IDEAS home Printed from https://ideas.repec.org/a/spr/metron/v73y2015i3p397-417.html

A Bayesian approach for sensitivity analysis of incomplete multivariate longitudinal data with potential nonrandom dropout

Author

Listed:
  • Samaneh Mahabadi

  • Mojtaba Ganjali

Abstract

Experiments involving repeated observations of multivariate outcomes are common in biomedical and public health researches which lead to multivariate longitudinal data. These kinds of data have a unique property in the sense that they allow the researcher to study the joint evolution of the multiple outcomes over the time. Recently, there has been a considerable amount of interest on using Bayesian modelling of longitudinal data, data which commonly suffer from incomplete observations. Those Bayesian models for longitudinal data that rely on the ignorability assumption of the dropout mechanism might give misleading inferences. Hence, there is a need to further study the impact of departures from the ignorability assumption on the Bayesian estimates of the model parameters. Current methodology for Bayesian sensitivity analysis mostly involves single response variable in both cross-sectional and longitudinal studies. In this paper, we propose a multivariate extension of the Bayesian index of sensitivity to non-ignorability for the general case of multivariate longitudinal studies with the possibility of having mixed correlated outcomes and a vector of multiple non-ignorability parameters in the missing mechanism. To simultaneously model the mixed responses over the time, we use a random effect latent variable approach. We illustrate the method conducting some simulation studies and analyzing a real data set from a longitudinal study for the comparison of two oral treatments for toenail dermatophyte onychomycosis. Copyright Sapienza Università di Roma 2015

Suggested Citation

  • Samaneh Mahabadi & Mojtaba Ganjali, 2015. "A Bayesian approach for sensitivity analysis of incomplete multivariate longitudinal data with potential nonrandom dropout," METRON, Springer;Sapienza Università di Roma, vol. 73(3), pages 397-417, December.
    • Handle: RePEc:spr:metron:v:73:y:2015:i:3:p:397-417
    DOI: 10.1007/s40300-015-0063-6
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s40300-015-0063-6
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s40300-015-0063-6?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Jiameng Zhang & Daniel F. Heitjan, 2006. "A Simple Local Sensitivity Analysis Tool for Nonignorable Coarsening: Application to Dependent Censoring," Biometrics, The International Biometric Society, vol. 62(4), pages 1260-1268, December.
    2. Heckman, James J, 1978. "Dummy Endogenous Variables in a Simultaneous Equation System," Econometrica, Econometric Society, vol. 46(4), pages 931-959, July.
    3. James Robins & Andrea Rotnitzky & Stijn Vansteelandt, 2007. "Discussions," Biometrics, The International Biometric Society, vol. 63(3), pages 650-653, September.
    4. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
    5. John Copas & Shinto Eguchi, 2001. "Local sensitivity approximations for selectivity bias," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 871-895.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Bryan S. Graham & Andrin Pelican, 2023. "Scenario Sampling for Large Supermodular Games," Papers 2307.11857, arXiv.org.
    3. Isabella Morlini, 2012. "A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model," 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. 6(1), pages 5-28, April.
    4. John S. Heywood & W.S. Siebert & Xiangdong Wei, 2011. "Estimating the Use of Agency Workers: Can Family-Friendly Practices Reduce Their Use?," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 50(3), pages 535-564, July.
    5. Adriaan R. Soetevent, 2006. "Empirics of the Identification of Social Interactions; An Evaluation of the Approaches and Their Results," Journal of Economic Surveys, Wiley Blackwell, vol. 20(2), pages 193-228, April.
    6. Tamara Broderick & Robert Gramacy, 2011. "Classification and Categorical Inputs with Treed Gaussian Process Models," Journal of Classification, Springer;The Classification Society, vol. 28(2), pages 244-270, July.
    7. Marc Henry & Ismael Mourifié, 2013. "Euclidean Revealed Preferences: Testing The Spatial Voting Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 650-666, June.
    8. Liqiong Lin & Weizhuo Wang & Christopher Gan & David A. Cohen & Quang T.T Nguyen, 2019. "Rural Credit Constraint and Informal Rural Credit Accessibility in China," Sustainability, MDPI, vol. 11(7), pages 1-20, April.
    9. Omesh Kini & Shehzad Mian & Michael Rebello & Anand Venkateswaran, 2009. "On the Structure of Analyst Research Portfolios and Forecast Accuracy," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 47(4), pages 867-909, September.
    10. Blundell, Richard & Macurdy, Thomas, 1999. "Labor supply: A review of alternative approaches," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 27, pages 1559-1695, Elsevier.
    11. James J. Heckman, 1991. "Randomization and Social Policy Evaluation Revisited," NBER Technical Working Papers 0107, National Bureau of Economic Research, Inc.
    12. Sophocles Mavroeidis, 2021. "Identification at the Zero Lower Bound," Econometrica, Econometric Society, vol. 89(6), pages 2855-2885, November.
    13. Xiao, Junji & Ju, Heng, 2016. "The determinants of dealership structure: Empirical analysis of the Chinese auto market," Journal of Comparative Economics, Elsevier, vol. 44(4), pages 961-981.
    14. Cappuccio Nunzio & Lubian Diego & Raggi Davide, 2004. "MCMC Bayesian Estimation of a Skew-GED Stochastic Volatility Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-31, May.
    15. Copley, Paul & Douthett, Edward & Zhang, Suning, 2021. "Venture capitalists and assurance services on initial public offerings," Journal of Business Research, Elsevier, vol. 131(C), pages 278-286.
    16. Kunxi Nie & Yueji Zhu, 2025. "Internet access, female’s non-farm employment, and income effects: insights from China," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 13(1), pages 1-29, December.
    17. Hosny, Amr, 2020. "What is the Cost of Political Instability in Tunisia? Evidence from 592 Private Firms," African Journal of Economic Review, African Journal of Economic Review, vol. 8(3), November.
    18. Cuong NGUYEN, 2016. "An Introduction to Alternative Methods in Program Impact Evaluation," Journal of Economic and Social Thought, KSP Journals, vol. 3(3), pages 349-375, September.
    19. Taniguchi, Kiyoshi & Chern, Wen S., 2000. "Income Elasticity Of Rice Demand In Japan And Its Implications: Cross-Sectional Data Analysis," 2000 Annual meeting, July 30-August 2, Tampa, FL 21755, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    20. Alexandridis, George & Chen, Zhong & Zeng, Yeqin, 2021. "Financial hedging and corporate investment," Journal of Corporate Finance, Elsevier, vol. 67(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:spr:metron:v:73:y:2015:i:3:p:397-417. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.