IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v56y2012i5p1287-1300.html
   My bibliography  Save this article

Analyzing longitudinal clinical trial data with nonignorable missingness and unknown missingness reasons

Author

Listed:
  • Xie, Hui

Abstract

Longitudinal clinical trials are often plagued by nonmonotone missingness due to both patient dropout and intermittent missingness. Standard analysis assumes that missingness is ignorable. Because the assumption can be questionable, the sensitivity of inferences to alternative assumptions about missingness needs to be evaluated. This need arises in the analysis of a longitudinal prostate cancer quality-of-life (QoL) clinical trial dataset, in which nonmonotone missingness occurs. The choice of the missing data model is studied in the analysis. A local sensitivity analysis method is then applied to analyze the dataset and to investigate the changes in parameter estimates in the neighborhood of the ignorable model. One advantage of the method is that it surmounts computational difficulty and completely avoids evaluating the high-dimensional integrals in the likelihood due to nonmonotone missingness. Another is that it can be implemented using the standard software without excessive additional computation. The method is especially advantageous for large clinical datasets for which alternative approaches can become computationally prohibitive. In addition, the analysis demonstrates the importance of exploiting information on reasons for missingness. When such information is unavailable for some missingness and therefore the missingness types (i.e., dropout versus intermittent missingness) are unknown, a bound analysis is proposed, combined with genetic algorithms, to account for unknown missingness types. The analysis demonstrates the usefulness of the method as a general approach to evaluating the sensitivity of standard analysis to nonignorable nonmonotone missingness in clinical trials.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:5:p:1287-1300
    DOI: 10.1016/j.csda.2010.11.021
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947310004494
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2010.11.021?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. Gad, Ahmed M. & Ahmed, Abeer S., 2006. "Analysis of longitudinal data with intermittent missing values using the stochastic EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2702-2714, June.
    2. Stijn Vansteelandt & Andrea Rotnitzky & James Robins, 2007. "Estimation of Regression Models for the Mean of Repeated Outcomes Under Nonignorable Nonmonotone Nonresponse," Biometrika, Biometrika Trust, vol. 94(4), pages 841-860.
    3. 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.
    4. Sotto, Cristina & Beunckens, Caroline & Molenberghs, Geert & Kenward, Michael G., 2011. "MCMC-based estimation methods for continuous longitudinal data with non-random (non)-monotone missingness," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 301-311, January.
    5. Paul S. Albert, 2000. "A Transitional Model for Longitudinal Binary Data Subject to Nonignorable Missing Data," Biometrics, The International Biometric Society, vol. 56(2), pages 602-608, June.
    6. Molenberghs, Geert & Verbeke, Geert & Thijs, Herbert & Lesaffre, Emmanuel & Kenward, Michael G., 2001. "Influence analysis to assess sensitivity of the dropout process," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 93-113, July.
    7. 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.
    8. 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.
    9. Yi Qian, 2007. "Do National Patent Laws Stimulate Domestic Innovation in a Global Patenting Environment? A Cross-Country Analysis of Pharmaceutical Patent Protection, 1978-2002," The Review of Economics and Statistics, MIT Press, vol. 89(3), pages 436-453, August.
    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. S. Eftekhari Mahabadi & M. Ganjali, 2012. "An index of local sensitivity to non-ignorability for parametric survival models with potential non-random missing covariate: an application to the SEER cancer registry data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2327-2348, July.
    2. Urko Aguirre-Larracoechea & Cruz E. Borges, 2021. "Imputation for Repeated Bounded Outcome Data: Statistical and Machine-Learning Approaches," Mathematics, MDPI, vol. 9(17), pages 1-27, August.

    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. Heng Chen & Daniel F. Heitjan, 2022. "Analysis of local sensitivity to nonignorability with missing outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(4), pages 1342-1352, December.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. D. Todem & J. Fine & L. Peng, 2010. "A Global Sensitivity Test for Evaluating Statistical Hypotheses with Nonidentifiable Models," Biometrics, The International Biometric Society, vol. 66(2), pages 558-566, June.
    7. Trias Wahyuni Rakhmawati & Geert Molenberghs & Geert Verbeke & Christel Faes, 2016. "Local influence diagnostics for incomplete overdispersed longitudinal counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1722-1737, July.
    8. Weiping Zhang & Feiyue Xie & Jiaxin Tan, 2020. "A robust joint modeling approach for longitudinal data with informative dropouts," Computational Statistics, Springer, vol. 35(4), pages 1759-1783, 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. Xiaoyan Shi & Hongtu Zhu & Joseph G. Ibrahim, 2009. "Local Influence for Generalized Linear Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 65(4), pages 1164-1174, December.
    11. Lu, Susan Feng & Dranove, David, 2013. "Profiting from gaizhi: Management buyouts during China’s privatization," Journal of Comparative Economics, Elsevier, vol. 41(2), pages 634-650.
    12. Tony Vangeneugden & Geert Molenberghs & Geert Verbeke & Clarice G.B. Dem�trio, 2011. "Marginal correlation from an extended random-effects model for repeated and overdispersed counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(2), pages 215-232, September.
    13. Aghion, Philippe & Akcigit, Ufuk & Howitt, Peter, 2014. "What Do We Learn From Schumpeterian Growth Theory?," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 2, chapter 0, pages 515-563, Elsevier.
    14. Parker Hevron, 2018. "Judicialization and Its Effects: Experiments as a Way Forward," Laws, MDPI, vol. 7(2), pages 1-21, May.
    15. Lin, Jenny X. & Lincoln, William F., 2017. "Pirate's treasure," Journal of International Economics, Elsevier, vol. 109(C), pages 235-245.
    16. Auriol, Emmanuelle & Biancini, Sara & Paillacar, Rodrigo, 2023. "Intellectual property rights protection and trade: An empirical analysis," World Development, Elsevier, vol. 162(C).
    17. Aalbers, Rob & Shestalova, Victoria & Kocsis, Viktória, 2013. "Innovation policy for directing technical change in the power sector," Energy Policy, Elsevier, vol. 63(C), pages 1240-1250.
    18. Sinha, Sanjoy K. & Kaushal, Amit & Xiao, Wenzhong, 2014. "Inference for longitudinal data with nonignorable nonmonotone missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 77-91.
    19. Youwei Wang & Yuxin Chen & Yi Qian, 2018. "The Causal Link between Relative Age Effect and Entrepreneurship: Evidence from 17 Million Users across 49 Years on Taobao," NBER Working Papers 25318, National Bureau of Economic Research, Inc.
    20. Fabian Gaessler & Stefan Wagner, 2022. "Patents, Data Exclusivity, and the Development of New Drugs," The Review of Economics and Statistics, MIT Press, vol. 104(3), pages 571-586, May.

    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:eee:csdana:v:56:y:2012:i:5:p:1287-1300. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.