Skip to main content Accessibility help
×
Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-23T15:50:05.268Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  20 April 2023

Jos W. R. Twisk
Affiliation:
Amsterdam University Medical Centers
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agresti, A., Booth, J.G., Hobart, J.P. and Caffo, B. (2000). Random-effects modelling of categorical response data. Sociological Methodology, 30, 2780.Google Scholar
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716723.Google Scholar
Albert, P.S. (1999). Longitudinal data analysis (repeated measures) in clinical trials. Statistics in Medicine, 18, 17071732.Google Scholar
Altman, D.G. (1991). Practical statistics for medical research. London: Chapman and Hall.Google Scholar
Apeldoorn, A.T., Ostelo, R.W., Helvoirt, van H., Fritz, J.M., Knol, D.L., Tulder, van M.W. and Vet, de H.C.W. (2012) A randomized controlled trial on the effectiveness of a classification-based system for subacute and chronic low back pain. Spine, 37, 13471356.CrossRefGoogle ScholarPubMed
Austin, P.C., White, I.R., Lee, D.S. and Buuren van, S.V. (2021). Missing data in clinical research: a tutorial on multiple imputation. Canadian Journal of Cardiology, 37, 13221331.Google Scholar
Barbosa, M.F. and Goldstein, H. (2000). Discrete response multilevel models for repeated measures: an application to intentions data. Quality and Quantity, 34, 323330.Google 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, 1736.Google Scholar
Baybak, M.A. (2004). What you see may not be what you get: A brief, nontechnical introduction to overfitting in regression-type models. Psychosomatic Medicine, 66, 411421.Google Scholar
Basagaña, X. and Spiegelman, D. (2010). Power and sample size calculations for longitudinal studies comparing rates of change with a time-varying exposure Statistics in Medicine, 29, 181192.Google Scholar
Berkhof, J., Knol, D.J., Rijmen, F., Twisk, J.W.R., Uitdehaag, B.J.M. and Boers, M. (2009). Relapse – remission and remission – relapse switches in rheumatoid arthritis patients were modeled by random effects. Journal of Clinical Epidemiology, 62, 10851094.Google 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, 13681382.Google Scholar
Blomquist, N. (1977). On the relation between change and initial value. Journal of the American Statistical Association, 72, 746749.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, 209210.Google Scholar
Box-Steffensmeier, J.M. and De Boef, S. (2006). Repeated events survival models: The conditional frailty model. Statistics in Medicine, 25, 35183533.CrossRefGoogle ScholarPubMed
Bozdogan, H. (1987). Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345370.Google Scholar
Breslow, N.E. and Clayton, D.G. (1993). Approximate inference in generalised linear models. Journal of the American Statistical Association, 88, 925.Google Scholar
Brown, C.A. and Lilford, R.J. (2006). The stepped wedge trial design: A systematic review. BMC Medical Research Methodology, 6, 54.Google Scholar
Bruce, B. and Fries, J.F. (2003). The Stanford Health Assessment Questionnaire: Dimensions and practical applications. Health Quality of Life Outcomes, 1, 20.Google Scholar
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, 12611291.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, 42794292.Google Scholar
Buuren, van S.V. (2018). Flexible imputation of missing data (2nd edn.). London: Chapman and Hall/CRC.Google Scholar
Carey, V., Zeger, S.L. and Diggle, P.J. (1993). Modeling multivariate binary data with alternating logistic regression. Biometrika, 80, 517526.Google 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, 7182.Google Scholar
Cleves, M.A., Gould, W.W., Gutierrez, R.G. and Marchenko, Y.V. (2010). An introduction to survival analysis using Stata (3rd edn.). College Station, TX: Stata Press.Google Scholar
Connell, A. and Frye, A. (2006a). Growth Mixture Modelling in developmental psychology: Overview and demonstration of heterogeneity in developmental trajectories of adolescent antisocial behaviour. Infant and Child Development, 15, 609621.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, 639642.Google Scholar
Conway, M.R. (1990). A random effects model for binary data. Biometrics, 46, 317328.Google Scholar
Crowder, M.J. and Hand, D.J. (1990). Analysis of repeated measures. London: Chapman and Hall.Google Scholar
Crowther, M.J., Abrams, K.R. and Lambert, P.C. (2013). Joint modeling of longitudinal and survival data. The Stata Journal, 13, 165184.Google Scholar
Curren, P.J. and Bauer, D.J. (2001). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Reviews in Psychology, 62, 583619.Google Scholar
Dalgaard, P. (2002). Introductory statistics with R. New York: 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: VU University Press.Google Scholar
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, 6984.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, 25532575.Google Scholar
Diggle, P.J. (1989). Testing for random dropouts in repeated measurement data. Biometrics, 45, 12551258.CrossRefGoogle Scholar
Dik, M.G., Jonker, C., Comijs, H.C., Bouter, L.M., Twisk, J.W.R., van Kamp, G.J. and Deeg, D.J.H. (2001). Memory complaints and Apo E ε4 accelerate cognitive decline in cognitively normal elderly. Neurology, 57, 22172222.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: Lawrence Erlbaum Associated Publishers.Google Scholar
Enders, C.K. (2010). Applied missing data analysis. New York: 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, 6173.Google Scholar
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, 652676.CrossRefGoogle ScholarPubMed
Fitzmaurice, G.M., Laird, N.M. and Lipsitz, S.R. (1994). Analysing incomplete longitudinal binary responses: A likelihood-based approach. Biometrics, 50, 601612.Google Scholar
Fitzmaurice, G.M., Laird, N.M. and Ware, J.H. (2004) Applied longitudinal data analysis. Hoboken, NJ: Wiley.Google Scholar
Fleiss, J.L. (1981). Statistical methods for rates and proportions. New York: Wiley.Google Scholar
Forbes, A.B., Carlin, J.B. (2005). “Residual change” analysis is not equivalent to analysis of covariance. Journal of Clinical Epidemiology, 58, 540541.CrossRefGoogle Scholar
Fox, J. (2002). An R and S-Plus comparison to applied regression. New York: Sage Publications.Google Scholar
Gandar, W. and Gautschi, W. (2000). Adaptive quadrature – revisited. BIT Numerical Mathematics, 40, 84101Google Scholar
Gebski, V., Leung, O., McNeil, D. and Lunn, D. (eds.) (1992). SPIDA user manual, version 6. Sydney, NSW: Macquarie University.Google Scholar
Gibbons, R.D. and Hedeker, D. (1997). Random effects probit and logistic regression models for three level data. Biometrics, 53, 15271537.Google Scholar
Glynn, R.J., Stukel, T.A., Sharp, S.M, Bubolz, T.A., Freeman, J.L. and Fisher, E.S. (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, 776786.Google Scholar
Goldstein, H. (1986). Multilevel mixed linear model analysis using iterative generalised least squares. Biometrika, 73, 4356.Google Scholar
Goldstein, H. (1989). Restricted unbiased iterative generalised least squares estimation. Biometrika, 76, 622623.Google Scholar
Goldstein, H. (1991). Nonlinear multilevel models with an application to discrete response data. Biometrika, 78, 4551.Google Scholar
Goldstein, H. (1995). Multilevel statistical models. London: 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, 505513.CrossRefGoogle Scholar
Goldstein, H., Rasbash, J., Plewis, I., Draper, D., Browne, W., Yang, M., Woodhouse, G. and Healy, M. (1998). A user’s guide to MLwiN. London: Institute of Education.Google Scholar
Graham, J.W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549576.Google Scholar
Green, J.A. (2021). Too many zeros and/or highly skewed? A tutorial on modelling health behavior as count data with Poisson and negative binomial regression. Health Psychology and Behavioral Medicine, 9, 436455.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, 12551264.CrossRefGoogle Scholar
Guo, Y., Logan, H.L., Glueck, D.H. and Muller, K.E. (2013). Selecting a sample size for studies with repeated measures. BMC Medical Research Methodology, 13, 100CrossRefGoogle ScholarPubMed
Guo, Y. and Pandis, N. (2015). Sample-size calculation for repeated-measures and longitudinal studies. The American Journal of Orthodontics and Dentofacial Orthopedics, 147, 146149.CrossRefGoogle ScholarPubMed
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, 4046.CrossRefGoogle ScholarPubMed
Hajos, T.R.S., Pouwer, F., de Grooth, R., Holleman, F., Twisk, J.W.R., Diamant, M. and Snoek, F. (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, 10961102.CrossRefGoogle ScholarPubMed
Hand, D.J. and Crowder, M.J. (1996). Practical longitudinal data analysis. London: Chapman and Hall.Google Scholar
Harville, D.A. (1977). Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association, 72, 320340.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, 7093.Google Scholar
Hoeksma, J. and Kelderman, H. (2006). On growth curves and mixture models. Infant and Child Development, 15, 627634.Google Scholar
Hogan, J.W., Roy, J. and Korkontzelou, C. (2004). Handling drop-out in longitudinal studies. Statistics in Medicine, 23, 14551497.Google 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, 239257.Google Scholar
Holford, T.R. (1992). Analysing the temporal effects of age, period and cohort. Statistical Methods in Medical Research, 1, 317337.Google Scholar
Holford, T.R., Armitage, P. and Colton, T. (2005). Age-period-cohort analysis encyclopedia of biostatistics (2nd vol., pp. 8299). New York: John Wiley & Sons, Ltd.Google Scholar
Hosmer, D.W. and Lemeshow, S. (1989). Applied logistic regression. New York: 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, 694703.Google Scholar
Hurvich, C.M. and Tsai, C-L. (1989). Regression and time series model selection in small samples. Biometrika, 76, 297307.Google Scholar
Imlach Gunasekara, F., Richardson, K., Carter, K. and Blakely, T. (2014). Fixed effects analysis of repeated measures data. International Journal of Epidemiology, 43, 264269.Google Scholar
Jennrich, R.I. and Schluchter, M.D. (1986). Unbalanced repeated measures models with structured covariance matrices. Biometrics, 42, 805820.Google Scholar
Jones, B., Nagin, D. and Roeder, K. (2001). A SAS procedure based on mixed models for estimating developmental trajectories. Social Methods Research, 229, 374393.Google Scholar
Ju, K., Lin, L., Chu, H., Cheng, L-L. and Xu, C. (2020). Laplace approximation, penalized quasilikelihood, and adaptive Gauss–Hermite quadrature for generalized linear mixed models: towards meta-analysis of binary outcome with sparse data. BMC Medical Research Methodology, 20, 152.Google Scholar
Judd, C.M., Smith, E.R. and Kidder, L.H. (1991). Research methods in social relations. Fort Worth, TX: 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, 302317.Google 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, 1333.Google 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: 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, 27232732.Google Scholar
Kenward, M.G. and Carpenter, J. (2007). Multiple imputation: Current perspectives. Statistical Methods in Medical Research, 16, 199218.CrossRefGoogle ScholarPubMed
Kenward, M.G. and Molenberghs, G. (1999). Parametric models for incomplete continuous and categorical longitudinal data. Statistical Methods in Medical Research, 8, 5184.Google Scholar
Kleinbaum, D.G. (1994). Logistic regression: A self-learning text. New York: Springer-Verlag.Google Scholar
Kotz, D., Spigt, M., Arts, I.C.W., Crutzen, R. and Viechtbauer, W. (2012). Use of the stepped wedge design cannot be recommended: A critical appraisal and comparison with the classic cluster randomised controlled trial design. Journal of Clinical Epidemiology, 65, 12491252.CrossRefGoogle Scholar
Krieger, N. and Davey Smith, G.D. (2016). The tale wagged by the DAG: Broadening the scope of causal inference and explanation for epidemiology. International Journal of Epidemiology, 45, 17871808.Google ScholarPubMed
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, 657662.Google 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, 811830.Google Scholar
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, 191196.Google Scholar
Laird, N.M. and Ware, J.H. (1982). Random effects models for longitudinal data. Biometrics, 38, 963974.Google Scholar
Lambert, P.C. and Royston, P. (2009). Further development of flexible parametric models for survival analysis. Stata Journal, 9, 265290.Google Scholar
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, S273–277.CrossRefGoogle ScholarPubMed
Lee, E.W. and Durbin, N. (1994). Estimation and sample size considerations for clustered binary responses. Statistics in Medicine, 13, 12411252.CrossRefGoogle ScholarPubMed
Lee, I-M., Paffenbarger, R.S. Jr. and Hsieh, C-C. (1992). Time trends in physical activity among college alumni, 1962–1988. American Journal of Epidemiology, 135, 915925.Google Scholar
Lee, K.J., Tilling, K.M., Cornish, R.P., Little, R.J.A., Bell, M.L., Goetghebeur, E., Hogan, J.W. and Carpenter, J.R. (2021). STRATOS initiative. Framework for the treatment and reporting of missing data in observational studies: The Treatment and Reporting of Missing Data in Observational Studies framework. Journal of Clinical Epidemiology, 134, 7988.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, 325335.Google Scholar
Liang, K-Y. and Zeger, S.L. (1986). Longitudinal data analysis using generalised linear models. Biometrica, 73, 4551.Google Scholar
Liang, K-Y. and Zeger, S.L. (1993). Regression analysis for correlated data. Annual Review of Public Health, 14, 4368.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, 340.Google Scholar
Lingsma, H. (2010). Covariate adjustment increases statistical power in randomised controlled trials. Journal of Clinical Epidemiology, 63, 1391.Google Scholar
Lipsitz, S.R. and Fitzmaurice, G.M. (1994). Sample size for repeated measures studies with binary responses. Statistics in Medicine, 13, 12331239.Google Scholar
Lipsitz, S.R. and Fitzmaurice, G.M. (1996). Estimating equations for measures of association between repeated binary responses. Biometrics, 52, 903912.Google 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, 270278.Google 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.Google Scholar
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, 153160.Google Scholar
Littel, R.C., Freund, R.J. and Spector, P.C. (1991). SAS system for linear models (3rd edn.). Cary, NC: SAS Institute Inc.Google Scholar
Littel, R.C., Milliken, G.A., Stroup, W.W. and Wolfinger, R.D. (1996). SAS system for mixed models. Cary, NC: SAS Institute Inc.Google Scholar
Littel, R.C., Pendergast, J. and Natarajan, R. (2000). Modelling covariance structures in the analysis of repeated measures data. Statistics in Medicine, 19, 17931819.Google Scholar
Little, R.J.A. (1993). Pattern-mixture models for multivariate incomplete data. Journal of the American Statistical Association, 88, 125134.Google Scholar
Little, R.J.A. (1994). A class of pattern-mixture models for normal incomplete data. Biometrika, 81, 471483.CrossRefGoogle Scholar
Little, R.J.A. (1995). Modelling the drop-out mechanism repeated measures studies. Journal of the American Statistical Association, 90, 11121121.Google Scholar
Liu, Q. and Pierce, D.A. (1994). A note on Gauss–Hermite quadrature. Biometrika, 81, 624629.Google Scholar
Liu, G. and Liang, K-Y. (1997). Sample size calculations for studies with correlated observations. Biometrics, 53, 937947.Google Scholar
Loeys, T., Moerkerke, B. and Vansteelandt, S. (2015). A cautionary note on the power of the test for the indirect effect in mediation analysis. Frontiers in Psychology, 5, 1549.CrossRefGoogle ScholarPubMed
Longford, N.T. (1993). Random coefficient models. Oxford: 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, 633641.Google Scholar
Maindonald, J. and Braun, J. (2003). Data analysis and graphics using R: An example-based approach. Cambridge: Cambridge University Press.Google Scholar
Mansournia, M.A., Etminan, M., Danaei, G., Kaufman, J.S. and Collins, G. (2017). Handling time varying confounding in observational research. British Medical Journal, 359, j4587.Google Scholar
MathSoft (2000). S-PLUS 2000 guide to statistics, Vol. 1. Data analysis product division. Seattle, WA: MathSoft Inc.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, ed. Magnusson, D. and Bergman, L.R., pp. 211232. Cambridge: Cambridge University Press.Google Scholar
Mazumdar, S., Tang, G., Houck, P.R., Dew, M.A., Begley, A.E., Scott, J., Mulsant, B.H. and ReynoldsIII, C.F. (2007). Statistical analysis of longitudinal psychiatric data with dropouts. Journal of Psychological Research, 41, 10321041.Google Scholar
McCullagh, P. (1983). Quasi-likelihood functions. Annals of Statistics, 11, 5967.Google Scholar
Mchunu, N.N., Mwambi, H.G., Reddy, T., Yende-Zuma, N. and Naidoo, K. (2020). Joint modelling of longitudinal and time-to-event data: an illustration using CD4 count and mortality in a cohort of patients initiated on antiretroviral therapy. BMC Infectious Diseases, 20, 256.Google Scholar
McNally, R.J., Alexander, F.E., Strains, A. and Cartaright, 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, 3246.Google Scholar
Mdege, N.D., Man, M-S., Taylor, C.A. and Torgerson, D.J. (2011). Systematic review of stepped wedge cluster randomized trials shows that design is particularly used to evaluate interventions during routine implementation. Journal of Clinical Epidemiology, 64, 936948.Google Scholar
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, 10331044.CrossRefGoogle ScholarPubMed
Molenberghs, G., Michiels, B., Kenward, M.G. and Diggle, P.J. (1998). Monotone missing data and pattern-mixture models. Statistica Neerlandica, 52, 153161.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: Sage Publications.Google Scholar
Muthén, B. (2006). The potential of growth mixture modelling. Infant and Child Development, 15, 623625.Google 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.. pp. 143165. Boca Raton: Chapman & Hall/CRC Press.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, 882891.Google Scholar
Muthén, B. and Shedden, K. (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics, 55, 463469.Google Scholar
Nagin, D. (1999). Analyzing developmental trajectories. A semi-parametric group-based approach. Psychological Methods, 6, 1834.Google Scholar
Nagin, D. and Tremblay, R. (2001). Analyzing developmental trajectories of distinct but related behaviors: A group-based method. Psychological Methods, 6, 1834.Google Scholar
Naimi, A.I., Cole, S.R. and Kennedy, E.H. (2017). An introduction to g methods. International Journal of Epidemiology, 46, 756762.Google Scholar
Nelder, J.A. and Lee, Y. (1992). Likelihood, quasi-likelihood and psuedo-likelihood: Some comparisons. Journal of the Royal Statistical Society Series B, 54, 273284.Google Scholar
Nelder, J.A. and Pregibon, D. (1987). An extended quasi-likelihood function. Biometrika, 74, 221232.Google 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, 2536.Google 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, 15871603.Google Scholar
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, 1235.Google Scholar
Pinheiro, J.C. and Bates, D.M. (2000). Mixed-effects models in S and S-PLUS. New York: Springer-Verlag.CrossRefGoogle Scholar
Porkka, K.V.K., Viikari, J.S.A. and Åkerblom, H.K. (1991). Tracking of serum HDL-cholesterol and other lipids in children and adolescents: The cardiovascular risk in young Finns study. Preventive Medicine, 20, 713724.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, 213234.Google Scholar
Prentice, R.L. (1988). Correlated binary regression with covariates specific to each binary observation. Biometrics, 44, 10331048.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, 218226.Google Scholar
Rabe-Hesketh, S. and Pickles, A. (1999). Generalised linear latent and mixed models. In Proceedings of the 14th International workshop on statistical modelling, ed. Friedl, H., Berghold, A. and Kauermann, G., pp. 332339. Graz, Austria.Google Scholar
Rabe-Hesketh, S., Pickles, A. and Skrondal, A. (2001a). GLAMM manual technical report 2001/01. 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), 1723.Google Scholar
Rabe-Hesketh, S., Pickles, A. and Taylor, C. (2000). sg129: generalized linear latent and mixed models. Stata Technical Bulletin, 53, 4757.Google Scholar
Rabe-Hesketh, S. and Skrondal, A. (2001). Parameterisation of multivariate random effects models for categorical data. Biometrics, 57, 12561264.Google Scholar
Rabe-Hesketh, S., Skrondal, A. and Pickles, A. (2002). Reliable estimation of generalized linear mixed models using adaptive quadrature. The Stata Journal, 2, 121.Google Scholar
Rabe-Hesketh, S., Skrondal, A. and Pickles, A. (2005). Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects. Journal of Econometrics, 128, 301323.Google Scholar
Rasbash, J., Browne, W., Goldstein, H., Yang, M., Plewis, I., Healy, M., Woodhouse, G. and Draper, D. (1999). A user’s guide to MLwiN (2nd edn.). London: Institute of Education.Google Scholar
Rhian, D.M., De Stavola, B.L. and Cousens, S.N. (2011). gformula: Estimating causal effects in the presence of time-varying confounding or mediation using the g-computational formula. The Stata Journal, 11, 479517.Google Scholar
Rice, J.C. (1975). A metalgorithm for adaptive quadrature. Journal of the Association for Computing Machinery, 22, 6182.CrossRefGoogle Scholar
Ridout, M.S. (1991). Testing for random dropouts in repeated measurement data. Reader reaction. Biometrics, 47, 16171621.Google Scholar
Rijnhart, J.J.M., Lamp, S.J., Valente, M.J., MacKinnon, D.P., Twisk, J.W.R. and Heymans, M.W. (2021). Mediation analysis methods used in observational research: A scoping review and recommendations. BMC Medical Research Methodology, 21, 226.Google Scholar
Rizopoulos, D. (2011). Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics, 67, 819829.Google Scholar
Robertson, C. and Boyle, P. (1998). Age–period–cohort analysis of chronic disease rates; I modelling approach. Statistics in Medicine, 17, 13021323.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, 569583.Google Scholar
Robins, J.M., Hernan, M.A. and Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550560.Google Scholar
Robins, J. and Wang, N. (2000). Inference for imputation estimators. Biometrika, 87, 113124.Google 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, 7389.Google 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, 339355.Google Scholar
Rogossa, D. (1995). Myths and methods: “Myths about longitudinal research” plus supplemental questions. In The analysis of change, ed. Gottman, J.M., pp. 366. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Rosenberg, P.S. and Anderson, W.F. (2010). Proportional hazards models and age-period-cohort analysis of cancer rates. Statistics in Medicine, 20, 12281238.Google Scholar
Rosner, B. and Munoz, A. (1988). Autoregressive modelling for the analysis of longitudinal data with unequally spaced examinations. Statistics in Medicine, 7, 5971.Google Scholar
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, 457467.Google Scholar
Royston, P. 2001. Flexible parametric alternatives to the Cox model, and more. Stata Journal, 1, 128.Google Scholar
Royston, P. (2004). Multiple imputation of missing values. Stata Journal, 4, 227241.CrossRefGoogle Scholar
Royston, P., Carlin, J.B., and White, I.R. (2009). Multiple imputation of missing values: New features for mim. Stata Journal, 2, 252-264.Google Scholar
Royston, P., and Lambert, P.C. (2011). Flexible parametric survival analysis using stata: Beyond the cox model. College Station, TX: Stata Press.Google Scholar
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581592.Google Scholar
Rubin, D.B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.Google Scholar
Rubin, D.B. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association, 91, 473489.Google Scholar
Schafer, J.L. (1999). Multiple imputation: a primer. Statistical Methods in Medical Research, 8, 315.Google Scholar
Schall, R. (1991). Estimation in generalized linear models with random effects. Biometrika, 40, 719727.CrossRefGoogle Scholar
Schwarz, G. (1978). Estimating the dimensions of a model. Annals of Statistics, 6, 461464.Google 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, 12251239.Google Scholar
Skrondal, A. and Rabe-Hesketh, S. (2004). Generalized latent variable modeling: multilevel, longitudinal and structural equation models. Boca Raton, FL: Chapman & Hall/ CRC Press.Google Scholar
Snijders, T.A.B. and Bosker, R.J. (1993). Standard errors and sample sizes for two-level research. Journal of Educational Statistics, 18, 237259.Google Scholar
Spriensma, A.S., Hajos, T.R.S., Boer de, M.R., Heymans, M.W. and Twisk, J.W.R. (2013) A new approach to analyse longitudinal epidemiological data with an excess of zeros. BMC Medical Research Methodology, 13, 27.Google Scholar
Stanek III, 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, 15231528.Google Scholar
STATA (2009). Multiple-imputation reference manual. Release 11. College Station, TX: StataCorp LP.Google Scholar
Stevens, J. (1996). Applied multivariate statistics for the social sciences (3rd edn.). Mahway, NJ: Lawrence Erlbaum.Google Scholar
Steyerberg, E.W. (2000) Clinical trials in acute myocardial infarction: Should we adjust for baseline characteristics? American Heart Journal, 139, 745751.CrossRefGoogle ScholarPubMed
Stroup, W. and Claassen, E. (2020). Pseudo-likelihood or quadrature? What we thought we knew, what we think we know, and what we are still trying to figure out. Journal of Agricultural, Biological, and Environmental Statistics, 25, 639656.Google Scholar
Sun, J. and Song, P.X-K. (2001). Statistical analysis of repeated measurements with informative censoring times. Statistics in Medicine, 20, 6373.Google Scholar
Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrics, 26, 2436.Google 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), S216–224.Google Scholar
Twisk, J.W. (2004). Longitudinal data analysis. A comparison between generalized estimating equations and random coefficient analysis. European Journal of Epidemiology, 19, 769776.Google Scholar
Twisk, J.W.R. (2006). Applied multilevel analysis. A practical guide. Cambridge: Cambridge University Press.Google Scholar
Twisk, J.W.R. (2013). Applied longitudinal data analysis for epidemiology: A practical guide. Cambridge: Cambridge University Press.Google Scholar
Twisk, J.W.R. (2014). Is it necessary to classify developmental trajectories over time? A critical note. Annals of Nutrition and Metabolism, 65, 236240.Google Scholar
Twisk, J.W.R. (2019). Applied mixed model analysis. A practical guide. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Twisk, J.W.R. (2022). Analysis of data from randomized controlled trials: A practical guide. Cham, Switzerland: Springer Nature.Google Scholar
Twisk, J., Bosman, L., Hoekstra, T., Rijnhart, J., Welten, M., Heymans, M. (2018). Different ways to estimate treatment effects in randomised controlled trials. Contemporary Clinical Trials Communications, 10, 8085.Google Scholar
Twisk, J.W.R. and de Vente, W. (2002). Attrition in longitudinal studies: How to deal with missing data. Journal of Clinical Epidemiology, 55, 329337.Google Scholar
Twisk, J.W.R., de Vente, W. Apeldoorn, A.T. and de Boer, MR. (2017). Should we use logistic mixed model analysis for the effect estimation in a longitudinal RCT with a dichotomous outcome variable? Epidemiology Biostatistics and Public Health, 3, e12613–12621.Google Scholar
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, 10781087.Google Scholar
Twisk, J.W.R., Hoogendijk, E.O., Zwijsen, S.A., Boer, M.R. de. (2016). Different methods to analyze stepped wedge trial designs revealed different aspects of intervention effects. Journal of Clinical Epidemiology, 72, 7583.Google 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, 157165.Google Scholar
Twisk, J. and Proper, K. (2005). Evaluation of the results of a randomized controlled trial: How to define changes between baseline and follow-up. Journal of Clinical Epidemiology, 57, 223228.Google Scholar
Twisk, J. and Rijmen, F. (2009). Longitudinal tobit regression: A new approach to analyze outcome variables with floor or ceiling effects. Journal of Clinical Epidemiology, 62, 953958.Google Scholar
Twisk, J.W.R., Staal, B.J., Brinkman, M.N., Kemper, H.C.G. and van Mechelen, W. (1998). Tracking of lung function parameters and the longitudinal relationship with lifestyle. European Respiratory Journal, 12, 627634.Google Scholar
VanderWeele, T.J. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford: Oxford University Press.Google Scholar
Venables, W.N. and Ripley, B.D. (1997). Modern applied statistics with S-PLUS (2nd edn.). New York: Springer-Verlag.Google Scholar
Venables, W.N. and Ripley, B.D. (2000). S programming. New York: Springer.Google Scholar
Venables, W.N. and Ripley, B.D. (2002). Modern applied statistics with S (4th edn.). New York: Springer.Google Scholar
Verbeke, G. and Molenberghs, G. (2000). Linear mixed models for longitudinal data. New York: Springer-Verlag.Google Scholar
Vermeulen, E.G.J., Stehouwer, C.D.A., Twisk, J.W.R., van den Berg, M., de Jong, S., Mackaay, A.J.C., van Campen, C.M.C., Visser, F.J., Jakobs, C.A.J.M., Bulterijs, E.J. and Rauwerda, J.A. (2000). Effect of homocysteine-lowering treatment with folic acid plus vitamin B6 on progression of subclinical atherosclerosis: A randomised, placebo-controlled trial. Lancet, 355, 517522.Google Scholar
Vickers, A.J. and Altman, D.G. (2001). Analysing controlled trials with baseline and follow up measurements. British Medical Journal, 323, 11231124.Google Scholar
Wiliamson, 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, 14321437.Google Scholar
Wing, D., Simon, K. and Bello-Gomez, R.A. (2018). Designing difference in difference studies: Best practices for public health policy research. Annual Review of Public Health, 39, 453469.Google Scholar
Wolfinger, R.D. (1998). Towards practical application of generalized linear mixed models. In Proceedings of the 13th International workshop on statistical modelling, ed. Marx, B. and Friedl, H., pp. 388395. New Orleans, LA.Google 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, 4962.Google 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, 28262849.CrossRefGoogle ScholarPubMed
Yucel, R.M., He, Y. and Zaslavsky, A.M. (2008). Using calibration to improve rounding in imputation. American Statistician, 62, 15.Google Scholar
Zeger, S.L. and Liang, K-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42, 121130.CrossRefGoogle ScholarPubMed
Zeger, S.L. and Liang, K-Y. (1992). An overview of methods for the analysis of longitudinal data. Statistics in Medicine, 11, 18251839.Google Scholar
Zeger, S.L., Liang, K-Y. and Albert, P.S. (1988). Models for longitudinal data: a generalised estimating equation approach. Biometrics, 44, 10491060.CrossRefGoogle Scholar
Zeger, S.L. and Qaqish, B. (1988). Markov regression models for time series: A quasi-likelihood approach. Biometrics, 44, 10191031.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • References
  • Jos W. R. Twisk, Amsterdam University Medical Centers
  • Book: Applied Longitudinal Data Analysis for Medical Science
  • Online publication: 20 April 2023
  • Chapter DOI: https://doi.org/10.1017/9781009288002.015
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • References
  • Jos W. R. Twisk, Amsterdam University Medical Centers
  • Book: Applied Longitudinal Data Analysis for Medical Science
  • Online publication: 20 April 2023
  • Chapter DOI: https://doi.org/10.1017/9781009288002.015
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • Jos W. R. Twisk, Amsterdam University Medical Centers
  • Book: Applied Longitudinal Data Analysis for Medical Science
  • Online publication: 20 April 2023
  • Chapter DOI: https://doi.org/10.1017/9781009288002.015
Available formats
×