Estimating inverse-probability weights for longitudinal data with dropout or truncation: The xtrccipw command
Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, 6: 241-258) developed a method to conduct re...
Gespeichert in:
| Veröffentlicht in: | The Stata journal Jg. 17; H. 2; S. 253 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
01.06.2017
|
| Schlagworte: | |
| ISSN: | 1536-867X |
| Online-Zugang: | Weitere Angaben |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005,
6: 241-258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipw command can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study. |
|---|---|
| AbstractList | Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241-258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipw command can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study.Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241-258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipw command can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study. Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, 6: 241-258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipw command can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study. |
| Author | Hudgens, Michael G Herring, Amy H Daza, Eric J |
| Author_xml | – sequence: 1 givenname: Eric J surname: Daza fullname: Daza, Eric J organization: Stanford Prevention Research Center, Stanford University, Stanford, CA – sequence: 2 givenname: Michael G surname: Hudgens fullname: Hudgens, Michael G organization: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC – sequence: 3 givenname: Amy H surname: Herring fullname: Herring, Amy H organization: Department of Biostatistics and Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29755297$$D View this record in MEDLINE/PubMed |
| BookMark | eNo1kEtLAzEUhbOo2If-AReSpZvRPCaT0Z2U-oCCmwruhrymjcwkY5Kx9t-bYoXLuXD4OHDOHEycdwaAK4xuMeb8DjNa1RX_wBzlQwSRCZgdzeLoTsE8xk-ESo4JOQdTcs8ZyzID_Som24tk3RZa921CNMUQvBTSdjYd4N7Y7S5F2PoAO--2No3aOtFBLZKAe5t2UAc_-DHBTKQwOpXDvHuAm52BPykoZYc9VL7vhdMX4KwVXTSXp78A70-rzfKlWL89vy4f14UqS5IKozCRTBqGKZemolSgqq0w1jXFumoN11xKXVLJ6lyUt6gVWFBSasZrZkpNFuDmLzdX-RpNTE1vozJdJ5zxY2wIojVHJI-R0esTOsre6GYIeY5waP4nIr8UP2tc |
| CitedBy_id | crossref_primary_10_1093_geronb_gbae195 crossref_primary_10_1186_s12874_018_0499_5 crossref_primary_10_1016_j_archger_2024_105701 crossref_primary_10_1111_pirs_12751 crossref_primary_10_2519_jospt_2024_12864 crossref_primary_10_1007_s00415_022_11203_x crossref_primary_10_1007_s11121_019_01050_0 crossref_primary_10_1016_j_socscimed_2025_117976 crossref_primary_10_1164_rccm_202308_1452OC crossref_primary_10_1017_S1041610220000447 crossref_primary_10_1016_j_spc_2023_06_001 crossref_primary_10_1016_j_bbih_2023_100705 crossref_primary_10_1002_pst_2258 crossref_primary_10_3389_fdgth_2025_1435917 |
| ContentType | Journal Article |
| DBID | NPM 7X8 |
| DOI | 10.1177/1536867X1701700202 |
| DatabaseName | PubMed MEDLINE - Academic |
| DatabaseTitle | PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed |
| 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 | no_fulltext_linktorsrc |
| Discipline | Statistics Mathematics |
| ExternalDocumentID | 29755297 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NICHD NIH HHS grantid: P2C HD050924 – fundername: NIAID NIH HHS grantid: P30 AI050410 – fundername: NIAID NIH HHS grantid: R01 AI085073 – fundername: NCCDPHP CDC HHS grantid: U48 DP001944 – fundername: ACL HHS grantid: U48DP001944 – fundername: NICHD NIH HHS grantid: R24 HD050924 – fundername: NIAID NIH HHS grantid: R01 AI029168 – fundername: NCCDPHP CDC HHS grantid: U48 DP000059 – fundername: NIEHS NIH HHS grantid: R01 ES020619 |
| GroupedDBID | -TM -~X 0R~ 123 51Z 54M AADUE AAGLT AAHPS AAPII AAQXI AARIX AATAA ABCCA ABDBF ABEHJ ABIDT ABKRH ABPNF ABRHV ABTDE ABUJY ACCVC ACDXX ACHQT ACJER ACOFE ACOXC ACROE ACSIQ ACUHS ACUIR ADEBD ADRRZ AEEHM AENEX AESZF AEWDL AEWHI AEXNY AFKRG AFMOU AFQAA AFUIA AGKLV AGNHF AHDMH AJGYC AJUZI AJVBE ALFTD ALMA_UNASSIGNED_HOLDINGS AMNSR ANDLU ARTOV BPACV DOPDO DV7 EAP EBS EJD ESX F5P FHBDP GROUPED_SAGE_PREMIER_JOURNAL_COLLECTION H13 IAO INS ITC J8X JAG NPM OJV OK1 SAFTQ SAUOL SCNPE SFC SJN YHZ ZPPRI 7X8 AJHME |
| ID | FETCH-LOGICAL-c442t-ec12b5be5137be633a06f611d831d6fe7d7bbd43b582027f0fa1a324d5785e4d2 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 19 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000406436600002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1536-867X |
| IngestDate | Sat Sep 27 19:47:06 EDT 2025 Mon Jul 21 06:03:13 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | xtrccipw weighted GEE st0474 generalized estimating equations dropout inverse-probability weights missing at random truncation longitudinal data |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c442t-ec12b5be5137be633a06f611d831d6fe7d7bbd43b582027f0fa1a324d5785e4d2 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://journals.sagepub.com/doi/pdf/10.1177/1536867X1701700202 |
| PMID | 29755297 |
| PQID | 2038702122 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2038702122 pubmed_primary_29755297 |
| PublicationCentury | 2000 |
| PublicationDate | 2017-06-01 |
| PublicationDateYYYYMMDD | 2017-06-01 |
| PublicationDate_xml | – month: 06 year: 2017 text: 2017-06-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | The Stata journal |
| PublicationTitleAlternate | Stata J |
| PublicationYear | 2017 |
| SSID | ssj0047122 |
| Score | 2.2323184 |
| Snippet | Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 253 |
| Title | Estimating inverse-probability weights for longitudinal data with dropout or truncation: The xtrccipw command |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/29755297 https://www.proquest.com/docview/2038702122 |
| Volume | 17 |
| WOSCitedRecordID | wos000406436600002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1JS8NAFB7UeqgHl7rVjRG8BppMMpN6EZEWD7b0oJBbyCyRgp3UJrH6730vSetJELwMIRvJzJs337zle4TcKCRRE4I7RgawQelzmFJJYByA3kwZJfxQVonCT2I8DqOoP2kMbnkTVrnSiZWi1plCGzls0hmIFiha727-7mDVKPSuNiU0NkmLAZTBkC4Rrb0IoHcrLwJMau6EXESrpBlMN4dzeArpyAViJu93iFktNcO9_37kPtltQCa9r6XigGwY2yE7ozVDa94hbUSZNUnzIZkN4ACv2Fc6tRioYRysNFNzeH_RZWU_zSkAXPqWYYWjUmM1LYrxpRRNuVRjtYWyoHBHsShtbQi8pSCF9LNYKDWdLyn8wyyx-oi8DAfPD49OU4fBUb7vFY5RricDaQKXCWk4Y0mPp9x1dchczVMjtJBS-0wGIZpS0l6auAkANY1EOsbX3jHZspk1p4SaIDSJJ6ViAt6tQcUZkQKEEzBUuueqLrledWwMco7Oi8SarMzjn67tkpN6dOJ5TcgRY3ZwAM3ZH54-J20PV-bKkHJBWinMcnNJttUHdPriqhIgaMeT0TfQM9Fo |
| linkProvider | ProQuest |
| 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=Estimating+inverse-probability+weights+for+longitudinal+data+with+dropout+or+truncation%3A+The+xtrccipw+command&rft.jtitle=The+Stata+journal&rft.au=Daza%2C+Eric+J&rft.au=Hudgens%2C+Michael+G&rft.au=Herring%2C+Amy+H&rft.date=2017-06-01&rft.issn=1536-867X&rft.volume=17&rft.issue=2&rft.spage=253&rft_id=info:doi/10.1177%2F1536867X1701700202&rft_id=info%3Apmid%2F29755297&rft_id=info%3Apmid%2F29755297&rft.externalDocID=29755297 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1536-867X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1536-867X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1536-867X&client=summon |