IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v7y2020i3d10.1007_s40745-020-00303-y.html
   My bibliography  Save this article

Estimation of Treatment Effect with Missing Observations for Three Arms and Three Periods Crossover Clinical Trials

Author

Listed:
  • Atanu Bhattacharjee

    (Centre for Cancer Epidemiology, Tata Memorial Centre
    Homi Bhaba National Institute)

Abstract

The statistical analysis in presence of missing data in any study is challenging. It gets more attention since last few years for clinical trials. There are several reasons for the occurrence of missing data in the crossover trial. However, attempts toward crossover trial data are negligible. This manuscript is dedicated towards development of missing data handling technique for three arms three periods crossover trial.Data obtained from a crossover trial having microarray gene expression values are considered. The gene expression values are considered as outcomes with therapeutic effects. The statistical methodology are explained through Multiple Imputation and Bayesian approach separately. Further, their performance with same data is documented. In Bayesian context, it becomes feasible to perform the causal effect relation jointly with imputation. However, we failed to perform it through mixed effect model jointly. We performed separately Multiple Imputation procedures to overcome the missing values in the dataset and thereafter performed with the mixed effect model to explore the causal effect relation between therapeutic arm on gene expression values.

Suggested Citation

  • Atanu Bhattacharjee, 2020. "Estimation of Treatment Effect with Missing Observations for Three Arms and Three Periods Crossover Clinical Trials," Annals of Data Science, Springer, vol. 7(3), pages 447-460, September.
    • Handle: RePEc:spr:aodasc:v:7:y:2020:i:3:d:10.1007_s40745-020-00303-y
    DOI: 10.1007/s40745-020-00303-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-020-00303-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-020-00303-y?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. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
    2. Constantine E. Frangakis & Donald B. Rubin & Ming-Wen An & Ellen MacKenzie, 2007. "Principal Stratification Designs to Estimate Input Data Missing Due to Death," Biometrics, The International Biometric Society, vol. 63(3), pages 641-649, September.
    3. Wei Yan & Yaqin Hu & Zhi Geng, 2012. "Identifiability of Causal Effects for Binary Variables with Baseline Data Missing Due to Death," Biometrics, The International Biometric Society, vol. 68(1), pages 121-128, March.
    4. P. Diggle & M. G. Kenward, 1994. "Informative Drop‐Out in Longitudinal Data Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 49-73, March.
    5. Yong Shi & Zhiguang Shan & Jianping Li & Yufei Fang, 2017. "How China Deals with Big Data," Annals of Data Science, Springer, vol. 4(4), pages 433-440, December.
    6. Martha Nason & Dean Follmann, 2010. "Design and Analysis of Crossover Trials for Absorbing Binary Endpoints," Biometrics, The International Biometric Society, vol. 66(3), pages 958-965, September.
    7. Kosuke Imai, 2009. "Statistical analysis of randomized experiments with non‐ignorable missing binary outcomes: an application to a voting experiment," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(1), pages 83-104, February.
    8. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.
    9. L. Taylor & X. H. Zhou, 2009. "Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical Trials," Biometrics, The International Biometric Society, vol. 65(1), pages 88-95, March.
    10. Hua Chen & Zhi Geng & Xiao-Hua Zhou, 2009. "Identifiability and Estimation of Causal Effects in Randomized Trials with Noncompliance and Completely Nonignorable Missing Data," Biometrics, The International Biometric Society, vol. 65(3), pages 675-682, September.
    11. Ma, Wen-Qing & Geng, Zhi & Hu, Yong-Hua, 2003. "Identification of graphical models for nonignorable nonresponse of binary outcomes in longitudinal studies," Journal of Multivariate Analysis, Elsevier, vol. 87(1), pages 24-45, October.
    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. Yi He & Linzhi Zheng & Peng Luo, 2023. "Treatment Benefit and Treatment Harm Rates with Nonignorable Missing Covariate, Endpoint, or Treatment," Mathematics, MDPI, vol. 11(21), pages 1-18, October.
    2. Jierui Du & Gao Wen & Xin Liang, 2024. "Estimating the Complier Average Causal Effect with Non-Ignorable Missing Outcomes Using Likelihood Analysis," Mathematics, MDPI, vol. 12(9), pages 1-16, April.
    3. Alessandra Mattei & Fabrizia Mealli & Barbara Pacini, 2014. "Identification of causal effects in the presence of nonignorable missing outcome values," Biometrics, The International Biometric Society, vol. 70(2), pages 278-288, June.
    4. Maria Josefsson & Michael J. Daniels, 2021. "Bayesian semi‐parametric G‐computation for causal inference in a cohort study with MNAR dropout and death," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 398-414, March.
    5. Anna M. Wilke & Donald P. Green & Jasper Cooper, 2020. "A placebo design to detect spillovers from an education–entertainment experiment in Uganda," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1075-1096, June.
    6. Michael E. Sobel & Bengt Muthén, 2012. "Compliance Mixture Modelling with a Zero-Effect Complier Class and Missing Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1037-1045, December.
    7. VanderWeele Tyler J, 2011. "Principal Stratification -- Uses and Limitations," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-14, July.
    8. Amelia M. Haviland & Bobby L. Jones & Daniel S. Nagin, 2011. "Group-based Trajectory Modeling Extended to Account for Nonrandom Participant Attrition," Sociological Methods & Research, , vol. 40(2), pages 367-390, May.
    9. Linbo Wang & Thomas S. Richardson & Xiao-Hua Zhou, 2017. "Causal analysis of ordinal treatments and binary outcomes under truncation by death," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 719-735, June.
    10. Bryan E. Shepherd & Peter B. Gilbert & Charles T. Dupont, 2011. "Sensitivity Analyses Comparing Time-to-Event Outcomes Only Existing in a Subset Selected Postrandomization and Relaxing Monotonicity," Biometrics, The International Biometric Society, vol. 67(3), pages 1100-1110, September.
    11. Chiba Yasutaka, 2012. "The Large Sample Bounds on the Principal Strata Effect with Application to a Prostate Cancer Prevention Trial," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, May.
    12. VanderWeele, Tyler J., 2008. "Simple relations between principal stratification and direct and indirect effects," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2957-2962, December.
    13. Bindele, Huybrechts F. & Nguelifack, Brice M., 2019. "Generalized signed-rank estimation for regression models with non-ignorable missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 14-33.
    14. Sylvie Goetgeluk & Stijn Vansteelandt & Els Goetghebeur, 2008. "Estimation of controlled direct effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 1049-1066, November.
    15. Pearl Judea, 2011. "Principal Stratification -- a Goal or a Tool?," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-13, March.
    16. David M. Murray & Jonathan L. Blitstein, 2003. "Methods To Reduce The Impact Of Intraclass Correlation In Group-Randomized Trials," Evaluation Review, , vol. 27(1), pages 79-103, February.
    17. Michael R. Elliott & Anna Conlon & Yun Li, 2013. "Discussion on “Surrogate Measures and Consistent Surrogates”," Biometrics, The International Biometric Society, vol. 69(3), pages 565-569, September.
    18. Ying Huang & Shibasish Dasgupta, 2019. "Likelihood-Based Methods for Assessing Principal Surrogate Endpoints in Vaccine Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 504-523, December.
    19. Patrick E. B. FitzGerald, 2002. "Extended Generalized Estimating Equations for Binary Familial Data with Incomplete Families," Biometrics, The International Biometric Society, vol. 58(4), pages 718-726, December.
    20. Rachel Axelrod & Daniel Nevo, 2023. "A sensitivity analysis approach for the causal hazard ratio in randomized and observational studies," Biometrics, The International Biometric Society, vol. 79(3), pages 2743-2756, September.

    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:aodasc:v:7:y:2020:i:3:d:10.1007_s40745-020-00303-y. 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.