402
Views
0
CrossRef citations to date
0
Altmetric
Testing and Inference

Testing Biased Randomization Assumptions and Quantifying Imperfect Matching and Residual Confounding in Matched Observational Studies

, , ORCID Icon & ORCID Icon
Pages 528-538 | Received 01 Nov 2021, Accepted 17 Aug 2022, Published online: 19 Oct 2022

References

  • Austin, P. C., and Stuart, E. A. (2015), “Moving Towards Best Practice When Using Inverse Probability of Treatment Weighting (IPTW) Using the Propensity Score to Estimate Causal Treatment Effects in Observational Studies.” Statistics in Medicine, 34, 3661–3679. DOI: 10.1002/sim.6607.
  • Bind, M.-A. C., and Rubin, D. B. (2019), “Bridging Observational Studies and Randomized Experiments by Embedding the Former in the Latter.” Statistical Methods in Medical Research, 28, 1958–1978. DOI: 10.1177/0962280217740609.
  • Branson, Z. (2020), “Randomization Tests to Assess Covariate Balance When Designing and Analyzing Matched Datasets.” Observational Studies, 7, 1–36. DOI: 10.1353/obs.2021.0031.
  • Branson, Z., and Keele, L. (2020), “Evaluating a Key Instrumental Variable Assumption Using Randomization Tests.” American Journal of Epidemiology, 189, 1412–1420. DOI: 10.1093/aje/kwaa089.
  • Connors, A. F., McCaffree, D. R., and Gray, B. A. (1983), “Evaluation of Right-Heart Catheterization in the Critically Ill Patient Without Acute Myocardial Infarction.” New England Journal of Medicine, 308, 263–267. DOI: 10.1056/NEJM198302033080508.
  • Connors, A. F., Speroff, T., Dawson, N. V., Thomas, C., Harrell, F. E., Wagner, D., Desbiens, N., Goldman, L., Wu, A. W., Califf, R. M. (1996), “The Effectiveness of Right Heart Catheterization in the Initial Care of Critically Ill Patients.” JAMA, 276, 889–897. DOI: 10.1001/jama.1996.03540110043030.
  • Dehejia, R. H., and Wahba, S. (2002), “Propensity Score-Matching Methods for Nonexperimental Causal Studies.” Review of Economics and Statistics, 84, 151–161. DOI: 10.1162/003465302317331982.
  • Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977), “Maximum Likelihood from Incomplete Data via the EM Algorithm.” Journal of the Royal Statistical Society, Series B, 39, 1–22.
  • Denby, L., and Landwehr, J. (2013), “A Conversation with Colin L. Mallows.” International Statistical Review, 81, 338–360. DOI: 10.1111/insr.12038.
  • Diamond, A., and Sekhon, J. S. (2013), “Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies.” Review of Economics and Statistics, 95, 932–945. DOI: 10.1162/REST_a_00318.
  • DiPrete, T. A., and Gangl, M. (2004), “Assessing Bias in the Estimation of Causal Effects: Rosenbaum Bounds on Matching Estimators and Instrumental Variables Estimation with Imperfect Instruments.” Sociological Methodology, 34, 271–310. DOI: 10.1111/j.0081-1750.2004.00154.x.
  • Fogarty, C. B. (2020), “Studentized Sensitivity Analysis for the Sample Average Treatment Effect in Paired Observational Studies.” Journal of the American Statistical Association, 115, 1518–1530. DOI: 10.1080/01621459.2019.1632072.
  • Franklin, J. M., Rassen, J. A., Ackermann, D., Bartels, D. B., and Schneeweiss, S. (2014), “Metrics for Covariate Balance in Cohort Studies of Causal Effects.” Statistics in Medicine, 33, 1685–1699. DOI: 10.1002/sim.6058.
  • Gagnon-Bartsch, J., and Shem-Tov, Y. (2019), “The Classification Permutation Test: A Flexible Approach to Testing for Covariate Imbalance in Observational Studies.” Annals of Applied Statistics, 13, 1464–1483.
  • Guyatt, G. (1991), “A Randomized Control Trial of Right-Heart Catheterization in Critically Ill Patients.” Journal of Intensive Care Medicine, 6, 91–95. DOI: 10.1177/088506669100600204.
  • Hansen, B. B., Rosenbaum, P. R., and Small, D. S. (2014), “Clustered Treatment Assignments and Sensitivity to Unmeasured Biases in Observational Studies.” Journal of the American Statistical Association, 109, 133–144. DOI: 10.1080/01621459.2013.863157.
  • Heller, R., Rosenbaum, P. R., and Small, D. S. (2010), “Using the Cross-Match Test to Appraise Covariate Balance in Matched Pairs.” The American Statistician, 64, 299–309. DOI: 10.1198/tast.2010.09210.
  • Heng, S., Kang, H., Small, D. S., and Fogarty, C. B. (2021), “Increasing Power for Observational Studies of Aberrant Response: An Adaptive Approach.” Journal of the Royal Statistical Society, Series B, 83, 482–504. DOI: 10.1111/rssb.12424.
  • Ho, D. E., Imai, K., King, G., and Stuart, E. A. (2007), “Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric causal Inference.” Political Analysis, 15, 199–236. DOI: 10.1093/pan/mpl013.
  • Iacus, S. M., King, G., and Porro, G. (2011), “Multivariate Matching Methods that are Monotonic Imbalance Bounding.” Journal of the American Statistical Association, 106, 345–361. DOI: 10.1198/jasa.2011.tm09599.
  • Keele, L. J. (2014), rbounds: Perform Rosenbaum Bounds Sensitivity Tests for Matched and Unmatched Data. R package version 2.1.
  • Pimentel, S. D. (2022), rcbsubset: Optimal Subset Matching with Refined Covariate Balance. R package version 1.1.7.
  • Pimentel, S. D., Kelz, R. R., Silber, J. H., and Rosenbaum, P. R. (2015), “Large, Sparse Optimal Matching with Refined Covariate Balance in an Observational Study of the Health Outcomes Produced by New Surgeons.” Journal of the American Statistical Association, 110, 515–527. DOI: 10.1080/01621459.2014.997879.
  • Rosenbaum, P. R. (1989), “Optimal Matching for Observational Studies.” Journal of the American Statistical Association, 84, 1024–1032. DOI: 10.1080/01621459.1989.10478868.
  • Rosenbaum, P. R. (2002), Observational Studies. New York: Springer.
  • Rosenbaum, P. R. (2005), “An Exact Distribution-Free Test Comparing Two Multivariate Distributions Based on Adjacency.” Journal of the Royal Statistical Society, Series B, 67, 515–530.
  • Rosenbaum, P. R. (2010), Design of Observational Studies. New York: Springer.
  • Rosenbaum, P. R. (2012), “Optimal Matching of an Optimally Chosen Subset in Observational Studies.” Journal of Computational and Graphical Statistics, 21, 57–71.
  • Rosenbaum, P. R., Ross, R. N., and Silber, J. H. (2007), “Minimum Distance Matched Sampling with Fine Balance in an Observational Study of Treatment for Ovarian Cancer.” Journal of the American Statistical Association, 102, 75–83. DOI: 10.1198/016214506000001059.
  • Rosenbaum, P. R., and Rubin, D. B. (1985), “Constructing a Control Group Using Multivariate Matched Sampling Methods that Incorporate the Propensity Score.” The American Statistician, 39, 33–38.
  • Rubin, D. B. (1979), “Using Multivariate Matched Sampling and Regression Adjustment to Control Bias in Observational Studies.” Journal of the American Statistical Association, 74, 318–328.
  • Rubin, D. B. (2007), “The Design Versus the Analysis of Observational Studies for Causal Effects: Parallels with the Design of Randomized Trials.” Statistics in Medicine, 26, 20–36.
  • Silber, J. H., Rosenbaum, P. R., Trudeau, M. E., Even-Shoshan, O., Chen, W., Zhang, X., and Mosher, R. E. (2001), “Multivariate Matching and Bias Reduction in the Surgical Outcomes Study.” Medical Care, 39, 1048–1064.
  • 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.
  • Wagstaff, K., Cardie, C., Rogers, S., and Schroedl, S. (2001), “Constrained k-Means Clustering with Background Knowledge.” In Proceedings of the Eighteenth International Conference on Machine Learning, Vol. 1, 577–584.
  • Wu, J., and Ding, P. (2020), “Randomization Tests for Weak Null Hypotheses in Randomized Experiments.” Journal of the American Statistical Association, 116, 1898–1913. DOI: 10.1080/01621459.2020.1750415.
  • Zhang, B. (2021), match2C: Match One Sample using Two Criteria. R package version 1.1.0.
  • Zhang, B. (2022), “Towards Better Reconciling Randomized Controlled Trial and Observational Study Findings: Efficient Algorithms for Building Representative Matched Samples with Enhanced External Validity.”
  • Zhang, B., Heng, S., Mackay, E. J., and Ye, T. (2021a), “Bridging Preference-Based Instrumental Variable Studies and Cluster-Randomized Encouragement Experiments: Study Design, Noncompliance, and Average Cluster Effect Ratio.” Biometrics. DOI: 10.1111/biom.13500.
  • Zhang, B., Mackay, E. J., and Baiocchi, M. (2022), “Statistical Matching and Subclassification with a Continuous Dose: Characterization, Algorithm, and Application to a Health Outcomes Study.” Annals of Applied Statistics.
  • Zhang, B., Small, D., Lasater, K., McHugh, M., Silber, J., and Rosenbaum, P. (2021b), “Matching One Sample According to Two Criteria in Observational Studies.” Journal of the American Statistical Association, 1–34. DOI: 10.1080/01621459.2021.1981337.
  • Zhao, Q. (2018), “On Sensitivity Value of Pair-Matched Observational Studies.” Journal of the American Statistical Association, 114, 713–722. DOI: 10.1080/01621459.2018.1429277.
  • Zubizarreta, J. R. (2012), “Using Mixed Integer Programming for Matching in an Observational Study of Kidney Failure After Surgery.” Journal of the American Statistical Association, 107, 1360–1371. DOI: 10.1080/01621459.2012.703874.

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.