Reference-based sensitivity analysis via multiple imputation for longitudinal trials with protocol deviation
Randomized controlled trials provide essential evidence for the evaluation of new and existing medical treatments. Unfortunately, the statistical analysis is often complicated by the occurrence of protocol deviations, which mean we cannot always measure the intended outcomes for individuals who devi...
Uloženo v:
| Vydáno v: | The Stata journal Ročník 16; číslo 2; s. 443 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
01.06.2016
|
| Témata: | |
| ISSN: | 1536-867X |
| On-line přístup: | Zjistit podrobnosti o přístupu |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Randomized controlled trials provide essential evidence for the evaluation of new and existing medical treatments. Unfortunately, the statistical analysis is often complicated by the occurrence of protocol deviations, which mean we cannot always measure the intended outcomes for individuals who deviate, resulting in a missing-data problem. In such settings, however one approaches the analysis, an untestable assumption about the distribution of the unobserved data must be made. To understand how far the results depend on these assumptions, the primary analysis should be supplemented by a range of sensitivity analyses, which explore how the conclusions vary over a range of different credible assumptions for the missing data. In this article, we describe a new command, mimix, that can be used to perform reference-based sensitivity analyses for randomized controlled trials with longitudinal quantitative outcome data, using the approach proposed by Carpenter, Roger, and Kenward (2013,
23: 1352-1371). Under this approach, we make qualitative assumptions about how individuals' missing outcomes relate to those observed in relevant groups in the trial, based on plausible clinical scenarios. Statistical analysis then proceeds using the method of multiple imputation. |
|---|---|
| AbstractList | Randomized controlled trials provide essential evidence for the evaluation of new and existing medical treatments. Unfortunately, the statistical analysis is often complicated by the occurrence of protocol deviations, which mean we cannot always measure the intended outcomes for individuals who deviate, resulting in a missing-data problem. In such settings, however one approaches the analysis, an untestable assumption about the distribution of the unobserved data must be made. To understand how far the results depend on these assumptions, the primary analysis should be supplemented by a range of sensitivity analyses, which explore how the conclusions vary over a range of different credible assumptions for the missing data. In this article, we describe a new command, mimix, that can be used to perform reference-based sensitivity analyses for randomized controlled trials with longitudinal quantitative outcome data, using the approach proposed by Carpenter, Roger, and Kenward (2013,
23: 1352-1371). Under this approach, we make qualitative assumptions about how individuals' missing outcomes relate to those observed in relevant groups in the trial, based on plausible clinical scenarios. Statistical analysis then proceeds using the method of multiple imputation. Randomized controlled trials provide essential evidence for the evaluation of new and existing medical treatments. Unfortunately, the statistical analysis is often complicated by the occurrence of protocol deviations, which mean we cannot always measure the intended outcomes for individuals who deviate, resulting in a missing-data problem. In such settings, however one approaches the analysis, an untestable assumption about the distribution of the unobserved data must be made. To understand how far the results depend on these assumptions, the primary analysis should be supplemented by a range of sensitivity analyses, which explore how the conclusions vary over a range of different credible assumptions for the missing data. In this article, we describe a new command, mimix, that can be used to perform reference-based sensitivity analyses for randomized controlled trials with longitudinal quantitative outcome data, using the approach proposed by Carpenter, Roger, and Kenward (2013, Journal of Biopharmaceutical Statistics 23: 1352-1371). Under this approach, we make qualitative assumptions about how individuals' missing outcomes relate to those observed in relevant groups in the trial, based on plausible clinical scenarios. Statistical analysis then proceeds using the method of multiple imputation.Randomized controlled trials provide essential evidence for the evaluation of new and existing medical treatments. Unfortunately, the statistical analysis is often complicated by the occurrence of protocol deviations, which mean we cannot always measure the intended outcomes for individuals who deviate, resulting in a missing-data problem. In such settings, however one approaches the analysis, an untestable assumption about the distribution of the unobserved data must be made. To understand how far the results depend on these assumptions, the primary analysis should be supplemented by a range of sensitivity analyses, which explore how the conclusions vary over a range of different credible assumptions for the missing data. In this article, we describe a new command, mimix, that can be used to perform reference-based sensitivity analyses for randomized controlled trials with longitudinal quantitative outcome data, using the approach proposed by Carpenter, Roger, and Kenward (2013, Journal of Biopharmaceutical Statistics 23: 1352-1371). Under this approach, we make qualitative assumptions about how individuals' missing outcomes relate to those observed in relevant groups in the trial, based on plausible clinical scenarios. Statistical analysis then proceeds using the method of multiple imputation. |
| Author | Cro, Suzie Morris, Tim P Kenward, Michael G Carpenter, James R |
| Author_xml | – sequence: 1 givenname: Suzie surname: Cro fullname: Cro, Suzie organization: MRC Clinical Trials Unit at UCL, London School of Hygiene and Tropical Medicine, London, UK – sequence: 2 givenname: Tim P surname: Morris fullname: Morris, Tim P organization: MRC Clinical Trials Unit at UCL, London School of Hygiene and Tropical Medicine, London, UK – sequence: 3 givenname: Michael G surname: Kenward fullname: Kenward, Michael G organization: London School of Hygiene and Tropical Medicine, London, UK – sequence: 4 givenname: James R surname: Carpenter fullname: Carpenter, James R organization: MRC Clinical Trials Unit at UCL, London School of Hygiene and Tropical Medicine, London, UK |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29398978$$D View this record in MEDLINE/PubMed |
| BookMark | eNo1kEtLBDEQhHNYcR_6BzxIjl5Gk8wrOcriCxYEUfA29GQ6Gskk6ySzsv_eWVyhoaH6q6aoJZn54JGQC86uOa_rG17mlazqd16xaZjgfEYWBzE7qHOyjPGLsaLmQpySuVC5kqqWC-Je0OCAXmPWQsSORvTRJruzaU_Bg9tHG-nOAu1Hl-zWIbX9dkyQbPDUhIG64D9sGjs7wTQNFlykPzZ90u0QUtDB0Q4n_4E_IydmOuP5ca_I2_3d6_ox2zw_PK1vN5kumUpZUUPLS8gBNe8MlKoSGlHyom4VGmkkY6ZSErgWRkg0ZZuXoJgyutBQVVqsyNXf3ynC94gxNb2NGp0Dj2GMDVeqyKeWinxCL4_o2PbYNdvB9jDsm_-GxC9jqmyG |
| CitedBy_id | crossref_primary_10_3390_cancers13153707 crossref_primary_10_1002_hec_3963 crossref_primary_10_1016_j_jand_2019_04_011 crossref_primary_10_1017_S0033291721002786 crossref_primary_10_1093_nutrit_nuaf060 crossref_primary_10_3390_nu15102402 crossref_primary_10_1177_0022146519887347 crossref_primary_10_1016_S2215_0366_25_00063_X crossref_primary_10_1111_resp_14231 crossref_primary_10_1056_NEJMoa2501440 crossref_primary_10_1016_j_jad_2025_04_075 crossref_primary_10_1093_advances_nmab072 crossref_primary_10_1186_s13063_023_07168_5 crossref_primary_10_1002_eat_24213 crossref_primary_10_1016_j_nutres_2024_09_002 crossref_primary_10_1186_s12889_023_15564_4 crossref_primary_10_1007_s43441_023_00575_5 crossref_primary_10_1038_s41366_020_00733_x crossref_primary_10_1111_jcpp_13590 crossref_primary_10_1186_s12889_021_12129_1 crossref_primary_10_1371_journal_pone_0289503 crossref_primary_10_1186_s12883_024_03843_5 crossref_primary_10_1093_ije_dyz032 crossref_primary_10_1371_journal_pmed_1004335 crossref_primary_10_1002_pst_2348 crossref_primary_10_1111_ijn_70044 crossref_primary_10_1111_rssa_12423 crossref_primary_10_1002_sim_10301 crossref_primary_10_1186_s12874_021_01261_6 crossref_primary_10_1017_S0033291724002800 crossref_primary_10_1093_advances_nmab024 crossref_primary_10_1177_17407745231176773 crossref_primary_10_1007_s10803_022_05809_3 crossref_primary_10_1002_fsn3_4396 crossref_primary_10_1080_19466315_2019_1700157 crossref_primary_10_1002_sim_8569 crossref_primary_10_2196_66463 crossref_primary_10_1017_S0033291720003426 crossref_primary_10_1111_stan_12250 crossref_primary_10_1002_ejhf_3669 crossref_primary_10_3390_jcm10112224 crossref_primary_10_1007_s43441_025_00843_6 crossref_primary_10_1002_eat_24466 crossref_primary_10_1002_pst_2214 crossref_primary_10_1080_19466315_2022_2151506 crossref_primary_10_1111_1471_0528_18333 crossref_primary_10_2196_68648 crossref_primary_10_1002_epi4_13014 |
| ContentType | Journal Article |
| DBID | NPM 7X8 |
| DOI | 10.1177/1536867X1601600211 |
| DatabaseName | PubMed MEDLINE - Academic |
| DatabaseTitle | PubMed MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic |
| 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 | 29398978 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: Medical Research Council grantid: MC_UU_12023/21 |
| 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-c509t-47ab15a3aec1dfa5962cee8147b9ef8f800f698a1c2f28ef5b35a909fc4ca66c2 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 49 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000379449600011&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 | Thu Oct 02 10:12:48 EDT 2025 Mon Jul 21 06:05:36 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | clinical trial mimix sensitivity analysis st0440 multiple imputation protocol deviation missing data |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c509t-47ab15a3aec1dfa5962cee8147b9ef8f800f698a1c2f28ef5b35a909fc4ca66c2 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://journals.sagepub.com/doi/pdf/10.1177/1536867X1601600211 |
| PMID | 29398978 |
| PQID | 1994360043 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_1994360043 pubmed_primary_29398978 |
| PublicationCentury | 2000 |
| PublicationDate | 2016-06-01 |
| PublicationDateYYYYMMDD | 2016-06-01 |
| PublicationDate_xml | – month: 06 year: 2016 text: 2016-06-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | The Stata journal |
| PublicationTitleAlternate | Stata J |
| PublicationYear | 2016 |
| SSID | ssj0047122 |
| Score | 2.3352592 |
| Snippet | Randomized controlled trials provide essential evidence for the evaluation of new and existing medical treatments. Unfortunately, the statistical analysis is... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 443 |
| Title | Reference-based sensitivity analysis via multiple imputation for longitudinal trials with protocol deviation |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/29398978 https://www.proquest.com/docview/1994360043 |
| Volume | 16 |
| WOSCitedRecordID | wos000379449600011&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/eLvHCXMwpV1LS8QwEA7qetCDj_W1vojgNbjZpk16EhEXL7vsQaG3kuYBC0u7Wnd_vzNpu54EwUsPIYUymc58yUy-j5B7HetYRtYyrQvBROEgDlormJVWOiscj7QIYhNyOlVZls7aA7e6bavsYmII1LYyeEb-gBy2UYKFq8flB0PVKKyuthIa26QXAZTBli6ZbaoIEHdDFQF-6oSpRGbdpRm8bg5jOMSRjgQTHf8dYoZUMz7870cekYMWZNKnxiuOyZYr-2R_smForftkD1FmQ9J8QhYbulmGWc3SGtvaG10JqlvaErqea9r1H9I5ikGEVaUAe-miQt2jlUWNLRqUQGqKR7wUeSAqcDZqIQOH-afkffzy9vzKWhkGZgBNfDEhdcFjHWlnuPUa5XogsyouZJE6rzxATp-kSnMz8iPlfFxEsU6HqTfC6CQxozOyU1aluyAU1h7gKYQEZCXkwighPGxwfDF0Fub7Abnr7JqDm2PtQpeuWtX5j2UH5LxZnHzZ8HHkgFhSBbvhyz-8fUX2APIkTbPXNel5sIe7IbtmDTb_vA3-A8_pbPINbZrTfw |
| 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=Reference-based+sensitivity+analysis+via+multiple+imputation+for+longitudinal+trials+with+protocol+deviation&rft.jtitle=The+Stata+journal&rft.au=Cro%2C+Suzie&rft.au=Morris%2C+Tim+P&rft.au=Kenward%2C+Michael+G&rft.au=Carpenter%2C+James+R&rft.date=2016-06-01&rft.issn=1536-867X&rft.volume=16&rft.issue=2&rft.spage=443&rft_id=info:doi/10.1177%2F1536867X1601600211&rft_id=info%3Apmid%2F29398978&rft_id=info%3Apmid%2F29398978&rft.externalDocID=29398978 |
| 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 |