IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v59y2003i4p829-836.html
   My bibliography  Save this article

Modeling Longitudinal Data with Nonignorable Dropouts Using a Latent Dropout Class Model

Author

Listed:
  • Jason Roy

Abstract

No abstract is available for this item.

Suggested Citation

  • Jason Roy, 2003. "Modeling Longitudinal Data with Nonignorable Dropouts Using a Latent Dropout Class Model," Biometrics, The International Biometric Society, vol. 59(4), pages 829-836, December.
    • Handle: RePEc:bla:biomet:v:59:y:2003:i:4:p:829-836
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2003.00097.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Akaike, Hirotugu, 1981. "Likelihood of a model and information criteria," Journal of Econometrics, Elsevier, vol. 16(1), pages 3-14, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bartolucci, Francesco & Giorgio E., Montanari & Pandolfi, Silvia, 2012. "Item selection by an extended Latent Class model: An application to nursing homes evaluation," MPRA Paper 38757, University Library of Munich, Germany.
    2. Eric J. Tchetgen Tchetgen & Kathleen E. Wirth, 2017. "A general instrumental variable framework for regression analysis with outcome missing not at random," Biometrics, The International Biometric Society, vol. 73(4), pages 1123-1131, December.
    3. Jung, Hyekyung & Schafer, Joseph L. & Seo, Byungtae, 2011. "A latent class selection model for nonignorably missing data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 802-812, January.
    4. Francesco Bartolucci & Alessio Farcomeni, 2015. "A discrete time event-history approach to informative drop-out in mixed latent Markov models with covariates," Biometrics, The International Biometric Society, vol. 71(1), pages 80-89, March.
    5. Jouni Kuha & Myrsini Katsikatsou & Irini Moustaki, 2018. "Latent variable modelling with non‐ignorable item non‐response: multigroup response propensity models for cross‐national analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1169-1192, October.
    6. Sehee Kim & Donglin Zeng & Jeremy M. G. Taylor, 2017. "Joint partially linear model for longitudinal data with informative drop-outs," Biometrics, The International Biometric Society, vol. 73(1), pages 72-82, March.
    7. Maria Marino & Marco Alfó, 2015. "Latent drop-out based transitions in linear quantile hidden Markov models for longitudinal responses with attrition," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(4), pages 483-502, December.
    8. Hélène Jacqmin-Gadda & Cécile Proust-Lima & Jeremy M.G. Taylor & Daniel Commenges, 2010. "Score Test for Conditional Independence Between Longitudinal Outcome and Time to Event Given the Classes in the Joint Latent Class Model," Biometrics, The International Biometric Society, vol. 66(1), pages 11-19, March.
    9. Yu Cao & Nitai D. Mukhopadhyay, 2021. "Statistical Modeling of Longitudinal Data with Non-Ignorable Non-Monotone Missingness with Semiparametric Bayesian and Machine Learning Components," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 152-169, May.
    10. Dantan Etienne & Proust-Lima Cécile & Letenneur Luc & Jacqmin-Gadda Helene, 2008. "Pattern Mixture Models and Latent Class Models for the Analysis of Multivariate Longitudinal Data with Informative Dropouts," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-26, July.
    11. Haiqun Lin & Charles E. McCulloch & Robert A. Rosenheck, 2004. "Latent Pattern Mixture Models for Informative Intermittent Missing Data in Longitudinal Studies," Biometrics, The International Biometric Society, vol. 60(2), pages 295-305, June.
    12. Galatzer-Levy, Isaac R. & Bonanno, George A., 2012. "Beyond normality in the study of bereavement: Heterogeneity in depression outcomes following loss in older adults," Social Science & Medicine, Elsevier, vol. 74(12), pages 1987-1994.
    13. Betsy J. Feldman & Sophia Rabe-Hesketh, 2012. "Modeling Achievement Trajectories When Attrition Is Informative," Journal of Educational and Behavioral Statistics, , vol. 37(6), pages 703-736, December.
    14. Jason Roy & Michael J. Daniels, 2008. "A General Class of Pattern Mixture Models for Nonignorable Dropout with Many Possible Dropout Times," Biometrics, The International Biometric Society, vol. 64(2), pages 538-545, June.
    15. Michael J. Daniels & Minji Lee & Wei Feng, 2023. "Dirichlet process mixture models for the analysis of repeated attempt designs," Biometrics, The International Biometric Society, vol. 79(4), pages 3907-3915, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. María Ayuda & Fernando Collantes & Vicente Pinilla, 2010. "From locational fundamentals to increasing returns: the spatial concentration of population in Spain, 1787–2000," Journal of Geographical Systems, Springer, vol. 12(1), pages 25-50, March.
    2. Asghar, Zahid & Abid, Irum, 2007. "Performance of lag length selection criteria in three different situations," MPRA Paper 40042, University Library of Munich, Germany.
    3. Karakotsios, Achillefs & Katrakilidis, Constantinos & Kroupis, Nikolaos, 2021. "The dynamic linkages between food prices and oil prices. Does asymmetry matter?," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).
    4. Ruiz Vargas, E. & Mitchell, D.G.V. & Greening, S.G. & Wahl, L.M., 2014. "Topology of whole-brain functional MRI networks: Improving the truncated scale-free model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 151-158.
    5. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    6. Farrell, Terence C. & Hopkins, David L., 2007. "A hedonic Model of Lamb Carcass Attributes," Australasian Agribusiness Review, University of Melbourne, Department of Agriculture and Food Systems, vol. 15.
    7. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    8. Morandi, Valentina & Malighetti, Paolo & Paleari, Stefano & Redondi, Renato, 2015. "Codesharing agreements by low-cost carriers: An explorative analysis," Journal of Air Transport Management, Elsevier, vol. 42(C), pages 184-191.
    9. Simons, Andrew M. & Beltramo, Theresa & Blalock, Garrick & Levine, David I., 2017. "Using unobtrusive sensors to measure and minimize Hawthorne effects: Evidence from cookstoves," Journal of Environmental Economics and Management, Elsevier, vol. 86(C), pages 68-80.
    10. Wei Zhang & Sriram Dasu & Reza Ahmadi, 2017. "Higher Prices for Larger Quantities? Nonmonotonic Price–Quantity Relations in B2B Markets," Management Science, INFORMS, vol. 63(7), pages 2108-2126, July.
    11. Vladimir Mihajlović & Gordana Marjanović, 2020. "Asymmetries in effects of domestic inflation drivers in the Baltic States: a Phillips curve-based nonlinear ARDL approach," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 20(1), pages 94-116.
    12. Zolnik, Edmund, 2021. "Geographically weighted regression models of residential property transactions: Walkability and value uplift," Journal of Transport Geography, Elsevier, vol. 92(C).
    13. Kenneth P. Burnham & David R. Anderson, 2004. "Multimodel Inference," Sociological Methods & Research, , vol. 33(2), pages 261-304, November.
    14. Elizabeth S. Garrett & Scott L. Zeger, 2000. "Latent Class Model Diagnosis," Biometrics, The International Biometric Society, vol. 56(4), pages 1055-1067, December.
    15. Bushara, Mohammed O. A. & Abdelmahmod, Murtada Kh. A., 2016. "Efficiency of selected camel markets in Sudan: A multivariate approach (1995-2011)," 2016 Fifth International Conference, September 23-26, 2016, Addis Ababa, Ethiopia 246910, African Association of Agricultural Economists (AAAE).
    16. Thi Mai Hoa Ha & Derrick Yong & Elizabeth Mei Yin Lee & Prathab Kumar & Yuan Kun Lee & Weibiao Zhou, 2017. "Activation and inactivation of Bacillus pumilus spores by kiloelectron volt X-ray irradiation," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-15, May.
    17. Dimitrios Gounopoulos & Kyriaki Kosmidou & Dimitrios Kousenidis & Victoria Patsika, 2019. "The investigation of the dynamic linkages between real estate market and stock market in Greece," The European Journal of Finance, Taylor & Francis Journals, vol. 25(7), pages 647-669, May.
    18. Mati, Sagiru, 2021. "Do as your neighbours do? Assessing the impact of lockdown and reopening on the active COVID-19 cases in Nigeria," Social Science & Medicine, Elsevier, vol. 270(C).
    19. Francisco Venegas & Enrique de Alba, 1995. "An Economist´s guide to the Kalman filter," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 10(2), pages 123-145.
    20. Husmann, Kai & Möhring, Bernhard, 2017. "Modelling the economically viable wood in the crown of European beech trees," Forest Policy and Economics, Elsevier, vol. 78(C), pages 67-77.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:59:y:2003:i:4:p:829-836. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.