A review of the use of controlled multiple imputation in randomised controlled trials with missing outcome data
Missing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the primary missing-data assumptions should be conducted, followed by sensitivity analysis under alternative justified assumptions to assess the robu...
Saved in:
Published in | BMC medical research methodology Vol. 21; no. 1; p. 72 |
---|---|
Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
England
BioMed Central Ltd
15.04.2021
BioMed Central BMC |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Missing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the primary missing-data assumptions should be conducted, followed by sensitivity analysis under alternative justified assumptions to assess the robustness of results. Controlled Multiple Imputation (MI) procedures, including delta-based and reference-based approaches, have been developed for analysis under missing-not-at-random assumptions. However, it is unclear how often these methods are used, how they are reported, and what their impact is on trial results. This review evaluates the current use and reporting of MI and controlled MI in RCTs.
A targeted review of phase II-IV RCTs (non-cluster randomised) published in two leading general medical journals (The Lancet and New England Journal of Medicine) between January 2014 and December 2019 using MI. Data was extracted on imputation methods, analysis status, and reporting of results. Results of primary and sensitivity analyses for trials using controlled MI analyses were compared.
A total of 118 RCTs (9% of published RCTs) used some form of MI. MI under missing-at-random was used in 110 trials; this was for primary analysis in 43/118 (36%), and in sensitivity analysis for 70/118 (59%) (3 used in both). Sixteen studies performed controlled MI (1.3% of published RCTs), either with a delta-based (n = 9) or reference-based approach (n = 7). Controlled MI was mostly used in sensitivity analysis (n = 14/16). Two trials used controlled MI for primary analysis, including one reporting no sensitivity analysis whilst the other reported similar results without imputation. Of the 14 trials using controlled MI in sensitivity analysis, 12 yielded comparable results to the primary analysis whereas 2 demonstrated contradicting results. Only 5/110 (5%) trials using missing-at-random MI and 5/16 (31%) trials using controlled MI reported complete details on MI methods.
Controlled MI enabled the impact of accessible contextually relevant missing data assumptions to be examined on trial results. The use of controlled MI is increasing but is still infrequent and poorly reported where used. There is a need for improved reporting on the implementation of MI analyses and choice of controlled MI parameters. |
---|---|
AbstractList | Missing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the primary missing-data assumptions should be conducted, followed by sensitivity analysis under alternative justified assumptions to assess the robustness of results. Controlled Multiple Imputation (MI) procedures, including delta-based and reference-based approaches, have been developed for analysis under missing-not-at-random assumptions. However, it is unclear how often these methods are used, how they are reported, and what their impact is on trial results. This review evaluates the current use and reporting of MI and controlled MI in RCTs. A targeted review of phase II-IV RCTs (non-cluster randomised) published in two leading general medical journals (The Lancet and New England Journal of Medicine) between January 2014 and December 2019 using MI. Data was extracted on imputation methods, analysis status, and reporting of results. Results of primary and sensitivity analyses for trials using controlled MI analyses were compared. A total of 118 RCTs (9% of published RCTs) used some form of MI. MI under missing-at-random was used in 110 trials; this was for primary analysis in 43/118 (36%), and in sensitivity analysis for 70/118 (59%) (3 used in both). Sixteen studies performed controlled MI (1.3% of published RCTs), either with a delta-based (n = 9) or reference-based approach (n = 7). Controlled MI was mostly used in sensitivity analysis (n = 14/16). Two trials used controlled MI for primary analysis, including one reporting no sensitivity analysis whilst the other reported similar results without imputation. Of the 14 trials using controlled MI in sensitivity analysis, 12 yielded comparable results to the primary analysis whereas 2 demonstrated contradicting results. Only 5/110 (5%) trials using missing-at-random MI and 5/16 (31%) trials using controlled MI reported complete details on MI methods. Controlled MI enabled the impact of accessible contextually relevant missing data assumptions to be examined on trial results. The use of controlled MI is increasing but is still infrequent and poorly reported where used. There is a need for improved reporting on the implementation of MI analyses and choice of controlled MI parameters. Missing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the primary missing-data assumptions should be conducted, followed by sensitivity analysis under alternative justified assumptions to assess the robustness of results. Controlled Multiple Imputation (MI) procedures, including delta-based and reference-based approaches, have been developed for analysis under missing-not-at-random assumptions. However, it is unclear how often these methods are used, how they are reported, and what their impact is on trial results. This review evaluates the current use and reporting of MI and controlled MI in RCTs. A targeted review of phase II-IV RCTs (non-cluster randomised) published in two leading general medical journals (The Lancet and New England Journal of Medicine) between January 2014 and December 2019 using MI. Data was extracted on imputation methods, analysis status, and reporting of results. Results of primary and sensitivity analyses for trials using controlled MI analyses were compared. A total of 118 RCTs (9% of published RCTs) used some form of MI. MI under missing-at-random was used in 110 trials; this was for primary analysis in 43/118 (36%), and in sensitivity analysis for 70/118 (59%) (3 used in both). Sixteen studies performed controlled MI (1.3% of published RCTs), either with a delta-based (n = 9) or reference-based approach (n = 7). Controlled MI was mostly used in sensitivity analysis (n = 14/16). Two trials used controlled MI for primary analysis, including one reporting no sensitivity analysis whilst the other reported similar results without imputation. Of the 14 trials using controlled MI in sensitivity analysis, 12 yielded comparable results to the primary analysis whereas 2 demonstrated contradicting results. Only 5/110 (5%) trials using missing-at-random MI and 5/16 (31%) trials using controlled MI reported complete details on MI methods. Controlled MI enabled the impact of accessible contextually relevant missing data assumptions to be examined on trial results. The use of controlled MI is increasing but is still infrequent and poorly reported where used. There is a need for improved reporting on the implementation of MI analyses and choice of controlled MI parameters. Abstract Background Missing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the primary missing-data assumptions should be conducted, followed by sensitivity analysis under alternative justified assumptions to assess the robustness of results. Controlled Multiple Imputation (MI) procedures, including delta-based and reference-based approaches, have been developed for analysis under missing-not-at-random assumptions. However, it is unclear how often these methods are used, how they are reported, and what their impact is on trial results. This review evaluates the current use and reporting of MI and controlled MI in RCTs. Methods A targeted review of phase II-IV RCTs (non-cluster randomised) published in two leading general medical journals (The Lancet and New England Journal of Medicine) between January 2014 and December 2019 using MI. Data was extracted on imputation methods, analysis status, and reporting of results. Results of primary and sensitivity analyses for trials using controlled MI analyses were compared. Results A total of 118 RCTs (9% of published RCTs) used some form of MI. MI under missing-at-random was used in 110 trials; this was for primary analysis in 43/118 (36%), and in sensitivity analysis for 70/118 (59%) (3 used in both). Sixteen studies performed controlled MI (1.3% of published RCTs), either with a delta-based (n = 9) or reference-based approach (n = 7). Controlled MI was mostly used in sensitivity analysis (n = 14/16). Two trials used controlled MI for primary analysis, including one reporting no sensitivity analysis whilst the other reported similar results without imputation. Of the 14 trials using controlled MI in sensitivity analysis, 12 yielded comparable results to the primary analysis whereas 2 demonstrated contradicting results. Only 5/110 (5%) trials using missing-at-random MI and 5/16 (31%) trials using controlled MI reported complete details on MI methods. Conclusions Controlled MI enabled the impact of accessible contextually relevant missing data assumptions to be examined on trial results. The use of controlled MI is increasing but is still infrequent and poorly reported where used. There is a need for improved reporting on the implementation of MI analyses and choice of controlled MI parameters. Background Missing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the primary missing-data assumptions should be conducted, followed by sensitivity analysis under alternative justified assumptions to assess the robustness of results. Controlled Multiple Imputation (MI) procedures, including delta-based and reference-based approaches, have been developed for analysis under missing-not-at-random assumptions. However, it is unclear how often these methods are used, how they are reported, and what their impact is on trial results. This review evaluates the current use and reporting of MI and controlled MI in RCTs. Methods A targeted review of phase II-IV RCTs (non-cluster randomised) published in two leading general medical journals (The Lancet and New England Journal of Medicine) between January 2014 and December 2019 using MI. Data was extracted on imputation methods, analysis status, and reporting of results. Results of primary and sensitivity analyses for trials using controlled MI analyses were compared. Results A total of 118 RCTs (9% of published RCTs) used some form of MI. MI under missing-at-random was used in 110 trials; this was for primary analysis in 43/118 (36%), and in sensitivity analysis for 70/118 (59%) (3 used in both). Sixteen studies performed controlled MI (1.3% of published RCTs), either with a delta-based (n = 9) or reference-based approach (n = 7). Controlled MI was mostly used in sensitivity analysis (n = 14/16). Two trials used controlled MI for primary analysis, including one reporting no sensitivity analysis whilst the other reported similar results without imputation. Of the 14 trials using controlled MI in sensitivity analysis, 12 yielded comparable results to the primary analysis whereas 2 demonstrated contradicting results. Only 5/110 (5%) trials using missing-at-random MI and 5/16 (31%) trials using controlled MI reported complete details on MI methods. Conclusions Controlled MI enabled the impact of accessible contextually relevant missing data assumptions to be examined on trial results. The use of controlled MI is increasing but is still infrequent and poorly reported where used. There is a need for improved reporting on the implementation of MI analyses and choice of controlled MI parameters. Abstract Background Missing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the primary missing-data assumptions should be conducted, followed by sensitivity analysis under alternative justified assumptions to assess the robustness of results. Controlled Multiple Imputation (MI) procedures, including delta-based and reference-based approaches, have been developed for analysis under missing-not-at-random assumptions. However, it is unclear how often these methods are used, how they are reported, and what their impact is on trial results. This review evaluates the current use and reporting of MI and controlled MI in RCTs. Methods A targeted review of phase II-IV RCTs (non-cluster randomised) published in two leading general medical journals (The Lancet and New England Journal of Medicine) between January 2014 and December 2019 using MI. Data was extracted on imputation methods, analysis status, and reporting of results. Results of primary and sensitivity analyses for trials using controlled MI analyses were compared. Results A total of 118 RCTs (9% of published RCTs) used some form of MI. MI under missing-at-random was used in 110 trials; this was for primary analysis in 43/118 (36%), and in sensitivity analysis for 70/118 (59%) (3 used in both). Sixteen studies performed controlled MI (1.3% of published RCTs), either with a delta-based ( n = 9) or reference-based approach ( n = 7). Controlled MI was mostly used in sensitivity analysis ( n = 14/16). Two trials used controlled MI for primary analysis, including one reporting no sensitivity analysis whilst the other reported similar results without imputation. Of the 14 trials using controlled MI in sensitivity analysis, 12 yielded comparable results to the primary analysis whereas 2 demonstrated contradicting results. Only 5/110 (5%) trials using missing-at-random MI and 5/16 (31%) trials using controlled MI reported complete details on MI methods. Conclusions Controlled MI enabled the impact of accessible contextually relevant missing data assumptions to be examined on trial results. The use of controlled MI is increasing but is still infrequent and poorly reported where used. There is a need for improved reporting on the implementation of MI analyses and choice of controlled MI parameters. Background Missing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the primary missing-data assumptions should be conducted, followed by sensitivity analysis under alternative justified assumptions to assess the robustness of results. Controlled Multiple Imputation (MI) procedures, including delta-based and reference-based approaches, have been developed for analysis under missing-not-at-random assumptions. However, it is unclear how often these methods are used, how they are reported, and what their impact is on trial results. This review evaluates the current use and reporting of MI and controlled MI in RCTs. Methods A targeted review of phase II-IV RCTs (non-cluster randomised) published in two leading general medical journals (The Lancet and New England Journal of Medicine) between January 2014 and December 2019 using MI. Data was extracted on imputation methods, analysis status, and reporting of results. Results of primary and sensitivity analyses for trials using controlled MI analyses were compared. Results A total of 118 RCTs (9% of published RCTs) used some form of MI. MI under missing-at-random was used in 110 trials; this was for primary analysis in 43/118 (36%), and in sensitivity analysis for 70/118 (59%) (3 used in both). Sixteen studies performed controlled MI (1.3% of published RCTs), either with a delta-based (n = 9) or reference-based approach (n = 7). Controlled MI was mostly used in sensitivity analysis (n = 14/16). Two trials used controlled MI for primary analysis, including one reporting no sensitivity analysis whilst the other reported similar results without imputation. Of the 14 trials using controlled MI in sensitivity analysis, 12 yielded comparable results to the primary analysis whereas 2 demonstrated contradicting results. Only 5/110 (5%) trials using missing-at-random MI and 5/16 (31%) trials using controlled MI reported complete details on MI methods. Conclusions Controlled MI enabled the impact of accessible contextually relevant missing data assumptions to be examined on trial results. The use of controlled MI is increasing but is still infrequent and poorly reported where used. There is a need for improved reporting on the implementation of MI analyses and choice of controlled MI parameters. Keywords: Controlled multiple imputation, Randomised controlled trials, Missing data, Sensitivity analysis, Multiple imputation BACKGROUNDMissing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the primary missing-data assumptions should be conducted, followed by sensitivity analysis under alternative justified assumptions to assess the robustness of results. Controlled Multiple Imputation (MI) procedures, including delta-based and reference-based approaches, have been developed for analysis under missing-not-at-random assumptions. However, it is unclear how often these methods are used, how they are reported, and what their impact is on trial results. This review evaluates the current use and reporting of MI and controlled MI in RCTs. METHODSA targeted review of phase II-IV RCTs (non-cluster randomised) published in two leading general medical journals (The Lancet and New England Journal of Medicine) between January 2014 and December 2019 using MI. Data was extracted on imputation methods, analysis status, and reporting of results. Results of primary and sensitivity analyses for trials using controlled MI analyses were compared. RESULTSA total of 118 RCTs (9% of published RCTs) used some form of MI. MI under missing-at-random was used in 110 trials; this was for primary analysis in 43/118 (36%), and in sensitivity analysis for 70/118 (59%) (3 used in both). Sixteen studies performed controlled MI (1.3% of published RCTs), either with a delta-based (n = 9) or reference-based approach (n = 7). Controlled MI was mostly used in sensitivity analysis (n = 14/16). Two trials used controlled MI for primary analysis, including one reporting no sensitivity analysis whilst the other reported similar results without imputation. Of the 14 trials using controlled MI in sensitivity analysis, 12 yielded comparable results to the primary analysis whereas 2 demonstrated contradicting results. Only 5/110 (5%) trials using missing-at-random MI and 5/16 (31%) trials using controlled MI reported complete details on MI methods. CONCLUSIONSControlled MI enabled the impact of accessible contextually relevant missing data assumptions to be examined on trial results. The use of controlled MI is increasing but is still infrequent and poorly reported where used. There is a need for improved reporting on the implementation of MI analyses and choice of controlled MI parameters. |
ArticleNumber | 72 |
Audience | Academic |
Author | Cornelius, Victoria R Szigeti, Matyas Cro, Suzie Tan, Ping-Tee Van Vogt, Eleanor |
Author_xml | – sequence: 1 givenname: Ping-Tee surname: Tan fullname: Tan, Ping-Tee organization: School of Public Health Imperial College London, Medical School Building, St Mary's Hospital, Norfolk Place, London, UK – sequence: 2 givenname: Suzie orcidid: 0000-0002-6113-1173 surname: Cro fullname: Cro, Suzie email: s.cro@imperial.ac.uk organization: Imperial Clinical Trials Unit, Imperial College London, Stadium House, 68 Wood Lane, London, UK. s.cro@imperial.ac.uk – sequence: 3 givenname: Eleanor surname: Van Vogt fullname: Van Vogt, Eleanor organization: Imperial Clinical Trials Unit, Imperial College London, Stadium House, 68 Wood Lane, London, UK – sequence: 4 givenname: Matyas surname: Szigeti fullname: Szigeti, Matyas organization: Imperial Clinical Trials Unit, Imperial College London, Stadium House, 68 Wood Lane, London, UK – sequence: 5 givenname: Victoria R surname: Cornelius fullname: Cornelius, Victoria R organization: Imperial Clinical Trials Unit, Imperial College London, Stadium House, 68 Wood Lane, London, UK |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33858355$$D View this record in MEDLINE/PubMed |
BookMark | eNptUk1v3CAQtapUzUf7B3qokHrpxSlgMPhSaRX1I1KkXtozwjDeZWXDFnCi_Pvi3TTdrSoOjGbevOEN77I688FDVb0l-JoQ2X5MhErBakxJjQltSd2-qC4IE6SmVMqzo_i8ukxpizERsmlfVedNI7lsOL-owgpFuHfwgMKA8gbQnGAJTfA5hnEEi6Z5zG43AnLTbs46u-CR8yhqb8PkUkEcgXN0ekzoweUNKsXk_BqFOZswAbI669fVy6EA4M3TfVX9_PL5x823-u7719ub1V1teNvkmhNBAA_M9sTahlGshQRChWCGamN6jbXse20wAy2FHqjgVljR94MAQbVsrqrbA68Neqt20U06PqqgndonQlwrHbMzIyjeUdlJzYWhjDVUdIy1guOuBdYbAwvXpwPXbu4nsAaKWj2ekJ5WvNuodbhXEjNJRVMIPjwRxPBrhpRVWY2BcdQewpwU5YTxjkvaFuj7f6DbMEdfVrVHMdlRzv-i1roIcH4IZa5ZSNWqbQnDRcgy9vo_qHIsTK78GQyu5E8a6KHBxJBShOFZI8FqsZw6WE4Vy6m95dTy4nfH23lu-eOx5jemltPD |
CitedBy_id | crossref_primary_10_1038_s41598_024_57514_z crossref_primary_10_1186_s12874_022_01782_8 crossref_primary_10_1136_bmjopen_2022_065576 crossref_primary_10_1016_j_cct_2024_107602 crossref_primary_10_2196_40719 crossref_primary_10_1007_s13300_024_01560_3 crossref_primary_10_3390_cancers15041008 crossref_primary_10_1177_15459683221119761 crossref_primary_10_3758_s13428_022_02043_8 crossref_primary_10_1080_19466315_2021_1983455 crossref_primary_10_1097_SLA_0000000000005407 crossref_primary_10_1016_j_ctcp_2023_101801 crossref_primary_10_1080_19466315_2022_2151506 crossref_primary_10_1093_biomtc_ujad036 crossref_primary_10_1080_24754269_2023_2261351 |
Cites_doi | 10.1136/bmj.b2393 10.1002/9781119942283.ch8 10.1002/sim.6008 10.1201/9781439821862 10.1002/sim.1923 10.1002/(SICI)1097-0258(19990330)18:6<681::AID-SIM71>3.0.CO;2-R 10.1002/pst.1821 10.1093/biomet/63.3.581 10.1002/9780470316696 10.1186/s12874-015-0022-1 10.1016/S0140-6736(19)31271-1 10.1002/9781119536604 10.1186/1471-2288-12-184 10.1111/rssa.12423 10.1002/sim.2697 10.1056/NEJMsr1203730 10.1002/pst.1624 10.1002/9780470510445 10.1002/pst.1954 10.1111/biom.12702 10.5705/ss.202016.0308 10.1093/biostatistics/kxr048 10.1080/10543406.2013.860769 10.1080/10543406.2013.834911 10.1111/j.1467-9868.2007.00590.x 10.1177/0962280216683570 10.1002/9781119942283.ch2 10.1093/aje/kww107 10.1136/adc.2004.058222 10.3310/hta2030 10.4155/cli.14.132 10.1016/S0140-6736(18)31773-2 10.1002/9781119942283 10.1016/j.cjca.2020.11.010 10.1080/01621459.1996.10476908 10.1177/1536867X1601600211 10.1111/j.0006-341X.2002.00510.x 10.1002/sim.8569 10.1002/sim.6274 10.1186/1471-2288-14-118 10.1191/1740774505cn128oa 10.1002/pst.1738 10.1111/j.0006-341X.2001.00404.x 10.1136/bmj.b2535 10.1002/sim.7583 10.1371/journal.pmed.1000217 10.1002/pst.1720 10.1080/01621459.1999.10473862 10.1080/19466315.2015.1053572 10.1002/sim.4067 10.1080/10543406.2014.928306 10.1002/sim.4076 10.1111/j.1365-2796.2010.02274.x |
ContentType | Journal Article |
Copyright | COPYRIGHT 2021 BioMed Central Ltd. 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2021 |
Copyright_xml | – notice: COPYRIGHT 2021 BioMed Central Ltd. – notice: 2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2021 |
DBID | NPM AAYXX CITATION 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PIMPY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
DOI | 10.1186/s12874-021-01261-6 |
DatabaseName | PubMed CrossRef ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) Medical Database Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
DatabaseTitle | PubMed CrossRef Publicly Available Content Database ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Central China ProQuest Hospital Collection (Alumni) ProQuest Central ProQuest Health & Medical Complete Health Research Premium Collection ProQuest Medical Library ProQuest One Academic UKI Edition Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest One Academic ProQuest Medical Library (Alumni) ProQuest Central (Alumni) MEDLINE - Academic |
DatabaseTitleList | PubMed Publicly Available Content Database CrossRef MEDLINE - Academic |
Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 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 – sequence: 3 dbid: 7X7 name: Health & Medical Collection url: https://search.proquest.com/healthcomplete sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine |
EISSN | 1471-2288 |
EndPage | 72 |
ExternalDocumentID | oai_doaj_org_article_592898a57c244327944675096e4bcce8 A661404433 10_1186_s12874_021_01261_6 33858355 |
Genre | Journal Article |
GeographicLocations | United Kingdom |
GeographicLocations_xml | – name: United Kingdom |
GroupedDBID | --- -A0 0R~ 23N 2WC 3V. 53G 5VS 6J9 6PF 7X7 88E 8FI 8FJ AAFWJ AAJSJ AAWTL ABDBF ABUWG ACGFO ACGFS ACIHN ACRMQ ADBBV ADINQ ADRAZ ADUKV AEAQA AENEX AFKRA AFPKN AHBYD AHMBA AHYZX ALIPV ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BCNDV BENPR BFQNJ BMC BPHCQ BVXVI C24 C6C CCPQU CS3 DIK DU5 E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GROUPED_DOAJ GX1 HMCUK IAO IHR INH INR ITC KQ8 M1P M48 MK0 M~E NPM O5R O5S OK1 P2P PGMZT PIMPY PQQKQ PROAC PSQYO RBZ RNS ROL RPM RSV SMD SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB AAYXX CITATION ABVAZ AFGXO AFNRJ 7XB 8FK AZQEC DWQXO K9. PQEST PQUKI PRINS 7X8 5PM |
ID | FETCH-LOGICAL-c563t-5171e0f4db1dd3420a78e12774c2accba0a8bbac04ea87af275d7d7bbf7e72a83 |
IEDL.DBID | RPM |
ISSN | 1471-2288 |
IngestDate | Thu Sep 05 15:48:08 EDT 2024 Tue Sep 17 20:42:13 EDT 2024 Fri Aug 16 07:43:20 EDT 2024 Thu Oct 10 16:40:44 EDT 2024 Thu Feb 22 23:27:31 EST 2024 Fri Feb 02 04:31:57 EST 2024 Thu Sep 12 18:48:02 EDT 2024 Sat Sep 28 08:29:02 EDT 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 1 |
Keywords | Missing data Sensitivity analysis Multiple imputation Randomised controlled trials Controlled multiple imputation |
Language | English |
License | Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c563t-5171e0f4db1dd3420a78e12774c2accba0a8bbac04ea87af275d7d7bbf7e72a83 |
Notes | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-3 content type line 23 ObjectType-Review-1 |
ORCID | 0000-0002-6113-1173 |
OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8048273/ |
PMID | 33858355 |
PQID | 2514489255 |
PQPubID | 42579 |
PageCount | 1 |
ParticipantIDs | doaj_primary_oai_doaj_org_article_592898a57c244327944675096e4bcce8 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8048273 proquest_miscellaneous_2514595826 proquest_journals_2514489255 gale_infotracmisc_A661404433 gale_infotracacademiconefile_A661404433 crossref_primary_10_1186_s12874_021_01261_6 pubmed_primary_33858355 |
PublicationCentury | 2000 |
PublicationDate | 2021-04-15 |
PublicationDateYYYYMMDD | 2021-04-15 |
PublicationDate_xml | – month: 04 year: 2021 text: 2021-04-15 day: 15 |
PublicationDecade | 2020 |
PublicationPlace | England |
PublicationPlace_xml | – name: England – name: London |
PublicationTitle | BMC medical research methodology |
PublicationTitleAlternate | BMC Med Res Methodol |
PublicationYear | 2021 |
Publisher | BioMed Central Ltd BioMed Central BMC |
Publisher_xml | – name: BioMed Central Ltd – name: BioMed Central – name: BMC |
References | DB Rubin (1261_CR16) 1987 M Shardell (1261_CR42) 2007; 26 RJ Little (1261_CR3) 2012; 367 M Bell (1261_CR10) 2014; 14 JR Carpenter (1261_CR13) 2013; 23 S van Buuren (1261_CR19) 1999; 18 1261_CR18 IR White (1261_CR51) 2018; 28 D Scharfstein (1261_CR36) 2001; 57 1261_CR53 A Atkinson (1261_CR44) 2019; 18 1261_CR54 M Kenward (1261_CR14) 2015; 5 1261_CR7 ON Keene (1261_CR28) 2014; 13 J Zhang (1261_CR37) 2005; 2 1261_CR8 D Jackson (1261_CR48) 2014; 33 1261_CR2 R Thiébaut (1261_CR40) 2005; 24 R Pratley (1261_CR55) 2019; 394 National Research Council (1261_CR6) 2010 S Cro (1261_CR25) 2016; 16 Y Tang (1261_CR60) 2017; 73 PT Bradshaw (1261_CR39) 2010; 29 A Rotnitzky (1261_CR38) 2007; 69 AK Akobeng (1261_CR1) 2005; 90 1261_CR26 1261_CR27 X Huang (1261_CR41) 2002; 58 K Lu (1261_CR59) 2014; 33 1261_CR61 S Cro (1261_CR49) 2019; 182 TE Raghunathan (1261_CR20) 2001; 27 European Medicines Agency (1261_CR57) 2010 D Moher (1261_CR50) 2009; 339 MK Murphy (1261_CR62) 1998; 2 M Akacha (1261_CR29) 2016; 15 P Hayati Rezvan (1261_CR11) 2015; 15 I Lipkovich (1261_CR34) 2016; 15 D Moher (1261_CR63) 2010; 7 NA Kaciroti (1261_CR43) 2012; 13 1261_CR32 IR White (1261_CR33) 2011; 30 DB Rubin (1261_CR5) 1976; 63 S Cro (1261_CR12) 2020; 39 DB Rubin (1261_CR17) 1978 JL Schafer (1261_CR21) 1997 SR Seaman (1261_CR58) 2014; 24 K Lu (1261_CR45) 2015; 7 JAC Sterne (1261_CR52) 2009; 338 F Gao (1261_CR30) 2017; 16 Y Tang (1261_CR31) 2018; 37 DO Scharfstein (1261_CR35) 1999; 94 X-L Meng (1261_CR22) 1994; 9 JR Carpenter (1261_CR4) 2007 G Molenberghs (1261_CR9) 2007 A Mackinnon (1261_CR15) 2010; 268 DB Rubin (1261_CR24) 1996; 91 FP Leacy (1261_CR47) 2017; 185 Y Zhao (1261_CR46) 2014; 24 PM O'Neil (1261_CR56) 2018; 392 J Hardt (1261_CR23) 2012; 12 |
References_xml | – volume: 338 start-page: b2393 issue: jun29 1 year: 2009 ident: 1261_CR52 publication-title: BMJ doi: 10.1136/bmj.b2393 contributor: fullname: JAC Sterne – ident: 1261_CR32 doi: 10.1002/9781119942283.ch8 – volume: 33 start-page: 1134 issue: 7 year: 2014 ident: 1261_CR59 publication-title: Stat Med doi: 10.1002/sim.6008 contributor: fullname: K Lu – volume-title: Analysis of incomplete multivariate data year: 1997 ident: 1261_CR21 doi: 10.1201/9781439821862 contributor: fullname: JL Schafer – volume: 24 start-page: 65 issue: 1 year: 2005 ident: 1261_CR40 publication-title: Stat Med doi: 10.1002/sim.1923 contributor: fullname: R Thiébaut – volume: 18 start-page: 681 issue: 6 year: 1999 ident: 1261_CR19 publication-title: Stat Med doi: 10.1002/(SICI)1097-0258(19990330)18:6<681::AID-SIM71>3.0.CO;2-R contributor: fullname: S van Buuren – volume: 9 start-page: 538 issue: 4 year: 1994 ident: 1261_CR22 publication-title: Stat Sci contributor: fullname: X-L Meng – start-page: 20 volume-title: Proceedings of the Survey Research Methods Section of the American Statistical Association year: 1978 ident: 1261_CR17 contributor: fullname: DB Rubin – volume: 16 start-page: 424 issue: 6 year: 2017 ident: 1261_CR30 publication-title: Pharm Stat doi: 10.1002/pst.1821 contributor: fullname: F Gao – volume: 63 start-page: 581 issue: 3 year: 1976 ident: 1261_CR5 publication-title: Biometrika doi: 10.1093/biomet/63.3.581 contributor: fullname: DB Rubin – volume-title: Multiple imputation for nonresponse in surveys year: 1987 ident: 1261_CR16 doi: 10.1002/9780470316696 contributor: fullname: DB Rubin – volume: 15 start-page: 30 issue: 1 year: 2015 ident: 1261_CR11 publication-title: BMC Med Res Methodol doi: 10.1186/s12874-015-0022-1 contributor: fullname: P Hayati Rezvan – volume: 394 start-page: 39 issue: 10192 year: 2019 ident: 1261_CR55 publication-title: Lancet doi: 10.1016/S0140-6736(19)31271-1 contributor: fullname: R Pratley – ident: 1261_CR61 doi: 10.1002/9781119536604 – volume: 12 start-page: 184 issue: 1 year: 2012 ident: 1261_CR23 publication-title: BMC Med Res Methodol doi: 10.1186/1471-2288-12-184 contributor: fullname: J Hardt – volume: 182 start-page: 623 issue: 2 year: 2019 ident: 1261_CR49 publication-title: J Royal Stat Soc Series A doi: 10.1111/rssa.12423 contributor: fullname: S Cro – volume: 26 start-page: 2184 issue: 10 year: 2007 ident: 1261_CR42 publication-title: Stat Med doi: 10.1002/sim.2697 contributor: fullname: M Shardell – ident: 1261_CR26 – volume: 367 start-page: 1355 issue: 14 year: 2012 ident: 1261_CR3 publication-title: N Engl J Med doi: 10.1056/NEJMsr1203730 contributor: fullname: RJ Little – volume-title: Missing data in randomised controlled trials: a practical guide year: 2007 ident: 1261_CR4 contributor: fullname: JR Carpenter – volume: 13 start-page: 258 issue: 4 year: 2014 ident: 1261_CR28 publication-title: Pharm Stat doi: 10.1002/pst.1624 contributor: fullname: ON Keene – volume-title: Guideline on missing data in confirmatory clinical trials year: 2010 ident: 1261_CR57 contributor: fullname: European Medicines Agency – volume-title: Missing data in clinical studies year: 2007 ident: 1261_CR9 doi: 10.1002/9780470510445 contributor: fullname: G Molenberghs – volume: 18 start-page: 645 issue: 6 year: 2019 ident: 1261_CR44 publication-title: Pharm Stat doi: 10.1002/pst.1954 contributor: fullname: A Atkinson – volume: 73 start-page: 1379 issue: 4 year: 2017 ident: 1261_CR60 publication-title: Biometrics doi: 10.1111/biom.12702 contributor: fullname: Y Tang – volume: 28 start-page: 1985 issue: 4 year: 2018 ident: 1261_CR51 publication-title: Stat Sin doi: 10.5705/ss.202016.0308 contributor: fullname: IR White – volume: 13 start-page: 341 issue: 2 year: 2012 ident: 1261_CR43 publication-title: Biostatistics doi: 10.1093/biostatistics/kxr048 contributor: fullname: NA Kaciroti – volume: 24 start-page: 229 issue: 2 year: 2014 ident: 1261_CR46 publication-title: J Biopharm Stat doi: 10.1080/10543406.2013.860769 contributor: fullname: Y Zhao – volume: 23 start-page: 1352 issue: 6 year: 2013 ident: 1261_CR13 publication-title: J Biopharm Stat doi: 10.1080/10543406.2013.834911 contributor: fullname: JR Carpenter – volume: 69 start-page: 307 issue: 3 year: 2007 ident: 1261_CR38 publication-title: J Royal Stat Soc Series B doi: 10.1111/j.1467-9868.2007.00590.x contributor: fullname: A Rotnitzky – ident: 1261_CR54 doi: 10.1177/0962280216683570 – ident: 1261_CR18 doi: 10.1002/9781119942283.ch2 – volume: 185 start-page: 304 issue: 4 year: 2017 ident: 1261_CR47 publication-title: Am J Epidemiol doi: 10.1093/aje/kww107 contributor: fullname: FP Leacy – volume: 90 start-page: 840 issue: 8 year: 2005 ident: 1261_CR1 publication-title: Arch Dis Child doi: 10.1136/adc.2004.058222 contributor: fullname: AK Akobeng – ident: 1261_CR27 – volume: 2 start-page: 1 issue: 3 year: 1998 ident: 1261_CR62 publication-title: Health Technol Assess doi: 10.3310/hta2030 contributor: fullname: MK Murphy – volume-title: Panel on handling missing data in clinical trials. Committee on National Statistics, Division of Behavioral and Social Sciences and Education year: 2010 ident: 1261_CR6 contributor: fullname: National Research Council – volume: 5 start-page: 311 issue: 3 year: 2015 ident: 1261_CR14 publication-title: Clin Invest doi: 10.4155/cli.14.132 contributor: fullname: M Kenward – volume: 392 start-page: 637 issue: 10148 year: 2018 ident: 1261_CR56 publication-title: Lancet doi: 10.1016/S0140-6736(18)31773-2 contributor: fullname: PM O'Neil – ident: 1261_CR8 doi: 10.1002/9781119942283 – ident: 1261_CR53 doi: 10.1016/j.cjca.2020.11.010 – volume: 91 start-page: 473 issue: 434 year: 1996 ident: 1261_CR24 publication-title: J Am Stat Assoc doi: 10.1080/01621459.1996.10476908 contributor: fullname: DB Rubin – volume: 16 start-page: 443 issue: 2 year: 2016 ident: 1261_CR25 publication-title: Stata J doi: 10.1177/1536867X1601600211 contributor: fullname: S Cro – volume: 58 start-page: 510 issue: 3 year: 2002 ident: 1261_CR41 publication-title: Biometrics doi: 10.1111/j.0006-341X.2002.00510.x contributor: fullname: X Huang – volume: 39 start-page: 2815 issue: 21 year: 2020 ident: 1261_CR12 publication-title: Stat Med doi: 10.1002/sim.8569 contributor: fullname: S Cro – volume: 33 start-page: 4681 issue: 27 year: 2014 ident: 1261_CR48 publication-title: Stat Med doi: 10.1002/sim.6274 contributor: fullname: D Jackson – volume: 14 start-page: 118 issue: 1 year: 2014 ident: 1261_CR10 publication-title: BMC Med Res Methodol doi: 10.1186/1471-2288-14-118 contributor: fullname: M Bell – ident: 1261_CR7 – volume: 2 start-page: 488 issue: 6 year: 2005 ident: 1261_CR37 publication-title: Clin Trials doi: 10.1191/1740774505cn128oa contributor: fullname: J Zhang – volume: 15 start-page: 216 issue: 3 year: 2016 ident: 1261_CR34 publication-title: Pharm Stat doi: 10.1002/pst.1738 contributor: fullname: I Lipkovich – volume: 57 start-page: 404 issue: 2 year: 2001 ident: 1261_CR36 publication-title: Biometrics doi: 10.1111/j.0006-341X.2001.00404.x contributor: fullname: D Scharfstein – volume: 339 start-page: b2535 issue: jul21 1 year: 2009 ident: 1261_CR50 publication-title: BMJ doi: 10.1136/bmj.b2535 contributor: fullname: D Moher – volume: 37 start-page: 1467 issue: 9 year: 2018 ident: 1261_CR31 publication-title: Stat Med doi: 10.1002/sim.7583 contributor: fullname: Y Tang – volume: 7 start-page: e1000217 issue: 2 year: 2010 ident: 1261_CR63 publication-title: PLoS Med doi: 10.1371/journal.pmed.1000217 contributor: fullname: D Moher – volume: 15 start-page: 4 issue: 1 year: 2016 ident: 1261_CR29 publication-title: Pharm Stat doi: 10.1002/pst.1720 contributor: fullname: M Akacha – volume: 94 start-page: 1096 issue: 448 year: 1999 ident: 1261_CR35 publication-title: J Am Stat Assoc doi: 10.1080/01621459.1999.10473862 contributor: fullname: DO Scharfstein – volume: 7 start-page: 199 issue: 3 year: 2015 ident: 1261_CR45 publication-title: Stat Biopharm Res doi: 10.1080/19466315.2015.1053572 contributor: fullname: K Lu – volume: 30 start-page: 377 issue: 4 year: 2011 ident: 1261_CR33 publication-title: Stat Med doi: 10.1002/sim.4067 contributor: fullname: IR White – volume: 24 start-page: 1358 issue: 6 year: 2014 ident: 1261_CR58 publication-title: J Biopharm Stat doi: 10.1080/10543406.2014.928306 contributor: fullname: SR Seaman – volume: 27 start-page: 85 year: 2001 ident: 1261_CR20 publication-title: Surv Methodol contributor: fullname: TE Raghunathan – ident: 1261_CR2 – volume: 29 start-page: 3017 issue: 29 year: 2010 ident: 1261_CR39 publication-title: Stat Med doi: 10.1002/sim.4076 contributor: fullname: PT Bradshaw – volume: 268 start-page: 586 issue: 6 year: 2010 ident: 1261_CR15 publication-title: J Intern Med doi: 10.1111/j.1365-2796.2010.02274.x contributor: fullname: A Mackinnon |
SSID | ssj0017836 |
Score | 2.443103 |
SecondaryResourceType | review_article |
Snippet | Missing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis under the... Abstract Background Missing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid... Background Missing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis... BACKGROUNDMissing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid analysis... Abstract Background Missing data are common in randomised controlled trials (RCTs) and can bias results if not handled appropriately. A statistically valid... |
SourceID | doaj pubmedcentral proquest gale crossref pubmed |
SourceType | Open Website Open Access Repository Aggregation Database Index Database |
StartPage | 72 |
SubjectTerms | Analysis Clinical trials Controlled multiple imputation Evaluation Medical research Methods Missing data Multiple imputation Multiple imputation (Statistics) Randomised controlled trials Research methodology Sensitivity analysis Statistical analysis Statistical methods Variables |
SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Ni9UwEA-yB_EifltdJYLgQcI2TdIkx7fisgjryYW9hXwVH0grvvf-f2eS9vHKHrx4ezSTR5vfTGemmfmFkI_Ca2U9IJCSMkzmPjPPk2TaRi5j5ikVLr2b7_31rfx2p-5OjvrCmrBKD1wX7kJZSAmMVzqCIxIdqA-YNnKWZBlizLXNl6slmZr3D7A3YWmRMf3FjiOtO8NyBHgh95z1KzdU2Prvv5NPnNK6YPLEA109IY_n0JFu6i0_JQ_y-Iw8vJk3x5-TaUNrIwqdBgpxHT3sMv6cq9F_5USX8kG6xbMcCih0O1LwV2kCwEHiRLgc6LGj-KWWwiB-VKDTYQ8qmikWlr4gt1dff3y5ZvN5CiyqXuyZ4prndpApAARCdq3XJvMOAsDY-RiDb70JwcdWZm-0Hzqtkk46hEFn3XkjXpKzcRrza0KTHcDZx8GnIKXtuBfG-LazIUr4Kxsa8nlZXve70ma4km6Y3lUwHIDhChiub8glInCURMrrcgEUwc2K4P6lCA35hPg5NEwAKfq5vwBuGCmu3AYjkRamioacryRhCeN6eNEANxv0zkEYCImshQSsIR-OwzgTi9TGPB2qjLIKEraGvKoKc3wkgRuwAmfrlSqtnnk9Mm5_Frpv0yJVq3jzPxbpLXnUFSuQjKtzcrb_c8jvIKrah_fFgP4CN8ce8g priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3di9QwEA96B-KL-G31lAiCDxKuSZMmfZI9ueMQ7hDx4N5CvqoLR3ted_9_Z9p03SL4VppJafKbzEwykxlCPlROq8YBAjEqw2SqE3M8SqabwGVIPMYxl97FZX1-Jb9eq-t84DbksMpZJo6COvYBz8iPQQ_DTqIBC_jz7W-GVaPQu5pLaNwnh4JLdNMenpxefvu-8yPgHYX5qoypjweO6d0ZhiWAYK45qxfqaMza_69s3lNOy8DJPU109pg8yiYkXU2YPyH3UveUPLjITvJnpF_R6UIK7VsK9h3dDgkfc1T6TYp0DiOka6zpMIJD1x0FvRV7AB4o9ojHwh4DxRNbCo14uED77QamLVEMMH1Ors5Of3w5Z7muAguqrjZMcc1T2croAYpKitJpk7gAQzAIF4J3pTPeu1DK5Ix2rdAq6qi9b3XSwpnqBTno-i69IjQ2LSj90LropWwEd5UxrhSNDxI-1fiCfJqn195O6TPsuO0wtZ3AsACGHcGwdUFOEIEdJaa-Hl_0dz9tXklWNbBHNE7pAJZJJUCegKzHJDZJ-hCSKchHxM_iAgWQgsv3DOCHMdWVXaFFUkLXqiBHC0qYwrBsnjnA5oU92L9sWJD3u2bsicFqXeq3E41qFGzcCvJyYpjdkCp0xFbYWy9YaTHmZUu3_jWm_TYlpmytXv__t96Qh2Lkb8m4OiIHm7ttegt208a_y4vjD0lnGWg priority: 102 providerName: ProQuest – databaseName: Scholars Portal Open Access Journals dbid: M48 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1bi9UwEA7rCuKLeN_qKhEEH6Ta3Jr0QWQVl0U4Pnlg30Ju1QNLq-cC-u-dSdvjKS6-lWYS0syXzEwzF0JeCqdV44ADMSpTylSn0rEoS90EJkNiMeZceosv9cVSfr5Ul0dkKnc0LuDmWtMO60kt11dvfv38_R42_Lu84U39dsMwaXuJzgZw3NasrG-Qm1wKiYhfyL-3ChixkKONNCs5N2YKorl2jJmgyvn8_z21D8TW3KXyQEad3yV3RuWSng1ouEeOUnef3FqM1-cPSH9Gh1AV2rcUND-62yR8HP3Vr1Kkk4MhXWG1h8w2uuooSLTYAySA4oA4l_zYUPyXS6ERfzvQfrcFECeKrqcPyfL809ePF-VYcaEMqhbbUjHNUtXK6IFJQvLKaZMYBxUxcBeCd5Uz3rtQyeSMdi3XKuqovW910twZ8Ygcd32XTgiNTQvqQGhd9FI2nDlhjKt444OEoRpfkNfT8tofQ2INmw0SU9uBGRaYYTMzbF2QD8iBPSUmxc4v-vU3O-4xqxqwHo1TOoDOIjicNCAFML1Nkj6EZAryCvlnEUzApODGCASYMCbBsmeoq1TQVRTkdEYJSxjmzRMC7IRYC4oimLoNmGgFebFvxp7oxtalfjfQqEaBSVeQxwNg9p8k8IpWYG89g9Lsm-ct3ep7TghuKkzmKp78f9ZPyW2e8S1Lpk7J8Xa9S89Ao9r653mb_AECQhy6 priority: 102 providerName: Scholars Portal |
Title | A review of the use of controlled multiple imputation in randomised controlled trials with missing outcome data |
URI | https://www.ncbi.nlm.nih.gov/pubmed/33858355 https://www.proquest.com/docview/2514489255 https://search.proquest.com/docview/2514595826 https://pubmed.ncbi.nlm.nih.gov/PMC8048273 https://doaj.org/article/592898a57c244327944675096e4bcce8 |
Volume | 21 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3da9swEBdtB2Mvo_v22gUNBnsYbmxLsuTHpLSUQUopK4S9CH15DbR2aZL_f3eyHWL2thdjrJOR_bvT3Ul3J0K-MSNFZQAB74VKeShDanLPU1m5nLuQex9r6S2uy6s7_nMplgdEDLkwMWjf2dVZ8_B41qzuY2zl06ObDnFi05vFucqweCWbHpJDydjgovdbB5iWMGTHqHK6zrGie4qRCDAXl3mKhxYx3A5jmN63p4xizf5_Z-Y91TQOm9zTQ5fH5HVvQNJZN9A35CA0b8nLRb9F_o60M9qlo9C2pmDd0e064G0fk_4QPB2CCOkKT3SI0NBVQ0Fr-RZgB4o94nisx5riei2FRlxaoO12A4waKIaXvid3lxe_zq_S_lSF1ImSbVKRyzxkNfcWgGC8yIxUIS_ADHSFcc6azChrjct4MEqaupDCSy-trWWQhVHsAzlq2iZ8ItRXNah8VxtvOa-K3DClTFZU1nF4VWUT8mP4vfqpK56ho9OhSt3hogEXHXHRZULmiMCOEgtfxwft8x_dw69FBR6iMkI6sEtYAbMJzPRYwiZw61xQCfmO-GkUTwDJmT7LAAaMha70DO2RDLqyhJyOKOEXunHzwAG6F-u1BmMQ3NkK3LCEfN01Y08MVWtCu-1oRCXAbUvIx45hdp808F1C5IiVRt88bgEZiEW_e57__N89T8irIkoBT3NxSo42z9vwBQyqjZ2AGC3lhLyYX1zf3E7isgRcF1zB9Xb-exIF7C8adCZe |
link.rule.ids | 230,315,733,786,790,870,891,2115,12083,21416,24346,27955,27956,31752,31753,33777,33778,43343,43838,53825,53827 |
linkProvider | National Library of Medicine |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3di9QwEA96gvoifls9NYLgg5RrmqRJn2QVj1Vv7-kO9i3kq-eCtOd19_93Jm3XLYJvpZmUJr_JfCSTGULec6tkbQGBEKTORaxiblkQuao9Ez6yEFIuvdV5tbwU39dyPW649WNY5SQTk6AOncc98hPQw-BJ1GABf7r-nWPVKDxdHUto3CZ3BOcC-Vyt9w4XwxsK00UZXZ30DJO75xiUAGK5Ynk1U0YpZ_-_kvlANc3DJg_00OlD8mA0IOliQPwRuRXbx-Tuajwif0K6BR2uo9CuoWDd0V0f8XGMSf8VA52CCOkGKzokaOimpaC1QgewA8UBcSrr0VPcr6XQiFsLtNttYdIixfDSp-Ty9OvFl2U-VlXIvaz4NpdMsVg0IjgAgouysEpHVoIZ6EvrvbOF1c5ZX4hotbJNqWRQQTnXqKhKq_kzctR2bXxBaKgbUPm-scEJUZfMcq1tUdbOC_hU7TLycZpecz0kzzDJ6dCVGcAwAIZJYJgqI58RgT0lJr5OL7qbKzOuIyNr8BC1lcqDXcJLkCYg6TGFTRTO-6gz8gHxM7g8ASRvx1sG8MOY6Mos0B4poCvPyPGMEqbQz5snDjDjsu7NXybMyLt9M_bEULU2druBRtYS3LaMPB8YZj8kjsewHHurGSvNxjxvaTc_U9JvXWDCVv7y_7_1ltxbXqzOzNm38x-vyP0y8brImTwmR9ubXXwNFtTWvUnL5A_rMRrv |
linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LbxMxELagSBUXyrMsFDASEge02Ze99h5DISqPVD1QqeJi-bUQ0e5GTXLpr2fGuxtl4dZbFI-jeGc8nll_8w0h7woteKVBA85xGTNf-lhnjsWishmzPnMucOnNT8uTc_b1gl_stPoKoH1rFpPm8mrSLH4HbOXyyiYDTiw5mx_LFMkri2Tp6uQuuQd7NhdDot5fIGBxwlAjI8tklSGve4x4BPDIZRZj66ICL8UKLPLbOZICc____nnngBqDJ3dOo9kB-TmsowOh_Jls1mZib_6heLzVQh-SB32MSqedyCNyxzePyf68v4V_Qtop7SpeaFtTCCDpZuXxYw97v_SODjhFusCmEUH7dNFQOBhdC5YFEjvCoXPIiuIrYQqD-PaCtps17AVPEcH6lJzPPv84Pon7xg2x5WWxjnkmMp_WzBnQdcHyVAvpsxwiTZtra41OtTRG25R5LYWuc8GdcMKYWniRa1k8I3tN2_jnhLqqhqjC1toZxqo804WUOs0rYxn8VGUi8mHQnVp2_Bwq5DWyVJ3SFShdBaWrMiIfUb1bSeTWDl-0179U_-AVryAJlZoLC6FPkYPDgsMEWXI8M9Z6GZH3aBwKPQBYgNV9IQP8YeTSUlMMeVKYWkTkaCQJj9COhwfzUr3nWCmINyFjriDTi8jb7TDORDRc49tNJ8MrDplhRA47a9wuaTDqiIiRnY7WPB4B6wu84r21vbj1zDdk_-zTTH3_cvrtJbmfh93G4owfkb319ca_gvBtbV6HjfoXKUVFdQ |
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=A+review+of+the+use+of+controlled+multiple+imputation+in+randomised+controlled+trials+with+missing+outcome+data&rft.jtitle=BMC+medical+research+methodology&rft.au=Tan%2C+Ping-Tee&rft.au=Cro%2C+Suzie&rft.au=Van+Vogt%2C+Eleanor&rft.au=Szigeti%2C+Matyas&rft.date=2021-04-15&rft.pub=BioMed+Central+Ltd&rft.issn=1471-2288&rft.eissn=1471-2288&rft.volume=21&rft.issue=1&rft_id=info:doi/10.1186%2Fs12874-021-01261-6&rft.externalDocID=A661404433 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2288&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2288&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2288&client=summon |