of the Royal Statistical Association, 164, 339–55.CrossRefGoogle Scholar

Rosenberg, P.S. and Anderson, W.F. (2010). Proportional hazards models and age–period–cohort analysis of cancer rates. Statistics in Medicine, 20, 1228–38.Google Scholar

Rosner, B. and Munoz, A. (1988). Autoregressive modelling for the analysis of longitudinal data with unequally spaced examinations. Statistics in Medicine, 7, 59–71.CrossRefGoogle ScholarPubMed

Rosner, B., Munoz, A., Tager, I., Speizer, F., and Weiss, S. (1985). The use of an autoregressive model for the analysis of longitudinal data in epidemiologic studies. Statistics in Medicine, 4, 457–67.CrossRefGoogle ScholarPubMed

Rothman, K.J. and Greenland, S. (1998). Modern Epidemiology. Philadelphia, PA, USA: Lippincott-Raven.Google Scholar

Royston, P. (2004). Multiple imputation of missing values. Stata Journal, 4, 227–41.Google Scholar

Royston, P. (2009). Multiple imputation of missing values: further update of ice, with an emphasis on categorical variables. Stata Journal, 9, 466–77.Google Scholar

Royston, P., Carlin, J.B., and White, I.R. (2009). Multiple imputation of missing values: new features for mim. Stata Journal, 2, 252–64.Google Scholar

Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York, NY, USA: Wiley.CrossRefGoogle Scholar

Rubin, D.B. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association, 91, 473–89.CrossRefGoogle Scholar

SAS Institute, Inc. (1997). SAS/STAT Software: Changes and Enhancements through Release 6.12. Cary, NC, USA: SAS Institute, Inc.Google Scholar

Schafer, J.L. (1997). Analysis of Incomplete Multivariate Data. New York, NY, USA: Chapman and Hall.CrossRefGoogle Scholar

Schafer, J.L. (1999). Multiple imputation: a primer. Statistical Methods in Medical Research, 8, 3–15.CrossRefGoogle ScholarPubMed

Schall, R. (1991). Estimation in generalized linear models with random effects. Biometrika, 40, 719–27.CrossRefGoogle Scholar

Schwarz, G. (1978). Estimating the dimensions of a model. Annals of Statistics, 6, 461–4.CrossRefGoogle Scholar

Shih, W.J. and Quan, H. (1997). Testing for treatment differences with dropouts present in clinical trials – a composite approach. Statistics in Medicine, 16, 1225–39.3.0.CO;2-Y>CrossRefGoogle ScholarPubMed

Snijders, T.A.B. and Bosker, R.J. (1993). Standard errors and sample sizes for two-level research. Journal of Educational Statistics, 18, 237–59.CrossRefGoogle Scholar

Spriensma, A.S., Hajos, T.R.S., Boer, M.R., Heymans, M.W., and Twisk, J.W.R. (2012). A new approach to analyse longitudinal epidemiological data with an excess of zeros. Submitted for publication.

SPSS (1997). Statistical Package for the Social Sciences, Advanced Statistics Reference Guide, Release 7.5. Chicago, IL, USA: SPSS.Google Scholar

SPSS (1998). Statistical Package for the Social Sciences, SPSS 9.0 Regression Models. Chicago, IL, USA: SPSS.Google Scholar

StanekIII, E.J., Shetterley, S.S., Allen, L.H., Pelto, G.H., and Chavez, A. (1989). A cautionary note on the use of autoregressive models in analysis of longitudinal data. Statistics in Medicine, 8, 1523–8.CrossRefGoogle ScholarPubMed

Stata (2001). Stata Reference Manual, Release 7. College Station, TX, USA: Stata Press.Google Scholar

Stata (2009). Multiple-Imputation Reference Manual, Release 11. College Station, TX, USA: STATA Press.Google Scholar

Stevens, J. (1996). Applied Multivariate Statistics for the Social Sciences, 3rd edn. Mahway, NJ, USA: Lawrence Erlbaum.Google Scholar

Steyerberg, E.W. (2000). Clinical trials in acute myocardial infarction: should we adjust for baseline characteristics? American Heart Journal, 139, 745–51.CrossRefGoogle ScholarPubMed

Stuart, E.A., Azur, M., Frangakis, C., and Leaf, P. (2009). Multiple imputation with large data sets: a case study of the children's mental health initiative. American Journal of Epidemiology, 169, 1133–9.CrossRefGoogle ScholarPubMed

Sun, J. and Song, P.X.-K. (2001). Statistical analysis of repeated measurements with informative censoring times. Statistics in Medicine, 20, 63–73.3.0.CO;2-2>CrossRefGoogle ScholarPubMed

Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrics, 26, 24–36.CrossRefGoogle Scholar

Twisk, J.W.R. (1997). Different statistical models to analyze epidemiological observational longitudinal data: an example from the Amsterdam Growth and Health Study. International Journal of Sports Medicine, 18(Suppl. 3), S216–24.CrossRefGoogle ScholarPubMed

Twisk, J.W.R. (2004). Longitudinal data analysis. A comparison between generalized estimating equations and random coefficient analysis. European Journal of Epidemiology, 19, 769–76.CrossRefGoogle ScholarPubMed

Twisk, J.W.R. (2006). Applied Multilevel Analysis. A Practical Guide. Cambridge, UK:Cambridge University Press.CrossRefGoogle Scholar

Twisk, J.W.R. and de Vente, W. (2008). The analysis of randomised controlled data with more than one follow-up measurement. A comparison between different approaches. European Journal of Epidemiology, 23, 655–60.CrossRefGoogle ScholarPubMed

Twisk, J.W.R. and Hoekstra, T. (2012). Classifying developmental trajectories over time should be done with great caution: a comparison between methods. Journal of Clinical Epidemiology, 65, 1078–87.CrossRefGoogle ScholarPubMed

Twisk, J.W.R. and Proper, K. (2004). Evaluation of the results of a randomized controlled trial: how to define changes between baseline and follow-up. Journal of Clinical Epidemiology, 57, 223–8.CrossRefGoogle ScholarPubMed

Twisk, J.W.R. and Rijmen, F. (2009). Longitudinal tobit regression: a new approach to analyze outcome variables with floor or ceiling effects. Journal of Clinical Epidemiology, 62, 953–8.CrossRefGoogle ScholarPubMed

Twisk, J.W.R., Kemper, H.C.G., and Mellenbergh, G.J. (1994). Mathematical and analytical aspects of tracking. Epidemiological Reviews, 16, 165–83.CrossRefGoogle ScholarPubMed

Twisk, J.W.R., Kemper, H.C.G., van Mechelen, W., and Post, G.B. (1997). Tracking of risk factors for coronary heart disease over a 14 year period: a comparison between lifestyle and biological risk factors with data from the Amsterdam Growth and Health Study. American Journal of Epidemiology, 145, 888–98.CrossRefGoogle Scholar

Twisk, J.W.R., Staal, B.J., Brinkman, M.N., Kemper, H.C.G., and van Mechelen, W. (1998a). Tracking of lung function parameters and the longitudinal relationship with lifestyle. European Respiratory Journal, 12, 627–34.CrossRefGoogle ScholarPubMed

Twisk, J.W.R., Kemper, H.C.G., van Mechelen, W., and van Lenthe, F.J. (1998b). Longitudinal relationship of body mass index and the sum of skinfolds with other risk factors for coronary heart disease. International Journal of Obesity, 22, 915–22.CrossRefGoogle ScholarPubMed

Twisk, J.W.R., van Mechelen, W., and Kemper, H.C.G. (2000). Tracking of activity and fitness and the relationship with CVD risk factors. Medicine Science in Sports and Exercise, 32, 1455–61.CrossRefGoogle Scholar

Twisk, J.W.R., Kemper, H.C.G., van Mechelen, W., and Post, G.B. (2001). Clustering of risk factors for coronary heart disease. The longitudinal relationship with lifestyle. Annals of Epidemiology, 11, 157–65.CrossRefGoogle ScholarPubMed

van ‘t Hof, M.A. and Kowalski, C.J. (1979). Analysis of mixed longitudinal data-sets. In A Mixed Longitudinal Interdisciplinary Study of Growth and Development, eds Prahl-Andersen, B., Kowalski, H. C. J., and Heyendael, P.. New York, NY, USA: Academic Press, pp. 161–72.Google Scholar

Venables, W.N. and Ripley, B.D. (2000). S Programming. New York, NY, USA: Springer.CrossRefGoogle Scholar

Venables, W.N. and Ripley, B.D. (2002). Modern Applied Statistics with S, 4th edn. New York, NY, USA: Springer.CrossRefGoogle Scholar

Verbeke, G. and Molenberghs, G. (2000). Linear Mixed Models for Longitudinal Data. New York, NY, USA: Springer-Verlag.Google Scholar

Vermeulen, E.G.J., Stehouwer, C.D.A., Twisk, J.W.R., et al. (2000). Effect of homocysteine-lowering treatment with folic acid plus vitamin B6 on progression of subclinical atherosclerosis: a randomised, placebo-controlled trial. Lancet, 355, 517–22.CrossRefGoogle ScholarPubMed

Vickers, A.J. and Altman, D.G. (2001). Analysing controlled trials with baseline and follow up measurements. British Medical Journal, 323, 1123–4.CrossRefGoogle ScholarPubMed

Williamson, J.M., Kim, K., and Lipsitz, S.R. (1995). Analyzing bivariate ordinal data using a global odds ratio. Journal of the American Statistical Association, 90, 1432–7.CrossRefGoogle Scholar

Wolfinger, R.D. (1998). Towards practical application of generalized linear mixed models. In Proceedings of the 13th International Workshop on Statistical Modelling, eds Marx, B. and Friedl, H.. New Orleans, LA, USA, pp. 388–95.Google Scholar

Wolfinger, R.D., Tobias, R., and Sall, J. (1994). Computing Gaussian likelihoods and their derivates for general linear mixed models. SIAM Journal of Scientific Computation, 15, 1294–310.CrossRefGoogle Scholar

Yang, M. and Goldstein, H. (2000). Multilevel models for repeated binary outcomes: attitudes and voting over the electoral cycle. Journal of the Royal Statistical Society, 163, 49–62.CrossRefGoogle Scholar

Yang, X., Li, J. and Shoptaw, S. (2008). Imputation-based strategies for clinical trial longitudinal data with nonignorable missing values. Statistics in Medicine, 27, 2826–49.CrossRefGoogle ScholarPubMed

Yucel, R.M., He, Y., and Zaslavsky, A.M. (2008). Using calibration to improve rounding in imputation. American Statistician, 62, 1–5.CrossRefGoogle Scholar

Zeger, S.L. and Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42, 121–30.CrossRefGoogle ScholarPubMed

Zeger, S.L. and Liang, K.-Y. (1992). An overview of methods for the analysis of longitudinal data. Statistics in Medicine, 11, 1825–39.CrossRefGoogle ScholarPubMed

Zeger, S.L. and Qaqish, B. (1988). Markov regression models for time series: a quasi-likelihood approach. Biometrics, 44, 1019–31.CrossRefGoogle ScholarPubMed

Zeger, S.L., Liang, K-Y., and Albert, P.S. (1988). Models for longitudinal data: a generalised estimating equation approach. Biometrics, 44, 1049–60.CrossRefGoogle Scholar