Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-26T02:21:41.228Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  15 May 2019

Jos W. R. Twisk
Jos W. R. Twisk
Affiliation:
Universiteit van Amsterdam
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Applied Mixed Model Analysis
A Practical Guide
 , pp. 227 - 233
Publisher: Cambridge University Press
Print publication year: 2019

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

Ahmed, I., Sutton, A. J. and Riley, R. (2011). Assessment of publication bias, selection bias, and unavailable data in meta-analyses using individual participant data: a database survey. BMJ, 344, d7762.CrossRefGoogle Scholar
Allison, P. D. (2001). Missing Data. Thousand Oaks, CA: Sage.Google Scholar
Atkinson, A. C. (1986). Masking unmasked. Biometrika, 73, 533–541.CrossRefGoogle Scholar
Audigier, V., White, I. R., Jolani, S., et al. (2017). Multiple imputation for multilevel data with continuous and binary variables. https://arxiv.org/abs/1702.00971Google Scholar
Barnett, V. and Lewis, T. (1994). Outliers in Statistical Data. New York: Wiley.Google Scholar
Browne, W. J. and Draper, D. (2006). A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Bayesian Analysis, 3, 473–514.Google Scholar
Bryk, A. S. and Raudenbush, S. W. (1992). Hierarchical Linear Models. Newbury Park, CA: Sage.Google Scholar
Burke, D. L., Ensor, J. and Riley, R. D. (2017). Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ. Statistics in Medicine, 36, 855–875.CrossRefGoogle ScholarPubMed
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
Cornell, J. E., Mulrow, C. D., Localio, R., et al. (2014). Random-effects meta-analysis of inconsistent effects: a time for change. Annals of Internal Medicine, 160, 267–270.CrossRefGoogle ScholarPubMed
Crouchley, R. and Davies, R. B. (2001). A comparison of GEE and random coefficient models for distinguishing heterogeneity, nonstationarity and state dependence in a collection of short binary event series. Statistical Modelling, 1, 271–285.Google Scholar
Curran, P. J. and Bauer, D. J. (2001). The disaggregation of within-Person and between-Person effects in longitudinal models of change. Annual Review of Psychology, 62, 583–619.CrossRefGoogle Scholar
Debray, T. P. A., Moons, K. G. M., van Valkenhoef, G., et al. and the GetReal Methods Review Group (2015). Get real in individual participant data (IPD) meta-analysis: a review of the methodology. Research Synthesis Methods, 6, 293–309.CrossRefGoogle ScholarPubMed
Demidenko, E. (2013). Mixed Models: Theory and Applications with R, 2nd edn. Hoboken, NJ: Wiley.Google Scholar
Diez Roux, A. V. (2002). A glossary for multilevel analysis. Journal of Epidemiology and Community Health, 56, 588–594.CrossRefGoogle ScholarPubMed
Donders, A., van der Heiden, G., Stijnen, T. and Moons, K. (2012). Review: a gentle introduction to imputation of missing values. Journal of Clinical Epidemiology, 59, 1087–1091.CrossRefGoogle Scholar
Duncan, T., Duncan, S., Stryker, L., Li, F. and Alpert, A. (Eds.). (1999). An Introduction to Latent Variable Modelling: Concepts, Issues and Applications. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Engel, B. (1998). A simple illustration of the failure of PQL, IRREML and AHPL as approximate ML methods for mixed models for binary data. Biometrical Journal, 40, 141–154.3.0.CO;2-A>CrossRefGoogle Scholar
Fisher, D. J., Copas, A. J., Tierney, J. F. and Parmar, M. K. B. (2011). A critical review of methods for the assessment of patient-level interactions in individual participant data meta-analysis of randomized trials, and guidance for practitioners. Journal of Clinical Epidemiology, 64, 949–967.CrossRefGoogle ScholarPubMed
Galecki, A. and Burzykowski, T. (2013). Linear Mixed-Effects Models Using R: A Step-by-Step Approach. New York: Springer Texts in Statistics.CrossRefGoogle Scholar
Gardner, W., Mulvey, E. P. and Shaw, E. C. (1995). Regression analyses of counts and rates: Poisson, overdispersed Poisson and negative binomial models. Psychological Bulletin, 118, 392–404.CrossRefGoogle ScholarPubMed
Gelwin, A. (2006). Prior distribution for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Analysis, 3, 513–534.Google Scholar
Ghith, N., Wagner, P., Frølich, A. and Merlo, J. (2016). Short term survival after admission for heart failure in Sweden: applying multilevel analyses of discriminatory accuracy to evaluate institutional performance. PLoS One, 11(2), e0148187.CrossRefGoogle ScholarPubMed
Goldstein, H. (1987). Multilevel Models in Educational and Social Research. London/New York: Griffin/Oxford University Press.Google Scholar
Goldstein, H. (1992). Statistical information and the measurement of education outcomes (editorial). Journal of the Royal Statistical Society, A, 155, 313–315.CrossRefGoogle Scholar
Goldstein, H. (1995). Multilevel Statistical Models. London: Edward Arnold.Google Scholar
Goldstein, H. (2003). Multilevel Statistical Models, 3rd edn. London: Edward Arnold.Google Scholar
Goldstein, H. and Cuttance, P. (1988). A note on national assessment and school comparisons. Journal of Education Policy, 3, 197–202.CrossRefGoogle Scholar
Goldstein, H. and Rasbash, J. (1996). Improved approximation for multilevel models with binary responses. Journal of the Royal Statistical Society, Series A, 159, 505–513.CrossRefGoogle Scholar
Greenland, S. (2000a). When should epidemiologic regressions use random coefficients? Biometrics, 56, 915–921.CrossRefGoogle ScholarPubMed
Greenland, S. (2000b). Principles of multilevel modelling. International Journal of Epidemiology, 29, 158–167.CrossRefGoogle ScholarPubMed
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
Heo, M. and Leon, A. C. (2005). Comparison of statistical methods for analysis of clustered binary outcomes. Statistics in Medicine, 24, 911–923.CrossRefGoogle Scholar
Higgins, J. P. T., Whitehead, A., Turner, R. M., Omar, R. Z. and Thompson, S. G. (2001). Meta-analysis of continuous outcome data from individual patients. Statistics in Medicine, 20, 2219–2241.CrossRefGoogle ScholarPubMed
Hox, J. J. (2002). Multilevel Analysis: Techniques and Applications. Mahwah, NJ: Lawrence Erlbaum Associates.CrossRefGoogle 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 ScholarPubMed
Hutchinson, M. K. and Holtman, M. C. (2005). Analysis of count data using poisson regression. Research in Nursing & Health, 28, 408–418.CrossRefGoogle ScholarPubMed
Jiang, J. (2007). Linear and Generalized Linear Mixed Models and Their Applications. New York: Springer-Verlag.Google Scholar
Jiang, J. (2016). Robust Mixed Model Analysis. World Scientific Publishing Co.Google Scholar
Jolani, S., Debray, T. P. A., Koffijberg, H., van Buuren, S. and Moons, K. G. M. (2015). Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE. Statistics in Medicine, 34, 1841–1863.CrossRefGoogle Scholar
Jöreskog, K. G. and Sörbom, D. (2001). LISREL 8.5. Chicago, IL: Scientific Software International.Google Scholar
Jung, S. H., Kang, S. H. and Ahn, C. (2001). Sample size calculations for clustered binary data. Statistics in Medicine, 20, 1971–1982.CrossRefGoogle ScholarPubMed
Kenward, M. G. and Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983–997.CrossRefGoogle ScholarPubMed
Korff, , , M., Koepsell, T., Curry, S. and Diehr, P. (1992). Multilevel analysis in epidemiologic research on health behaviors and outcomes. American Journal of Epidemiology, 135, 1077–1082.CrossRefGoogle Scholar
Kreft, I. and De Leeuw, J. (1998). Introducing Multilevel Modelling. London: Sage.CrossRefGoogle Scholar
Lambert, P. C. and Royston, P. (2009). Further development of flexible parametric models for survival analysis. Stata Journal, 9, 265–290.CrossRefGoogle Scholar
Langford, I. H. and Lewis, T. (1998). Outliers in multilevel models (with discussion). Journal of the Royal Statistical Society, A, 161, 121–160.CrossRefGoogle Scholar
Larsen, K., Petersen, J. H., Budtz-Jorgensen, E. and Endahl, L. (2000). Interpreting parameters in the logistic regression model with random effects. Biometrics, 56, 909–914.CrossRefGoogle ScholarPubMed
Lawrence, A. J. (1995). Deletion, influence and masking in regression. Journal of the Royal Statistical Society, B, 57, 181–189.Google Scholar
Lee, E. W. and Durbin, N. (1994). Estimation and sample size considerations for clustered binary responses. Statistics in Medicine, 13, 1241–1252.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, 325–335.Google Scholar
Leyland, A. H. and Groenewegen, P. P. (2003). Multilevel modelling and public health policy. Scandinavian Journal of Public Health, 31, 267–274.CrossRefGoogle ScholarPubMed
Liang, K.-Y. and Zeger, S. L. (1993). Regression analysis for correlated data. Annual Review of Public Health, 14, 43–68.CrossRefGoogle ScholarPubMed
Lin, X. (1997). Variance component testing in generalised linear models with random effects. Biometrika, 84, 309–325.CrossRefGoogle 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, 153–160.CrossRefGoogle Scholar
Littel, R. C., Pendergast, J. and Natarajan, R. (2000). Modelling covariance structures in the analysis of repeated measures data. Statistics in Medicine, 19, 1793–1819.3.0.CO;2-Q>CrossRefGoogle Scholar
Little, R. J. A. (1995). Modelling the drop-out mechanism repeated measures studies. Journal of the American Statistical Association, 90, 1112–1121.CrossRefGoogle Scholar
Little, R. J. A. and Rubin, D. B. (1987). Statistical Analysis with Missing Data. New York: Wiley.Google Scholar
Little, T. D., Schabel, K. U. and Baumert, J. (Eds.). (2000). Modeling Longitudinal and Multilevel Data. Practical Issues, Applied Approaches, and Specific Examples. Mahwah, NJ: Lawrence Erlbaum Associates.CrossRefGoogle Scholar
Liu, G. and Liang, K.-Y. (1997). Sample size calculations for studies with correlated observations. Biometrics, 53, 937–947.CrossRefGoogle ScholarPubMed
Liu, Q. and Pierce, D. A. (1994). A note on Gauss-Hermite quadrature. Biometrika, 81, 624–629.Google Scholar
Livert, D., Rindskopf, D., Saxe, L. and Stirratt, M. (2001). Using multilevel modelling in the evaluation of community-based treatment programs. Multivariate Behavioral Research, 36, 155–183.CrossRefGoogle ScholarPubMed
Ludtke, O., Marsh, H., Robitzsch, A., et al. (2008). The multilevel latent covariate model: a new, more reliable approach to group-level effects in contextual studies. Psychological Methods, 13, 203–229.CrossRefGoogle Scholar
McCullagh, P. and Searle, S. R. (2001). Generalized, Linear and Mixed Models. New York: Wiley.Google Scholar
Merlo, J. (2003). Multilevel analytical approaches in social epidemiology: measures of health variation compared with traditional measures of association. Journal of Epidemiology and Community Health, 57, 550–552.CrossRefGoogle ScholarPubMed
Moerbeek, M., Breukelen van, G. J. P. and Berger, M. P. F. (2000). Design issues for multilevel experiments. Journal of Educational and Behavioral Statistics, 25, 271–284.CrossRefGoogle Scholar
Moerbeek, M., Breukelen van, G. J. P. and Berger, M. P. F. (2001). Optimal experimental designs for multilevel logistic models. The Statistician, 50, 17–30.CrossRefGoogle Scholar
Moerbeek, M., van Breukelen, G. J. P. and Berger, M. P .F. (2003a). A comparison of estimation methods for multilevel logistic models. Computational Statistics, 18, 19–37.CrossRefGoogle Scholar
Moerbeek, M., van Breukelen, G. J. P., Ausems, M. and Berger, M. P. F. (2003b). Optimal sample sizes in experimental designs with individuals nested within clusters. Understanding Statistics, 2, 151–175.CrossRefGoogle Scholar
Morrell, C. H. (1998). Likelihood ratio testing of variance components in the linear mixed-effects model using restricted maximum likelihood. Biometrics, 54, 1560–1568.CrossRefGoogle ScholarPubMed
Muthén, B. (1984). A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika, 49, 115–132.CrossRefGoogle Scholar
Nelder, J. A. and Lee, Y. (1992). Likelihood, Quasi-likelihood and Pseudolikelihood: some comparisons. Journal of the Royal Statistical Society, B, 54, 273–284.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, 25–36.CrossRefGoogle Scholar
Neuhaus, J. M. and Lesparance, M. L. (1996). Estimation efficiency in a binary mixed-effects model setting. Biometrika, 83, 441–446.CrossRefGoogle Scholar
Nuttall, D. L., Goldstein, H., Prosser, R. and Rasbash, J. (1989). Differential school effectiveness. International Journal of Educational Research, 13, 769–776.CrossRefGoogle Scholar
Oehlert, G. W. (2012). A few words about REML. Stat 5303, 1–11.Google Scholar
Omar, R. Z., Wright, E. M., Turner, R. M. and Thompson, S. G. (1999). Analyzing repeated measurements data: a practical comparison of methods. Statistics in Medicine, 18, 1587–1603.3.0.CO;2-Z>CrossRefGoogle Scholar
Pinheiro, J. C. and Bates, D. M. (2000). Mixed-Effects Models in S and S-PLUS. New York: Springer-Verlag.CrossRefGoogle Scholar
Plewis, I. (1991). Using multilevel models to link educational progress with curriculum coverage. In Schools, Classrooms and Pupils. International Studies of Schooling from a Multilevel Perspective, ed. Raudenbush, S. W. and Willms, J. D., San Diego: Academic Press.Google Scholar
Plewis, I. and Hurry, J. (1998). A multilevel perspective on the design and analysis of intervention studies. Educational Research and Evaluation, 4, 13–26.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, ed. Friedl, H., Bughold, A. and Kauermann, G., pp. 332–339. Graz, Austria: TU Graz Press.Google 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). 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, 17–23.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, 1–21.CrossRefGoogle Scholar
Rabe-Hesketh, S., Skrondal, A. and Pickles, A. (2004). GLLAMM Manual. U.C. Berekely Division of Biostatistics Working Paper Series, paper 160, www.bepres.com/ucbbiostat/paper160.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, 301–323.CrossRefGoogle Scholar
Raudenbush, S. W. and Bryk, A. S. (2002). Hierarchical Linear Models. Applications and Data Analysis Methods. Thousand Oaks, CA: Sage.Google Scholar
Reise, S. P. and Duan, N. (2003). Multilevel Modelling Methodological Advances, Issues, and Applications. Mahwah, NJ: Lawrence Erlbaum Associates.CrossRefGoogle Scholar
Rice, N. and Leyland, A. (1996). Multilevel models: applications to health data. Journal of Health Services Research Policy, 1, 154–164.CrossRefGoogle ScholarPubMed
Riley, R. D., Lambert, P. C. and Abo-Zaid, G. (2010). Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ, 340, c221.CrossRefGoogle ScholarPubMed
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. (1997). Multilevel models with binary response: a comparison of estimation procedures. Journal of the Royal Statistical Society, A, 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, 339–355.CrossRefGoogle 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. (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.CrossRefGoogle Scholar
Rubin, D. B. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association, 91, 473–489.CrossRefGoogle Scholar
Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. New York: Chapman & Hall.CrossRefGoogle Scholar
Schafer, J. L. (1999). Multiple imputation: a primer. Statistical Methods in Medical Research, 8, 3–15.CrossRefGoogle ScholarPubMed
Schüle, S. A., von Kries, R., Fromme, H. and Bolte, G. (2016). Neighbourhood socioeconomic context, individual socioeconomic position, and overweight in young children: a multilevel study in a large German city. BMC Obesity, 3, 25.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, 1225–1239.3.0.CO;2-Y>CrossRefGoogle ScholarPubMed
Simmonds, M. C., Higgins, J. P. T., Stewart, L. A., et al. (2005). Meta-analysis of individual patient data from randomized trials: a review of methods used in practice. Clinical Trials, 2, 209–217.CrossRefGoogle ScholarPubMed
Simmonds, M., Stewart, G. and Stewart, L. (2015). A decade of individual participant data meta-analyses: a review of current practice. Contemporary Clinical Trials, 45, 76–83.CrossRefGoogle ScholarPubMed
Skrondal, A. and Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models. Boca Raton, FL: Chapman & Hall/CRC Press.CrossRefGoogle Scholar
Snijders, T. A. B. and Bosker, R. J. (1993). Standard errors and sample sizes for two-level research. Journal of Educational Statistics, 18, 237–259.CrossRefGoogle Scholar
Snijders, T. A. B. and Bosker, R. J. (1999). Multilevel Analysis. An Introduction to Basic and Advanced Multilevel Modelling. London: Sage.Google Scholar
Sterne, J. A. C., White, I. A., Carlin, J. B., et al. (2009). Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ, 338, b2393.CrossRefGoogle ScholarPubMed
Stewart, L. A. and Parmar, M. K. B. (1993). Meta-analysis of the literature or of individual patient data: is there a difference? Lancet, 341, 418–422.CrossRefGoogle ScholarPubMed
Steyerberg, E. W. (2009). Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York: Springer-Verlag.CrossRefGoogle Scholar
Ten Have, T. R., Ratcliffe, S. J., Reboussin, B. A. and Miller, M. E. (2004). Deviations from the population-averaged cluster-specific relationship for clustered binary data. Statistical Methods in Medical Research, 13, 3–16.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, 769–776.CrossRefGoogle ScholarPubMed
Twisk, J. W. R. (2013). Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide, 2nd edn. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Twisk, J., de Boer, M., de Vente, W. and Heymans, M. (2013). Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis. Journal of Clinical Epidemiology, 66, 1022–1028.CrossRefGoogle Scholar
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, 223–228.CrossRefGoogle ScholarPubMed
Twisk, J. W. R. and de Vente, W. (2002). Attrition in longitudinal studies: how to deal with missing data. Journal of Clinical Epidemiology, 55, 329–337.CrossRefGoogle ScholarPubMed
Twisk, J. W. R., de Vente, W., Apeldoorn, A. T. and Boer, M. R. de (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, 14, 3.Google Scholar
Weaver, C. G., Ravani, P., Oliver, M. J., Austin, P. C. and Quinn, R. R. (2015). Analyzing hospitalisation data: potential limitations of Poisson regression. Nephrology Dialysis Transplantation, 30, 1244–1249.CrossRefGoogle ScholarPubMed
West, B. T., Welch, K. B. and Galecki, A. T. (2015). Linear Mixed Models: A Practical Guide Using Statistical Software, 2nd edn. Boca Raton, FL: CRC Press.Google Scholar
Woodhouse, G. and Goldstein, H. (1989). Educational Performance Indicators and LEA league tables. Oxford Review of Education, 14, 301–319.CrossRefGoogle Scholar
Zeger, S. L. and Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42, 121–130.CrossRefGoogle ScholarPubMed
Zeger, S. L. and Liang, K.-Y. (1992). An overview of methods for the analysis of longitudinal data. Statistics in Medicine, 11, 1825–1839.CrossRefGoogle ScholarPubMed
Zheng, X. and Rabe-Hesketh, S. (2007). Estimating parameters of dichotomous and ordinal item response models with gllamm. The Stata Journal, 3, 313–333.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] 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, Universiteit van Amsterdam
  • Book: Applied Mixed Model Analysis
  • Online publication: 15 May 2019
  • Chapter DOI: https://doi.org/10.1017/9781108635660.015
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, Universiteit van Amsterdam
  • Book: Applied Mixed Model Analysis
  • Online publication: 15 May 2019
  • Chapter DOI: https://doi.org/10.1017/9781108635660.015
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, Universiteit van Amsterdam
  • Book: Applied Mixed Model Analysis
  • Online publication: 15 May 2019
  • Chapter DOI: https://doi.org/10.1017/9781108635660.015
Available formats
×