IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i14p3233-d1200291.html
   My bibliography  Save this article

An Improved Dunnett’s Procedure for Comparing Multiple Treatments with a Control in the Presence of Missing Observations

Author

Listed:
  • Wenqing Jiang

    (Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, China)

  • Jiangjie Zhou

    (Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, China)

  • Baosheng Liang

    (Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, China)

Abstract

Dunnett’s procedure has been frequently used for multiple comparisons of group means of several treatments with a control, in drug development and other areas. However, in practice, researchers usually face missing observations when performing Dunnett’s procedure. This paper presents an improved Dunnett’s procedure that can construct unique ensemble confidence intervals for comparing group means of several treatments with a control, in the presence of missing observations, using a derived multivariate t distribution under the framework of Rubin’s rule. This procedure fills the current research gap that Rubin’s repeated-imputation inferences cannot adjust for multiplicity and, thereby, cannot give a unified confidence interval to control the family-wise error rate (FWER) when dealing with this problem. Simulation results show that the constructed pooled confidence intervals archive nominal joint coverage and the interval estimations preserve comparable precision to Rubin’s repeated-imputation inference as the missing rate increases. The proposed procedure with propensity-score imputation method is shown to produce more accurate interval estimations and control the FWER well.

Suggested Citation

  • Wenqing Jiang & Jiangjie Zhou & Baosheng Liang, 2023. "An Improved Dunnett’s Procedure for Comparing Multiple Treatments with a Control in the Presence of Missing Observations," Mathematics, MDPI, vol. 11(14), pages 1-16, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3233-:d:1200291
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/14/3233/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/14/3233/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. F. Bretz & J. C. Pinheiro & M. Branson, 2005. "Combining Multiple Comparisons and Modeling Techniques in Dose-Response Studies," Biometrics, The International Biometric Society, vol. 61(3), pages 738-748, September.
    2. Jingya Gao & Pei‐Fang Su & Feifang Hu & Siu Hung Cheung, 2020. "Adaptive treatment allocation for comparative clinical studies with recurrent events data," Biometrics, The International Biometric Society, vol. 76(1), pages 183-196, March.
    3. Chauhan, Rajvir Singh & Singh, Parminder & Kumar, Narinder, 2013. "Multiple comparisons with a control in direction-mixed families of hypothesis under heteroscedasticity," Statistics & Probability Letters, Elsevier, vol. 83(12), pages 2679-2687.
    4. Schenker, Nathaniel & Taylor, Jeremy M. G., 1996. "Partially parametric techniques for multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 22(4), pages 425-446, August.
    5. Björn Bornkamp, 2018. "Calculating quantiles of noisy distribution functions using local linear regressions," Computational Statistics, Springer, vol. 33(1), pages 487-501, March.
    6. S. Chakraborti & M.M. Desu, 1991. "Linear rank tests for comparing treatments with a control when data are subject to unequal patterns of censorship," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 45(3), pages 227-254, September.
    7. Daniel F. Heitjan & Roderick J. A. Little, 1991. "Multiple Imputation for the Fatal Accident Reporting System," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(1), pages 13-29, March.
    8. Zhengyu Yang & Guo-Liang Tian & Xiaobin Liu & Chang-Xing Ma, 2021. "Simultaneous confidence interval construction for many-to-one comparisons of proportion differences based on correlated paired data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(8), pages 1442-1456, June.
    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. Chaton, Corinne & Gouraud, Alexandre, 2020. "Simulation of fuel poverty in France," Energy Policy, Elsevier, vol. 140(C).
    2. Lana Salih Joelsson & Evangelia Elenis & Kjell Wanggren & Anna Berglund & Anastasia N Iliadou & Carolyn E Cesta & Sunni L Mumford & Richard White & Tanja Tydén & Alkistis Skalkidou, 2019. "Investigating the effect of lifestyle risk factors upon number of aspirated and mature oocytes in in vitro fertilization cycles: Interaction with antral follicle count," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-15, August.
    3. Chaton, Corinne & Gouraud, Alexandre, 2020. "Simulation of fuel poverty in France," Energy Policy, Elsevier, vol. 140(C).
    4. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    5. Patrick Lloyd‐Smith, 2021. "The economic benefits of recreation in Canada," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 54(4), pages 1684-1715, November.
    6. Kwon, Tae Yeon & Park, Yousung, 2015. "A new multiple imputation method for bounded missing values," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 204-209.
    7. repec:jss:jstsof:45:i02 is not listed on IDEAS
    8. Taylor, Jeremy M. G. & Murray, Susan & Hsu, Chiu-Hsieh, 2002. "Survival estimation and testing via multiple imputation," Statistics & Probability Letters, Elsevier, vol. 58(3), pages 221-232, July.
    9. Patrick M. Joyce & Donald Malec & Roderick J. A. Little & Aaron Gilary & Alfredo Navarro & Mark E. Asiala, 2014. "Statistical Modeling Methodology for the Voting Rights Act Section 203 Language Assistance Determinations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 36-47, March.
    10. Encarnita Mariotti-Ferrandiz & Hang-Phuong Pham & Sophie Dulauroy & Olivier Gorgette & David Klatzmann & Pierre-André Cazenave & Sylviane Pied & Adrien Six, 2016. "A TCRβ Repertoire Signature Can Predict Experimental Cerebral Malaria," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    11. Gabriele Beissel Durrant, 2009. "Imputation Methods for Handling Item-Nonresponse in the Social Sciences: A Methodological Review," Working Papers id:2007, eSocialSciences.
    12. Shu Yang & Jae Kwang Kim, 2020. "Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 839-861, September.
    13. Timm Bönke & Markus M. Grabka & Carsten Schröder & Edward N. Wolff & Lennard Zyska, 2019. "The Joint Distribution of Net Worth and Pension Wealth in Germany," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(4), pages 834-871, December.
    14. Moritz Kuhn & Moritz Schularick & Ulrike I. Steins, 2020. "Income and Wealth Inequality in America, 1949–2016," Journal of Political Economy, University of Chicago Press, vol. 128(9), pages 3469-3519.
    15. Kathrin Möllenhoff & Frank Bretz & Holger Dette, 2020. "Equivalence of regression curves sharing common parameters," Biometrics, The International Biometric Society, vol. 76(2), pages 518-529, June.
    16. Alina K. Bartscher & Moritz Kuhn & Moritz Schularick, 2020. "The College Wealth Divide: Education and Inequality in America, 1956-2016," Review, Federal Reserve Bank of St. Louis, vol. 102(1), pages 19-49.
    17. Francesco De Pretis & Barbara Osimani, 2019. "New Insights in Computational Methods for Pharmacovigilance: E-Synthesis , a Bayesian Framework for Causal Assessment," IJERPH, MDPI, vol. 16(12), pages 1-19, June.
    18. Johan Verbeeck & Martin Geroldinger & Konstantin Thiel & Andrew Craig Hooker & Sebastian Ueckert & Mats Karlsson & Arne Cornelius Bathke & Johann Wolfgang Bauer & Geert Molenberghs & Georg Zimmermann, 2023. "How to analyze continuous and discrete repeated measures in small‐sample cross‐over trials?," Biometrics, The International Biometric Society, vol. 79(4), pages 3998-4011, December.
    19. Qiqi Deng & Kun Wang & Xiaofei Bai & Naitee Ting, 2019. "A Cautionary Note When a Dose-Ranging Study is Used for Proving the Concept," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(1), pages 127-140, April.
    20. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
    21. Beibei Guo & Ying Yuan, 2023. "DROID: dose‐ranging approach to optimizing dose in oncology drug development," Biometrics, The International Biometric Society, vol. 79(4), pages 2907-2919, December.

    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:gam:jmathe:v:11:y:2023:i:14:p:3233-:d:1200291. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.