Change surface regression for nonlinear subgroup identification with application to warfarin pharmacogenomics data
Pharmacogenomics stands as a pivotal driver toward personalized medicine, aiming to optimize drug efficacy while minimizing adverse effects by uncovering the impact of genetic variations on inter-individual outcome variability. Despite its promise, the intricate landscape of drug metabolism introduc...
Uložené v:
| Vydané v: | Biometrics Ročník 81; číslo 1 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
07.01.2025
|
| Predmet: | |
| ISSN: | 0006-341X, 1541-0420, 1541-0420 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Pharmacogenomics stands as a pivotal driver toward personalized medicine, aiming to optimize drug efficacy while minimizing adverse effects by uncovering the impact of genetic variations on inter-individual outcome variability. Despite its promise, the intricate landscape of drug metabolism introduces complexity, where the correlation between drug response and genes can be shaped by numerous nongenetic factors, often exhibiting heterogeneity across diverse subpopulations. This challenge is particularly pronounced in datasets such as the International Warfarin Pharmacogenetic Consortium (IWPC), which encompasses diverse patient information from multiple nations. To capture the between-patient heterogeneity in dosing requirement, we formulate a novel change surface model as a model-based approach for multiple subgroup identification in complex datasets. A key feature of our approach is its ability to accommodate nonlinear subgroup divisions, providing a clearer understanding of dynamic drug-gene associations. Furthermore, our model effectively handles high-dimensional data through a doubly penalized approach, ensuring both interpretability and adaptability. We propose an iterative 2-stage method that combines a change point detection technique in the first stage with a smoothed local adaptive majorize-minimization algorithm for surface regression in the second stage. Performance of the proposed methods is evaluated through extensive numerical studies. Application of our method to the IWPC dataset leads to significant new findings, where 3 subgroups subject to different pharmacogenomic relationships are identified, contributing valuable insights into the complex dynamics of drug-gene associations in patients. |
|---|---|
| AbstractList | Pharmacogenomics stands as a pivotal driver toward personalized medicine, aiming to optimize drug efficacy while minimizing adverse effects by uncovering the impact of genetic variations on inter-individual outcome variability. Despite its promise, the intricate landscape of drug metabolism introduces complexity, where the correlation between drug response and genes can be shaped by numerous nongenetic factors, often exhibiting heterogeneity across diverse subpopulations. This challenge is particularly pronounced in datasets such as the International Warfarin Pharmacogenetic Consortium (IWPC), which encompasses diverse patient information from multiple nations. To capture the between-patient heterogeneity in dosing requirement, we formulate a novel change surface model as a model-based approach for multiple subgroup identification in complex datasets. A key feature of our approach is its ability to accommodate nonlinear subgroup divisions, providing a clearer understanding of dynamic drug-gene associations. Furthermore, our model effectively handles high-dimensional data through a doubly penalized approach, ensuring both interpretability and adaptability. We propose an iterative 2-stage method that combines a change point detection technique in the first stage with a smoothed local adaptive majorize-minimization algorithm for surface regression in the second stage. Performance of the proposed methods is evaluated through extensive numerical studies. Application of our method to the IWPC dataset leads to significant new findings, where 3 subgroups subject to different pharmacogenomic relationships are identified, contributing valuable insights into the complex dynamics of drug-gene associations in patients. Pharmacogenomics stands as a pivotal driver toward personalized medicine, aiming to optimize drug efficacy while minimizing adverse effects by uncovering the impact of genetic variations on inter-individual outcome variability. Despite its promise, the intricate landscape of drug metabolism introduces complexity, where the correlation between drug response and genes can be shaped by numerous nongenetic factors, often exhibiting heterogeneity across diverse subpopulations. This challenge is particularly pronounced in datasets such as the International Warfarin Pharmacogenetic Consortium (IWPC), which encompasses diverse patient information from multiple nations. To capture the between-patient heterogeneity in dosing requirement, we formulate a novel change surface model as a model-based approach for multiple subgroup identification in complex datasets. A key feature of our approach is its ability to accommodate nonlinear subgroup divisions, providing a clearer understanding of dynamic drug-gene associations. Furthermore, our model effectively handles high-dimensional data through a doubly penalized approach, ensuring both interpretability and adaptability. We propose an iterative 2-stage method that combines a change point detection technique in the first stage with a smoothed local adaptive majorize-minimization algorithm for surface regression in the second stage. Performance of the proposed methods is evaluated through extensive numerical studies. Application of our method to the IWPC dataset leads to significant new findings, where 3 subgroups subject to different pharmacogenomic relationships are identified, contributing valuable insights into the complex dynamics of drug-gene associations in patients.Pharmacogenomics stands as a pivotal driver toward personalized medicine, aiming to optimize drug efficacy while minimizing adverse effects by uncovering the impact of genetic variations on inter-individual outcome variability. Despite its promise, the intricate landscape of drug metabolism introduces complexity, where the correlation between drug response and genes can be shaped by numerous nongenetic factors, often exhibiting heterogeneity across diverse subpopulations. This challenge is particularly pronounced in datasets such as the International Warfarin Pharmacogenetic Consortium (IWPC), which encompasses diverse patient information from multiple nations. To capture the between-patient heterogeneity in dosing requirement, we formulate a novel change surface model as a model-based approach for multiple subgroup identification in complex datasets. A key feature of our approach is its ability to accommodate nonlinear subgroup divisions, providing a clearer understanding of dynamic drug-gene associations. Furthermore, our model effectively handles high-dimensional data through a doubly penalized approach, ensuring both interpretability and adaptability. We propose an iterative 2-stage method that combines a change point detection technique in the first stage with a smoothed local adaptive majorize-minimization algorithm for surface regression in the second stage. Performance of the proposed methods is evaluated through extensive numerical studies. Application of our method to the IWPC dataset leads to significant new findings, where 3 subgroups subject to different pharmacogenomic relationships are identified, contributing valuable insights into the complex dynamics of drug-gene associations in patients. |
| Author | Li, Yaguang Li, Jialiang Liu, Pan |
| Author_xml | – sequence: 1 givenname: Pan surname: Liu fullname: Liu, Pan – sequence: 2 givenname: Yaguang surname: Li fullname: Li, Yaguang – sequence: 3 givenname: Jialiang orcidid: 0000-0002-9704-4135 surname: Li fullname: Li, Jialiang |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39817854$$D View this record in MEDLINE/PubMed |
| BookMark | eNp1kc1LAzEQxYNUtFavHiVHL1uTTbLbHKX4BYIXBW9Lkp1tU3aTNclS_O_d2upB8DTM8Htv4L0zNHHeAUKXlMwpkexGW98lczNsFNBCHqEpFZxmhOdkgqaEkCJjnL6forMYN-MqBclP0CmTC1ouBJ-isFwrtwIch9AoAzjAKkCM1jvc-IDHb611oMII6FXwQ49tDS7ZxhqVdtTWpjVWfd_-HJLHWzWaBetwv1ahU8avwPnOmohrldQ5Om5UG-HiMGfo7f7udfmYPb88PC1vnzPDmEhZKRlZlLkCUxDKC85ZrfPcCBC8KHKiuaHSEMM1y3UjJbBG1lrQmkoiSl3XbIau97598B8DxFR1NhpoW-XAD7FiVBSCEUbKEb06oIPuoK76YDsVPqufnEZgvgdM8DEGaH4RSqpdEdW-iOpQxCjgfwTGpu-AUlC2_U_2Be4fks4 |
| CitedBy_id | crossref_primary_10_1002_sim_70179 |
| Cites_doi | 10.1214/20-AOS2017 10.1080/01621459.2019.1705308 10.1002/cpt.2714 10.1002/sim.8976 10.1038/s41573-019-0024-5 10.1080/10618600.2020.1763808 10.1186/s12859-017-1863-x 10.1002/sim.7064 10.1198/106186008X319331 10.1007/s40264-018-0767-7 10.2202/1557-4679.1071 10.1001/archinte.165.10.1095 10.1080/10618600.2022.2032723 10.1093/bioinformatics/btr159 10.1214/18-AOS1757 10.1002/sim.6454 10.1111/rssb.12108 10.1038/jhg.2013.40 10.1038/s41576-022-00572-8 10.1002/sim.4289 10.1111/rssb.12216 10.1038/tpj.2012.45 10.1080/01621459.2016.1166115 10.1016/j.jaad.2008.12.039 10.1080/07350015.2020.1740712 10.1073/pnas.1507583112 10.7326/0003-4819-145-10-200611210-00007 10.1002/sim.6343 10.1016/S1471-4914(01)01986-4 10.1186/1479-7364-2-5-318 10.1002/sim.7236 10.1038/clpt.2011.355 10.1002/sim.9229 10.1111/bcp.14608 10.1080/01621459.2021.1915319 10.1093/ajcp/aqw049 10.1214/17-AOS1568 10.1038/clpt.2011.185 10.1038/73439 10.1002/sim.8214 10.1002/sim.4322 10.1056/NEJMoa0809329 10.1214/009053607000000415 10.1002/sim.7638 10.1111/j.1467-9868.2009.00718.x 10.2217/pgs.09.154 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2025. Published by Oxford University Press on behalf of The International Biometric Society. |
| Copyright_xml | – notice: The Author(s) 2025. Published by Oxford University Press on behalf of The International Biometric Society. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1093/biomtc/ujae169 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | CrossRef MEDLINE - Academic MEDLINE |
| 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 | Statistics Biology Mathematics |
| EISSN | 1541-0420 |
| ExternalDocumentID | 39817854 10_1093_biomtc_ujae169 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 72331005 – fundername: Fundamental Research Funds for the Central Universities grantid: WK2040000068 – fundername: MOE grantid: A-8000016-0-0 |
| GroupedDBID | --- -~X .3N .4S .DC .GA 05W 0R~ 10A 1OC 23N 33P 36B 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5HH 5LA 5RE 5VS 66C 6J9 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 A8Z AAESR AAEVG AAHBH AANLZ AAONW AAUAY AAXRX AAYCA AAYXX AAZKR ABCQN ABCUV ABDBF ABDFA ABEJV ABEML ABFAN ABGNP ABJNI ABLJU ABMNT ABPPZ ABPVW ABXVV ABYWD ACAHQ ACCZN ACFBH ACGFO ACGFS ACGOD ACIWK ACMTB ACNCT ACPOU ACPRK ACSCC ACTMH ACXBN ACXQS ADBBV ADEOM ADIPN ADIZJ ADKYN ADMGS ADNBA ADOZA ADVOB ADXAS ADZMN AEGXH AEIGN AEIMD AENEX AEOTA AEUYR AFBPY AFEBI AFGKR AFVYC AFWVQ AFZJQ AGTJU AHGBF AHMBA AIAGR AIURR AJBYB AJNCP AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS AMBMR AMYDB ATUGU AUFTA AZBYB AZVAB BAFTC BCRHZ BDRZF BENPR BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CITATION CS3 D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DXH EAP EBS ESX F00 F01 F04 F5P FD6 G-S G.N GODZA H.T H.X H13 HZI HZ~ IX1 J0M JAC K48 KOP LATKE LC2 LC3 LEEKS LITHE LOXES LP6 LP7 LUTES LYRES MK4 MRFUL MRSTM MSFUL MSSTM MVM MXFUL MXSTM N04 N05 N9A NF~ O66 O8X O9- OIG OJZSN OWPYF P2P P2W P2X P4D PQQKQ Q.N Q11 QB0 R.K ROX RX1 RXW SUPJJ TN5 TUS UB1 V8K W8V W99 WBKPD WH7 WIH WIK WOHZO WQJ WYISQ X6Y XBAML XG1 XSW ZZTAW ~02 ~IA ~KM ~WT ACUHS AGORE ALIPV CGR CUY CVF ECM EIF GS5 NPM 7X8 |
| ID | FETCH-LOGICAL-c335t-7930872aec60146443db22c5e546620b4c19c0c4b32bf99e3f9db51d19057bdd3 |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001396681400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0006-341X 1541-0420 |
| IngestDate | Sat Sep 27 17:10:25 EDT 2025 Mon Jul 21 05:57:27 EDT 2025 Tue Nov 18 22:36:17 EST 2025 Sat Nov 29 08:10:59 EST 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | change surface pharmacogenomics warfarin dosing treatment recommendation personalized medicine subgroup identification |
| Language | English |
| License | https://creativecommons.org/licenses/by-nc/4.0 The Author(s) 2025. Published by Oxford University Press on behalf of The International Biometric Society. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c335t-7930872aec60146443db22c5e546620b4c19c0c4b32bf99e3f9db51d19057bdd3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-9704-4135 |
| OpenAccessLink | https://academic.oup.com/biometrics/article-pdf/81/1/ujae169/61458930/ujae169.pdf |
| PMID | 39817854 |
| PQID | 3156530307 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_3156530307 pubmed_primary_39817854 crossref_primary_10_1093_biomtc_ujae169 crossref_citationtrail_10_1093_biomtc_ujae169 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-Jan-07 |
| PublicationDateYYYYMMDD | 2025-01-07 |
| PublicationDate_xml | – month: 01 year: 2025 text: 2025-Jan-07 day: 07 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Biometrics |
| PublicationTitleAlternate | Biometrics |
| PublicationYear | 2025 |
| References | Fan (2025030423594851000_bib8) 2017; 79 Holbrook (2025030423594851000_bib12) 2005; 165 Vamathevan (2025030423594851000_bib43) 2019; 18 Spear (2025030423594851000_bib35) 2001; 7 Lee (2025030423594851000_bib18) 2021; 49 Asiimwe (2025030423594851000_bib1) 2021; 87 Steingrimsson (2025030423594851000_bib37) 2019; 38 Motsinger (2025030423594851000_bib26) 2006; 2 Limdi (2025030423594851000_bib21) 2010; 115 Yuan (2025030423594851000_bib46) 2022; 41 Zhang (2025030423594851000_bib48) 2021; 117 Sim (2025030423594851000_bib33) 2013; 13 Pirmohamed (2025030423594851000_bib29) 2023; 24 Stack (2025030423594851000_bib36) 2016; 145 Horowitz (2025030423594851000_bib13) 2007; 35 Taylor (2025030423594851000_bib41) 2015; 112 Chen (2025030423594851000_bib2) 2015; 34 Roden (2025030423594851000_bib31) 2006; 145 Peng (2025030423594851000_bib28) 2022; 31 Sissung (2025030423594851000_bib34) 2010; 11 Su (2025030423594851000_bib38) 2008; 4 Lipkovich (2025030423594851000_bib22) 2017; 36 Johnson (2025030423594851000_bib15) 2011; 90 Lee (2025030423594851000_bib17) 2019; 42 Yuan (2025030423594851000_bib45) 2018; 37 Lee (2025030423594851000_bib19) 2016; 78 Tang (2025030423594851000_bib40) 2021; 116 Chen (2025030423594851000_bib3) 2021; 30 Huang (2025030423594851000_bib14) 2017; 36 Lipkovich (2025030423594851000_bib23) 2011; 30 Nazarian (2025030423594851000_bib27) 2009; 61 International Warfarin Pharmacogenetics Consortium (2025030423594851000_bib4) 2009; 360 Yu (2025030423594851000_bib44) 2021; 39 Loh (2025030423594851000_bib24) 2015; 34 Thompson (2025030423594851000_bib42) 2023; 113 Tan (2025030423594851000_bib39) 2019; 47 Scherf (2025030423594851000_bib32) 2000; 24 Fan (2025030423594851000_bib7) 2017; 112 Foster (2025030423594851000_bib11) 2011; 30 Fan (2025030423594851000_bib9) 2018; 46 Martin (2025030423594851000_bib25) 2012; 91 Doliner (2025030423594851000_bib6) 2019; 7 Ravikumar (2025030423594851000_bib30) 2009; 71 Zeileis (2025030423594851000_bib47) 2008; 17 Fong (2025030423594851000_bib10) 2017; 18 Lee (2025030423594851000_bib16) 2013; 58 Cosgun (2025030423594851000_bib5) 2011; 27 Li (2025030423594851000_bib20) 2021; 40 |
| References_xml | – volume: 49 start-page: 1656 year: 2021 ident: 2025030423594851000_bib18 article-title: Factor-driven two-regime regression publication-title: The Annals of Statistics doi: 10.1214/20-AOS2017 – volume: 116 start-page: 1280 year: 2021 ident: 2025030423594851000_bib40 article-title: Individualized multidirectional variable selection publication-title: Journal of the American Statistical Association doi: 10.1080/01621459.2019.1705308 – volume: 113 start-page: 585 year: 2023 ident: 2025030423594851000_bib42 article-title: Personalizing direct oral anticoagulant therapy for a diverse population: role of race, kidney function, drug interactions, and pharmacogenetics publication-title: Clinical Pharmacology & Therapeutics doi: 10.1002/cpt.2714 – volume: 40 start-page: 3440 year: 2021 ident: 2025030423594851000_bib20 article-title: Multithreshold change plane model: estimation theory and applications in subgroup identification publication-title: Statistics in Medicine doi: 10.1002/sim.8976 – volume: 18 start-page: 463 year: 2019 ident: 2025030423594851000_bib43 article-title: Applications of machine learning in drug discovery and development publication-title: Nature Reviews Drug Discovery doi: 10.1038/s41573-019-0024-5 – volume: 30 start-page: 43 year: 2021 ident: 2025030423594851000_bib3 article-title: Identifying heterogeneous effect using latent supervised clustering with adaptive fusion publication-title: Journal of Computational and Graphical Statistics doi: 10.1080/10618600.2020.1763808 – volume: 18 start-page: 1 year: 2017 ident: 2025030423594851000_bib10 article-title: chngpt: Threshold regression model estimation and inference publication-title: BMC Bioinformatics doi: 10.1186/s12859-017-1863-x – volume: 36 start-page: 136 year: 2017 ident: 2025030423594851000_bib22 article-title: Tutorial in biostatistics: data-driven subgroup identification and analysis in clinical trials publication-title: Statistics in Medicine doi: 10.1002/sim.7064 – volume: 17 start-page: 492 year: 2008 ident: 2025030423594851000_bib47 article-title: Model-based recursive partitioning publication-title: Journal of Computational and Graphical Statistics doi: 10.1198/106186008X319331 – volume: 42 start-page: 657 year: 2019 ident: 2025030423594851000_bib17 article-title: Development of a controlled vocabulary-based adverse drug reaction signal dictionary for multicenter electronic health record-based pharmacovigilance publication-title: Drug Safety doi: 10.1007/s40264-018-0767-7 – volume: 4 start-page: 1 year: 2008 ident: 2025030423594851000_bib38 article-title: Interaction trees with censored survival data publication-title: The International Journal of Biostatistics doi: 10.2202/1557-4679.1071 – volume: 165 start-page: 1095 year: 2005 ident: 2025030423594851000_bib12 article-title: Systematic overview of warfarin and its drug and food interactions publication-title: Archives of Internal Medicine doi: 10.1001/archinte.165.10.1095 – volume: 31 start-page: 824 year: 2022 ident: 2025030423594851000_bib28 article-title: A generalized quantile tree method for subgroup identification publication-title: Journal of Computational and Graphical Statistics doi: 10.1080/10618600.2022.2032723 – volume: 27 start-page: 1384 year: 2011 ident: 2025030423594851000_bib5 article-title: High-dimensional pharmacogenetic prediction of a continuous trait using machine learning techniques with application to warfarin dose prediction in African Americans publication-title: Bioinformatics doi: 10.1093/bioinformatics/btr159 – volume: 47 start-page: 2567 year: 2019 ident: 2025030423594851000_bib39 article-title: Doubly penalized estimation in additive regression with high-dimensional data publication-title: The Annals of Statistics doi: 10.1214/18-AOS1757 – volume: 34 start-page: 1818 year: 2015 ident: 2025030423594851000_bib24 article-title: A regression tree approach to identifying subgroups with differential treatment effects publication-title: Statistics in Medicine doi: 10.1002/sim.6454 – volume: 78 start-page: 193 year: 2016 ident: 2025030423594851000_bib19 article-title: The lasso for high dimensional regression with a possible change point publication-title: Journal of the Royal Statistical Society: Series B (Statistical Methodology) doi: 10.1111/rssb.12108 – volume: 58 start-page: 334 year: 2013 ident: 2025030423594851000_bib16 article-title: Pharmacogenetics of warfarin: challenges and opportunities publication-title: Journal of Human Genetics doi: 10.1038/jhg.2013.40 – volume: 24 start-page: 350 year: 2023 ident: 2025030423594851000_bib29 article-title: Pharmacogenomics: current status and future perspectives publication-title: Nature Reviews Genetics doi: 10.1038/s41576-022-00572-8 – volume: 30 start-page: 2601 year: 2011 ident: 2025030423594851000_bib23 article-title: Subgroup identification based on differential effect search—a recursive partitioning method for establishing response to treatment in patient subpopulations publication-title: Statistics in Medicine doi: 10.1002/sim.4289 – volume: 79 start-page: 1565 year: 2017 ident: 2025030423594851000_bib8 article-title: Concordance-assisted learning for estimating optimal individualized treatment regimes publication-title: Journal of the Royal Statistical Society: Series B (Statistical Methodology) doi: 10.1111/rssb.12216 – volume: 13 start-page: 1 year: 2013 ident: 2025030423594851000_bib33 article-title: Pharmacogenomics of drug-metabolizing enzymes: a recent update on clinical implications and endogenous effects publication-title: The Pharmacogenomics Journal doi: 10.1038/tpj.2012.45 – volume: 112 start-page: 769 year: 2017 ident: 2025030423594851000_bib7 article-title: Change-plane analysis for subgroup detection and sample size calculation publication-title: Journal of the American Statistical Association doi: 10.1080/01621459.2016.1166115 – volume: 61 start-page: 325 year: 2009 ident: 2025030423594851000_bib27 article-title: Warfarin-induced skin necrosis publication-title: Journal of the American Academy of Dermatology doi: 10.1016/j.jaad.2008.12.039 – volume: 39 start-page: 953 year: 2021 ident: 2025030423594851000_bib44 article-title: Threshold regression with a threshold boundary publication-title: Journal of Business & Economic Statistics doi: 10.1080/07350015.2020.1740712 – volume: 112 start-page: 7629 year: 2015 ident: 2025030423594851000_bib41 article-title: Statistical learning and selective inference publication-title: Proceedings of the National Academy of Sciences doi: 10.1073/pnas.1507583112 – volume: 7 start-page: 260 year: 2019 ident: 2025030423594851000_bib6 article-title: Treatments to prevent primary venous ulceration after deep venous thrombosis publication-title: Journal of Vascular Surgery: Venous and Lymphatic Disorders – volume: 145 start-page: 749 year: 2006 ident: 2025030423594851000_bib31 article-title: Pharmacogenomics: challenges and opportunities publication-title: Annals of Internal Medicine doi: 10.7326/0003-4819-145-10-200611210-00007 – volume: 115 start-page: 3827 year: 2010 ident: 2025030423594851000_bib21 article-title: Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups publication-title: Blood, The Journal of the American Society of Hematology – volume: 34 start-page: 317 year: 2015 ident: 2025030423594851000_bib2 article-title: A PRIM approach to predictive-signature development for patient stratification publication-title: Statistics in Medicine doi: 10.1002/sim.6343 – volume: 7 start-page: 201 year: 2001 ident: 2025030423594851000_bib35 article-title: Clinical application of pharmacogenetics publication-title: Trends in Molecular Medicine doi: 10.1016/S1471-4914(01)01986-4 – volume: 2 start-page: 1 year: 2006 ident: 2025030423594851000_bib26 article-title: Multifactor dimensionality reduction: an analysis strategy for modelling and detecting gene-gene interactions in human genetics and pharmacogenomics studies publication-title: Human Genomics doi: 10.1186/1479-7364-2-5-318 – volume: 36 start-page: 1414 year: 2017 ident: 2025030423594851000_bib14 article-title: Patient subgroup identification for clinical drug development publication-title: Statistics in Medicine doi: 10.1002/sim.7236 – volume: 91 start-page: 734 year: 2012 ident: 2025030423594851000_bib25 article-title: Clinical Pharmacogenetics Implementation Consortium Guidelines for HLA-B Genotype and Abacavir Dosing publication-title: Clinical Pharmacology & Therapeutics doi: 10.1038/clpt.2011.355 – volume: 41 start-page: 180 year: 2022 ident: 2025030423594851000_bib46 article-title: Set-regression with applications to subgroup analysis publication-title: Statistics in Medicine doi: 10.1002/sim.9229 – volume: 87 start-page: 1717 year: 2021 ident: 2025030423594851000_bib1 article-title: Warfarin dosing algorithms: s systematic review publication-title: British Journal of Clinical Pharmacology doi: 10.1111/bcp.14608 – volume: 117 start-page: 2222 year: 2021 ident: 2025030423594851000_bib48 article-title: Single-index thresholding in quantile regression publication-title: Journal of the American Statistical Association doi: 10.1080/01621459.2021.1915319 – volume: 145 start-page: 671 year: 2016 ident: 2025030423594851000_bib36 article-title: Warfarin pharmacogenetics reevaluated: subgroup analysis reveals a likely underestimation of the maximum pharmacogenetic benefit by clinical trials publication-title: American Journal of Clinical Pathology doi: 10.1093/ajcp/aqw049 – volume: 46 start-page: 814 year: 2018 ident: 2025030423594851000_bib9 article-title: I-LAMM for sparse learning: simultaneous control of algorithmic complexity and statistical error publication-title: The Annals of Statistics doi: 10.1214/17-AOS1568 – volume: 90 start-page: 625 year: 2011 ident: 2025030423594851000_bib15 article-title: Clinical Pharmacogenetics Implementation Consortium Guidelines for CYP2C9 and VKORC1 genotypes and warfarin dosing publication-title: Clinical Pharmacology & Therapeutics doi: 10.1038/clpt.2011.185 – volume: 24 start-page: 236 year: 2000 ident: 2025030423594851000_bib32 article-title: A gene expression database for the molecular pharmacology of cancer publication-title: Nature Genetics doi: 10.1038/73439 – volume: 38 start-page: 3974 year: 2019 ident: 2025030423594851000_bib37 article-title: Subgroup identification using covariate-adjusted interaction trees publication-title: Statistics in Medicine doi: 10.1002/sim.8214 – volume: 30 start-page: 2867 year: 2011 ident: 2025030423594851000_bib11 article-title: Subgroup identification from randomized clinical trial data publication-title: Statistics in Medicine doi: 10.1002/sim.4322 – volume: 360 start-page: 753 year: 2009 ident: 2025030423594851000_bib4 article-title: Estimation of the warfarin dose with clinical and pharmacogenetic data publication-title: New England Journal of Medicine doi: 10.1056/NEJMoa0809329 – volume: 35 start-page: 2589 year: 2007 ident: 2025030423594851000_bib13 article-title: Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions publication-title: The Annals of Statistics doi: 10.1214/009053607000000415 – volume: 37 start-page: 1830 year: 2018 ident: 2025030423594851000_bib45 article-title: Subgroup analysis with semiparametric models toward precision medicine publication-title: Statistics in Medicine doi: 10.1002/sim.7638 – volume: 71 start-page: 1009 year: 2009 ident: 2025030423594851000_bib30 article-title: Sparse additive models publication-title: Journal of the Royal Statistical Society: Series B (Statistical Methodology) doi: 10.1111/j.1467-9868.2009.00718.x – volume: 11 start-page: 89 year: 2010 ident: 2025030423594851000_bib34 article-title: Clinical pharmacology and pharmacogenetics in a genomics era: the DMET platform publication-title: Pharmacogenomics doi: 10.2217/pgs.09.154 |
| SSID | ssj0009502 |
| Score | 2.4457445 |
| Snippet | Pharmacogenomics stands as a pivotal driver toward personalized medicine, aiming to optimize drug efficacy while minimizing adverse effects by uncovering the... |
| SourceID | proquest pubmed crossref |
| SourceType | Aggregation Database Index Database Enrichment Source |
| SubjectTerms | Algorithms Anticoagulants - administration & dosage Computer Simulation Humans Models, Statistical Nonlinear Dynamics Pharmacogenetics - methods Pharmacogenetics - statistics & numerical data Precision Medicine Regression Analysis Warfarin - administration & dosage Warfarin - pharmacokinetics |
| Title | Change surface regression for nonlinear subgroup identification with application to warfarin pharmacogenomics data |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39817854 https://www.proquest.com/docview/3156530307 |
| Volume | 81 |
| WOSCitedRecordID | wos001396681400001&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: 1541-0420 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009502 issn: 0006-341X databaseCode: DRFUL dateStart: 19990101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3fb9MwELbYBtJ4QFAYlB-TkRA8TNES_0jiRwSrEBoFoU4qT5HtOFURdCVtYPz3nHNu2gKTxgMvUWW5TtTv6-Xsu_uOkGexMiKX1kaJNc6HGWWktcyitI1qxcxmyrTNJrLhMB-P1YdQQrBo2wlks1l-caHm_xVqGAOwfensP8DdLQoD8BlAhyvADtcrAY_1AkeLpq609U1RJpjqihmFM5TG0DVMMG1Jx9G0DBlDyAWsdlvHtb13-kPDYrVPOA9K117a9asXeA61bevAsC_n96r_na9-Om3QVV0n_7QZBJ_0pNHhvdkNvp225y6TzcMIJtvDiGzTfgoYEAxDLe4vY8HoYp-WTXL9YctR58qrEPhm74Pms3YJtnXZls0evi8GZ6enxehkPHo-_xb5jmI-8h7aq-yQPZZJBRZv7_VHmLghyYwZqavH6yQ9-THe9DjccttluWQf0vojo9vkVthI0JdIgDvkmpv1yA1sLfqzR26-6_R4Fz2y7_cUKMl9l9TIEBoYQtcMocAQ2jGErhhCtxlCPUPoBkPo8pyuGEJ_Zwj1DLlHzgYno1dvotB6I7Kcy2UEVjvOM6adTb26kBC8NIxZ6aRIUxYbYRNlYysMZ6ZSyvFKlUYmJbiXMjNlyQ_ILjyue0BoxaTlQlR5mlRCpSI3LpawS3ZO55VgSZ9Eq1-3sEGX3rdH-VJgfgQvEI0ioNEnL7r5c1RkuXTm0xVYBRhNHwnTM3feLAqewD6G-_dbn9xHFLu1uMqTLJfi4RW-_Yjsr_8Fj8nusm7cE3LdfgdI60Oyk43zw0C7XymgoOk |
| linkProvider | Wiley-Blackwell |
| 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=Change+surface+regression+for+nonlinear+subgroup+identification+with+application+to+warfarin+pharmacogenomics+data&rft.jtitle=Biometrics&rft.au=Liu%2C+Pan&rft.au=Li%2C+Yaguang&rft.au=Li%2C+Jialiang&rft.date=2025-01-07&rft.issn=1541-0420&rft.eissn=1541-0420&rft.volume=81&rft.issue=1&rft_id=info:doi/10.1093%2Fbiomtc%2Fujae169&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0006-341X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0006-341X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0006-341X&client=summon |