2,805
Views
27
CrossRef citations to date
0
Altmetric
Theory and Methods

Estimating Optimal Dynamic Treatment Regimes With Survival Outcomes

ORCID Icon, ORCID Icon, , ORCID Icon &
Pages 1531-1539 | Received 17 Aug 2018, Accepted 22 May 2019, Published online: 22 Jul 2019

References

  • Bai, X., Tsiatis, A. A., Lu, W., and Song, R. (2017), “Optimal Treatment Regimes for Survival Endpoints Using a Locally-Efficient Doubly-Robust Estimator From a Classification Perspective,” Lifetime Data Analysis, 23, 585–604. DOI: 10.1007/s10985-016-9376-x.
  • Dale, J., Paterson, C., Tierney, A., Ralston, S. H., Reid, D. M., Basu, N., Harvie, J., McKay, N. D., Saunders, S., Wilson, H., and Munro, R. (2016), “The Scottish Early Rheumatoid Arthritis (SERA) Study: An Inception Cohort and Biobank,” BMC Musculoskeletal Disorders, 17, 461. DOI: 10.1186/s12891-016-1318-y.
  • Felson, D. T., Smolen, J. S., Wells, G., Zhang, B., van Tuyl, L. H. D., Funovits, J., Aletaha, D., Allaart, C. F., Bathon, J., Bombardieri, S., and Brooks, P. (2011), “American College of Rheumatology/European League Against Rheumatism Provisional Definition of Remission in Rheumatoid Arthritis for Clinical Trials,” Arthritis & Rheumatology, 63, 573–586.
  • Gill, R. D., van der Laan, M. J., and Robins, J. M. (1997), “Coarsening at Random: Characterizations, Conjectures, Counter-Examples,” in Proceedings of the First Seattle Symposium in Biostatistics, New York: Springer, pp. 255–294.
  • Goldberg, Y., and Kosorok, M. R. (2012), “Q-Learning With Censored Data,” Annals of Statistics, 40, 529–560. DOI: 10.1214/12-AOS968.
  • Hager, R., Tsiatis, A. A., and Davidian, M. (2018), “Optimal Two-Stage Dynamic Treatment Regimes From a Classification Perspective With Censored Survival Data,” Biometrics, 74, 1180–1192. DOI: 10.1111/biom.12894.
  • Hernán, M. A., Cole, S. R., Margolick, J., Cohen, M., and Robins, J. M. (2005), “Structural Accelerated Failure Time Models for Survival Analysis in Studies With Time-Varying Treatments,” Pharmacoepidemiology and Drug Safety, 14, 477–491. DOI: 10.1002/pds.1064.
  • Hernán, M. A., and Robins, J. M. (2010), Causal Inference, Boca Raton, FL: CRC Press.
  • Huang, X., Ning, J., and Wahed, A. S. (2014), “Optimization of Individualized Dynamic Treatment Regimes for Recurrent Diseases,” Statistics in Medicine, 33, 2363–2378. DOI: 10.1002/sim.6104.
  • Inoue, E., Yamanaka, H., Hara, M., Tomatsu, T., and Kamatani, N. (2007), “Comparison of Disease Activity Score (DAS) 28-Erythrocyte Sedimentation Rate and DAS28-C-Reactive Protein Threshold Values,” Annals of the Rheumatic Diseases, 66, 407–409. DOI: 10.1136/ard.2006.054205.
  • Jiang, R., Lu, W., Song, R., and Davidian, M. (2017), “On Estimation of Optimal Treatment Regimes for Maximizing t-Year Survival Probability,” Journal of the Royal Statistical Society, Series B, 79, 1165–1185. DOI: 10.1111/rssb.12201.
  • Joffe, M. M. (2001), “Administrative and Artificial Censoring in Censored Regression Models,” Statistics in Medicine, 20, 2287–2304. DOI: 10.1002/sim.850.
  • Joffe, M. M., Yang, W. P., and Feldman, H. (2012), “G-Estimation and Artificial Censoring: Problems, Challenges, and Applications,” Biometrics, 68, 275–286. DOI: 10.1111/j.1541-0420.2011.01656.x.
  • Karrison, T. G. (1997), “Use of Irwin’s Restricted Mean as an Index for Comparing Survival in Different Treatment Groups—Interpretation and Power Considerations,” Controlled Clinical Trials, 18, 151–167. DOI: 10.1016/S0197-2456(96)00089-X.
  • Kuriya, B., Schieir, O., Lin, D., Xiong, J., Pope, J., Boire, G., Haraoui, B., Thorne, J. C., Tin, D., Hitchon, C., and Jamal, S. (2017), “Thresholds for the 28-Joint Disease Activity Score (DAS28) Using C-Reactive Protein Are Lower Compared to DAS28 Using Erythrocyte Sedimentation Rate in Early Rheumatoid Arthritis,” Clinical and Experimental Rheumatology, 35, 799–803.
  • Li, F., Morgan, K. L., and Zaslavsky, A. M. (2018), “Balancing Covariates via Propensity Score Weighting,” Journal of the American Statistical Association, 113, 390–400. DOI: 10.1080/01621459.2016.1260466.
  • Moodie, E. E. M. (2009), “A Note on the Variance of Doubly-Robust G-Estimators,” Biometrika, 96, 998–1004. DOI: 10.1093/biomet/asp043.
  • Moodie, E. E. M., and Richardson, T. S. (2010), “Estimating Optimal Dynamic Regimes: Correcting Bias Under the Null,” Scandinavian Journal of Statistics, 37, 126–146. DOI: 10.1111/j.1467-9469.2009.00661.x.
  • Murphy, S. A. (2003), “Optimal Dynamic Treatment Regimes,” Journal of the Royal Statistical Society, Series B, 65, 331–355. DOI: 10.1111/1467-9868.00389.
  • Robins, J. M. (2000), “Robust Estimation in Sequentially Ignorable Missing Data and Causal Inference Models,” in Proceedings of the American Statistical Association (Vol. 1999), pp. 6–10.
  • Robins, J. M. (2004), “Optimal Structural Nested Models for Optimal Sequential Decisions,” in Proceedings of the Second Seattle Symposium in Biostatistics, New York: Springer, pp. 189–326.
  • Robins, J. M., and Greenland, S. (1994), “Adjusting for Differential Rates of Prophylaxis Therapy for PCP in High-Versus Low-Dose AZT Treatment Arms in an AIDS Randomized Trial,” Journal of the American Statistical Association, 89, 737–749. DOI: 10.1080/01621459.1994.10476807.
  • Robins, J. M., and Rotnitzky, A. (1992), “Recovery of Information and Adjustment for Dependent Censoring Using Surrogate Markers,” in AIDS Epidemiology, New York: Springer, pp. 297–331.
  • Rubin, D. B. (1980), “Randomization Analysis of Experimental Data: The Fisher Randomization Test Comment,” Journal of the American Statistical Association, 75, 591–593. DOI: 10.2307/2287653.
  • Sengul, I., Akcay-Yalbuzdag, S., Ince, B., Goksel-Karatepe, A., and Kaya, T. (2015), “Comparison of the DAS28-CRP and DAS28-ESR in Patients With Rheumatoid Arthritis,” International Journal of Rheumatic Diseases, 18, 640–645. DOI: 10.1111/1756-185X.12695.
  • Singh, J. A., Saag, K. G., Bridges, S. L., Akl, E. A., Bannuru, R. R., Sullivan, M. C., Vaysbrot, E., McNaughton, C., Osani, M., Shmerling, R. H., and Curtis, J. R. (2016), “2015 American College of Rheumatology Guideline for the Treatment of Rheumatoid Arthritis,” Arthritis & Rheumatology, 68, 1–26. DOI: 10.1002/art.39480.
  • Son, K. M., Kim, S. Y., Lee, S. H., Yang, C. M., Seo, Y. I., and Kim, H. A. (2016), “Comparison of the Disease Activity Score Using the Erythrocyte Sedimentation Rate and C-Reactive Protein Levels in Koreans With Rheumatoid Arthritis,” International Journal of Rheumatic Diseases, 19, 1278–1283. DOI: 10.1111/1756-185X.12698.
  • Wallace, M. P., and Moodie, E. E. M. (2015), “Doubly-Robust Dynamic Treatment Regimen Estimation via Weighted Least Squares,” Biometrics, 71, 636–644. DOI: 10.1111/biom.12306.
  • Wallace, M. P., Moodie, E. E. M., and Stephens, D. A. (2016), “Model Assessment in Dynamic Treatment Regimen Estimation via Double Robustness,” Biometrics, 72, 855–864. DOI: 10.1111/biom.12468.
  • Wallace, M. P., Moodie, E. E. M., and Stephens, D. A. (2017a), “Dynamic Treatment Regimen Estimation via Regression-Based Techniques: Introducing R Package DTRreg,” Journal of Statistical Software, 80, 1–20. DOI: 10.18637/jss.v080.i02.
  • Wallace, M. P., Moodie, E. E. M., and Stephens, D. A. (2017b), “Model Validation and Selection for Personalized Medicine Using Dynamic-Weighted Ordinary Least Squares,” Statistical Methods in Medical Research, 26, 1641–1653. DOI: 10.1177/0962280217708665.
  • White, I. R., Royston, P., and Wood, A. M. (2011), “Multiple Imputation Using Chained Equations: Issues and Guidance for Practice,” Statistics in Medicine, 30, 377–399. DOI: 10.1002/sim.4067.
  • Zhang, B., Tsiatis, A. A., Laber, E. B., and Davidian, M. (2013), “Robust Estimation of Optimal Dynamic Treatment Regimes for Sequential Treatment Decisions,” Biometrika, 100, 681–694. DOI: 10.1093/biomet/ast014.
  • Zhao, Y. Q., Zeng, D., Laber, E. B., and Kosorok, M. R. (2015), “New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes,” Journal of the American Statistical Association, 110, 583–598. DOI: 10.1080/01621459.2014.937488.
  • Zhao, Y. Q., Zeng, D., Laber, E. B., Song, R., Yuan, M., and Kosorok, M. R. (2014), “Doubly Robust Learning for Estimating Individualized Treatment With Censored Data,” Biometrika, 102, 151–168. DOI: 10.1093/biomet/asu050.

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.