Individual participant data meta‐analysis to examine interactions between treatment effect and participant‐level covariates: Statistical recommendations for conduct and planning

Precision medicine research often searches for treatment‐covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant‐level covariate (eg, age, gender, biomarker). Single trials do not usually hav...

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Vydané v:Statistics in medicine Ročník 39; číslo 15; s. 2115 - 2137
Hlavní autori: Riley, Richard D., Debray, Thomas P.A., Fisher, David, Hattle, Miriam, Marlin, Nadine, Hoogland, Jeroen, Gueyffier, Francois, Staessen, Jan A., Wang, Jiguang, Moons, Karel G.M., Reitsma, Johannes B., Ensor, Joie
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken, USA John Wiley & Sons, Inc 10.07.2020
Wiley Subscription Services, Inc
Predmet:
Clinical trials
Data Analysis
effect modifier
Humans
individual participant data (IPD)
Medical statistics
Meta-analysis
Meta-Analysis as Topic
Models, Statistical
Precision medicine
Proportional Hazards Models
subgroup effect
treatment‐covariate interaction
Tutorial in Biostatistics
individual participant data (IPD)
meta-analysis
treatment-covariate interaction
subgroup effect
effect modifier
ISSN:0277-6715, 1097-0258, 1097-0258
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Abstract Precision medicine research often searches for treatment‐covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant‐level covariate (eg, age, gender, biomarker). Single trials do not usually have sufficient power to detect genuine treatment‐covariate interactions, which motivate the sharing of individual participant data (IPD) from multiple trials for meta‐analysis. Here, we provide statistical recommendations for conducting and planning an IPD meta‐analysis of randomized trials to examine treatment‐covariate interactions. For conduct, two‐stage and one‐stage statistical models are described, and we recommend: (i) interactions should be estimated directly, and not by calculating differences in meta‐analysis results for subgroups; (ii) interaction estimates should be based solely on within‐study information; (iii) continuous covariates and outcomes should be analyzed on their continuous scale; (iv) nonlinear relationships should be examined for continuous covariates, using a multivariate meta‐analysis of the trend (eg, using restricted cubic spline functions); and (v) translation of interactions into clinical practice is nontrivial, requiring individualized treatment effect prediction. For planning, we describe first why the decision to initiate an IPD meta‐analysis project should not be based on between‐study heterogeneity in the overall treatment effect; and second, how to calculate the power of a potential IPD meta‐analysis project in advance of IPD collection, conditional on characteristics (eg, number of participants, standard deviation of covariates) of the trials (potentially) promising their IPD. Real IPD meta‐analysis projects are used for illustration throughout.
AbstractList Precision medicine research often searches for treatment‐covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant‐level covariate (eg, age, gender, biomarker). Single trials do not usually have sufficient power to detect genuine treatment‐covariate interactions, which motivate the sharing of individual participant data (IPD) from multiple trials for meta‐analysis. Here, we provide statistical recommendations for conducting and planning an IPD meta‐analysis of randomized trials to examine treatment‐covariate interactions. For conduct, two‐stage and one‐stage statistical models are described, and we recommend: (i) interactions should be estimated directly, and not by calculating differences in meta‐analysis results for subgroups; (ii) interaction estimates should be based solely on within‐study information; (iii) continuous covariates and outcomes should be analyzed on their continuous scale; (iv) nonlinear relationships should be examined for continuous covariates, using a multivariate meta‐analysis of the trend (eg, using restricted cubic spline functions); and (v) translation of interactions into clinical practice is nontrivial, requiring individualized treatment effect prediction. For planning, we describe first why the decision to initiate an IPD meta‐analysis project should not be based on between‐study heterogeneity in the overall treatment effect; and second, how to calculate the power of a potential IPD meta‐analysis project in advance of IPD collection, conditional on characteristics (eg, number of participants, standard deviation of covariates) of the trials (potentially) promising their IPD. Real IPD meta‐analysis projects are used for illustration throughout.
Precision medicine research often searches for treatment-covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant-level covariate (eg, age, gender, biomarker). Single trials do not usually have sufficient power to detect genuine treatment-covariate interactions, which motivate the sharing of individual participant data (IPD) from multiple trials for meta-analysis. Here, we provide statistical recommendations for conducting and planning an IPD meta-analysis of randomized trials to examine treatment-covariate interactions. For conduct, two-stage and one-stage statistical models are described, and we recommend: (i) interactions should be estimated directly, and not by calculating differences in meta-analysis results for subgroups; (ii) interaction estimates should be based solely on within-study information; (iii) continuous covariates and outcomes should be analyzed on their continuous scale; (iv) nonlinear relationships should be examined for continuous covariates, using a multivariate meta-analysis of the trend (eg, using restricted cubic spline functions); and (v) translation of interactions into clinical practice is nontrivial, requiring individualized treatment effect prediction. For planning, we describe first why the decision to initiate an IPD meta-analysis project should not be based on between-study heterogeneity in the overall treatment effect; and second, how to calculate the power of a potential IPD meta-analysis project in advance of IPD collection, conditional on characteristics (eg, number of participants, standard deviation of covariates) of the trials (potentially) promising their IPD. Real IPD meta-analysis projects are used for illustration throughout.Precision medicine research often searches for treatment-covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference, odds ratio, hazard ratio) changes across values of a participant-level covariate (eg, age, gender, biomarker). Single trials do not usually have sufficient power to detect genuine treatment-covariate interactions, which motivate the sharing of individual participant data (IPD) from multiple trials for meta-analysis. Here, we provide statistical recommendations for conducting and planning an IPD meta-analysis of randomized trials to examine treatment-covariate interactions. For conduct, two-stage and one-stage statistical models are described, and we recommend: (i) interactions should be estimated directly, and not by calculating differences in meta-analysis results for subgroups; (ii) interaction estimates should be based solely on within-study information; (iii) continuous covariates and outcomes should be analyzed on their continuous scale; (iv) nonlinear relationships should be examined for continuous covariates, using a multivariate meta-analysis of the trend (eg, using restricted cubic spline functions); and (v) translation of interactions into clinical practice is nontrivial, requiring individualized treatment effect prediction. For planning, we describe first why the decision to initiate an IPD meta-analysis project should not be based on between-study heterogeneity in the overall treatment effect; and second, how to calculate the power of a potential IPD meta-analysis project in advance of IPD collection, conditional on characteristics (eg, number of participants, standard deviation of covariates) of the trials (potentially) promising their IPD. Real IPD meta-analysis projects are used for illustration throughout.
Author Staessen, Jan A.
Debray, Thomas P.A.
Marlin, Nadine
Gueyffier, Francois
Moons, Karel G.M.
Ensor, Joie
Fisher, David
Riley, Richard D.
Hoogland, Jeroen
Wang, Jiguang
Hattle, Miriam
Reitsma, Johannes B.
AuthorAffiliation 3 MRC Clinical Trials Unit, Institute of Clinical Trials & Methodology, Faculty of Population Health Sciences University College London London UK
6 Department of Cardiovascular Sciences, Research Unit Hypertension and Cardiovascular Epidemiology, Studies Coordinating Centre KU Leuven Leuven Belgium
1 Centre for Prognosis Research, School of Primary, Community and Social Care Keele University Staffordshire UK
2 Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands
5 Inserm Lyon France
7 Centre for Epidemiological Studies and Clinical Trials, Ruijin Hospital Shanghai Jiaotong University School of Medicine Shanghai China
4 Blizard Institute, Barts and The London School of Medicine and Dentistry Queen Mary University of London London UK
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– name: 6 Department of Cardiovascular Sciences, Research Unit Hypertension and Cardiovascular Epidemiology, Studies Coordinating Centre KU Leuven Leuven Belgium
– name: 1 Centre for Prognosis Research, School of Primary, Community and Social Care Keele University Staffordshire UK
– name: 7 Centre for Epidemiological Studies and Clinical Trials, Ruijin Hospital Shanghai Jiaotong University School of Medicine Shanghai China
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/32350891$$D View this record in MEDLINE/PubMed
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Cites_doi 10.1016/j.jclinepi.2014.12.012
10.1017/S0266462308080471
10.1186/s12874-018-0492-z
10.1002/sim.4333
10.1002/sim.7609
10.1136/bmj.j573
10.1136/bmj.332.7549.1080
10.1136/bmj.329.7472.966
10.1002/sim.5471
10.1002/jrsm.1356
10.1177/1536867X0200200201
10.1002/sim.1844
10.1002/sim.7974
10.1200/JCO.2007.14.8981
10.1002/jrsm.4
10.1002/sim.3165
10.1177/1536867X0900900103
10.1002/sim.2768
10.1002/sim.1009
10.1002/sim.7171
10.1136/bmjopen-2013-004188
10.1016/j.jclinepi.2003.08.009
10.1177/096228029700600206
10.1161/01.HYP.0000165020.14745.79
10.1016/j.jclinepi.2019.05.029
10.1001/jama.298.10.1209
10.1002/sim.6739
10.1002/sim.4172
10.1371/journal.pone.0060650
10.1136/bmj.j3932
10.1016/S0895-4356(01)00341-9
10.1177/0962280212439578
10.2307/2986270
10.1002/sim.7930
10.1371/journal.pmed.1001855
10.1001/jamainternmed.2016.9125
10.2307/3002019
10.1186/2046-4053-3-46
10.1002/sim.1815
10.1111/j.1467-985X.2008.00552.x
10.1002/9780470770771
10.1093/jnci/86.11.829
10.1136/bmj.k4245
10.1186/1471-2288-11-94
10.1016/j.jclinepi.2007.01.018
10.1136/bmjopen-2016-011148
10.1177/1536867X1101100206
10.1111/1467-985X.00122
10.1002/jrsm.1331
10.1002/jrsm.1129
10.1289/ehp.93101s459
10.1002/sim.1524
10.1136/bmj.d549
10.1177/1536867X1501500203
10.1007/978-3-319-19425-7
10.1093/ije/dyy239
10.1002/bimj.201100167
10.1371/journal.pmed.1001886
10.1136/bmj.e5793
10.1016/S0140-6736(05)70200-2
10.1186/s12874-019-0817-6
10.1002/sim.7064
10.1136/bmj.326.7382.219
10.1002/sim.4040
10.1038/d41586-018-07535-2
10.1016/j.jclinepi.2010.11.016
10.1002/sim.2331
10.1056/NEJMra043186
10.1007/978-0-387-77244-8
10.1136/bmj.c117
10.1002/sim.6191
10.7326/0003-4819-154-10-201105170-00008
10.1371/journal.pone.0046042
10.1002/sim.7141
10.1093/med/9780198796619.001.0001
10.1002/sim.918
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Copyright 2020 The Authors. published by John Wiley & Sons, Ltd.
2020 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
2020. This article is published 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.
Copyright_xml – notice: 2020 The Authors. published by John Wiley & Sons, Ltd.
– notice: 2020 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
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ISSN 0277-6715
1097-0258
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Issue 15
Keywords individual participant data (IPD)
meta-analysis
treatment-covariate interaction
subgroup effect
effect modifier
Language English
License Attribution
2020 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Notes Funding information
NIHR Health Technology Assessment Programme, programme 12/01; National Institute for Health Research (NIHR) Clinical Trials Unit Support Funding, TOP grant of the Netherlands Organisation for Health Research and Development (ZonMw), 91215058; NIHR Doctoral Fellowship, DRF‐2018‐11‐ST2‐077; Netherlands Organisation for Health Research and Development, Keele University
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ObjectType-Evidence Based Healthcare-1
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Funding information NIHR Health Technology Assessment Programme, programme 12/01; National Institute for Health Research (NIHR) Clinical Trials Unit Support Funding, TOP grant of the Netherlands Organisation for Health Research and Development (ZonMw), 91215058; NIHR Doctoral Fellowship, DRF‐2018‐11‐ST2‐077; Netherlands Organisation for Health Research and Development, Keele University
ORCID 0000-0002-1790-2719
0000-0002-2397-6052
0000-0001-7481-0282
0000-0001-8699-0735
OpenAccessLink https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fsim.8516
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References 2018; 363
2013; 22
2019; 10
2018; 563
2004; 23
2019; 19
2011; 11
1999; 162
2010; 340
2004; 329
2013; 8
1997; 6
2017; 356
2006; 332
2012; 54
2017; 358
2016; 35
2011; 154
2014; 4
2010; 1
2003; 326
2014; 3
2017; 36
2010; 29
2007; 298
2008; 27
2006; 25
2011; 64
2008; 26
2008; 24
2019; 114
2007; 60
2018; 37
2007; 26
2016; 45
2001; 54
2015; 12
2015; 15
2015; 6
2013; 346
2009
2011; 30
2019; 38
2008
2002; 2
2009; 172
2017; 177
2005; 45
2012; 31
2001; 20
1994; 43
1993; 101
1994; 86
2015; 68
2007; 357
2018; 18
2016; 6
1946; 2
2005; 365
2019; 48
2004; 57
2019
2009; 9
2015
2012; 7
2011; 342
2014; 33
2003; 22
e_1_2_9_75_1
e_1_2_9_31_1
e_1_2_9_52_1
e_1_2_9_50_1
e_1_2_9_73_1
e_1_2_9_79_1
e_1_2_9_10_1
e_1_2_9_35_1
e_1_2_9_56_1
e_1_2_9_77_1
e_1_2_9_12_1
e_1_2_9_33_1
e_1_2_9_71_1
e_1_2_9_14_1
e_1_2_9_39_1
e_1_2_9_16_1
e_1_2_9_37_1
e_1_2_9_58_1
e_1_2_9_18_1
e_1_2_9_41_1
e_1_2_9_64_1
e_1_2_9_20_1
e_1_2_9_62_1
e_1_2_9_22_1
e_1_2_9_45_1
e_1_2_9_68_1
e_1_2_9_24_1
e_1_2_9_43_1
e_1_2_9_66_1
e_1_2_9_8_1
e_1_2_9_6_1
e_1_2_9_81_1
e_1_2_9_4_1
e_1_2_9_60_1
e_1_2_9_2_1
e_1_2_9_26_1
e_1_2_9_49_1
e_1_2_9_28_1
e_1_2_9_47_1
e_1_2_9_30_1
e_1_2_9_53_1
e_1_2_9_74_1
e_1_2_9_51_1
e_1_2_9_72_1
e_1_2_9_11_1
e_1_2_9_34_1
e_1_2_9_57_1
e_1_2_9_13_1
e_1_2_9_32_1
e_1_2_9_55_1
e_1_2_9_76_1
e_1_2_9_15_1
e_1_2_9_38_1
Browne WJ (e_1_2_9_70_1) 2009
e_1_2_9_17_1
e_1_2_9_36_1
e_1_2_9_59_1
e_1_2_9_19_1
e_1_2_9_42_1
Kent DM (e_1_2_9_63_1) 2016; 45
e_1_2_9_40_1
e_1_2_9_61_1
e_1_2_9_21_1
e_1_2_9_46_1
e_1_2_9_67_1
e_1_2_9_23_1
e_1_2_9_44_1
e_1_2_9_65_1
e_1_2_9_7_1
e_1_2_9_80_1
e_1_2_9_5_1
e_1_2_9_3_1
VanderWeele Tyler J (e_1_2_9_78_1) 2014; 3
e_1_2_9_9_1
e_1_2_9_25_1
e_1_2_9_27_1
e_1_2_9_48_1
e_1_2_9_69_1
Royston P (e_1_2_9_54_1) 2008
e_1_2_9_29_1
References_xml – volume: 26
  start-page: 1397
  issue: 9
  year: 2008
  end-page: 1399
  article-title: Interactions between treatment and continuous covariates: a step toward individualizing therapy
  publication-title: J Clin Oncol
– year: 2009
– volume: 2
  start-page: 107
  issue: 2
  year: 2002
  end-page: 124
  article-title: Power by simulation
  publication-title: Stata J
– volume: 12
  issue: 10
  year: 2015
  article-title: Individual Participant Data (IPD) meta‐analyses of diagnostic and prognostic modeling studies: guidance on their use
  publication-title: PLoS Med
– volume: 356
  start-page: j573
  year: 2017
  article-title: Meta‐analytical methods to identify who benefits most from treatments: daft, deluded, or deft approach?
  publication-title: BMJ
– volume: 45
  start-page: 2075
  issue: 6
  year: 2016
  end-page: 2088
  article-title: Risk and treatment effect heterogeneity: re‐analysis of individual participant data from 32 large clinical trials
  publication-title: Int J Epidemiol
– volume: 11
  start-page: 94
  year: 2011
  article-title: Simulation methods to estimate design power: an overview for applied research
  publication-title: BMC Med Res Methodol
– volume: 4
  issue: 1
  year: 2014
  article-title: Stratified medicine in European Medicines Agency licensing: a systematic review of predictive biomarkers
  publication-title: BMJ Open
– volume: 340
  start-page: c117
  year: 2010
  article-title: Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses
  publication-title: BMJ
– volume: 45
  start-page: 907
  issue: 5
  year: 2005
  end-page: 913
  article-title: Systolic and diastolic blood pressure lowering as determinants of cardiovascular outcome
  publication-title: Hypertension
– volume: 43
  start-page: 429
  issue: 3
  year: 1994
  end-page: 467
  article-title: Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling
  publication-title: J R Stat Soc Ser C Appl Stat
– volume: 35
  start-page: 966
  issue: 7
  year: 2016
  end-page: 977
  article-title: Mastering variation: variance components and personalised medicine
  publication-title: Stat Med
– volume: 19
  start-page: 183
  issue: 1
  year: 2019
  article-title: Statistical approaches to identify subgroups in meta‐analysis of individual participant data: a simulation study
  publication-title: BMC Med Res Methodol
– volume: 38
  start-page: 326
  issue: 3
  year: 2019
  end-page: 38
  article-title: Meta‐analysis of non‐linear exposure‐outcome relationships using individual participant data: a comparison of two methods
  publication-title: Stat Med
– volume: 29
  start-page: 3046
  year: 2010
  end-page: 3067
  article-title: Random effects meta‐analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data
  publication-title: Stat Med
– volume: 6
  start-page: 157
  year: 2015
  end-page: 174
  article-title: Multivariate meta‐analysis using individual participant data
  publication-title: Res Synth Methods
– volume: 365
  start-page: 341
  issue: 9456
  year: 2005
  end-page: 346
  article-title: Treating individuals 4: can meta‐analysis help target interventions at individuals most likely to benefit?
  publication-title: Lancet
– volume: 6
  issue: 9
  year: 2016
  article-title: Multivariable fractional polynomial interaction to investigate continuous effect modifiers in a meta‐analysis on higher versus lower PEEP for patients with ARDS
  publication-title: BMJ Open
– volume: 162
  start-page: 71
  year: 1999
  end-page: 94
  article-title: Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials
  publication-title: J R Stat Soc Ser A
– volume: 363
  year: 2018
  article-title: Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects
  publication-title: BMJ
– volume: 154
  start-page: 680
  issue: 10
  year: 2011
  end-page: 683
  article-title: Interpretation of subgroup analyses in randomized trials: heterogeneity versus secondary interventions
  publication-title: Ann Intern Med
– volume: 24
  start-page: 358
  issue: 3
  year: 2008
  end-page: 361
  article-title: Empirical comparison of subgroup effects in conventional and individual patient data meta‐analyses
  publication-title: Int J Technol Assess Health Care
– volume: 9
  start-page: 40
  year: 2009
  end-page: 56
  article-title: Multivariate meta‐analysis
  publication-title: Stata J
– volume: 1
  start-page: 2
  year: 2010
  end-page: 9
  article-title: Meta‐analysis of a binary outcome using individual participant data and aggregate data
  publication-title: Res Synth Methods
– volume: 33
  start-page: 3844
  issue: 22
  year: 2014
  end-page: 3858
  article-title: Multilevel mixed effects parametric survival models using adaptive Gauss‐Hermite quadrature with application to recurrent events and individual participant data meta‐analysis
  publication-title: Stat Med
– volume: 25
  start-page: 127
  issue: 1
  year: 2006
  end-page: 141
  article-title: Dichotomizing continuous predictors in multiple regression: a bad idea
  publication-title: Stat Med
– volume: 48
  start-page: 596
  issue: 2
  year: 2019
  end-page: 608
  article-title: How often can meta‐analyses of individual‐level data individualize treatment? A meta‐epidemiologic study
  publication-title: Int J Epidemiol
– volume: 8
  issue: 4
  year: 2013
  article-title: Individual participant data meta‐analysis for a binary outcome: one‐stage or two‐stage?
  publication-title: PLoS One
– volume: 60
  start-page: 1002
  issue: 10
  year: 2007
  end-page: 1009
  article-title: A systematic review of analytical methods used to study subgroups in (individual patient data) meta‐analyses
  publication-title: J Clin Epidemiol
– volume: 20
  start-page: 3875
  issue: 24
  year: 2001
  end-page: 3889
  article-title: A refined method for the meta‐analysis of controlled clinical trials with binary outcome
  publication-title: Stat Med
– year: 2008
– volume: 64
  start-page: 949
  issue: 9
  year: 2011
  end-page: 967
  article-title: A critical review of methods for the assessment of patient‐level interactions in individual participant data meta‐analysis of randomized trials, and guidance for practitioners
  publication-title: J Clin Epidemiol
– volume: 54
  start-page: 774
  year: 2001
  end-page: 781
  article-title: Internal validation of predictive models: efficiency of some procedures for logistic regression analysis
  publication-title: J Clin Epidemiol
– volume: 332
  start-page: 1080
  year: 2006
  article-title: Statistics notes: the cost of dichotomising continuous variables
  publication-title: BMJ
– volume: 27
  start-page: 1870
  issue: 11
  year: 2008
  end-page: 1893
  article-title: Meta‐analysis of continuous outcomes combining individual patient data and aggregate data
  publication-title: Stat Med
– volume: 86
  start-page: 829
  issue: 11
  year: 1994
  end-page: 835
  article-title: Dangers of using “optimal” cutpoints in the evaluation of prognostic factors
  publication-title: J Natl Cancer Inst
– volume: 342
  start-page: d549
  year: 2011
  article-title: Interpretation of random effects meta‐analyses
  publication-title: BMJ
– volume: 12
  issue: 7
  year: 2015
  article-title: Individual Participant Data (IPD) meta‐analyses of randomised controlled trials: guidance on their use
  publication-title: PLoS Med
– volume: 36
  start-page: 136
  issue: 1
  year: 2017
  end-page: 196
  article-title: Tutorial in biostatistics: data‐driven subgroup identification and analysis in clinical trials
  publication-title: Stat Med
– year: 2019
– volume: 10
  start-page: 515
  issue: 4
  year: 2019
  end-page: 527
  article-title: A new justification of the Hartung‐Knapp method for random‐effects meta‐analysis based on weighted least squares regression
  publication-title: Res Synth Methods
– year: 2015
– volume: 18
  start-page: 41
  issue: 1
  year: 2018
  article-title: Simulation‐based power calculations for planning a two‐stage individual participant data meta‐analysis
  publication-title: BMC Med Res Methodol
– volume: 23
  start-page: 2567
  issue: 16
  year: 2004
  end-page: 2586
  article-title: Validation and updating of predictive logistic regression models: a study on sample size and shrinkage
  publication-title: Stat Med
– volume: 68
  start-page: 470
  issue: 4
  year: 2015
  end-page: 474
  article-title: Confounding, effect modification, and the odds ratio: common misinterpretations
  publication-title: J Clin Epidemiol
– volume: 3
  start-page: 33
  issue: 1
  year: 2014
  article-title: A tutorial on interaction
  publication-title: Epidemiol Methods
– volume: 101
  start-page: 59
  issue: suppl 4
  year: 1993
  end-page: 66
  article-title: Basic problems in interaction assessment
  publication-title: Environ Health Perspect
– volume: 6
  start-page: 167
  issue: 2
  year: 1997
  end-page: 183
  article-title: Using regression models for prediction: shrinkage and regression to the mean
  publication-title: Stat Methods Med Res
– volume: 22
  start-page: 2591
  issue: 16
  year: 2003
  end-page: 2602
  article-title: Separation of individual‐level and cluster‐level covariate effects in regression analysis of correlated data
  publication-title: Stat Med
– volume: 54
  start-page: 370
  issue: 3
  year: 2012
  end-page: 384
  article-title: Using aggregate data to estimate the standard error of a treatment‐covariate interaction in an individual patient data meta‐analysis
  publication-title: Biom J
– volume: 37
  start-page: 1550
  issue: 9
  year: 2018
  end-page: 1561
  article-title: A recursive partitioning approach for subgroup identification in individual patient data meta‐analysis
  publication-title: Stat Med
– volume: 36
  start-page: 855
  issue: 5
  year: 2017
  end-page: 875
  article-title: Meta‐analysis using individual participant data: one‐stage and two‐stage approaches, and why they may differ
  publication-title: Stat Med
– volume: 10
  start-page: 360
  issue: 3
  year: 2019
  end-page: 75
  article-title: One‐stage random effects meta‐analysis using linear mixed models for aggregate continuous outcome data
  publication-title: Res Synth Methods
– volume: 172
  start-page: 137
  year: 2009
  end-page: 159
  article-title: A re‐evaluation of random‐effects meta‐analysis
  publication-title: J Royal Stat Soc Ser A
– volume: 2
  start-page: 110
  issue: 6
  year: 1946
  end-page: 114
  article-title: An approximate distribution of estimates of variance components
  publication-title: Biometrics
– volume: 31
  start-page: 3821
  year: 2012
  end-page: 3839
  article-title: Multivariate meta‐analysis for non‐linear and other multi‐parameter associations
  publication-title: Stat Med
– volume: 20
  start-page: 2219
  issue: 15
  year: 2001
  end-page: 2241
  article-title: Meta‐analysis of continuous outcome data from individual patients
  publication-title: Stat Med
– volume: 326
  start-page: 219
  issue: 7382
  year: 2003
  article-title: Interaction revisited: the difference between two estimates
  publication-title: BMJ
– volume: 298
  start-page: 1209
  issue: 10
  year: 2007
  end-page: 1212
  article-title: Limitations of applying summary results of clinical trials to individual patients: the need for risk stratification
  publication-title: JAMA
– volume: 37
  start-page: 4404
  issue: 29
  year: 2018
  end-page: 4420
  article-title: Individual participant data meta‐analysis of continuous outcomes: a comparison of approaches for specifying and estimating one‐stage models
  publication-title: Stat Med
– volume: 177
  start-page: 554
  issue: 4
  year: 2017
  end-page: 560
  article-title: Evaluation of evidence of statistical support and corroboration of subgroup claims in randomized clinical trials
  publication-title: JAMA Intern Med
– volume: 30
  start-page: 3341
  issue: 28
  year: 2011
  end-page: 3360
  article-title: A new strategy for meta‐analysis of continuous covariates in observational studies
  publication-title: Stat Med
– volume: 57
  start-page: 229
  issue: 3
  year: 2004
  end-page: 236
  article-title: Subgroup analyses in randomized trials: risks of subgroup‐specific analyses; power and sample size for the interaction test
  publication-title: J Clin Epidemiol
– volume: 36
  start-page: 772
  issue: 5
  year: 2017
  end-page: 789
  article-title: One‐stage individual participant data meta‐analysis models: estimation of treatment‐covariate interactions must avoid ecological bias by separating out within‐trial and across‐trial information
  publication-title: Stat Med
– volume: 3
  start-page: 46
  year: 2014
  article-title: Investigation of continuous effect modifiers in a meta‐analysis on higher versus lower PEEP in patients requiring mechanical ventilation—protocol of the ICEM study
  publication-title: Syst Rev
– volume: 23
  start-page: 2509
  issue: 16
  year: 2004
  end-page: 2525
  article-title: A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials
  publication-title: Stat Med
– volume: 7
  issue: 10
  year: 2012
  article-title: Statistical analysis of individual participant data meta‐analyses: a comparison of methods and recommendations for practice
  publication-title: PLoS One
– volume: 15
  start-page: 369
  issue: 2
  year: 2015
  end-page: 396
  article-title: Two‐stage individual participant data meta‐analysis and generalized forest plots
  publication-title: Stata J
– volume: 30
  start-page: 2481
  year: 2011
  end-page: 2498
  article-title: Multivariate meta‐analysis: potential and promise
  publication-title: Stat Med
– volume: 329
  start-page: 966
  issue: 7472
  year: 2004
  end-page: 968
  article-title: Individual response to treatment: is it a valid assumption?
  publication-title: BMJ
– volume: 26
  start-page: 2982
  issue: 15
  year: 2007
  end-page: 2999
  article-title: Covariate heterogeneity in meta‐analysis: criteria for deciding between meta‐regression and individual patient data
  publication-title: Stat Med
– volume: 358
  year: 2017
  article-title: Multivariate and network meta‐analysis of multiple outcomes and multiple treatments: rationale, concepts, and examples
  publication-title: BMJ
– volume: 346
  year: 2013
  article-title: Prognosis research strategy (PROGRESS) 4: stratified medicine research
  publication-title: BMJ
– volume: 357
  start-page: 39
  issue: 1
  year: 2007
  end-page: 51
  article-title: Trastuzumab—mechanism of action and use in clinical practice
  publication-title: N Engl J Med
– volume: 114
  start-page: 72
  year: 2019
  end-page: 83
  article-title: Models with interactions overestimated heterogeneity of treatment effects and were prone to treatment mistargeting
  publication-title: J Clin Epidemiol
– volume: 22
  start-page: 324
  issue: 3
  year: 2013
  end-page: 345
  article-title: Sample size and power calculations for medical studies by simulation when closed form expressions are not available
  publication-title: Stat Methods Med Res
– volume: 11
  start-page: 255
  year: 2011
  end-page: 270
  article-title: Multivariate random‐effects meta‐regression: updates to mvmeta
  publication-title: Stata J
– volume: 563
  start-page: 619
  issue: 7733
  year: 2018
  end-page: 621
  article-title: Statistical pitfalls of personalized medicine
  publication-title: Nature
– ident: e_1_2_9_79_1
  doi: 10.1016/j.jclinepi.2014.12.012
– volume: 45
  start-page: 2075
  issue: 6
  year: 2016
  ident: e_1_2_9_63_1
  article-title: Risk and treatment effect heterogeneity: re‐analysis of individual participant data from 32 large clinical trials
  publication-title: Int J Epidemiol
– ident: e_1_2_9_14_1
  doi: 10.1017/S0266462308080471
– ident: e_1_2_9_42_1
  doi: 10.1186/s12874-018-0492-z
– ident: e_1_2_9_56_1
  doi: 10.1002/sim.4333
– ident: e_1_2_9_81_1
  doi: 10.1002/sim.7609
– volume-title: MLPowSim Manual
  year: 2009
  ident: e_1_2_9_70_1
– ident: e_1_2_9_10_1
  doi: 10.1136/bmj.j573
– ident: e_1_2_9_40_1
  doi: 10.1136/bmj.332.7549.1080
– ident: e_1_2_9_43_1
  doi: 10.1136/bmj.329.7472.966
– ident: e_1_2_9_47_1
  doi: 10.1002/sim.5471
– ident: e_1_2_9_24_1
  doi: 10.1002/jrsm.1356
– ident: e_1_2_9_69_1
  doi: 10.1177/1536867X0200200201
– volume: 3
  start-page: 33
  issue: 1
  year: 2014
  ident: e_1_2_9_78_1
  article-title: A tutorial on interaction
  publication-title: Epidemiol Methods
– ident: e_1_2_9_60_1
  doi: 10.1002/sim.1844
– ident: e_1_2_9_52_1
  doi: 10.1002/sim.7974
– ident: e_1_2_9_16_1
  doi: 10.1200/JCO.2007.14.8981
– ident: e_1_2_9_30_1
  doi: 10.1002/jrsm.4
– ident: e_1_2_9_9_1
  doi: 10.1002/sim.3165
– ident: e_1_2_9_51_1
  doi: 10.1177/1536867X0900900103
– ident: e_1_2_9_11_1
  doi: 10.1002/sim.2768
– ident: e_1_2_9_23_1
  doi: 10.1002/sim.1009
– ident: e_1_2_9_8_1
  doi: 10.1002/sim.7171
– ident: e_1_2_9_74_1
  doi: 10.1136/bmjopen-2013-004188
– ident: e_1_2_9_5_1
  doi: 10.1016/j.jclinepi.2003.08.009
– ident: e_1_2_9_58_1
  doi: 10.1177/096228029700600206
– ident: e_1_2_9_18_1
  doi: 10.1161/01.HYP.0000165020.14745.79
– ident: e_1_2_9_62_1
  doi: 10.1016/j.jclinepi.2019.05.029
– ident: e_1_2_9_64_1
  doi: 10.1001/jama.298.10.1209
– ident: e_1_2_9_76_1
  doi: 10.1002/sim.6739
– ident: e_1_2_9_49_1
  doi: 10.1002/sim.4172
– ident: e_1_2_9_25_1
  doi: 10.1371/journal.pone.0060650
– ident: e_1_2_9_48_1
  doi: 10.1136/bmj.j3932
– ident: e_1_2_9_59_1
  doi: 10.1016/S0895-4356(01)00341-9
– ident: e_1_2_9_68_1
  doi: 10.1177/0962280212439578
– ident: e_1_2_9_55_1
  doi: 10.2307/2986270
– ident: e_1_2_9_35_1
  doi: 10.1002/sim.7930
– ident: e_1_2_9_71_1
  doi: 10.1371/journal.pmed.1001855
– ident: e_1_2_9_12_1
  doi: 10.1001/jamainternmed.2016.9125
– ident: e_1_2_9_36_1
  doi: 10.2307/3002019
– ident: e_1_2_9_45_1
  doi: 10.1186/2046-4053-3-46
– ident: e_1_2_9_17_1
  doi: 10.1002/sim.1815
– ident: e_1_2_9_22_1
  doi: 10.1111/j.1467-985X.2008.00552.x
– volume-title: Multivariable Model‐Building—A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables
  year: 2008
  ident: e_1_2_9_54_1
  doi: 10.1002/9780470770771
– ident: e_1_2_9_39_1
  doi: 10.1093/jnci/86.11.829
– ident: e_1_2_9_61_1
  doi: 10.1136/bmj.k4245
– ident: e_1_2_9_67_1
  doi: 10.1186/1471-2288-11-94
– ident: e_1_2_9_15_1
  doi: 10.1016/j.jclinepi.2007.01.018
– ident: e_1_2_9_44_1
  doi: 10.1136/bmjopen-2016-011148
– ident: e_1_2_9_50_1
  doi: 10.1177/1536867X1101100206
– ident: e_1_2_9_53_1
  doi: 10.1111/1467-985X.00122
– ident: e_1_2_9_19_1
  doi: 10.1002/jrsm.1331
– ident: e_1_2_9_65_1
  doi: 10.1002/jrsm.1129
– ident: e_1_2_9_73_1
  doi: 10.1289/ehp.93101s459
– ident: e_1_2_9_33_1
  doi: 10.1002/sim.1524
– ident: e_1_2_9_21_1
  doi: 10.1136/bmj.d549
– ident: e_1_2_9_34_1
  doi: 10.1177/1536867X1501500203
– ident: e_1_2_9_46_1
  doi: 10.1007/978-3-319-19425-7
– ident: e_1_2_9_75_1
  doi: 10.1093/ije/dyy239
– ident: e_1_2_9_66_1
  doi: 10.1002/bimj.201100167
– ident: e_1_2_9_72_1
  doi: 10.1371/journal.pmed.1001886
– ident: e_1_2_9_2_1
  doi: 10.1136/bmj.e5793
– ident: e_1_2_9_6_1
  doi: 10.1016/S0140-6736(05)70200-2
– ident: e_1_2_9_32_1
– ident: e_1_2_9_37_1
  doi: 10.1186/s12874-019-0817-6
– ident: e_1_2_9_80_1
  doi: 10.1002/sim.7064
– ident: e_1_2_9_38_1
  doi: 10.1136/bmj.326.7382.219
– ident: e_1_2_9_28_1
  doi: 10.1002/sim.4040
– ident: e_1_2_9_77_1
  doi: 10.1038/d41586-018-07535-2
– ident: e_1_2_9_7_1
  doi: 10.1016/j.jclinepi.2010.11.016
– ident: e_1_2_9_41_1
  doi: 10.1002/sim.2331
– ident: e_1_2_9_4_1
  doi: 10.1056/NEJMra043186
– ident: e_1_2_9_57_1
  doi: 10.1007/978-0-387-77244-8
– ident: e_1_2_9_13_1
  doi: 10.1136/bmj.c117
– ident: e_1_2_9_27_1
  doi: 10.1002/sim.6191
– ident: e_1_2_9_20_1
  doi: 10.7326/0003-4819-154-10-201105170-00008
– ident: e_1_2_9_26_1
  doi: 10.1371/journal.pone.0046042
– ident: e_1_2_9_29_1
  doi: 10.1002/sim.7141
– ident: e_1_2_9_3_1
  doi: 10.1093/med/9780198796619.001.0001
– ident: e_1_2_9_31_1
  doi: 10.1002/sim.918
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Snippet Precision medicine research often searches for treatment‐covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference,...
Precision medicine research often searches for treatment-covariate interactions, which refers to when a treatment effect (eg, measured as a mean difference,...
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StartPage 2115
SubjectTerms Clinical trials
Data Analysis
effect modifier
Humans
individual participant data (IPD)
Medical statistics
Meta-analysis
Meta-Analysis as Topic
Models, Statistical
Precision medicine
Proportional Hazards Models
subgroup effect
treatment‐covariate interaction
Tutorial in Biostatistics
Title Individual participant data meta‐analysis to examine interactions between treatment effect and participant‐level covariates: Statistical recommendations for conduct and planning
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fsim.8516
https://www.ncbi.nlm.nih.gov/pubmed/32350891
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https://pubmed.ncbi.nlm.nih.gov/PMC7401032
Volume 39
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