Facilitating Longitudinal Exposure-Response Modeling of a Composite Endpoint Using the Joint Modeling of Sparsely and Frequently Collected Subcomponents
Longitudinal exposure–response modeling plays an important role in optimizing dose and dosing regimens in clinical drug development. Certain clinical trials contain induction and maintenance phases where the maintenance treatment depends on the subjects’ achieving the main endpoint outcome in the in...
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Published in | The AAPS journal Vol. 22; no. 4; p. 79 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
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Springer International Publishing
26.05.2020
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Online Access | Get full text |
ISSN | 1550-7416 1550-7416 |
DOI | 10.1208/s12248-020-00452-1 |
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Abstract | Longitudinal exposure–response modeling plays an important role in optimizing dose and dosing regimens in clinical drug development. Certain clinical trials contain induction and maintenance phases where the maintenance treatment depends on the subjects’ achieving the main endpoint outcome in the induction phase. Due to logistic difficulties and cost considerations, the main endpoint is usually collected more sparsely than a subcomponent (or other related endpoints). The sparse collection of the main endpoint hampers its longitudinal modeling. In principle, the frequent collection of a subcomponent allows its longitudinal modeling. However, the model evaluation via the visual predictive check (VPC) in the maintenance phase is difficult due to the requirement of the main-endpoint model to identify the treatment subgroups. This manuscript proposes a solution to this dilemma via the joint modeling of the main endpoint and the subcomponent. The challenges are illustrated by analyzing the data collected up to 60 weeks from a phase III trial of ustekinumab in patients with moderate-to-severe ulcerative colitis (UC). The main endpoint Mayo score, a commonly used composite endpoint to measure the severity of UC, was collected only at baseline, the end of the induction phase, and the end of the maintenance phase. The partial Mayo score, which is a major subset of the Mayo score, was collected at nearly every 4 weeks. A longitudinal joint exposure-response model, developed under a latent-variable Indirect Response modeling framework, described the Mayo score time course and facilitated the VPC model evaluation under a response-adaptive trial design. |
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AbstractList | Longitudinal exposure–response modeling plays an important role in optimizing dose and dosing regimens in clinical drug development. Certain clinical trials contain induction and maintenance phases where the maintenance treatment depends on the subjects’ achieving the main endpoint outcome in the induction phase. Due to logistic difficulties and cost considerations, the main endpoint is usually collected more sparsely than a subcomponent (or other related endpoints). The sparse collection of the main endpoint hampers its longitudinal modeling. In principle, the frequent collection of a subcomponent allows its longitudinal modeling. However, the model evaluation via the visual predictive check (VPC) in the maintenance phase is difficult due to the requirement of the main-endpoint model to identify the treatment subgroups. This manuscript proposes a solution to this dilemma via the joint modeling of the main endpoint and the subcomponent. The challenges are illustrated by analyzing the data collected up to 60 weeks from a phase III trial of ustekinumab in patients with moderate-to-severe ulcerative colitis (UC). The main endpoint Mayo score, a commonly used composite endpoint to measure the severity of UC, was collected only at baseline, the end of the induction phase, and the end of the maintenance phase. The partial Mayo score, which is a major subset of the Mayo score, was collected at nearly every 4 weeks. A longitudinal joint exposure-response model, developed under a latent-variable Indirect Response modeling framework, described the Mayo score time course and facilitated the VPC model evaluation under a response-adaptive trial design. Longitudinal exposure-response modeling plays an important role in optimizing dose and dosing regimens in clinical drug development. Certain clinical trials contain induction and maintenance phases where the maintenance treatment depends on the subjects' achieving the main endpoint outcome in the induction phase. Due to logistic difficulties and cost considerations, the main endpoint is usually collected more sparsely than a subcomponent (or other related endpoints). The sparse collection of the main endpoint hampers its longitudinal modeling. In principle, the frequent collection of a subcomponent allows its longitudinal modeling. However, the model evaluation via the visual predictive check (VPC) in the maintenance phase is difficult due to the requirement of the main-endpoint model to identify the treatment subgroups. This manuscript proposes a solution to this dilemma via the joint modeling of the main endpoint and the subcomponent. The challenges are illustrated by analyzing the data collected up to 60 weeks from a phase III trial of ustekinumab in patients with moderate-to-severe ulcerative colitis (UC). The main endpoint Mayo score, a commonly used composite endpoint to measure the severity of UC, was collected only at baseline, the end of the induction phase, and the end of the maintenance phase. The partial Mayo score, which is a major subset of the Mayo score, was collected at nearly every 4 weeks. A longitudinal joint exposure-response model, developed under a latent-variable Indirect Response modeling framework, described the Mayo score time course and facilitated the VPC model evaluation under a response-adaptive trial design.Longitudinal exposure-response modeling plays an important role in optimizing dose and dosing regimens in clinical drug development. Certain clinical trials contain induction and maintenance phases where the maintenance treatment depends on the subjects' achieving the main endpoint outcome in the induction phase. Due to logistic difficulties and cost considerations, the main endpoint is usually collected more sparsely than a subcomponent (or other related endpoints). The sparse collection of the main endpoint hampers its longitudinal modeling. In principle, the frequent collection of a subcomponent allows its longitudinal modeling. However, the model evaluation via the visual predictive check (VPC) in the maintenance phase is difficult due to the requirement of the main-endpoint model to identify the treatment subgroups. This manuscript proposes a solution to this dilemma via the joint modeling of the main endpoint and the subcomponent. The challenges are illustrated by analyzing the data collected up to 60 weeks from a phase III trial of ustekinumab in patients with moderate-to-severe ulcerative colitis (UC). The main endpoint Mayo score, a commonly used composite endpoint to measure the severity of UC, was collected only at baseline, the end of the induction phase, and the end of the maintenance phase. The partial Mayo score, which is a major subset of the Mayo score, was collected at nearly every 4 weeks. A longitudinal joint exposure-response model, developed under a latent-variable Indirect Response modeling framework, described the Mayo score time course and facilitated the VPC model evaluation under a response-adaptive trial design. |
ArticleNumber | 79 |
Author | Hu, Chuanpu Sharma, Amarnath Zhou, Honghui |
Author_xml | – sequence: 1 givenname: Chuanpu surname: Hu fullname: Hu, Chuanpu email: CHu25@its.jnj.com organization: Clinical Pharmacology and Pharmacometrics, LLC, Janssen Research & Development – sequence: 2 givenname: Honghui surname: Zhou fullname: Zhou, Honghui organization: Clinical Pharmacology and Pharmacometrics, LLC, Janssen Research & Development – sequence: 3 givenname: Amarnath surname: Sharma fullname: Sharma, Amarnath organization: Clinical Pharmacology and Pharmacometrics, LLC, Janssen Research & Development |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32700158$$D View this record in MEDLINE/PubMed |
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Cites_doi | 10.1111/apt.13291 10.1007/s10928-017-9529-x 10.1208/s12248-019-0370-6 10.1007/s10928-017-9531-3 10.1007/s10928-018-9581-1 10.1023/B:JOPA.0000012998.04442.1f 10.1007/s10928-014-9366-0 10.1093/biostatistics/kxj034 10.1007/s10928-011-9191-7 10.1007/s10928-018-9598-5 10.1007/s11095-017-2216-1 10.1002/psp4.12015 10.1056/NEJMoa1602773 10.1007/BF02353483 10.1007/s10928-011-9222-4 10.1007/s10928-018-9610-0 10.1007/s10928-007-9080-2 10.1002/sim.7433 10.1007/s11095-014-1315-5 10.1208/s12248-020-00441-4 10.1056/NEJMoa1203572 10.1208/s12248-016-9977-z 10.1007/s10928-017-9534-0 10.1023/A:1023249510224 10.1177/0091270008329556 10.1007/s10928-015-9453-x 10.1002/psp4.12280 10.1038/psp.2014.15 10.1007/s40300-016-0091-x 10.1056/NEJMoa1900750 10.1177/0962280216661370 10.1007/s10928-012-9288-7 10.1002/jcph.1582 10.1007/978-1-4899-3242-6 10.1002/psp4.12422 10.1093/ecco-jcc/jjy222.034 10.1093/ecco-jcc/jjy222.088 |
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References | Ueckert (CR35) 2018; 7 Hutmacher, Krishnaswami, Kowalski (CR13) 2008; 35 Lesaffre, Rizopoulos, Tsonaka (CR31) 2007; 8 Hu, Adedokun, Chen, Szapary, Gasink, Sharma, Zhou (CR4) 2017; 44 Zhang, Beal, Sheiner (CR23) 2003; 30 CR17 CR16 Novakovic, Krekels, Munafo, Ueckert, Karlsson (CR37) 2017; 19 Hu (CR29) 2019; 21 Hu, Xu, Zhuang, Hsu, Sharma, Xu, Zhang, Zhou (CR8) 2018; 45 Hu, Szapary, Yeilding, Zhou (CR30) 2011; 38 Hu, Sale (CR25) 2003; 30 Hu, Zhou (CR6) 2016; 43 Buatois, Retout, Frey, Ueckert (CR36) 2017; 34 Overgaard, Ingwersen, Tornoe (CR1) 2015; 4 Hu, Adedokun, Zhang, Sharma, Zhou (CR5) 2018; 45 Hu, Zhou, Sharma (CR28) 2020; 22 Sandborn, Gasink, Gao, Blank, Johanns, Guzzo, Sands, Hanauer, Targan, Rutgeerts, Ghosh, de Villiers, Panaccione, Greenberg, Schreiber, Lichtiger, Feagan (CR19) 2012; 367 Hu, Yao, Chen, Randazzo, Zhang, Xu, Sharma, Zhou (CR9) 2018; 45 CR3 Hu, Randazzo, Sharma, Zhou (CR7) 2017; 44 Hu, Xu, Mendelsohn, Zhou (CR14) 2013; 40 CR26 Hu (CR12) 2014; 3 Hu, Szapary, Mendelsohn, Zhou (CR15) 2014; 41 Ursino, Gasparini (CR33) 2018; 27 CR24 Iannario, D. (CR32) 2016; 74 Rutgeerts, Feagan, Marano, Padgett, Strauss, Johanns, Adedokun, Guzzo, Zhang, Colombel, Reinisch, Gibson, Sandborn (CR10) 2015; 42 CR21 Zhu, Hu, Lu, Liao, Marini, Yohrling, Yeilding, Davis, Zhou (CR22) 2009; 49 Hu, Zhou, Sharma (CR2) 2017; 44 Hutmacher, French (CR27) 2011; 38 Sands, Sandborn, Panaccione, O'Brien, Zhang, Johanns, Adedokun, Li, Peyrin-Biroulet, van Assche, Danese, Targan, Abreu, Hisamatsu, Szapary, Marano (CR20) 2019; 381 Sharma, Jusko (CR11) 1996; 24 Feagan, Sandborn, Gasink, Jacobstein, Lang, Friedman, Blank, Johanns, Gao, Miao, Adedokun, Sands, Hanauer, Vermeire, Targan, Ghosh, de Villiers, Colombel, Tulassay, Seidler, Salzberg, Desreumaux, Lee, Loftus, Dieleman, Katz, Rutgeerts (CR18) 2016; 375 Ueckert, Plan, Ito, Karlsson, Corrigan, Hooker (CR38) 2014; 31 Liu, Shepherd, Li, Harrell (CR34) 2017; 36 C Hu (452_CR14) 2013; 40 C Hu (452_CR25) 2003; 30 C Hu (452_CR9) 2018; 45 452_CR21 AM Novakovic (452_CR37) 2017; 19 Q Liu (452_CR34) 2017; 36 452_CR24 MM Hutmacher (452_CR27) 2011; 38 S Buatois (452_CR36) 2017; 34 WJ Sandborn (452_CR19) 2012; 367 BE Sands (452_CR20) 2019; 381 Y Zhu (452_CR22) 2009; 49 452_CR16 452_CR3 452_CR17 C Hu (452_CR30) 2011; 38 S Ueckert (452_CR38) 2014; 31 C Hu (452_CR28) 2020; 22 MM Hutmacher (452_CR13) 2008; 35 L Zhang (452_CR23) 2003; 30 E Lesaffre (452_CR31) 2007; 8 MP Iannario (452_CR32) 2016; 74 M Ursino (452_CR33) 2018; 27 A Sharma (452_CR11) 1996; 24 C Hu (452_CR12) 2014; 3 BG Feagan (452_CR18) 2016; 375 C Hu (452_CR7) 2017; 44 P Rutgeerts (452_CR10) 2015; 42 C Hu (452_CR8) 2018; 45 C Hu (452_CR29) 2019; 21 RV Overgaard (452_CR1) 2015; 4 452_CR26 C Hu (452_CR2) 2017; 44 S Ueckert (452_CR35) 2018; 7 C Hu (452_CR15) 2014; 41 C Hu (452_CR4) 2017; 44 C Hu (452_CR5) 2018; 45 C Hu (452_CR6) 2016; 43 |
References_xml | – volume: 42 start-page: 504 issue: 5 year: 2015 end-page: 514 ident: CR10 article-title: Randomised clinical trial: a placebo-controlled study of intravenous golimumab induction therapy for ulcerative colitis publication-title: Aliment Pharmacol Ther doi: 10.1111/apt.13291 – volume: 44 start-page: 425 issue: 5 year: 2017 end-page: 436 ident: CR4 article-title: Challenges in longitudinal exposure-response modeling of data from complex study designs: a case study of modeling CDAI score for ustekinumab in patients with Crohn's disease publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-017-9529-x – volume: 21 start-page: 102 issue: 6 year: 2019 ident: CR29 article-title: On the comparison of methods in analyzing bounded outcome score data publication-title: AAPS J doi: 10.1208/s12248-019-0370-6 – ident: CR16 – volume: 44 start-page: 437 issue: 5 year: 2017 end-page: 448 ident: CR7 article-title: Improvement in latent variable indirect response modeling of multiple categorical clinical endpoints: application to modeling of guselkumab treatment effects in psoriatic patients publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-017-9531-3 – volume: 45 start-page: 523 issue: 4 year: 2018 end-page: 535 ident: CR9 article-title: A comprehensive evaluation of exposure-response relationships in clinical trials: application to support guselkumab dose selection for patients with psoriasis publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-018-9581-1 – volume: 30 start-page: 387 issue: 6 year: 2003 end-page: 404 ident: CR23 article-title: Simultaneous vs. sequential analysis for population PK/PD data I: best-case performance publication-title: J Pharmacokinet Pharmacodyn doi: 10.1023/B:JOPA.0000012998.04442.1f – volume: 41 start-page: 335 issue: 4 year: 2014 end-page: 349 ident: CR15 article-title: Latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-014-9366-0 – volume: 8 start-page: 72 issue: 1 year: 2007 end-page: 85 ident: CR31 article-title: The logistic transform for bounded outcome scores publication-title: Biostatistics doi: 10.1093/biostatistics/kxj034 – volume: 38 start-page: 237 issue: 2 year: 2011 end-page: 260 ident: CR30 article-title: Informative dropout modeling of longitudinal ordered categorical data and model validation: application to exposure-response modeling of physician's global assessment score for ustekinumab in patients with psoriasis publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-011-9191-7 – volume: 45 start-page: 679 issue: 5 year: 2018 end-page: 691 ident: CR8 article-title: Joint longitudinal model development: application to exposure-response modeling of ACR and DAS scores in rheumatoid arthritis patients treated with sirukumab publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-018-9598-5 – volume: 34 start-page: 2109 issue: 10 year: 2017 end-page: 2118 ident: CR36 article-title: Item response theory as an efficient tool to describe a heterogeneous clinical rating scale in De novo idiopathic Parkinson's disease patients publication-title: Pharm Res doi: 10.1007/s11095-017-2216-1 – volume: 4 start-page: 565 issue: 10 year: 2015 end-page: 575 ident: CR1 article-title: Establishing good practices for exposure-response analysis of clinical endpoints in drug development publication-title: CPT Pharmacometrics Syst Pharmacol doi: 10.1002/psp4.12015 – volume: 375 start-page: 1946 issue: 20 year: 2016 end-page: 1960 ident: CR18 article-title: Ustekinumab as induction and maintenance therapy for Crohn’s disease publication-title: N Engl J Med doi: 10.1056/NEJMoa1602773 – volume: 24 start-page: 611 issue: 6 year: 1996 end-page: 635 ident: CR11 article-title: Characterization of four basic models of indirect pharmacodynamic responses publication-title: J Pharmacokinet Biopharm doi: 10.1007/BF02353483 – volume: 38 start-page: 833 year: 2011 end-page: 859 ident: CR27 article-title: Extending the latent variable model for extra correlated longitudinal dichotomous responses publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-011-9222-4 – volume: 45 start-page: 803 issue: 6 year: 2018 end-page: 816 ident: CR5 article-title: Modeling near-continuous clinical endpoint as categorical: application to longitudinal exposure-response modeling of Mayo scores for golimumab in patients with ulcerative colitis publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-018-9610-0 – volume: 35 start-page: 139 year: 2008 end-page: 157 ident: CR13 article-title: Exposure-response modeling using latent variables for the efficacy of a JAK3 inhibitor administered to rheumatoid arthritis patients publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-007-9080-2 – volume: 36 start-page: 4316 issue: 27 year: 2017 end-page: 4335 ident: CR34 article-title: Modeling continuous response variables using ordinal regression publication-title: Stat Med doi: 10.1002/sim.7433 – volume: 31 start-page: 2152 issue: 8 year: 2014 end-page: 2165 ident: CR38 article-title: Improved utilization of ADAS-cog assessment data through item response theory based pharmacometric modeling publication-title: Pharm Res doi: 10.1007/s11095-014-1315-5 – ident: CR21 – volume: 22 start-page: 61 issue: 3 year: 2020 ident: CR28 article-title: Applying Beta distribution in analyzing bounded outcome score data publication-title: AAPS J doi: 10.1208/s12248-020-00441-4 – ident: CR3 – volume: 367 start-page: 1519 issue: 16 year: 2012 end-page: 1528 ident: CR19 article-title: Ustekinumab induction and maintenance therapy in refractory Crohn’s disease publication-title: N Engl J Med doi: 10.1056/NEJMoa1203572 – volume: 19 start-page: 172 issue: 1 year: 2017 end-page: 179 ident: CR37 article-title: Application of item response theory to modeling of expanded disability status scale in multiple sclerosis publication-title: AAPS J doi: 10.1208/s12248-016-9977-z – volume: 44 start-page: 503 issue: 5 year: 2017 end-page: 507 ident: CR2 article-title: Landmark and longitudinal exposure-response analyses in drug development publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-017-9534-0 – ident: CR17 – volume: 30 start-page: 83 issue: 1 year: 2003 end-page: 103 ident: CR25 article-title: A joint model for nonlinear longitudinal data with informative dropout publication-title: J Pharmacokinet Pharmacodyn doi: 10.1023/A:1023249510224 – volume: 49 start-page: 162 issue: 2 year: 2009 end-page: 175 ident: CR22 article-title: Population pharmacokinetic modeling of ustekinumab, a human monoclonal antibody targeting IL-12/23p40, in patients with moderate to severe plaque psoriasis publication-title: J Clin Pharmacol doi: 10.1177/0091270008329556 – volume: 43 start-page: 45 issue: 1 year: 2016 end-page: 54 ident: CR6 article-title: Improvement in latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint in rheumatoid arthritis publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-015-9453-x – volume: 7 start-page: 205 issue: 4 year: 2018 end-page: 218 ident: CR35 article-title: Modeling composite assessment data using item response theory publication-title: CPT Pharmacometrics Syst Pharmacol doi: 10.1002/psp4.12280 – volume: 3 year: 2014 ident: CR12 article-title: Exposure-response modeling of clinical end points using latent variable indirect response models publication-title: CPT Pharmacometrics Syst Pharmacol doi: 10.1038/psp.2014.15 – volume: 74 start-page: 233 issue: 2 year: 2016 end-page: 252 ident: CR32 article-title: A comprehensive framework of regression models for ordinal data publication-title: METRON. doi: 10.1007/s40300-016-0091-x – ident: CR26 – ident: CR24 – volume: 381 start-page: 1201 issue: 13 year: 2019 end-page: 1214 ident: CR20 article-title: Ustekinumab as induction and maintenance therapy for ulcerative colitis publication-title: N Engl J Med doi: 10.1056/NEJMoa1900750 – volume: 27 start-page: 1376 issue: 5 year: 2018 end-page: 1393 ident: CR33 article-title: A new parsimonious model for ordinal longitudinal data with application to subjective evaluations of a gastrointestinal disease publication-title: Stat Methods Med Res doi: 10.1177/0962280216661370 – volume: 40 start-page: 81 issue: 1 year: 2013 end-page: 91 ident: CR14 article-title: Latent variable indirect response modeling of categorical endpoints representing change from baseline publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-012-9288-7 – ident: 452_CR21 doi: 10.1002/jcph.1582 – volume: 35 start-page: 139 year: 2008 ident: 452_CR13 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-007-9080-2 – volume: 44 start-page: 437 issue: 5 year: 2017 ident: 452_CR7 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-017-9531-3 – volume: 30 start-page: 387 issue: 6 year: 2003 ident: 452_CR23 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1023/B:JOPA.0000012998.04442.1f – volume: 40 start-page: 81 issue: 1 year: 2013 ident: 452_CR14 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-012-9288-7 – volume: 3 year: 2014 ident: 452_CR12 publication-title: CPT Pharmacometrics Syst Pharmacol doi: 10.1038/psp.2014.15 – volume: 21 start-page: 102 issue: 6 year: 2019 ident: 452_CR29 publication-title: AAPS J doi: 10.1208/s12248-019-0370-6 – ident: 452_CR26 doi: 10.1007/978-1-4899-3242-6 – volume: 45 start-page: 523 issue: 4 year: 2018 ident: 452_CR9 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-018-9581-1 – volume: 38 start-page: 237 issue: 2 year: 2011 ident: 452_CR30 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-011-9191-7 – volume: 42 start-page: 504 issue: 5 year: 2015 ident: 452_CR10 publication-title: Aliment Pharmacol Ther doi: 10.1111/apt.13291 – volume: 44 start-page: 425 issue: 5 year: 2017 ident: 452_CR4 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-017-9529-x – volume: 45 start-page: 803 issue: 6 year: 2018 ident: 452_CR5 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-018-9610-0 – ident: 452_CR24 doi: 10.1002/psp4.12422 – volume: 36 start-page: 4316 issue: 27 year: 2017 ident: 452_CR34 publication-title: Stat Med doi: 10.1002/sim.7433 – volume: 44 start-page: 503 issue: 5 year: 2017 ident: 452_CR2 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-017-9534-0 – ident: 452_CR17 doi: 10.1093/ecco-jcc/jjy222.034 – volume: 19 start-page: 172 issue: 1 year: 2017 ident: 452_CR37 publication-title: AAPS J doi: 10.1208/s12248-016-9977-z – volume: 43 start-page: 45 issue: 1 year: 2016 ident: 452_CR6 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-015-9453-x – volume: 22 start-page: 61 issue: 3 year: 2020 ident: 452_CR28 publication-title: AAPS J doi: 10.1208/s12248-020-00441-4 – volume: 381 start-page: 1201 issue: 13 year: 2019 ident: 452_CR20 publication-title: N Engl J Med doi: 10.1056/NEJMoa1900750 – volume: 45 start-page: 679 issue: 5 year: 2018 ident: 452_CR8 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-018-9598-5 – volume: 41 start-page: 335 issue: 4 year: 2014 ident: 452_CR15 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-014-9366-0 – ident: 452_CR3 – volume: 4 start-page: 565 issue: 10 year: 2015 ident: 452_CR1 publication-title: CPT Pharmacometrics Syst Pharmacol doi: 10.1002/psp4.12015 – volume: 38 start-page: 833 year: 2011 ident: 452_CR27 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1007/s10928-011-9222-4 – volume: 8 start-page: 72 issue: 1 year: 2007 ident: 452_CR31 publication-title: Biostatistics doi: 10.1093/biostatistics/kxj034 – volume: 367 start-page: 1519 issue: 16 year: 2012 ident: 452_CR19 publication-title: N Engl J Med doi: 10.1056/NEJMoa1203572 – volume: 30 start-page: 83 issue: 1 year: 2003 ident: 452_CR25 publication-title: J Pharmacokinet Pharmacodyn doi: 10.1023/A:1023249510224 – volume: 34 start-page: 2109 issue: 10 year: 2017 ident: 452_CR36 publication-title: Pharm Res doi: 10.1007/s11095-017-2216-1 – volume: 49 start-page: 162 issue: 2 year: 2009 ident: 452_CR22 publication-title: J Clin Pharmacol doi: 10.1177/0091270008329556 – volume: 27 start-page: 1376 issue: 5 year: 2018 ident: 452_CR33 publication-title: Stat Methods Med Res doi: 10.1177/0962280216661370 – volume: 7 start-page: 205 issue: 4 year: 2018 ident: 452_CR35 publication-title: CPT Pharmacometrics Syst Pharmacol doi: 10.1002/psp4.12280 – volume: 24 start-page: 611 issue: 6 year: 1996 ident: 452_CR11 publication-title: J Pharmacokinet Biopharm doi: 10.1007/BF02353483 – ident: 452_CR16 doi: 10.1093/ecco-jcc/jjy222.088 – volume: 375 start-page: 1946 issue: 20 year: 2016 ident: 452_CR18 publication-title: N Engl J Med doi: 10.1056/NEJMoa1602773 – volume: 74 start-page: 233 issue: 2 year: 2016 ident: 452_CR32 publication-title: METRON. doi: 10.1007/s40300-016-0091-x – volume: 31 start-page: 2152 issue: 8 year: 2014 ident: 452_CR38 publication-title: Pharm Res doi: 10.1007/s11095-014-1315-5 |
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Snippet | Longitudinal exposure–response modeling plays an important role in optimizing dose and dosing regimens in clinical drug development. Certain clinical trials... Longitudinal exposure-response modeling plays an important role in optimizing dose and dosing regimens in clinical drug development. Certain clinical trials... |
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SubjectTerms | Adaptive Clinical Trials as Topic Biochemistry Biomedical and Life Sciences Biomedicine Biotechnology Clinical Trials, Phase III as Topic Colitis, Ulcerative - drug therapy Colitis, Ulcerative - metabolism Dermatologic Agents - metabolism Dermatologic Agents - therapeutic use Double-Blind Method Endpoint Determination - methods Endpoint Determination - trends Humans Longitudinal Studies Models, Biological Multicenter Studies as Topic Pharmacology/Toxicology Pharmacy Research Article Ustekinumab - metabolism Ustekinumab - therapeutic use |
Title | Facilitating Longitudinal Exposure-Response Modeling of a Composite Endpoint Using the Joint Modeling of Sparsely and Frequently Collected Subcomponents |
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