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...

Full description

Saved in:
Bibliographic Details
Published in:The Stata journal Vol. 16; no. 2; p. 443
Main Authors: Cro, Suzie, Morris, Tim P, Kenward, Michael G, Carpenter, James R
Format: Journal Article
Language:English
Published: United States 01.06.2016
Subjects:
clinical trial
mimix
sensitivity analysis
st0440
multiple imputation
protocol deviation
missing data
ISSN:1536-867X
Online Access:Get more information
Tags: Add Tag
No Tags, Be the first to tag this record!
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 2.3351672
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_W1vojgNbh9JjmJiIsHd9mDSm8lzQMWlna17v5-Z9J2PQmClx5CC2Uyzfc1M_k-Qm6BgcdDYYfMSGtYDIDFlLEFC7XkBTBgF3rZxfcXPpmILJPTdsOtbtsquzXRL9Sm0rhHfocatlGKhav7xQdD1yisrrYWGpukFwGVwZYunq2rCLDu-ioCfNQpEynPukMzeNwcxnAoQDkSBLrgd4rpoWa0_9-XPCB7LcmkD01WHJINW_bJ7nit0Fr3yQ6yzEak-YjM13KzDFHN0Brb2htfCapa2RK6mina9R_SGZpB-FmlQHvpvELfo6VBjy3qnUBqilu8FHUgKkg2agCB_f3H5G309Pr4zFobBqaBTXyxmKsiSFSkrA6MU2jXA8gqgpgX0jrhgHK6VAoV6NCFwrqkiBIlh9LpWKs01eEJ2Sqr0p4RynUiFOeaR1rEVltpAAw1uvDayPFADchNF9cc0hxrF6q01bLOfyI7IKfN5OSLRo8jB8YiBfwNn__h6QuyA5QnbZq9LknPQTzsFdnWK4j557XPH7hOpuNvz1TS5Q
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.externalDBID=NO_FULL_TEXT
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