Subgroups from regression trees with adjustment for prognostic effects and postselection inference
Identification of subgroups with differential treatment effects in randomized trials is attracting much attention. Many methods use regression tree algorithms. This article addresses 2 important questions arising from the subgroups: how to ensure that treatment effects in subgroups are not confounde...
Uložené v:
| Vydané v: | Statistics in medicine Ročník 38; číslo 4; s. 545 - 557 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
Wiley Subscription Services, Inc
20.02.2019
|
| Predmet: | |
| ISSN: | 0277-6715, 1097-0258, 1097-0258 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Identification of subgroups with differential treatment effects in randomized trials is attracting much attention. Many methods use regression tree algorithms. This article addresses 2 important questions arising from the subgroups: how to ensure that treatment effects in subgroups are not confounded with effects of prognostic variables and how to determine the statistical significance of treatment effects in the subgroups. We address the first question by selectively including linear prognostic effects in the subgroups in a regression tree model. The second question is more difficult because it falls within the subject of postselection inference. We use a bootstrap technique to calibrate normal‐theory t intervals so that their expected coverage probability, averaged over all the subgroups in a fitted model, approximates the desired confidence level. It can also provide simultaneous confidence intervals for all subgroups. The first solution is implemented in the GUIDE algorithm and is applicable to data with missing covariate values, 2 or more treatment arms, and outcomes subject to right censoring. Bootstrap calibration is applicable to any subgroup identification method; it is not restricted to regression tree models. Two real examples are used for illustration: a diabetes trial where the outcomes are completely observed but some covariate values are missing and a breast cancer trial where the outcome is right censored. |
|---|---|
| AbstractList | Identification of subgroups with differential treatment effects in randomized trials is attracting much attention. Many methods use regression tree algorithms. This article addresses 2 important questions arising from the subgroups: how to ensure that treatment effects in subgroups are not confounded with effects of prognostic variables and how to determine the statistical significance of treatment effects in the subgroups. We address the first question by selectively including linear prognostic effects in the subgroups in a regression tree model. The second question is more difficult because it falls within the subject of postselection inference. We use a bootstrap technique to calibrate normal-theory t intervals so that their expected coverage probability, averaged over all the subgroups in a fitted model, approximates the desired confidence level. It can also provide simultaneous confidence intervals for all subgroups. The first solution is implemented in the GUIDE algorithm and is applicable to data with missing covariate values, 2 or more treatment arms, and outcomes subject to right censoring. Bootstrap calibration is applicable to any subgroup identification method; it is not restricted to regression tree models. Two real examples are used for illustration: a diabetes trial where the outcomes are completely observed but some covariate values are missing and a breast cancer trial where the outcome is right censored.Identification of subgroups with differential treatment effects in randomized trials is attracting much attention. Many methods use regression tree algorithms. This article addresses 2 important questions arising from the subgroups: how to ensure that treatment effects in subgroups are not confounded with effects of prognostic variables and how to determine the statistical significance of treatment effects in the subgroups. We address the first question by selectively including linear prognostic effects in the subgroups in a regression tree model. The second question is more difficult because it falls within the subject of postselection inference. We use a bootstrap technique to calibrate normal-theory t intervals so that their expected coverage probability, averaged over all the subgroups in a fitted model, approximates the desired confidence level. It can also provide simultaneous confidence intervals for all subgroups. The first solution is implemented in the GUIDE algorithm and is applicable to data with missing covariate values, 2 or more treatment arms, and outcomes subject to right censoring. Bootstrap calibration is applicable to any subgroup identification method; it is not restricted to regression tree models. Two real examples are used for illustration: a diabetes trial where the outcomes are completely observed but some covariate values are missing and a breast cancer trial where the outcome is right censored. Identification of subgroups with differential treatment effects in randomized trials is attracting much attention. Many methods use regression tree algorithms. This article addresses 2 important questions arising from the subgroups: how to ensure that treatment effects in subgroups are not confounded with effects of prognostic variables and how to determine the statistical significance of treatment effects in the subgroups. We address the first question by selectively including linear prognostic effects in the subgroups in a regression tree model. The second question is more difficult because it falls within the subject of postselection inference. We use a bootstrap technique to calibrate normal‐theory t intervals so that their expected coverage probability, averaged over all the subgroups in a fitted model, approximates the desired confidence level. It can also provide simultaneous confidence intervals for all subgroups. The first solution is implemented in the GUIDE algorithm and is applicable to data with missing covariate values, 2 or more treatment arms, and outcomes subject to right censoring. Bootstrap calibration is applicable to any subgroup identification method; it is not restricted to regression tree models. Two real examples are used for illustration: a diabetes trial where the outcomes are completely observed but some covariate values are missing and a breast cancer trial where the outcome is right censored. Identification of subgroups with differential treatment effects in randomized trials is attracting much attention. Many methods use regression tree algorithms. This article addresses 2 important questions arising from the subgroups: how to ensure that treatment effects in subgroups are not confounded with effects of prognostic variables and how to determine the statistical significance of treatment effects in the subgroups. We address the first question by selectively including linear prognostic effects in the subgroups in a regression tree model. The second question is more difficult because it falls within the subject of postselection inference. We use a bootstrap technique to calibrate normal‐theory t intervals so that their expected coverage probability, averaged over all the subgroups in a fitted model, approximates the desired confidence level. It can also provide simultaneous confidence intervals for all subgroups. The first solution is implemented in the GUIDE algorithm and is applicable to data with missing covariate values, 2 or more treatment arms, and outcomes subject to right censoring. Bootstrap calibration is applicable to any subgroup identification method; it is not restricted to regression tree models. Two real examples are used for illustration: a diabetes trial where the outcomes are completely observed but some covariate values are missing and a breast cancer trial where the outcome is right censored. |
| Author | Wang, Shuaicheng Loh, Wei‐Yin Man, Michael |
| Author_xml | – sequence: 1 givenname: Wei‐Yin orcidid: 0000-0001-6983-2495 surname: Loh fullname: Loh, Wei‐Yin email: loh@stat.wisc.edu organization: University of Wisconsin‐Madison – sequence: 2 givenname: Michael surname: Man fullname: Man, Michael organization: Eli Lilly and Company – sequence: 3 givenname: Shuaicheng surname: Wang fullname: Wang, Shuaicheng organization: BioStat Solutions, Inc |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29671896$$D View this record in MEDLINE/PubMed |
| BookMark | eNp1kUFr3DAQhUVJaDZpob8gCHrJxRtJtiz7GELTBlJ6SHsWsjzaarGljUYm5N9X201aKOlpmOGb4b15p-QoxACEfOBszRkTl-jntWqVekNWnPWqYkJ2R2TFhFJVq7g8IaeIW8Y4l0K9JSeiL9Oub1dkuF-GTYrLDqlLcaYJNgkQfQw0JwCkjz7_pGbcLphnCJm6mOguxU2ImL2l4BzYjNSEke7KCGEq_X7dBwcJgoV35NiZCeH9cz0jP24-fb_-Ut19-3x7fXVX2bopmsdW1qblg3QcnHGj6ZpRDYMbBWsssLrtJQNobOtqMLxpukExKRzrFFOueKnPyMXhbpH3sABmPXu0ME0mQFxQi_IO2XU16wv68R90G5cUijoteKt6KZtGFOr8mVqGGUa9S3426Um_fK8A6wNgU0RM4LT12ezd52T8pDnT-3h0iUfv4_kr8c_Cy81X0OqAPvoJnv7L6fvbr7_5X1kun5E |
| CitedBy_id | crossref_primary_10_1002_sim_10163 crossref_primary_10_1177_17534666221107314 crossref_primary_10_1016_j_csda_2025_108142 crossref_primary_10_1002_sim_10167 crossref_primary_10_1007_s10985_024_09618_x crossref_primary_10_3233_IDA_205367 crossref_primary_10_1002_widm_1326 |
| Cites_doi | 10.1002/sim.6454 10.1002/sim.4289 10.1080/10543406.2013.856026 10.1080/10543406.2013.856024 10.1080/01621459.1987.10478408 10.1111/j.1464-5491.2004.01426.x 10.1200/JCO.2011.38.3729 10.7326/0003-4819-116-1-78 10.1214/aos/1176344247 10.1007/s11222-005-1311-z 10.1200/JCO.1994.12.10.2086 10.1111/insr.12016 10.2307/2986301 10.1186/s12874-016-0122-6 10.1080/01621459.1963.10500855 10.1002/sim.7020 10.1198/016214501753168271 10.1136/bmj.e1553 10.1002/sim.7416 10.1080/10543406.2013.856021 10.2202/1557-4679.1071 10.1056/NEJMsr077003 10.1023/A:1010933404324 10.1080/01621459.1981.10477634 10.1198/1061860032049 10.1002/sim.4322 10.1001/jamainternmed.2016.9138 10.1111/j.1467-9868.2005.00490.x 10.1002/sim.5933 |
| ContentType | Journal Article |
| Copyright | Copyright © 2018 John Wiley & Sons, Ltd. 2019 John Wiley & Sons, Ltd. |
| Copyright_xml | – notice: Copyright © 2018 John Wiley & Sons, Ltd. – notice: 2019 John Wiley & Sons, Ltd. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM K9. 7X8 |
| DOI | 10.1002/sim.7677 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Health & Medical Complete (Alumni) MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Health & Medical Complete (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic ProQuest Health & Medical Complete (Alumni) MEDLINE CrossRef |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Statistics Public Health |
| EISSN | 1097-0258 |
| EndPage | 557 |
| ExternalDocumentID | 29671896 10_1002_sim_7677 SIM7677 |
| Genre | article Research Support, U.S. Gov't, Non-P.H.S Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: National Institutes of Health funderid: 1P01CA180945-01 – fundername: National Science Foundation funderid: DMS-1305725 – fundername: NCI NIH HHS grantid: P01 CA180945 – fundername: NIH HHS grantid: 1P01CA180945-01 |
| GroupedDBID | --- .3N .GA 05W 0R~ 10A 123 1L6 1OB 1OC 1ZS 33P 3SF 3WU 4.4 4ZD 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5RE 5VS 66C 6PF 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHQN AAMMB AAMNL AANLZ AAONW AASGY AAWTL AAXRX AAYCA AAZKR ABCQN ABCUV ABIJN ABJNI ABOCM ABPVW ACAHQ ACCZN ACGFS ACPOU ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADOZA ADXAS ADZMN AEFGJ AEIGN AEIMD AENEX AEUYR AEYWJ AFBPY AFFPM AFGKR AFWVQ AFZJQ AGHNM AGXDD AGYGG AHBTC AHMBA AIDQK AIDYY AITYG AIURR AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALVPJ AMBMR AMVHM AMYDB ATUGU AUFTA AZBYB AZVAB BAFTC BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CS3 D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DU5 EBD EBS EJD EMOBN F00 F01 F04 F5P G-S G.N GNP GODZA H.T H.X HBH HGLYW HHY HHZ HZ~ IX1 J0M JPC KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LYRES MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ NNB O66 O9- OIG P2P P2W P2X P4D PALCI PQQKQ Q.N Q11 QB0 QRW R.K ROL RX1 RYL SUPJJ SV3 TN5 UB1 V2E W8V W99 WBKPD WH7 WIB WIH WIK WJL WOHZO WQJ WXSBR WYISQ XBAML XG1 XV2 ZZTAW ~IA ~WT AAYXX CITATION O8X ALUQN CGR CUY CVF ECM EIF NPM K9. 7X8 |
| ID | FETCH-LOGICAL-c3497-d653a61b5f1efafda84d7bbfd204ce036950ee4c6f3ea1448b7052f08707f8963 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 9 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000456205500004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0277-6715 1097-0258 |
| IngestDate | Thu Oct 02 12:01:52 EDT 2025 Tue Oct 07 05:23:55 EDT 2025 Mon Jul 21 05:59:50 EDT 2025 Tue Nov 18 22:32:41 EST 2025 Sat Nov 29 05:32:41 EST 2025 Tue Nov 11 03:12:24 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Keywords | precision medicine missing value differential treatment effect bootstrap |
| Language | English |
| License | Copyright © 2018 John Wiley & Sons, Ltd. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3497-d653a61b5f1efafda84d7bbfd204ce036950ee4c6f3ea1448b7052f08707f8963 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-6983-2495 |
| PMID | 29671896 |
| PQID | 2167955442 |
| PQPubID | 48361 |
| PageCount | 13 |
| ParticipantIDs | proquest_miscellaneous_2027588309 proquest_journals_2167955442 pubmed_primary_29671896 crossref_citationtrail_10_1002_sim_7677 crossref_primary_10_1002_sim_7677 wiley_primary_10_1002_sim_7677_SIM7677 |
| PublicationCentury | 2000 |
| PublicationDate | 20 February 2019 |
| PublicationDateYYYYMMDD | 2019-02-20 |
| PublicationDate_xml | – month: 02 year: 2019 text: 20 February 2019 day: 20 |
| PublicationDecade | 2010 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: New York |
| PublicationTitle | Statistics in medicine |
| PublicationTitleAlternate | Stat Med |
| PublicationYear | 2019 |
| Publisher | Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley Subscription Services, Inc |
| References | 2015; 34 2004; 22 1991; 1 2012; 344 1980; 29 2002; 12 2011; 30 2014; 24 2008; 4 2017; 177 2001; 45 2016; 16 2016; 35 1995; 5 2005; 67 1997; 7 1978; 6 2003; 12 2007; 357 1963; 58 2009; 52 2009; 10 1987; 82 2017; 36 1994; 12 1992; 116 1984 2017 1982 2005; 15 1972; 34 2011; 29 2014; 34 1981; 76 2014; 33 2001; 96 1994; 4 e_1_2_8_29_1 e_1_2_8_24_1 Loh WY (e_1_2_8_20_1) 1991; 1 e_1_2_8_25_1 e_1_2_8_27_1 Loh WY (e_1_2_8_11_1) 2002; 12 Loh WY (e_1_2_8_19_1) 1987; 82 Lawless JF (e_1_2_8_38_1) 1982 e_1_2_8_3_1 Loh WY (e_1_2_8_26_1) 1997; 7 e_1_2_8_2_1 e_1_2_8_5_1 Su X (e_1_2_8_28_1) 2009; 10 e_1_2_8_4_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 Riviere MK (e_1_2_8_31_1) 2017 e_1_2_8_21_1 Dijkman B (e_1_2_8_12_1) 2009; 52 e_1_2_8_23_1 e_1_2_8_40_1 e_1_2_8_17_1 Breslow N (e_1_2_8_37_1) 1972; 34 e_1_2_8_18_1 e_1_2_8_39_1 Breiman L (e_1_2_8_22_1) 1984 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_14_1 Chaudhuri P (e_1_2_8_41_1) 1994; 4 e_1_2_8_15_1 e_1_2_8_16_1 Chaudhuri P (e_1_2_8_35_1) 1995; 5 e_1_2_8_32_1 e_1_2_8_10_1 e_1_2_8_34_1 e_1_2_8_33_1 e_1_2_8_30_1 |
| References_xml | – volume: 34 start-page: 216 year: 1972 end-page: 217 article-title: Contribution to the discussion of regression models and life tables by D. R. Cox publication-title: J R Stat Soc B – volume: 4 year: 2008 article-title: Interaction trees with censored survival data publication-title: Int J Biostat – volume: 7 start-page: 815 year: 1997 end-page: 840 article-title: Split selection methods for classification trees publication-title: Stat Sin – volume: 36 start-page: 4446 year: 2017 end-page: 4454 article-title: Multiplicity considerations in subgroup analysis publication-title: Stat Med – volume: 67 start-page: 91 year: 2005 end-page: 108 article-title: Sparsity and smoothness via the fused lasso publication-title: J R Stat Soc B – volume: 12 start-page: 512 year: 2003 end-page: 530 article-title: Classification trees with bivariate linear discriminant node models publication-title: J Comput Graph Stat – volume: 5 start-page: 641 year: 1995 end-page: 666 article-title: Generalized regression trees publication-title: Stat Sin – volume: 35 start-page: 4837 year: 2016 end-page: 4855 article-title: Identification of subgroups with differential treatment effects for longitudinal and multiresponse variables publication-title: Stat Med – volume: 30 start-page: 2601 year: 2011 end-page: 2621 article-title: Subgroup identification based on differential effect search—a recursive partitioning method for establishing response to treatment in patient subpopulations publication-title: Stat Med – volume: 15 start-page: 231 year: 2005 end-page: 239 article-title: Tree‐structured subgroup analysis for censored survival data: validation of computationally inexpensive model selection criteria publication-title: Stat Comput – volume: 4 start-page: 143 year: 1994 end-page: 167 article-title: Piecewise‐polynomial regression trees publication-title: Stat Sin – volume: 12 start-page: 361 year: 2002 end-page: 386 article-title: Regression trees with unbiased variable selection and interaction detection publication-title: Stat Sin – volume: 29 start-page: 156 year: 1980 end-page: 163 article-title: The fitting of exponential, Weibull and extreme value distributions to complex censored survival data using GLIM publication-title: Appl Stat – volume: 10 start-page: 141 year: 2009 end-page: 158 article-title: Subgroup analysis via recursive partitioning publication-title: J Mach Learn Res – volume: 96 start-page: 589 year: 2001 end-page: 604 article-title: Classification trees with unbiased multiway splits publication-title: J Am Stat Assoc – volume: 16 start-page: 1 year: 2016 end-page: 15 article-title: Subgroup analyses in confirmatory clinical trials: time to be specific about their purposes publication-title: BMC Med Res Methodol – volume: 24 start-page: 110 year: 2014 end-page: 129 article-title: A Bayesian approach to subgroup identification publication-title: J Biopharm Stat – volume: 1 start-page: 477 year: 1991 end-page: 491 article-title: Bootstrap calibration for confidence interval construction and selection publication-title: Stat Sin – year: 1984 – year: 1982 – volume: 29 start-page: 4718 year: 2011 article-title: Prognostic or predictive? It's time to get back to definitions! publication-title: J Clin Oncol – volume: 45 start-page: 5 year: 2001 end-page: 32 article-title: Random forests publication-title: Mach Learn – volume: 33 start-page: 219 year: 2014 end-page: 237 article-title: Qualitative interaction trees: a tool to identify qualitative treatment‐subgroup interactions publication-title: Stat Med – volume: 357 start-page: 2189 year: 2007 end-page: 2194 article-title: Statistics in medicine—reporting of subgroup analyses in clinical trials publication-title: N Engl J Med – volume: 58 start-page: 415 year: 1963 end-page: 434 article-title: Problems in the analysis of survey data, and a proposal publication-title: J Am Stat Assoc – volume: 76 start-page: 231 year: 1981 end-page: 240 article-title: Covariance analysis of censored survival data using log‐linear analysis techniques publication-title: J Am Stat Assoc – volume: 24 start-page: 130 year: 2014 end-page: 153 article-title: Strategies for identifying predictive biomarkers and subgroups with enhanced treatment effect in clinical trials using SIDES publication-title: J Biopharm Stat – volume: 30 start-page: 2867 year: 2011 end-page: 2880 article-title: Subgroup identification from randomized clinical trial data publication-title: Stat Med – volume: 34 start-page: 1818 year: 2015 end-page: 1833 article-title: A regression tree approach to identifying subgroups with differential treatment effects publication-title: Stat Med – volume: 82 start-page: 155 year: 1987 end-page: 162 article-title: Calibrating confidence coefficients publication-title: J Am Stat Assoc – volume: 6 start-page: 701 year: 1978 end-page: 726 article-title: Nonparametric inference for a family of counting processes publication-title: Ann Stat – volume: 24 start-page: 72 year: 2014 end-page: 93 article-title: An overview of statistical planning to address subgroups in confirmatory clinical trials publication-title: J Biopharm Stat – volume: 177 start-page: 561 issue: 4 year: 2017 end-page: 562 article-title: The challenges of generating evidence to support precision medicine publication-title: JAMA Intern Med – volume: 116 start-page: 78 year: 1992 end-page: 84 article-title: A consumer's guide to subgroup analyses publication-title: Ann Intern Med – volume: 344 year: 2012 article-title: Credibility of claims of subgroup effects in randomised controlled trials: systematic review publication-title: BMJ – volume: 34 start-page: 329 year: 2014 end-page: 370 article-title: Fifty years of classification and regression trees (with discussion) publication-title: Int Stat Rev – year: 2017 – volume: 52 start-page: 515 year: 2009 end-page: 522 article-title: How to work with a subgroup analysis publication-title: Can J Surg – volume: 22 start-page: 399 year: 2004 end-page: 405 article-title: A long‐term comparison of pioglitazone and gliclazide in patients with type 2 diabetes mellitus: a randomized, double‐blind, parallel‐group comparison trial publication-title: Diabet Med – volume: 12 start-page: 2086 year: 1994 end-page: 2093 article-title: Randomized 2 × 2 trial evaluating hormonal treatment and the duration of chemotherapy in node‐positive breast cancer patients publication-title: J Clin Oncol – ident: e_1_2_8_6_1 doi: 10.1002/sim.6454 – ident: e_1_2_8_4_1 doi: 10.1002/sim.4289 – ident: e_1_2_8_18_1 doi: 10.1080/10543406.2013.856026 – ident: e_1_2_8_30_1 doi: 10.1080/10543406.2013.856024 – volume: 4 start-page: 143 year: 1994 ident: e_1_2_8_41_1 article-title: Piecewise‐polynomial regression trees publication-title: Stat Sin – volume: 82 start-page: 155 year: 1987 ident: e_1_2_8_19_1 article-title: Calibrating confidence coefficients publication-title: J Am Stat Assoc doi: 10.1080/01621459.1987.10478408 – volume: 34 start-page: 216 year: 1972 ident: e_1_2_8_37_1 article-title: Contribution to the discussion of regression models and life tables by D. R. Cox publication-title: J R Stat Soc B – volume: 10 start-page: 141 year: 2009 ident: e_1_2_8_28_1 article-title: Subgroup analysis via recursive partitioning publication-title: J Mach Learn Res – ident: e_1_2_8_32_1 doi: 10.1111/j.1464-5491.2004.01426.x – ident: e_1_2_8_8_1 doi: 10.1200/JCO.2011.38.3729 – ident: e_1_2_8_13_1 doi: 10.7326/0003-4819-116-1-78 – ident: e_1_2_8_36_1 doi: 10.1214/aos/1176344247 – ident: e_1_2_8_27_1 doi: 10.1007/s11222-005-1311-z – volume-title: SIDES: Subgroup Identification Based on Differential Effect Search year: 2017 ident: e_1_2_8_31_1 – ident: e_1_2_8_10_1 doi: 10.1200/JCO.1994.12.10.2086 – volume: 12 start-page: 361 year: 2002 ident: e_1_2_8_11_1 article-title: Regression trees with unbiased variable selection and interaction detection publication-title: Stat Sin – ident: e_1_2_8_25_1 doi: 10.1111/insr.12016 – ident: e_1_2_8_33_1 doi: 10.2307/2986301 – volume-title: Statistical Models and Methods for Lifetime Data year: 1982 ident: e_1_2_8_38_1 – ident: e_1_2_8_16_1 doi: 10.1186/s12874-016-0122-6 – volume: 5 start-page: 641 year: 1995 ident: e_1_2_8_35_1 article-title: Generalized regression trees publication-title: Stat Sin – ident: e_1_2_8_21_1 doi: 10.1080/01621459.1963.10500855 – ident: e_1_2_8_5_1 doi: 10.1002/sim.7020 – ident: e_1_2_8_24_1 doi: 10.1198/016214501753168271 – ident: e_1_2_8_15_1 doi: 10.1136/bmj.e1553 – volume: 1 start-page: 477 year: 1991 ident: e_1_2_8_20_1 article-title: Bootstrap calibration for confidence interval construction and selection publication-title: Stat Sin – ident: e_1_2_8_40_1 doi: 10.1002/sim.7416 – ident: e_1_2_8_9_1 doi: 10.1080/10543406.2013.856021 – volume-title: Classification and Regression Trees year: 1984 ident: e_1_2_8_22_1 – ident: e_1_2_8_7_1 doi: 10.2202/1557-4679.1071 – ident: e_1_2_8_17_1 doi: 10.1056/NEJMsr077003 – ident: e_1_2_8_29_1 doi: 10.1023/A:1010933404324 – ident: e_1_2_8_34_1 doi: 10.1080/01621459.1981.10477634 – ident: e_1_2_8_23_1 doi: 10.1198/1061860032049 – ident: e_1_2_8_3_1 doi: 10.1002/sim.4322 – volume: 7 start-page: 815 year: 1997 ident: e_1_2_8_26_1 article-title: Split selection methods for classification trees publication-title: Stat Sin – ident: e_1_2_8_14_1 doi: 10.1001/jamainternmed.2016.9138 – ident: e_1_2_8_39_1 doi: 10.1111/j.1467-9868.2005.00490.x – ident: e_1_2_8_2_1 doi: 10.1002/sim.5933 – volume: 52 start-page: 515 year: 2009 ident: e_1_2_8_12_1 article-title: How to work with a subgroup analysis publication-title: Can J Surg |
| SSID | ssj0011527 |
| Score | 2.3354192 |
| Snippet | Identification of subgroups with differential treatment effects in randomized trials is attracting much attention. Many methods use regression tree algorithms.... |
| SourceID | proquest pubmed crossref wiley |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 545 |
| SubjectTerms | Algorithms bootstrap Bootstrap method Breast cancer Clinical trials differential treatment effect Humans Kaplan-Meier Estimate Medical prognosis Medical statistics Medical treatment missing value Models, Statistical Precision medicine Prognosis Randomized Controlled Trials as Topic - methods Regression Analysis Treatment Outcome |
| Title | Subgroups from regression trees with adjustment for prognostic effects and postselection inference |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fsim.7677 https://www.ncbi.nlm.nih.gov/pubmed/29671896 https://www.proquest.com/docview/2167955442 https://www.proquest.com/docview/2027588309 |
| Volume | 38 |
| WOSCitedRecordID | wos000456205500004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1097-0258 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0011527 issn: 0277-6715 databaseCode: DRFUL dateStart: 19960101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3_a9QwFH9styGDofP8stM5Ioj-1K2Xpk3y45geE7YxnJP7rSRNIhPtjavn3-97TVsZKgz2U6F9Sdu895JP8r4BvEEE7CvPQ-LVNCTChizRIneJtFZpl1WVa2sDfjmV5-dqPtcXnVclxcLE_BDDgRtpRjtfk4Ib2xz-SRraXP84kIWU67DBUWzFCDbef5pdnQ42hL5gKxkpCznN-9SzKT_s295ejP5CmLcBa7vizB7d51t34GGHM9lRFIzHsObrMTw46yzpY9iO53UshiGNYYtQZ0za_AQsTidtuEfDKPyELf3X6C5bMzJiN4xOb5lx31ZN66TOEPky8vSqF9QD65xEmKkdu8FbTVtsh5pf9_GFT-Fq9uHz8UnSFWNIqkxombgiz0wxtXmY-mCCM0o4ZGhwPBVUc6zQeeq9qIqQeYO7NGVlmvOQ4nwgg0I1fwajelH7XWAKIZyxUhmujAiBG6FCbnBvlSlTaG4n8K7nSll1mcqpYMb3MuZY5iWOZ0njOYHXA-VNzM7xD5q9nrFlp59Nycn6hEhKcOxieIyaReYSU_vFCmnIoqtUluoJPI8CMbyEa5Qo_KsJvG35_t-3l5cfz-j64q6EL2ELMZluo-bTPRj9XK78K9isfqEMLPdhXc7VfifpvwEVLALU |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Rb9QwDLbGDbFJaMCxwbEBQULsqayXpk0inqbBaRN3JwQb2luVNsm0afSmK8fvx27aogmQkHiq1DppG9vJFzu2AV4jAnal4z5yauwjUfgk0iK1kSwKpW1SlrapDfh1KudzdX6uP63Buy4WJuSH6A1upBnNfE0KTgbpg19ZQ-vLb29lJuUdWBcoRekA1t9_npxNeydCV7GVvJSZHKdd7tmYH3Rtb69Gv0HM24i1WXImD_7rYx_CVos02WEQjUew5qoh3Ju1vvQh3A8WOxYCkYawSbgzpG1-DAVOKE3AR80oAIUt3UU4MFsxcmPXjOy3zNirVd0cU2eIfRmd9aoW1ANrj4kwU1l2g7fqptwONb_sIgy34Wzy4fToOGrLMURlIrSMbJYmJhsXqR87b7w1Slhkqbc8FlR1LNNp7JwoM584g_s0Vcg45T7GGUF6hYq-A4NqUbmnwBSCOFNIZbgywntuhPKpwd1VokymeTGC_Y4tednmKqeSGdd5yLLMcxzPnMZzBK96ypuQn-MPNHsdZ_NWQ-uck_8JsZTg2EX_GHWLHCamcosV0pBPV6kk1iN4EiSifwnXKFL4VyN40zD-r2_Pv5zM6PrsXwlfwsbx6WyaT0_mH3dhExGabmLo4z0YfF-u3HO4W_5AeVi-aAX-J27IBdw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3_a9QwFH_MTcZAnN50Ozc1wnA_deulaZOwn2TzcHg7hnOy30raJDLR3nH1_Pt9r2krwwkDfyq0L2mb917ySd43gH1EwK503EdOjXwkCp9EWqQ2kkWhtE3K0ja1Ab9M5HSqrq_1xQocd7EwIT9Ef-BGmtHM16Tgbm790Z-sofXNj0OZSfkA1kSqM9TKtdNP46tJb0ToKraSlTKTo7TLPRvzo67t7dXoL4h5G7E2S854878-9gk8bpEmexdE4ymsuGoA6-etLX0Aj8KJHQuBSAPYINwZ0jZvQYETShPwUTMKQGEL9zU4zFaMzNg1o_NbZuy3Zd24qTPEvox8vaoZ9cBaNxFmKsvmeKtuyu1Q85suwvAZXI3ffz75ELXlGKIyEVpGNksTk42K1I-cN94aJSyy1FseC6o6luk0dk6UmU-cwX2aKmScch_jjCC9QkV_DqvVrHI7wBSCOFNIZbgywntuhPKpwd1VokymeTGEg44tednmKqeSGd_zkGWZ5zieOY3nEN70lPOQn-MOmr2Os3mroXXOyf6EWEpw7KJ_jLpFBhNTudkSacimq1QS6yFsB4noX8I1ihT-1RDeNoz_59vzy7Nzur64L-FrWL84HeeTs-nHXdhAgKabEPp4D1Z_LpbuJTwsf6E4LF618v4b8tQFVw |
| 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=Subgroups+from+regression+trees+with+adjustment+for+prognostic+effects+and+postselection+inference&rft.jtitle=Statistics+in+medicine&rft.au=Loh%2C+Wei%E2%80%90Yin&rft.au=Man%2C+Michael&rft.au=Wang%2C+Shuaicheng&rft.date=2019-02-20&rft.issn=0277-6715&rft.eissn=1097-0258&rft.volume=38&rft.issue=4&rft.spage=545&rft.epage=557&rft_id=info:doi/10.1002%2Fsim.7677&rft.externalDBID=10.1002%252Fsim.7677&rft.externalDocID=SIM7677 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0277-6715&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0277-6715&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0277-6715&client=summon |