Robust method for optimal treatment decision making based on survival data
dc.contributor.author | Fang, Yuexin | |
dc.contributor.author | Zhang, Baqun | |
dc.contributor.author | Zhang, Min | |
dc.date.accessioned | 2021-12-02T02:30:31Z | |
dc.date.available | 2023-01-01 21:30:29 | en |
dc.date.available | 2021-12-02T02:30:31Z | |
dc.date.issued | 2021-12-20 | |
dc.identifier.citation | Fang, Yuexin; Zhang, Baqun; Zhang, Min (2021). "Robust method for optimal treatment decision making based on survival data." Statistics in Medicine 40(29): 6558-6576. | |
dc.identifier.issn | 0277-6715 | |
dc.identifier.issn | 1097-0258 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/171005 | |
dc.publisher | CRC Press | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.subject.other | optimal treatment regime | |
dc.subject.other | subgroup identification | |
dc.subject.other | variable selection | |
dc.subject.other | augmented inverse probability weighted estimator | |
dc.subject.other | decision rule | |
dc.subject.other | doubly robust | |
dc.title | Robust method for optimal treatment decision making based on survival data | |
dc.type | Article | |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | |
dc.subject.hlbsecondlevel | Public Health | |
dc.subject.hlbsecondlevel | Medicine (General) | |
dc.subject.hlbtoplevel | Health Sciences | |
dc.subject.hlbtoplevel | Science | |
dc.subject.hlbtoplevel | Social Sciences | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/171005/1/sim9198-sup-0001-supinfo.pdf | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/171005/2/sim9198.pdf | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/171005/3/sim9198_am.pdf | |
dc.identifier.doi | 10.1002/sim.9198 | |
dc.identifier.source | Statistics in Medicine | |
dc.identifier.citedreference | Komárek A, Komárek MA. Package "smoothSurv"; 2020. | |
dc.identifier.citedreference | Imai K, Ratkovic M. Estimating treatment effect heterogeneity in randomized program evaluation. Ann Appl Stat. 2013; 7 ( 1 ): 443 ‐ 470. | |
dc.identifier.citedreference | Murphy SA. Optimal dynamic treatment regimes. J Royal Stat Soc Ser B (Stat Methodol). 2003; 65 ( 2 ): 331 ‐ 355. | |
dc.identifier.citedreference | Watkins CJ, Dayan P. Q‐learning. Mach Learn. 1992; 8 ( 3‐4 ): 279 ‐ 292. | |
dc.identifier.citedreference | Moodie EE, Platt RW, Kramer MS. Estimating response‐maximized decision rules with applications to breastfeeding. J Am Stat Assoc. 2009; 104 ( 485 ): 155 ‐ 165. | |
dc.identifier.citedreference | Cai T, Tian L, Wong PH, Wei L. Analysis of randomized comparative clinical trial data for personalized treatment selections. Biostatistics. 2011; 12 ( 2 ): 270 ‐ 282. | |
dc.identifier.citedreference | Schulte PJ, Tsiatis AA, Laber EB, Davidian M. Q‐and A‐learning methods for estimating optimal dynamic treatment regimes. Stat Sci Rev J Inst Math Stat. 2014; 29 ( 4 ): 640. | |
dc.identifier.citedreference | Tsiatis AA. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine. Boca Raton, FL: CRC Press; 2019. | |
dc.identifier.citedreference | Tian L, Alizadeh AA, Gentles AJ, Tibshirani R. A simple method for estimating interactions between a treatment and a large number of covariates. J Am Stat Assoc. 2014; 109 ( 508 ): 1517 ‐ 1532. | |
dc.identifier.citedreference | Lu W, Zhang HH, Zeng D. Variable selection for optimal treatment decision. Stat Methods Med Res. 2013; 22 ( 5 ): 493 ‐ 504. | |
dc.identifier.citedreference | Geng Y, Zhang HH, Lu W. On optimal treatment regimes selection for mean survival time. Stat Med. 2015; 34 ( 7 ): 1169 ‐ 1184. | |
dc.identifier.citedreference | Goldberg Y, Kosorok MR. Q‐learning with censored data. Ann Stat. 2012; 40 ( 1 ): 529. | |
dc.identifier.citedreference | Bai X, Tsiatis AA, Lu W, Song R. Optimal treatment regimes for survival endpoints using a locally‐efficient doubly‐robust estimator from a classification perspective. Lifetime Data Anal. 2017; 23 ( 4 ): 585 ‐ 604. | |
dc.identifier.citedreference | Jiang R, Lu W, Song R, Davidian M. On estimation of optimal treatment regimes for maximizing t‐year survival probability. J Royal Stat Soc Ser B (Stat Methodol). 2017; 79 ( 4 ): 1165 ‐ 1185. | |
dc.identifier.citedreference | Hager R, Tsiatis AA, Davidian M. Optimal two‐stage dynamic treatment regimes from a classification perspective with censored survival data. Biometrics. 2018; 74 ( 4 ): 1180 ‐ 1192. | |
dc.identifier.citedreference | Simoneau G, Moodie EE, Nijjar JS, Platt RW. Investigators SERAIC, others. Estimating optimal dynamic treatment regimes with survival outcomes. J Am Stat Assoc. 2020; 115 ( 531 ): 1531 ‐ 1539. | |
dc.identifier.citedreference | Zhang B, Tsiatis AA, Laber EB, Davidian M. A robust method for estimating optimal treatment regimes. Biometrics. 2012; 68 ( 4 ): 1010 ‐ 1018. | |
dc.identifier.citedreference | Zhang B, Tsiatis AA, Davidian M, Zhang M, Laber E. Estimating optimal treatment regimes from a classification perspective. Stat. 2012; 1 ( 1 ): 103 ‐ 114. | |
dc.identifier.citedreference | Robins JM. Optimal structural nested models for optimal sequential decisions. Paper presented at: Proceedings of the 2nd Seattle Symposium in Biostatistics; 2004:189‐326; Springer, New York, NY. | |
dc.identifier.citedreference | Komárek A, Lesaffre E, Hilton JF. Accelerated failure time model for arbitrarily censored data with smoothed error distribution. J Comput Graph Stat. 2005; 14 ( 3 ): 726 ‐ 745. | |
dc.identifier.citedreference | Khan MHR, Shaw JEH. Variable selection for survival data with a class of adaptive elastic net techniques. Stat Comput. 2016; 26 ( 3 ): 725 ‐ 741. | |
dc.identifier.citedreference | Stefanski LA, Boos DD. The calculus of M‐estimation. Am Stat. 2002; 56 ( 1 ): 29 ‐ 38. | |
dc.identifier.citedreference | Zhang Y, Laber EB, Davidian M, Tsiatis AA. Interpretable dynamic treatment regimes. J Am Stat Assoc. 2018; 113 ( 524 ): 1541 ‐ 1549. | |
dc.identifier.citedreference | Zhang M, Tsiatis AA, Davidian M. Improving efficiency of inferences in randomized clinical trials using auxiliary covariates. Biometrics. 2008; 64 ( 3 ): 707 ‐ 715. | |
dc.identifier.citedreference | Luedtke AR, Van Der Laan MJ. Statistical inference for the mean outcome under a possibly non‐unique optimal treatment strategy. Ann Stat. 2016; 44 ( 2 ): 713. | |
dc.identifier.citedreference | Shi C, Lu W, Song R. Breaking the curse of nonregularity with subagging—inference of the mean outcome under optimal treatment regimes. J Mach Learn Res. 2020; 21 ( 176 ): 1 ‐ 67. | |
dc.identifier.citedreference | Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc. 2018; 113 ( 523 ): 1228 ‐ 1242. | |
dc.identifier.citedreference | Davison A. Treatment effect heterogeneity in paired data. Biometrika. 1992; 79 ( 3 ): 463 ‐ 474. | |
dc.identifier.citedreference | Gail M, Simon R. Testing for qualitative interactions between treatment effects and patient subsets. Biometrics. 1985; 41: 361 ‐ 372. | |
dc.identifier.citedreference | Lagakos SW. The challenge of subgroup analyses‐reporting without distorting. N Engl J Med. 2006; 354 ( 16 ): 1667. | |
dc.working.doi | NO | en |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.