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

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  • 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
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    References listed on IDEAS

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    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.
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