Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

References

• Akacha, M., Branson, J., Bretz, F., Dharan, B., Gallo, P., Gathmann, I., Hemmings, R., Jones, J., Xi, D., and Zuber, E. (2020), “Challenges in Assessing the Impact of the Covid-19 Pandemic on the Integrity and Interpretability of Clinical Trials,” Statistics in Biopharmaceutical Research, 12, 419–426. DOI: 10.1080/19466315.2020.1788984.
   PubMed Web of Science ®Google Scholar
• Albertus, M. (2022), “Asymptotic z and Chi-Squared Tests with Auxiliary Information,” Metrika. DOI: 10.1007/s00184-021-00853-y.
   Web of Science ®Google Scholar
• Alemayehu, D., Hemmings, R., Natarajan, K., and Roychoudhury, S. (2022), “Perspectives on Virtual (Remote) Clinical Trials as the “New Normal” to Accelerate Drug Development,” Clinical Pharmacology & Therapeutics, 111, 373–381.
   PubMed Web of Science ®Google Scholar
• Bang, H., and Robins, J. M. (2005), “Doubly Robust Estimation in Missing Data and Causal Inference Models,” Biometrics, 61, 962–973. DOI: 10.1111/j.1541-0420.2005.00377.x.
   PubMed Web of Science ®Google Scholar
• Benkeser, D., Díaz, I., Luedtke, A., Segal, J., Scharfstein, D., and Rosenblum, M. (2020), “Improving Precision and Power in Randomized Trials for Covid-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes,” Biometrics, 77, 1467–1481. DOI: 10.1111/biom.13377.
   PubMed Web of Science ®Google Scholar
• Carpenter, J. R., Kenward, M. G., and Vansteelandt, S. (2006), “A Comparison of Multiple Imputation and Doubly Robust Estimation for Analyses with Missing Data,” Journal of the Royal Statistical Society, Series A, 169, 571–584. DOI: 10.1111/j.1467-985X.2006.00407.x.
   Web of Science ®Google Scholar
• Clayton, D., Spiegelhalter, D., Dunn, G., and Pickles, A. (1998), “Analysis of Longitudinal Binary Data from Multiphase Sampling,” Journal of the Royal Statistical Society, Series B, 60, 71–87. DOI: 10.1111/1467-9868.00109.
   Google Scholar
• Collins, L., Schafer, J. L., and Kam, C. M. (2001), “A Comparison of Inclusive and Restrictive Strategies in Modern Missing Data Procedures,” Psychological Methods, 6, 330–351. DOI: 10.1037/1082-989X.6.4.330.
   PubMed Web of Science ®Google Scholar
• Cro, S., Morris, T. P., Kahan, B. C., Cornelius, V. R., and Carpenter, J. R. (2020), “A Four-Step Strategy for Handling Missing Outcome Data in Randomised Trials Affected by a Pandemic,” BMC Medical Research Methodology, 20, 1–12. DOI: 10.1186/s12874-020-01089-6.
   Web of Science ®Google Scholar
• Cro, S., Morris, T. P., Kenward, M. G., and Carpenter, J. R. (2020), “Sensitivity Analysis for Clinical Trials with Missing Continuous Outcome Data Using Controlled Multiple Imputation: A Practical Guide,” Statistics in Medicine, 39, 2815–2842. DOI: 10.1002/sim.8569.
   PubMed Web of Science ®Google Scholar
• Degtyarev, E., Rufibach, K., Shentu, Y., Yung, G., Casey, M., Englert, S., Liu, F., Liu, Y., Sailer, O., Siegel, J., Sun, S., Tang, R., Zhou, J., and Industry Working Group on Estimands in Oncology. (2020), “Assessing the Impact of Covid-19 on the Clinical Trial Objective and Analysis of Oncology Clinical Trials—Application of the Estimand Framework,” Statistics in Biopharmaceutical Research, 12, 427–437. DOI: 10.1080/19466315.2020.1785543.
   PubMed Web of Science ®Google Scholar
• Dmitriev, Y., Tarassenko, P., and Ustinov, Y. (2014), “On Estimation of Linear Functional by Utilizing a Prior Guess,” in Information Technologies and Mathematical Modelling, eds. A. Dudin, A. Nazarov, R. Yakupov, and A. Gortsev, pp. 82–90, Cham: Springer.
   Google Scholar
• EMA (2020), “Points to Consider on Implications of Coronavirus Disease (COVID-19) on Methodological Aspects of Ongoing Clinical Trials,” European Medicines Agency. Available at https://www.ema.europa.eu/en/documents/scientific-guideline/points-consider-implications-coronavirus-disease-covid-19-methodological-aspects-ongoing-clinical_en.pdf.
   Google Scholar
• EMA (2022), “Points to Consider on the Impact of the War in Ukraine on Methodological Aspects of Ongoing Clinical Trials,” European Medicines Agency. Available at https://www.ema.europa.eu/en/documents/scientific-guideline/points-consider-impact-war-ukraine-methodological-aspects-ongoing-clinical-trials_en.pdf.
   Google Scholar
• FDA (2020), “Statistical Considerations for Clinical Trials During the COVID-19 Public Health Emergency,” Food and Drug Administration. Available at https://www.fda.gov/media/139145/download.
   Google Scholar
• Gravestock, I., Held, L., and On behalf of the COMBACTE-Net Consortium. (2017), “Adaptive Power Priors with Empirical Bayes for Clinical Trials,” Pharmaceutical Statistics, 16, 349–360. DOI: 10.1002/pst.1814.
   PubMed Web of Science ®Google Scholar
• Hernán, M., and Robins, J. M. (2020), Causal Inference: What If, Boca Raton, FL: Chapman & Hall/CRC. Taylor & Francis.
   Google Scholar
• Hernán, M. A., Brumback, B., and Robins, J. M. (2001), “Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments,” Journal of the American Statistical Association, 96, 440–448. DOI: 10.1198/016214501753168154.
   Web of Science ®Google Scholar
• Hobbs, B. P., Sargent, D. J., and Carlin, B. P. (2012), “Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models,” Bayesian Analysis, 7, 639–674. DOI: 10.1214/12-BA722.
   PubMed Web of Science ®Google Scholar
• ICH (2019), “International Council for Hamonisation Topic E9(R1) on Estimands and Sensitivity Analysis in Clinical Trials,” Available at www.ich.org.
   Google Scholar
• Kang, J. D., and Schafer, J. L. (2007), “Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data,” Statistical science, 22, 523–539.
   Web of Science ®Google Scholar
• Kenward, M. G. (2015), “Controlled Multiple Imputation Methods for Sensitivity Analyses in Longitudinal Clinical Trials with Dropout and Protocol Deviation,” Clinical Investigation, 5, 311–320. DOI: 10.4155/cli.14.132.
   Google Scholar
• Leacy, F. P., Floyd, S., Yates, T. A., and White, I. R. (2017), “Analyses of Sensitivity to the Missing-at-Random Assumption Using Multiple Imputation With Delta Adjustment: Application to a Tuberculosis/HIV Prevalence Survey With Incomplete HIV-Status Data,” American Journal of Epidemiology, 185, 304–315. DOI: 10.1093/aje/kww107.
   PubMed Web of Science ®Google Scholar
• Lipkovich, I., Ratitch, B., and Mallinckrodt, C. H. (2020), “Causal Inference and Estimands in Clinical Trials,” Statistics in Biopharmaceutical Research, 12, 54–67. DOI: 10.1080/19466315.2019.1697739.
   Web of Science ®Google Scholar
• Little, R. J. A., and Rubin, D. B. (1987), Statistical Analysis with Missing data, New York: Wiley.
   Google Scholar
• Meyer, R. D., Ratitch, B., Wolbers, M., Marchenko, O., Quan, H., Li, D., Fletcher, C., Li, X., Wright, D., Shentu, Y., Englert, S., Shen, W., Dey, J., Liu, T., Zhou, M., Bohidar, N., Zhao, P.-L., and Hale, M. (2020), “Statistical Issues and Recommendations for Clinical Trials Conducted During the Covid-19 Pandemic,” Statistics in Biopharmaceutical Research, 12, 399–411. DOI: 10.1080/19466315.2020.1779122.
   PubMed Web of Science ®Google Scholar
• Molenberghs, G., Fitzmaurice, G., Kenward, M. G., Tsiatis, A., and Verbeke, G. (2014), Handbook of Missing Data Methodology. Chapman & Hall/CRC Handbooks of Modern Statistical Methods. Boca Raton, FL: Taylor & Francis.
   Google Scholar
• Olarte Parra, C., Daniel, R. M., and Bartlett, J. W. (in press), “Hypothetical Estimands in Clinical Trials: A Unification of Causal Inference and Missing Data Methods,” Statistics in Biopharmaceutical Research, 1–26. DOI: 10.1080/19466315.2022.2081599.
   Web of Science ®Google Scholar
• Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1995), “Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data,” Journal of the American Statistical Association, 90, 106–121. DOI: 10.1080/01621459.1995.10476493.
   Web of Science ®Google Scholar
• Robins, J. M., Ángel Hernán, M., and Brumback, B. (2000), “Marginal Structural Models and Causal Inference in Epidemiology,” Epidemiology, 11, 550–560.
   PubMed Web of Science ®Google Scholar
• Rubin, D. B. (1976), “Inference and Missing Data,” Biometrika, 63, 581–592. DOI: 10.1093/biomet/63.3.581.
   Web of Science ®Google Scholar
• Rubin, D. B. (1987), Multiple Imputation for Nonreponse in Surveys. Wiley Series in Probability and Mathematical Statistics. New York: Wiley.
   Google Scholar
• Spiegelhalter, D. J., Abrams, K. R., and Myles, J. P. (2004), Bayesian Approaches to Clinical Trials and Health-Care Evaluation, Chichester: Wiley.
   Google Scholar
• Sun, B., Liu, L., Miao, W., Wirth, K., Robins, J., and Tchetgen, E. J. T. (2018), “Semiparametric Estimation with Data Missing not at Random Using an Instrumental Variable,” Statistica Sinica, 28, 1965–1983.
   PubMed Web of Science ®Google Scholar
• Sun, B., and Tchetgen Tchetgen, E. J. (2018), “On Inverse Probability Weighting for Nonmonotone Missing at Random Data,” Journal of the American Statistical Association, 113, 369–379. DOI: 10.1080/01621459.2016.1256814.
   PubMed Web of Science ®Google Scholar
• Tarima, S., and Pavlov, D. (2006), “Using Auxiliary Information in Statistical Function Estimation,” ESAIM: Probability and Statistics, 10, 11–23. DOI: 10.1051/ps:2005019.
   Google Scholar
• Tarima, S., Tuyishimire, B., Sparapani, R., Rein, L., and Meurer, J. (2020), “Estimation Combining Unbiased and Possibly Biased Estimators,” Journal of Statistical Theory and Practice, 14, 1–20. DOI: 10.1007/s42519-020-0083-2.
   Web of Science ®Google Scholar
• van der Laan, M. J., and Gruber, S. (2012), “Targeted Minimum Loss Based Estimation of Causal Effects of Multiple Time Point Interventions,” The International Journal of Biostatistics, 8. DOI: 10.1515/1557-4679.1370.
   Google Scholar
• Van Lancker, K. (2021), “Optimizing Treatment Effect Estimation in Randomized Trials by Leveraging Baseline Covariates and Early Read-outs,” Ph. D. thesis, Ghent University.
   Google Scholar
• Van Lancker, K., Vandebosch, A., and Vansteelandt, S. (2020), “Improving Interim Decisions in Randomized Trials by Exploiting Information on Short-Term Endpoints and Prognostic Baseline Covariates,” Pharmaceutical Statistics, 19, 583–601. DOI: 10.1002/pst.2014.
   PubMed Web of Science ®Google Scholar
• Vansteelandt, S. (2021), “Estimands, Estimators, Estimates: Discussion (online presentation, webenar, November 19, 2021),” Available at https://www.enar.org/education/past-webinars.cfm. Accessed April 18, 2022.
   Google Scholar
• Vansteelandt, S., Rotnitzky, A., and Robins, J. (2007), “Estimation of Regression Models for the Mean of Repeated Outcomes under Nonignorable Nonmonotone Nonresponse,” Biometrika, 94, 841–860. DOI: 10.1093/biomet/asm070.
   PubMed Web of Science ®Google Scholar
• Vermeulen, K., and Vansteelandt, S. (2015), “Bias-Reduced Doubly Robust Estimation,” Journal of the American Statistical Association, 110, 1024–1036. DOI: 10.1080/01621459.2014.958155.
   Web of Science ®Google Scholar
• White, I., Daniel, R., and Royston, P. (2010), “Avoiding Bias due to Perfect Prediction in Multiple Imputation of Incomplete Categorical Variables,” Computational Statistics & Data Analysis, 54, 2267–2275.
   PubMed Web of Science ®Google Scholar
• Zhou, X.-H., Eckert, G. J., and Tierney, W. M. (2001), “Multiple Imputation in Public Health Research,” Statistics in Medicine, 20, 1541–1549. DOI: 10.1002/sim.689.
   PubMed Web of Science ®Google Scholar