Non-parametric individual treatment effect estimation for survival data with random forests
Abstract Motivation Personalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the subject’s baseline covariates but additional complications arise for a time-to-event response subject to censoring. In this paper, the treatm...
Saved in:
| Published in | Bioinformatics Vol. 36; no. 2; pp. 629 - 636 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
15.01.2020
|
| Online Access | Get full text |
Cover
Loading…
| Abstract | Abstract
Motivation
Personalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the subject’s baseline covariates but additional complications arise for a time-to-event response subject to censoring. In this paper, the treatment effect is measured as the difference between the mean survival time of a treated subject and the mean survival time of a control subject. We propose a new random forest method for estimating the individual treatment effect with survival data. The random forest is formed by individual trees built with a splitting rule specifically designed to partition the data according to the individual treatment effect. For a new subject, the forest provides a set of similar subjects from the training dataset that can be used to compute an estimation of the individual treatment effect with any adequate method.
Results
The merits of the proposed method are investigated with a simulation study where it is compared to numerous competitors, including recent state-of-the-art methods. The results indicate that the proposed method has a very good and stable performance to estimate the individual treatment effects. Two examples of application with a colon cancer data and breast cancer data show that the proposed method can detect a treatment effect in a sub-population even when the overall effect is small or nonexistent.
Availability and implementation
The authors are working on an R package implementing the proposed method and it will be available soon. In the meantime, the code can be obtained from the first author at sami.tabib@hec.ca.
Supplementary information
Supplementary data are available at Bioinformatics online. |
|---|---|
| AbstractList | Abstract
Motivation
Personalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the subject’s baseline covariates but additional complications arise for a time-to-event response subject to censoring. In this paper, the treatment effect is measured as the difference between the mean survival time of a treated subject and the mean survival time of a control subject. We propose a new random forest method for estimating the individual treatment effect with survival data. The random forest is formed by individual trees built with a splitting rule specifically designed to partition the data according to the individual treatment effect. For a new subject, the forest provides a set of similar subjects from the training dataset that can be used to compute an estimation of the individual treatment effect with any adequate method.
Results
The merits of the proposed method are investigated with a simulation study where it is compared to numerous competitors, including recent state-of-the-art methods. The results indicate that the proposed method has a very good and stable performance to estimate the individual treatment effects. Two examples of application with a colon cancer data and breast cancer data show that the proposed method can detect a treatment effect in a sub-population even when the overall effect is small or nonexistent.
Availability and implementation
The authors are working on an R package implementing the proposed method and it will be available soon. In the meantime, the code can be obtained from the first author at sami.tabib@hec.ca.
Supplementary information
Supplementary data are available at Bioinformatics online. Personalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the subject's baseline covariates but additional complications arise for a time-to-event response subject to censoring. In this paper, the treatment effect is measured as the difference between the mean survival time of a treated subject and the mean survival time of a control subject. We propose a new random forest method for estimating the individual treatment effect with survival data. The random forest is formed by individual trees built with a splitting rule specifically designed to partition the data according to the individual treatment effect. For a new subject, the forest provides a set of similar subjects from the training dataset that can be used to compute an estimation of the individual treatment effect with any adequate method.MOTIVATIONPersonalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the subject's baseline covariates but additional complications arise for a time-to-event response subject to censoring. In this paper, the treatment effect is measured as the difference between the mean survival time of a treated subject and the mean survival time of a control subject. We propose a new random forest method for estimating the individual treatment effect with survival data. The random forest is formed by individual trees built with a splitting rule specifically designed to partition the data according to the individual treatment effect. For a new subject, the forest provides a set of similar subjects from the training dataset that can be used to compute an estimation of the individual treatment effect with any adequate method.The merits of the proposed method are investigated with a simulation study where it is compared to numerous competitors, including recent state-of-the-art methods. The results indicate that the proposed method has a very good and stable performance to estimate the individual treatment effects. Two examples of application with a colon cancer data and breast cancer data show that the proposed method can detect a treatment effect in a sub-population even when the overall effect is small or nonexistent.RESULTSThe merits of the proposed method are investigated with a simulation study where it is compared to numerous competitors, including recent state-of-the-art methods. The results indicate that the proposed method has a very good and stable performance to estimate the individual treatment effects. Two examples of application with a colon cancer data and breast cancer data show that the proposed method can detect a treatment effect in a sub-population even when the overall effect is small or nonexistent.The authors are working on an R package implementing the proposed method and it will be available soon. In the meantime, the code can be obtained from the first author at sami.tabib@hec.ca.AVAILABILITY AND IMPLEMENTATIONThe authors are working on an R package implementing the proposed method and it will be available soon. In the meantime, the code can be obtained from the first author at sami.tabib@hec.ca.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. Personalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the subject's baseline covariates but additional complications arise for a time-to-event response subject to censoring. In this paper, the treatment effect is measured as the difference between the mean survival time of a treated subject and the mean survival time of a control subject. We propose a new random forest method for estimating the individual treatment effect with survival data. The random forest is formed by individual trees built with a splitting rule specifically designed to partition the data according to the individual treatment effect. For a new subject, the forest provides a set of similar subjects from the training dataset that can be used to compute an estimation of the individual treatment effect with any adequate method. The merits of the proposed method are investigated with a simulation study where it is compared to numerous competitors, including recent state-of-the-art methods. The results indicate that the proposed method has a very good and stable performance to estimate the individual treatment effects. Two examples of application with a colon cancer data and breast cancer data show that the proposed method can detect a treatment effect in a sub-population even when the overall effect is small or nonexistent. The authors are working on an R package implementing the proposed method and it will be available soon. In the meantime, the code can be obtained from the first author at sami.tabib@hec.ca. Supplementary data are available at Bioinformatics online. |
| Author | Tabib, Sami Larocque, Denis |
| Author_xml | – sequence: 1 givenname: Sami surname: Tabib fullname: Tabib, Sami organization: Department of Decision Sciences, HEC Montréal, Montréal, QC H3T 2A7, Canada – sequence: 2 givenname: Denis orcidid: 0000-0002-7372-7943 surname: Larocque fullname: Larocque, Denis email: denis.larocque@hec.ca organization: Department of Decision Sciences, HEC Montréal, Montréal, QC H3T 2A7, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31373350$$D View this record in MEDLINE/PubMed |
| BookMark | eNqNkDtPwzAYRS0Eoi3wE0AZWQKOX03EhCpeEoIFJgbL8UMYJXaxnSL49bikDLDA9FnyOb7-7gxsO-80AIcVPKlgg09b660zPvQiWRlP2_TBINoC04owWCJIm-18xmxekhriCZjF-AIhrQghu2CCKzzHmMIpeLrzrlyKIHqdgpWFdcqurBpEV6SgReq1S4U2Rss8YrLrOO-KHFzEIazsKoNKJFG82fRcBOGU79e3mY37YMeILuqDzdwDj5cXD4vr8vb-6mZxflvK_IVUUqmgbhk1BhIsDFW0kg2iGuuG0FqJmhJkGGsQqQkUkqFKq7pBEmlW4xYbvAeOx3eXwb8OOZn3NkrddcJpP0SOUAZhQyDL6NEGHdpeK74MeaPwzr8LycDZCMjgYwzacGnT184pCNvxCvJ1_fxn_XysP9v0l_0d8JcHR88Py38qn1IupAo |
| CitedBy_id | crossref_primary_10_1016_j_jbi_2020_103474 crossref_primary_10_1515_ijb_2023_0056 crossref_primary_10_1155_2021_4602465 crossref_primary_10_1177_09622802231224628 crossref_primary_10_1002_sim_9090 crossref_primary_10_1109_TNNLS_2023_3266429 crossref_primary_10_59717_j_xinn_med_2023_100023 crossref_primary_10_1093_bioinformatics_btab158 crossref_primary_10_1145_3466818 crossref_primary_10_1177_09622802241275401 crossref_primary_10_1177_0272989X241263356 crossref_primary_10_1186_s12859_023_05377_y crossref_primary_10_2147_CMAR_S239795 crossref_primary_10_6339_24_JDS1119 |
| Cites_doi | 10.1007/s10618-014-0383-9 10.1198/106186008X319331 10.1002/sim.4780132105 10.1023/A:1010933404324 10.1214/09-AOAS285 10.1111/1467-985X.00122 10.1198/016214505000001230 10.1002/sim.6929 10.1200/JCO.1994.12.10.2086 10.1111/j.0006-341X.2001.01207.x 10.1002/sim.1593 10.1515/ijb-2015-0032 10.1093/bioinformatics/btx174 10.1002/sim.7594 10.1145/772862.772872 10.1001/jama.1982.03320430047030 10.1007/s10985-016-9372-1 10.1093/bioinformatics/btr295 10.1017/CBO9781139025751 10.1002/sim.6454 10.1200/JCO.1989.7.10.1447 10.1093/bib/bbr001 10.7326/0003-4819-122-5-199503010-00001 10.1080/01969722.2015.1012892 10.1037/h0037350 10.1002/sim.7297 10.1186/1471-2288-13-152 10.1007/s10115-011-0434-0 10.1177/0962280217727314 10.1002/sim.6246 10.1002/dir.10035 10.1056/NEJM199002083220602 10.1002/sim.7661 10.1214/18-AOS1709 10.1093/bioinformatics/btw391 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2019 The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. |
| Copyright_xml | – notice: The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com 2019 – notice: The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. |
| DBID | AAYXX CITATION NPM 7X8 |
| DOI | 10.1093/bioinformatics/btz602 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1460-2059 1367-4811 |
| EndPage | 636 |
| ExternalDocumentID | 31373350 10_1093_bioinformatics_btz602 10.1093/bioinformatics/btz602 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Natural Sciences and Engineering Research Council of Canada (NSERC) – fundername: Fondation HEC Montréal |
| GroupedDBID | -~X .2P 5GY AAMVS ABPTD ACGFS ADZXQ ALMA_UNASSIGNED_HOLDINGS BCRHZ F5P HW0 KOP Q5Y RD5 ROX TLC TN5 TOX WH7 --- -E4 .DC .I3 0R~ 23N 2WC 4.4 48X 53G 5WA 70D AAIJN AAIMJ AAJKP AAKPC AAMDB AAOGV AAPQZ AAPXW AAUQX AAVAP AAVLN AAYXX ABEJV ABEUO ABGNP ABIXL ABNKS ABPQP ABQLI ABWST ABXVV ABZBJ ACIWK ACPRK ACUFI ACUXJ ACYTK ADBBV ADEYI ADEZT ADFTL ADGKP ADGZP ADHKW ADHZD ADMLS ADOCK ADPDF ADRDM ADRTK ADVEK ADYVW ADZTZ AECKG AEGPL AEJOX AEKKA AEKSI AELWJ AEMDU AENEX AENZO AEPUE AETBJ AEWNT AFFZL AFGWE AFIYH AFOFC AFRAH AGINJ AGKEF AGQXC AGSYK AHMBA AHXPO AIJHB AJEEA AJEUX AKHUL AKWXX ALTZX ALUQC AMNDL APIBT APWMN ARIXL ASPBG AVWKF AXUDD AYOIW AZVOD BAWUL BAYMD BHONS BQDIO BQUQU BSWAC BTQHN C45 CDBKE CITATION CS3 CZ4 DAKXR DIK DILTD DU5 D~K EBD EBS EE~ EMOBN F9B FEDTE FHSFR FLIZI FLUFQ FOEOM FQBLK GAUVT GJXCC GROUPED_DOAJ GX1 H13 H5~ HAR HZ~ IOX J21 JXSIZ KAQDR KQ8 KSI KSN M-Z MK~ ML0 N9A NGC NLBLG NMDNZ NOMLY NU- O9- OAWHX ODMLO OJQWA OK1 OVD OVEED P2P PAFKI PEELM PQQKQ Q1. R44 RNS ROL RPM RUSNO RW1 RXO SV3 TEORI TJP TR2 W8F WOQ X7H YAYTL YKOAZ YXANX ZKX ~91 ~KM M49 NPM 7X8 |
| ID | FETCH-LOGICAL-c350t-5cd0eb65ff043af5d51c925e3e9458da8542f66924840ac621ed892c2e683b3f3 |
| IEDL.DBID | TOX |
| ISSN | 1367-4803 1367-4811 |
| IngestDate | Fri Jul 11 16:38:59 EDT 2025 Thu Apr 03 07:08:48 EDT 2025 Tue Jul 01 02:33:49 EDT 2025 Thu Apr 24 22:58:37 EDT 2025 Wed Aug 28 03:17:43 EDT 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| License | This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c350t-5cd0eb65ff043af5d51c925e3e9458da8542f66924840ac621ed892c2e683b3f3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-7372-7943 |
| PMID | 31373350 |
| PQID | 2268309406 |
| PQPubID | 23479 |
| PageCount | 8 |
| ParticipantIDs | proquest_miscellaneous_2268309406 pubmed_primary_31373350 crossref_citationtrail_10_1093_bioinformatics_btz602 crossref_primary_10_1093_bioinformatics_btz602 oup_primary_10_1093_bioinformatics_btz602 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20200115 2020-01-15 2020-Jan-15 |
| PublicationDateYYYYMMDD | 2020-01-15 |
| PublicationDate_xml | – month: 01 year: 2020 text: 20200115 day: 15 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Bioinformatics |
| PublicationTitleAlternate | Bioinformatics |
| PublicationYear | 2020 |
| Publisher | Oxford University Press |
| Publisher_xml | – name: Oxford University Press |
| References | Hansotia (2023013112062320500_btz602-B11) 2002; 16 Fernald (2023013112062320500_btz602-B8) 2011; 27 Radcliffe (2023013112062320500_btz602-B31) 2011 Lin (2023013112062320500_btz602-B21) 1994; 13 Moradian (2023013112062320500_btz602-B28) 2017; 23 Wey (2023013112062320500_btz602-B45) 2016; 35 Horiguchi (2023013112062320500_btz602-B14) 2018; 37 Ishwaran (2023013112062320500_btz602-B18) 2017 Athey (2023013112062320500_btz602-B4) 2019; 47 (2023013112062320500_btz602-B30) 2017 Zhang (2023013112062320500_btz602-B47) 2017; 33 Royston (2023013112062320500_btz602-B34) 2013; 13 Breiman (2023013112062320500_btz602-B5) 2001; 45 Therneau (2023013112062320500_btz602-B42) 2015 Rubin (2023013112062320500_btz602-B35) 1974; 66 Lo (2023013112062320500_btz602-B23) 2002; 4 Zeileis (2023013112062320500_btz602-B46) 2008; 17 Moradian (2023013112062320500_btz602-B29) 2019; 28 Gérardy (2023013112062320500_btz602-B9) 2016; 67 Breiman (2023013112062320500_btz602-B6) 1984 Schumacher (2023013112062320500_btz602-B38) 1994; 12 Jaroszewicz (2023013112062320500_btz602-B19) 2014 Rzepakowski (2023013112062320500_btz602-B36) 2012; 32 Guelman (2023013112062320500_btz602-B10) 2015; 46 Andersen (2023013112062320500_btz602-B1) 2017; 36 Harrell (2023013112062320500_btz602-B12) 1982; 247 Imbens (2023013112062320500_btz602-B17) 2015 Riccardo (2023013112062320500_btz602-B32) 2014; 33 Seibold (2023013112062320500_btz602-B39) 2016; 12 Wang (2023013112062320500_btz602-B44) 2016; 32 Hothorn (2023013112062320500_btz602-B15) 2017 Simon (2023013112062320500_btz602-B40) 2011; 12 Athey (2023013112062320500_btz602-B3) 2015; 1050 Loh (2023013112062320500_btz602-B25) 2015; 34 Sauerbrei (2023013112062320500_btz602-B37) 1999; 162 Hothorn (2023013112062320500_btz602-B16) 2004; 23 Anstrom (2023013112062320500_btz602-B2) 2001; 57 Lin (2023013112062320500_btz602-B22) 2006; 101 Roy (2023013112062320500_btz602-B33) 2019 Moertel (2023013112062320500_btz602-B27) 1995; 122 Sołtys (2023013112062320500_btz602-B41) 2015; 29 Loh (2023013112062320500_btz602-B24) 2002; 12 Chipman (2023013112062320500_btz602-B7) 2010; 4 Moertel (2023013112062320500_btz602-B26) 1990; 322 Thomas (2023013112062320500_btz602-B43) 2018; 37 Henderson (2023013112062320500_btz602-B13) 2018 Laurie (2023013112062320500_btz602-B20) 1989; 7 |
| References_xml | – volume: 29 start-page: 1531 year: 2015 ident: 2023013112062320500_btz602-B41 article-title: Ensemble methods for uplift modeling publication-title: Data Min. Knowl. Disc doi: 10.1007/s10618-014-0383-9 – volume-title: Classification and Regression Trees year: 1984 ident: 2023013112062320500_btz602-B6 – volume: 17 start-page: 492 year: 2008 ident: 2023013112062320500_btz602-B46 article-title: Model-based recursive partitioning publication-title: J. Comput. Graph. Stat doi: 10.1198/106186008X319331 – volume: 13 start-page: 2233 year: 1994 ident: 2023013112062320500_btz602-B21 article-title: Cox regression analysis of multivariate failure time data: the marginal approach publication-title: Stat. Med doi: 10.1002/sim.4780132105 – volume: 45 start-page: 5 year: 2001 ident: 2023013112062320500_btz602-B5 article-title: Random forests publication-title: Mach. Learn doi: 10.1023/A:1010933404324 – volume: 4 start-page: 266 year: 2010 ident: 2023013112062320500_btz602-B7 article-title: BART: Bayesian Additive Regression Trees publication-title: Ann. Appl. Stat doi: 10.1214/09-AOAS285 – volume: 162 start-page: 71 year: 1999 ident: 2023013112062320500_btz602-B37 article-title: Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials publication-title: J. R. Stat. Soc. Ser. A (Stat. Soc.) doi: 10.1111/1467-985X.00122 – volume: 101 start-page: 578 year: 2006 ident: 2023013112062320500_btz602-B22 article-title: Random forests and adaptive nearest neighbors publication-title: J. Am. Stat. Assoc doi: 10.1198/016214505000001230 – volume: 35 start-page: 3319 year: 2016 ident: 2023013112062320500_btz602-B45 article-title: Estimating restricted mean treatment effects with stacked survival models publication-title: Stat. Med doi: 10.1002/sim.6929 – volume: 12 start-page: 2086 year: 1994 ident: 2023013112062320500_btz602-B38 article-title: Randomized 2 x 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients. German Breast Cancer Study Group publication-title: J. Clin. Oncol doi: 10.1200/JCO.1994.12.10.2086 – volume: 57 start-page: 1207 year: 2001 ident: 2023013112062320500_btz602-B2 article-title: Utilizing propensity scores to estimate causal treatment effects with censored time-lagged data publication-title: Biometrics doi: 10.1111/j.0006-341X.2001.01207.x – volume: 23 start-page: 77 year: 2004 ident: 2023013112062320500_btz602-B16 article-title: Bagging survival trees publication-title: Stat. Med doi: 10.1002/sim.1593 – volume: 12 start-page: 45 year: 2016 ident: 2023013112062320500_btz602-B39 article-title: Model-based recursive partitioning for subgroup analyses publication-title: Int. J. Biostat doi: 10.1515/ijb-2015-0032 – volume: 33 start-page: 2372 year: 2017 ident: 2023013112062320500_btz602-B47 article-title: Mining heterogeneous causal effects for personalized cancer treatment publication-title: Bioinformatics doi: 10.1093/bioinformatics/btx174 – volume: 12 start-page: 361 year: 2002 ident: 2023013112062320500_btz602-B24 article-title: Regression tress with unbiased variable selection and interaction detection publication-title: Stat. Si – volume: 37 start-page: 1608 year: 2018 ident: 2023013112062320500_btz602-B43 article-title: Subgroup identification in dose-finding trials via model-based recursive partitioning publication-title: Stat. Med doi: 10.1002/sim.7594 – volume: 4 start-page: 78 year: 2002 ident: 2023013112062320500_btz602-B23 article-title: The true lift model: a novel data mining approach to response modeling in database marketing publication-title: ACM SIGKDD Explor. Newslett doi: 10.1145/772862.772872 – volume: 247 start-page: 2543 year: 1982 ident: 2023013112062320500_btz602-B12 article-title: Evaluating the yield of medical tests publication-title: JAMA doi: 10.1001/jama.1982.03320430047030 – year: 2014 ident: 2023013112062320500_btz602-B19 – year: 2018 ident: 2023013112062320500_btz602-B13 – volume: 23 start-page: 671 year: 2017 ident: 2023013112062320500_btz602-B28 article-title: L1 rules in survival forests publication-title: Lifetime Data Anal doi: 10.1007/s10985-016-9372-1 – year: 2011 ident: 2023013112062320500_btz602-B31 – volume: 67 start-page: 1 year: 2016 ident: 2023013112062320500_btz602-B9 article-title: Causal inference and uplift modeling: a review of the literature publication-title: JMLR Workshop Conf. Proc – volume: 27 start-page: 1741 year: 2011 ident: 2023013112062320500_btz602-B8 article-title: Bioinformatics challenges for personalized medicine publication-title: Bioinformatics doi: 10.1093/bioinformatics/btr295 – volume-title: Causal Inference in Statistics, Social, and Biomedical Sciences year: 2015 ident: 2023013112062320500_btz602-B17 doi: 10.1017/CBO9781139025751 – volume: 34 start-page: 1818 year: 2015 ident: 2023013112062320500_btz602-B25 article-title: A regression tree approach to identifying subgroups with differential treatment effects publication-title: Stat. Med doi: 10.1002/sim.6454 – volume: 7 start-page: 1447 year: 1989 ident: 2023013112062320500_btz602-B20 article-title: Surgical adjuvant therapy of large-bowel carcinoma: an evaluation of levamisole and the combination of levamisole and fluorouracil. The North Central Cancer Treatment Group and the Mayo Clinic publication-title: J. Clin. Oncol doi: 10.1200/JCO.1989.7.10.1447 – volume: 12 start-page: 203 year: 2011 ident: 2023013112062320500_btz602-B40 article-title: Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data publication-title: Brief. Bioinf doi: 10.1093/bib/bbr001 – volume: 122 start-page: 321 year: 1995 ident: 2023013112062320500_btz602-B27 article-title: Fluorouracil plus levamisole as effective adjuvant therapy after resection of stage III colon carcinoma: a final report publication-title: Ann. Internal Med doi: 10.7326/0003-4819-122-5-199503010-00001 – volume: 46 start-page: 230 year: 2015 ident: 2023013112062320500_btz602-B10 article-title: Uplift random forests publication-title: Cybern. Syst doi: 10.1080/01969722.2015.1012892 – volume: 1050 start-page: 1 year: 2015 ident: 2023013112062320500_btz602-B3 article-title: Machine learning methods for estimating heterogeneous causal effects publication-title: Stat – volume: 66 start-page: 688 year: 1974 ident: 2023013112062320500_btz602-B35 article-title: Estimating causal effects of treatments in randomized and nonrandomized studies publication-title: J. Educ. Psychol doi: 10.1037/h0037350 – volume: 36 start-page: 2669 year: 2017 ident: 2023013112062320500_btz602-B1 article-title: Causal inference in survival analysis using pseudo-observations publication-title: Stat. Med doi: 10.1002/sim.7297 – year: 2017 ident: 2023013112062320500_btz602-B15 – year: 2019 ident: 2023013112062320500_btz602-B33 article-title: Prediction intervals with random forests publication-title: Stat. Methods Med. Res – volume: 13 start-page: 152 year: 2013 ident: 2023013112062320500_btz602-B34 article-title: Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome publication-title: BMC Med. Res. Methodol doi: 10.1186/1471-2288-13-152 – volume: 32 start-page: 303 year: 2012 ident: 2023013112062320500_btz602-B36 article-title: Decision trees for uplift modeling with single and multiple treatments publication-title: Knowl. Inf. Syst doi: 10.1007/s10115-011-0434-0 – volume: 28 start-page: 445 year: 2019 ident: 2023013112062320500_btz602-B29 article-title: Survival forests for data with dependent censoring publication-title: Stat. Methods Med. Res doi: 10.1177/0962280217727314 – volume-title: R: A Language and Environment for Statistical Computing year: 2017 ident: 2023013112062320500_btz602-B30 – year: 2017 ident: 2023013112062320500_btz602-B18 – volume: 33 start-page: 5310 year: 2014 ident: 2023013112062320500_btz602-B32 article-title: Investigating the prediction ability of survival models based on both clinical and omics data: two case studies publication-title: Stat. Med doi: 10.1002/sim.6246 – volume: 16 start-page: 35. year: 2002 ident: 2023013112062320500_btz602-B11 article-title: Incremental value modeling publication-title: J. Interact. Market doi: 10.1002/dir.10035 – volume: 322 start-page: 352 year: 1990 ident: 2023013112062320500_btz602-B26 article-title: Levamisole and fluorouracil for adjuvant therapy of resected colon carcinoma publication-title: N. Engl. J. Med doi: 10.1056/NEJM199002083220602 – volume: 37 start-page: 2307 year: 2018 ident: 2023013112062320500_btz602-B14 article-title: A flexible and coherent test/estimation procedure based on restricted mean survival times for censored time-to-event data in randomized clinical trials publication-title: Stat. Med doi: 10.1002/sim.7661 – year: 2015 ident: 2023013112062320500_btz602-B42 – volume: 47 start-page: 1148 year: 2019 ident: 2023013112062320500_btz602-B4 article-title: Generalized random forests publication-title: Ann. Stat doi: 10.1214/18-AOS1709 – volume: 32 start-page: 3348 year: 2016 ident: 2023013112062320500_btz602-B44 article-title: TwoPhaseInd: an R package for estimating gene–treatment interactions and discovering predictive markers in randomized clinical trials publication-title: Bioinformatics doi: 10.1093/bioinformatics/btw391 |
| SSID | ssj0051444 ssj0005056 |
| Score | 2.408014 |
| Snippet | Abstract
Motivation
Personalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the... Personalized medicine often relies on accurate estimation of a treatment effect for specific subjects. This estimation can be based on the subject's baseline... |
| SourceID | proquest pubmed crossref oup |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 629 |
| Title | Non-parametric individual treatment effect estimation for survival data with random forests |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/31373350 https://www.proquest.com/docview/2268309406 |
| Volume | 36 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3dS8MwEA9jIPgifju_iOCLD3FtLunHo4hjCM6XDQY-lCZNYOBacd2D_vVelq0yRdTH0iQtd2nurne_3xFyGVqr8iS0TMexZgJsypTMFUtdo9sYDZzUDij8MIj6I3E_luMWCVZYmK8p_BS6alItSUQdcXFX1e_Rgj0SDbEjyx8-jj9rOgLHDOMv0BMQvqWtY_ZOAljhd35acs0yraHdvjmdC-PT2yZbS6-R3ng175CWKXfJhu8j-bZHngZVyRyJ99T1x9J00qCsaFNITn3lBnWsGh6uSPHV6GyOZwXuNupKRan7K0vRehXV1N3FsbN9MurdDW_7bNk1gWmQQc2kLgKjImltICC3spChTrk0YFIhkyJPpOA2ijDuwtgu1xEPTZGkXHMTJaDAwgFpl1VpjghVghsOEAtbKCGtVMC1LAo8I5XkqMQOESuJZXpJKe46WzxnPrUN2bqgMy_oDrlupr14To3fJlyhOv469mKltAy_FJf-yEtTzWcZOpoJOLrAqEMOvTabJSGEGFB8x_940gnZ5C74DkIWylPSrl_n5gw9lFqdL3blB9iD6ec |
| linkProvider | Oxford University Press |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Non-parametric+individual+treatment+effect+estimation+for+survival+data+with+random+forests&rft.jtitle=Bioinformatics&rft.au=Tabib%2C+Sami&rft.au=Larocque%2C+Denis&rft.date=2020-01-15&rft.pub=Oxford+University+Press&rft.issn=1367-4803&rft.eissn=1460-2059&rft.volume=36&rft.issue=2&rft.spage=629&rft.epage=636&rft_id=info:doi/10.1093%2Fbioinformatics%2Fbtz602&rft.externalDocID=10.1093%2Fbioinformatics%2Fbtz602 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1367-4803&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1367-4803&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1367-4803&client=summon |