IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v4y2008i1n14.html
   My bibliography  Save this article

Pattern Mixture Models and Latent Class Models for the Analysis of Multivariate Longitudinal Data with Informative Dropouts

Author

Listed:
  • Dantan Etienne

    (INSERM, U897, Center of Epidemiology and Biostatistics of Bordeaux)

  • Proust-Lima Cécile

    (Université Victor Segalen Bordeaux II)

  • Letenneur Luc

    (INSERM, U897, Center of Epidemiology and Biostatistics of Bordeaux)

  • Jacqmin-Gadda Helene

    (INSERM, U897, Center of Epidemiology and Biostatistics of Bordeaux)

Abstract

Missing data and especially dropouts frequently arise in longitudinal data. Maximum likelihood estimates are consistent when data are missing at random (MAR) but, as this assumption is not checkable, pattern mixture models (PMM) have been developed to deal with informative dropout. More recently, latent class models (LCM) have been proposed as a way to relax PMM assumptions. The aim of this paper is to compare PMM and LCM in order to tackle informative dropout in a longitudinal study of cognitive ageing measured by several psychometric tests. Using a multivariate longitudinal model with a latent process, a sensitivity analysis was performed to compare estimates under the MAR assumption, from a PMM and from two LCM. In the PMM, dropout patterns are included as covariates in the multivariate longitudinal model. In the simple LCM, they are predictors of the class membership probabilities while, in the joint LCM, the dropout time is jointly modeled using a proportional hazard model depending on latent classes. We show that parameter interpretation is different in the two kinds of models and thus can lead to different estimated values. PMM parameters are adjusted on the dropout patterns while LCM parameters are adjusted on the latent classes. This difference is highlighted in our data set because the latent classes exhibit much more heterogeneity than dropout patterns. We suggest several complementary analyses to investigate the characteristics of latent classes in order to understand the meaning of the parameters when using LCM to deal with informative dropout.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:ijbist:v:4:y:2008:i:1:n:14
    DOI: 10.2202/1557-4679.1088
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1088
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1088?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Kenneth J. Wilkins & Garrett M. Fitzmaurice, 2006. "A Hybrid Model for Nonignorable Dropout in Longitudinal Binary Responses," Biometrics, The International Biometric Society, vol. 62(1), pages 168-176, March.
    2. Geert Verbeke & Geert Molenberghs & Herbert Thijs & Emmanuel Lesaffre & Michael G. Kenward, 2001. "Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach," Biometrics, The International Biometric Society, vol. 57(1), pages 7-14, March.
    3. 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.
    4. Cécile Proust & Hélène Jacqmin-Gadda & Jeremy M. G. Taylor & Julien Ganiayre & Daniel Commenges, 2006. "A Nonlinear Model with Latent Process for Cognitive Evolution Using Multivariate Longitudinal Data," Biometrics, The International Biometric Society, vol. 62(4), pages 1014-1024, December.
    5. 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.
    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. Sanjoy Sinha, 2012. "Robust analysis of longitudinal data with nonignorable missing responses," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(7), pages 913-938, October.
    2. 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.
    3. Kano, Yutaka & Takai, Keiji, 2011. "Analysis of NMAR missing data without specifying missing-data mechanisms in a linear latent variate model," Journal of Multivariate Analysis, Elsevier, vol. 102(9), pages 1241-1255, October.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Proust-Lima, Cécile & Joly, Pierre & Dartigues, Jean-François & Jacqmin-Gadda, Hélène, 2009. "Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1142-1154, February.
    7. 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.
    8. 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.
    9. O'Hara Hines, R.J. & Hines, W.G.S., 2007. "Covariance miss-specification and the local influence approach in sensitivity analyses of longitudinal data with drop-outs," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5537-5546, August.
    10. 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.
    11. Xie, Hui, 2012. "Analyzing longitudinal clinical trial data with nonignorable missingness and unknown missingness reasons," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1287-1300.
    12. Baojiang Chen & Xiao-Hua Zhou, 2011. "Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates," Biometrics, The International Biometric Society, vol. 67(3), pages 830-842, September.
    13. Igari, Ryosuke & Hoshino, Takahiro, 2018. "A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 150-166.
    14. Ivy Jansen & Geert Molenberghs, 2008. "A flexible marginal modelling strategy for non‐monotone missing data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 347-373, April.
    15. D. Nitsch & B. L. DeStavola & S. M. B. Morton & D. A. Leon, 2006. "Linkage bias in estimating the association between childhood exposures and propensity to become a mother: an example of simple sensitivity analyses," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 493-505, July.
    16. Jan R. Magnus & Andrey L. Vasnev, 2007. "Local sensitivity and diagnostic tests," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 166-192, March.
    17. 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.
    18. Morikawa, Kosuke & Kano, Yutaka, 2018. "Identification problem of transition models for repeated measurement data with nonignorable missing values," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 216-230.
    19. Charles E. McCulloch & John M. Neuhaus & Rebecca L. Olin, 2016. "Biased and unbiased estimation in longitudinal studies with informative visit processes," Biometrics, The International Biometric Society, vol. 72(4), pages 1315-1324, December.
    20. David Todem & KyungMann Kim & Jason Fine & Limin Peng, 2010. "Semiparametric regression models and sensitivity analysis of longitudinal data with non‐random dropouts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(2), pages 133-156, May.

    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:bpj:ijbist:v:4:y:2008:i:1:n:14. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.