5,461
Views
37
CrossRef citations to date
0
Altmetric
Estimands and Missing Data

Causal Inference and Estimands in Clinical Trials

, &
Pages 54-67 | Received 22 Apr 2019, Accepted 21 Nov 2019, Published online: 23 Jan 2020

References

  • Aalen, O. O., Cook, R., and Røysland, K. (2015), “Does Cox Analysis of a Randomized Survival Study Yield a Causal Treatment Effect?,” Lifetime Data Analysis, 21, 579–593. DOI: 10.1007/s10985-015-9335-y.
  • Andersen, P. K., Syriopoulou, E., and Parner, E. T. (2017), “Causal Inference in Survival Analysis Using Pseudo-Observations,” Statistics in Medicine, 36, 2669–2681. DOI: 10.1002/sim.7297.
  • Angrist, J. D., Imbens, G. W., and Rubin, D. B. (1996), “Identification of Causal Effects Using Instrumental Variables,” Journal of the American Statistical Association, 91, 444–455. DOI: 10.1080/01621459.1996.10476902.
  • Austin, P. C. (2008), “A Critical Appraisal of Propensity Score Matching in the Medical Literature from 1996 to 2003,” Statistics in Medicine, 27, 2037–2049. DOI: 10.1002/sim.3150.
  • Billingham, L. J., and Abrams, K. R. (2002), “Simultaneous Analysis of Quality of Life and Survival Data,” Statistical Methods in Medical Research, 11, 25–48. DOI: 10.1191/0962280202sm269ra.
  • Bornkamp, B., and Bermann, G. (2019), “Estimating the Treatment Effect in a Subgroup Defined by an Early Post-Baseline Biomarker Measurement in Randomized Clinical Trials With Time-to-event Endpoint,” Statistics in Biopharmaceutical Research, DOI: 10.1080/19466315.2019.1575280.
  • Carpenter, J. R., Roger, J. H., and Kenward, M. G. (2013), “Analysis of Longitudinal Trials With Protocol Deviation: A Framework for Relevant, Accessible Assumptions, and Inference via Multiple Imputation,” Journal of Biopharmaceutical Statistics, 23, 1352–1371. DOI: 10.1080/10543406.2013.834911.
  • D’Agostino, R. B. (1998), “Tutorial in Biostatistics: Propensity Score Methods for Bias Reduction in the Comparison of a Treatment to a Non-Randomized Control Group,” Statistics in Medicine, 17, 2265–2281. DOI: 10.1002/(SICI)1097-0258(19981015)17:19<2265::AID-SIM918>3.0.CO;2-B.
  • Daniel, R. M., Cousens, S. N., De Stavola, B. L., Kenward, M. G., and Sterne, J. A. (2013), “Methods for Dealing With Time-Dependent Confounding,” Statistics in Medicine, 32, 1584–1618. DOI: 10.1002/sim.5686.
  • Dawid, A. P. (2000), “Causal Inference Without Counterfactuals (with discussion),” Journal of the American Statistical Association, 95, 407–424. DOI: 10.1080/01621459.2000.10474210.
  • Ding, P., and Lu, J. (2017), “Principal Stratification Analysis Using Principal Scores,” Journal of the Royal Statistical Society, Series B, 79, 757–777. DOI: 10.1111/rssb.12191.
  • ICH Harmonised Guideline E9(R1) (2017), “Estimands and Sensitivity Analysis in Clinical Trials” (Step 1 version dated 16 June 2017).
  • Imbens, G. W., and Rubin, D. B. (1997), “Bayesian Inference for Causal Effects in Randomized Experiments With Noncompliance,” The Annals of Statistics, 25, 305–327. DOI: 10.1214/aos/1034276631.
  • Frangakis, C. E., and Rubin, D. B. (2002), “Principal Stratification in Causal Inference,” Biometrics, 58, 21–29. DOI: 10.1111/j.0006-341X.2002.00021.x.
  • Gail, M. H., Wieand, S., and Piantadosi, S. (1984), “Biased Estimates of Treatment Effect in Randomized Experiments With Nonlinear Regressions and Omitted Covariates,” Biometrika, 71, 431–444. DOI: 10.1093/biomet/71.3.431.
  • Greenland, S., Pearl, J., and Robins, J. M. (1999), “Causal Diagrams for Epidemiological Research,” Epidemiology, 10, 37–48. DOI: 10.1097/00001648-199901000-00008.
  • Hayden, D., Pauler, D. K., and Schoenfeld, D. (2005), “An Estimator for Treatment Comparisons among Survivors in Randomized Trials,” Biometrics, 61, 305–310. DOI: 10.1111/j.0006-341X.2005.030227.x.
  • Hernán, M. A., and Scharfstein, D. (2018), “Cautions as Regulators Move to End Exclusive Reliance on Intention to Treat,” Annals of Internal Medicine, 168, 515–516. DOI: 10.7326/M17-3354.
  • Jo, B., and Stuart, E. A. (2009), “On the Use of Propensity Scores in Principal Causal Effect Estimation,” Statistics in Medicine, 28, 2857–2875. DOI: 10.1002/sim.3669.
  • Johnson, B. A., and Tsiatis, A. A. (2004), “Estimating Mean Response as a Function of Treatment Duration in an Observational Study, Where Duration May Be Informatively Censored,” Biometrics, 60, 315–323. DOI: 10.1111/j.0006-341X.2004.00175.x.
  • Johnson, B. A., and Tsiatis, A. A. (2005), “Semiparametric Inference in Observational Duration-Response Studies, with Duration Possibly Right-Censored,” Biometrika, 92, 605–618.
  • Kosuke, I. (2008), “Sharp Bounds on the Causal Effects in Randomized Experiments with ‘Truncation-by-Death ‘,” Statistics & Probability Letters, 78, 144–149.
  • Lachin, J. M. (1999), “Worst-Rank Score Analysis With Informatively Missing Observations in Clinical Trials,” Controlled Clinical Trials, 20, 408–422. DOI: 10.1016/S0197-2456(99)00022-7.
  • Lieberman, J. A., Stroup, T. S., McEvoy, J. P., Swartz, M. S., Rosenheck, R. A., Perkins, D. O., Keefe, R. S. E., Davis, S. M., Davis, C. E., Lebowitz, B. D., Severe, J., and Hsiao, J. K. (2005), “Effectiveness of Antipsychotic Drugs in Patients With Chronic Schozophrenia,” New England Journal of Medicine, 353, 1209–1223. DOI: 10.1056/NEJMoa051688.
  • Little, R., and Kang, S. (2015), “Intention-To-Treat Analysis With Treatment Discontinuation and Missing Data in Clinical Trials,” Statistics in Medicine, 34, 2381–2390. DOI: 10.1002/sim.6352.
  • Little, R. J., and Rubin, D. B. (2000), “Causal Effects in Clinical and Epidemiological Studies via Potential Outcomes,” Annual Review of Public Health, 21, 121–145. DOI: 10.1146/annurev.publhealth.21.1.121.
  • Lou, Y., Jones, M. P., and Sun, W. (2019), “Estimation of Causal Effects in Clinical Endpoint Bioequivalence Studies in the Presence of Intercurrent Events: Noncompliance and Missing Data,” Journal of Biopharmaceutical Statistics, 29, 151–173. DOI: 10.1080/10543406.2018.1489408.
  • Lu, X., Mehrotra, D. V., and Shepherd, B. E. (2013), “Rank-Based Principal Stratum Sensitivity Analyses,” Statistics in Medicine, 2, 4526–4539. DOI: 10.1002/sim.5849.
  • Lunceford, J. K., and Davidian, M. (2004), “Stratification and Weighting via the Propensity Score in Estimation of Causal Treatment Effects: A Comparative Study,” Statistics in Medicine, 23, 2937–2960. DOI: 10.1002/sim.1903.
  • Magnusson, B. P., Schmidli, H., Rouyrre, N., and Scharfstein, D. O. (2019), “Bayesian Inference for a Principal Stratum Estimand to Assess the Treatment Effect in a Subgroup Characterized By Post-Randomization Events,” Statistics in Medicine, 38, 4761–4771. DOI: 10.1002/sim.8333.
  • Mallinckrodt, C., Molenberghs, G., and Rathmann, S. (2017), “Choosing Estimands in Clinical Trials With Missing Data,” Pharmaceutical Statistics, 16, 29–36. DOI: 10.1002/pst.1765.
  • Miratrix, L., Furey, J., Feller, A., Grindal, T., and Page, L. C. (2018), “Bounding, an Accessible Method for Estimating Principal Causal Effects, Examined and Explained,” Journal of Research on Educational Effectiveness, 11, 133–162. DOI: 10.1080/19345747.2017.1379576.
  • Mehrotra, D., Li, X., and Gilbert, P. (2006), “A Comparison of Eight Methods for the Dual-Endpoint Evaluation of Efficacy in a Proof-of-Concept HIV Vaccine Trial,” Biometrics, 62, 893–900. DOI: 10.1111/j.1541-0420.2005.00516.x.
  • Mehrotra, D. V., Liu, F., and Permutt, T. (2017), “Missing Data in Clinical Trials: Control-Based Mean Imputation and Sensitivity Analysis,” Pharmaceutical Statistics, 16, 378–392. DOI: 10.1002/pst.1817.
  • Moodie, E. E. M., Richardson, T. S., and Stephens, D. A. (2007), “Demystifying Optimal Dynamic Treatment Regimes,” Biometrics, 63, 447–455. DOI: 10.1111/j.1541-0420.2006.00686.x.
  • Murphy, S. A. (2003), “Optimal Dynamic Treatment Regimes” (with discussion), Journal of the Royal Statistical Society, Series B, 65, 331–366. DOI: 10.1111/1467-9868.00389.
  • Murphy, S. A. (2005), “An Experimental Design for the Development of Adaptive Treatment Strategies,” Statistics in Medicine, 24, 1455–1481.
  • National Research Council (NRC) (2010), “The Prevention and Treatment of Missing Data in Clinical Trials,” in Panel on Handling Missing Data in Clinical Trials, Committee on National Statistics, Division of Behavioral and Social Sciences and Education, Washington, DC: The National Academies Press.
  • Neyman, J. (1923), “On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9,” Statistical Science, 5, 465–480.
  • Pearl, J. (2001), “Causal Inference in the Health Sciences: A Conceptual Introduction,” Health Services and Outcomes Research Methodology, 2, 189–220. DOI: 10.1023/A:1020315127304.
  • Pearl, J. (2009), “Causal Inference in Statistics: An Overview,” Statistics Surveys, 3, 96–146.
  • Permutt, T. (2016a), “A Taxonomy of Estimands for Regulatory Clinical Trials With Discontinuations,” Statistics in Medicine, 35, 2865–2875. DOI: 10.1002/sim.6841.
  • Permutt, T. (2016b), “Sensitivity Analysis for Missing Data in Regulatory Submissions,” Statistics in Medicine, 35, 2876–2879.
  • Permutt, T. (2018), “Effects in Adherent Subjects,” Statistics in Biopharmaceutical Research, 10, 233–235.
  • Permutt, T. 2019), “Treatment Effects, Comparisons, and Randomization,” Statistics in Biopharmaceutical Research,5.2019.1624192. DOI: 10.1080/1946631.
  • Permutt, T., and Li, F. (2017), “Trimmed Means for Symptom Trials With Dropouts,” Pharmaceutical Statistics, 16, 20–28. DOI: 10.1002/pst.1768.
  • Qu, Y., Fu, H., Luo, J., and Ruberg, S. J. (2019), “A General Framework for Treatment Effect Estimators Considering Patient Adherence,” Statistics in Biopharmaceutical Research, DOI: 10.1080/19466315.2019.1700157.
  • Ratitch, B., Bell, J., Mallinckrodt, C., Bartlett, J. W., Goel, N., Molenberghs, G., O’Kelly, M., Singh, P., and Lipkovich, I. (2019), “Choosing Estimands in Clinical Trials: Putting the ICH E9(R1) Into Practice,” Therapeutic Innovation & Regulatory Science, DOI: 10.1177/2168479019838827.
  • Richardson, T. S., and Robins, J. M. (2013), “Single World Intervention Graphs (SWIGs): A Unification of the Counterfactual and Graphical Approaches to Causality,” Working Paper Number 128, Center for Statistics and the Social Sciences, University of Washington, available at https://www.csss.washington.edu/Papers/wp128.pdf.
  • Robins, J. M. (1986), “A New Approach to Causal Inference in Mortality Studies With a Sustained Exposure Period—Application to Control of the Healthy Worker Survivor Effect,” Mathematical Modelling, 7, 1393–1512. DOI: 10.1016/0270-0255(86)90088-6.
  • Robins, J. M. (1998), “Correction for Non-Compliance in Equivalence Trials,” Statistics in Medicine, 17, 269–302.
  • Robins, J. M., and Hernán, M. A. (2009), “Estimation of the Causal Effects of Time-Varying Exposures,” in Longitudinal Data Analysis, eds. G. Fitzmaurice, M. Davidian, G. Verbeke, and G. Molenberghs, New York: Chapman and Hall/CRC Press, pp. 553–599.
  • Robins, J. M., Hernán, M. A., and Brumback, B. (2000), “Marginal Structural Models and Causal Inference in Epidemiology,” Epidemiology, 11, 550–560. DOI: 10.1097/00001648-200009000-00011.
  • Rubin, D. B. (1978a), “Bayesian Inference for Causal Effects: The Role of Randomization,” The Annals of Statistics, 6, 34–58. DOI: 10.1214/aos/1176344064.
  • Rubin, D. B. (1978b), “Multiple Imputations in Sample Surveys—A Phenomenological Bayesian Approach to Nonresponse,” in Imputation and Editing of Faulty or Missing Survey Data, Washington, DC: U.S. Department of Commerce, pp. 1–23.
  • Rubin, D. B. (1987), Multiple Imputation for Nonresponse in Surveys, New York: Wiley.
  • Rubin, D. B. (1998), “More Powerful Randomization-Based p-Values in Double-Blind Trials With Noncompliance” (with discussion), Statistics in Medicine, 17, 371–389.
  • Rufibach, K. (2018), “Treatment Effect Quantification for Time-To-Event Endpoints—Estimands, Analysis Strategies, and Beyond,” arXiv no. 1711.07518v4.
  • Shepherd, B. E., Gilbert, P. B., and Dupont, C. T. (2011), “Sensitivity Analyses Comparing Time-To-Event Outcomes Only Existing in a Subset Selected Postrandomization and Relaxing Monotonicity,” Biometrics, 67, 1100–1110. DOI: 10.1111/j.1541-0420.2010.01508.x.
  • Shortreed, S. M., and Moodie, E. E. M. (2012), “Estimating the Optimal Dynamic Antipsychotic Treatment Regime: Evidence from the Sequential Multiple Assignment Randomized CATIE Schizophrenia Study,” Journal of the Royal Statistical Society, Series C, 61, 577–599. DOI: 10.1111/j.1467-9876.2012.01041.x.
  • Stuart, E. A. (2010), “Matching Methods for Causal Inference: A Review and a Look Forward,” Statistical Science, 25, 1–21. DOI: 10.1214/09-STS313.
  • VanderWeele, T. J. (2011), “Principal Stratification—Uses and Limitations,” The International Journal of Biostatistics, 7, 1–14. DOI: 10.2202/1557-4679.1329.
  • VanderWeele, T. J., and Hernán, M. A. (2013), “Causal Inference Under Multiple Versions of Treatment,” Journal of Causal Inference, 1, 1–20. DOI: 10.1515/jci-2012-0002.
  • VanderWeele, T. J., and Vansteelandt, S. (2009), “Conceptual Issues Concerning Mediation, Interventions and Composition,” Statistics and Its Interface, 2, 457–468. DOI: 10.4310/SII.2009.v2.n4.a7.
  • Wang, D., and Pocock, S. (2016), “A Win Ratio Approach to Comparing Continuous Non-Normal Outcomes in Clinical Trials,” Pharmaceutical Statistics, 15, 238–245. DOI: 10.1002/pst.1743.
  • White, I. R., Joseph, R., and Best, N. A. (2019), “A Causal Modelling Framework for Reference-Based Imputation and Tipping Point Analysis,” Journal of Biopharmaceutical Statistics, DOI: 10.1080/10543406.2019.1684308.
  • Zhang, J. L., and Rubin, D. B. (2003), “Estimation of Causal Effects via Principal Stratification When Some Outcomes Are Truncated by ‘Death’,” Journal of Educational and Behavioral Statistics, 28, 353–368. DOI: 10.3102/10769986028004353.
  • Zheng, D., Chen, Q., Chen, M.-H., and Ibrahim, J. G. (2012), “Estimating Treatment Effects With Treatment Switching via Semicompeting Risks Models: An Application to a Colorectal Cancer Study,” Biometrika, 99, 167–184. DOI: 10.1093/biomet/asr062.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.