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  ["project_title"]=>
  string(58) "Bayesian nonparametric methods for pediatric extrapolation"
  ["project_narrative_summary"]=>
  string(2668) "The challenges in pediatric drug development include recruitment difficulties and ethical concerns due to the vulnerable nature of the population. To mitigate these issues, it has been proposed to leverage external data (e.g., from a similar adult trial) in order to reduce the recruitment burden necessary to power a pediatric study. However, leveraging this information is controversial since the pediatric and adult populations are clearly heterogeneous. In the case of polyarticular juvenile idiopathic arthritis (PJIA), borrowing information from adult trials in rheumatoid arthritis (RA) has been proposed. Although pharmacokentic / pharmacodynamic studies have shown that the response to treatments between the PJIA and RA populations can be similar, borrowing information from two different disease areas can result in substantially inflated type I error rates, complicating clinical and regulatory decision making.
Bayesian dynamic borrowing (BDB) methods have been a promising approach to leverage adult data in pediatric studies. However, most of these approaches rely on simple parametric models. PJIA and RA are notably heterogeneous, both within and between the diseases. Thus, assumptions from parametric models may be untenable for these disease areas. As a result, the amount of borrowing could be too little (resulting in a loss of statistical power) or too much (resulting in inflated type I error rates). Moreover, traditional BDB approaches conduct uniform discounting of the external data. Considering the heterogeneity of the populations, it is crucial to discount less relevant individuals in the external data set(s) more than pertinent individuals.
Our overarching objective is to decrease the duration of pediatric trials where an established adult therapy is being tested while minimizing type I error inflation. We will develop model-free borrowing techniques of external data via: incorporating adult clinical trial data in a pediatric study (Aim 1); incorporating real-world data in a pediatric study (Aim 2); and development of Bayesian adaptive designs incorporating sequential monitoring (i.e., stopping early due to efficacy or futility) while borrowing information from adult trials and/or RWD (Aim 3). By protecting against model misspecification, our proposed approach will have superior power and/or type I error properties compared to currently existing approaches. We will demonstrate our proposed approach using real pediatric clinical trial data. Our strategy has the potential to dramatically shift the conduct of pediatric clinical trials where it is suitable to borrow information from external data.
" ["project_learn_source"]=> string(5) "other" ["project_learn_source_exp"]=> string(5) "Vivli" ["principal_investigator"]=> array(7) { ["first_name"]=> string(5) "Ethan" ["last_name"]=> string(3) "Alt" ["degree"]=> string(3) "PhD" ["primary_affiliation"]=> string(43) "University of North Carolina at Chapel Hill" ["email"]=> string(21) "ethanalt@live.unc.edu" ["state_or_province"]=> string(14) "North Carolina" ["country"]=> string(3) "USA" } ["project_key_personnel"]=> array(2) { [0]=> array(6) { ["p_pers_f_name"]=> string(6) "Joseph" ["p_pers_l_name"]=> string(7) "Ibrahim" ["p_pers_degree"]=> string(3) "PhD" ["p_pers_pr_affil"]=> string(28) "University of North Carolina" ["p_pers_scop_id"]=> string(10) "7005341361" ["requires_data_access"]=> string(0) "" } [1]=> array(6) { ["p_pers_f_name"]=> string(7) "Eveline" ["p_pers_l_name"]=> string(2) "Wu" ["p_pers_degree"]=> string(2) "MD" ["p_pers_pr_affil"]=> string(28) "University of North Carolina" ["p_pers_scop_id"]=> string(11) "56182677100" ["requires_data_access"]=> string(0) "" } } ["project_ext_grants"]=> array(2) { ["value"]=> string(2) "no" ["label"]=> string(68) "No external grants or funds are being used to support this research." } ["project_funding_source"]=> string(48) "We will use this data to apply for an NIH grant." ["project_date_type"]=> string(18) "full_crs_supp_docs" ["property_scientific_abstract"]=> string(1980) "Background: The challenges in pediatric drug development include recruitment difficulties and ethical concerns due to the vulnerable nature of the population. To mitigate these issues, it has been proposed to leverage external data (e.g., from a similar adult trial) in order to reduce the recruitment burden necessary to power a pediatric study. However, leveraging this information is controversial since the pediatric and adult populations are clearly heterogeneous. In the case of polyarticular juvenile idiopathic arthritis (PJIA), borrowing information from adult trials in rheumatoid arthritis (RA) has been proposed. Although pharmacokentic / pharmacodynamic studies have shown that the response to treatments between the PJIA and RA populations can be similar, borrowing information from two different disease areas can result in substantially inflated type I error rates, complicating clinical and regulatory decision making.

Objective: To develop Bayesian nonparametric methods to borrow information from adults in pediatric studies, with an application to PJIA.

Study Design: We will demonstrate our novel methodology on real clinical trial examples. Pairs of adult RA / pediatric PJIA studies will be identified, and analysis will be conducted using the proposed methodology. We will compare our approach with existing approaches such as the power prior, meta-analytic predictive prior, and commensurate prior.

Participants: Our study population consists of children diagnosed with PJIA. We will borrow information from adults with RA to increase the precision of our estimates.

Primary and Secondary Outcome Measure(s): We will determine the outcomes after the methodology is developed based upon which outcome(s) are appropriate to borrow from adults.

Statistical Analysis : We will develop novel statistical methodology to enable robust borrowing from pediatrics to adults in PJIA trials." ["project_brief_bg"]=> string(3107) "Pediatric drug development poses a unique set of challenges ranging from recruitment difficulties of a small population[1–3] to ethical issues of experimenting on a vulnerable population that cannot provide informed consent.[1,4–7] In the case of polyarticular juvenile idiopathic arthritis (PJIA), a rare autoimmune disease that causes children to have arthritis in five or more joints, experts have recently called for more treatments, citing a failure of patients to achieve remission based on currently approved drugs.[8] Many have proposed leveraging adult trial data in rheumatoid arthritis (RA) in PJIA trial design.[9–12]
Bayesian dynamic borrowing (BDB) methods, which can result in increased power to detect a treatment effect, have emerged as a promising approach to extrapolate information from adults to pediatrics, resulting in a lower sample size to power an effect. However, incorporating adult data is controversial as the populations are not homogeneous, which can jeopardize decision making. On the other hand, not allowing for such borrowing presents a deterrent for sponsors to conduct pediatric trial, impeding the availability of drugs available for children. A workshop hosted between key stakeholders and FDA, coming to the conclusion that polyarticular course JIAs may be more similar to adult diseases than other pediatric arthritides, specifically mentioned Bayesian approaches to borrow information from adults in PJIA clinical trials.[11]
However, the dose-response relationships between and within PJIA and RA are heterogeneous.[13] It thus seems unreasonable to borrow information from adults to children using simple parametric models failing to account for this heterogeneity. Specifically, the amount of information borrowed may be too much (yielding substantial bias and inflating type I error rates) or too little (resulting in a lack of precision and insufficient power). Unfortunately, most external data priors currently existing, including the power prior,[14] robust meta-analtyic predictive prior,[15] and commensurate prior,[16] have been developed only for parametric models and conduct blanket discounting of the external data set. There is thus an unmet need for methods that can (i) flexibly model the the response of PJIA to a given treatment and (ii) robustly borrow information from adults, discounting adult trial participants who are less relevant for the pediatric study in a data-driven fashion.
Our overarching goal is to develop Bayesian nonparametric dynamic borrowing (BNPDB) methods for the design and analysis of pediatric clinical trials that leverage external data. Our proposed approach approach allows for observation-specific discounting of the external data, where the degree of discounting is data-driven, depending on how well a given participant fits to the pediatric trial data. This makes the framework more robust than existing approaches and could result in substantial efficiency gains when only a subset of the adult population is relevant for the pediatric trial data (perhaps due to disease subtypes). " ["project_specific_aims"]=> string(1469) "Aim 1: Develop a BNPDB framework to borrow information from adult trials in a pediatric study (no confounders). We will: (i) use BNP approaches to make model-free assumptions about the outcomes in the pediatric and adult trial data; (ii) use mixture modeling to estimate the probability that an adult trial participant has the same density as a pediatric participant; and (iii) evaluate the power gains and type I error inflation compared to traditional borrowing approaches.
Aim 2: Develop a BNP approach to incorporate adult and pediatric real-world data in pediatric clinical trials (where confounding is a concern). We will: (i) use BNP approaches and propensity score methods to conduct unbiased, model-free inference on the treatment effect of the pediatric trial and real-world data sets; (ii) use mixture modeling to estimate the probability that an individual from an external data set has the same outcome distribution as the pediatric population; and (iii) evaluate the power gains and type I error inflation under the proposed approach.
Aim 3: Develop an adaptive design for pediatric trials borrowing information from other trials and/or realworld data. We will: (i) use our BNPDB approaches to develop a sequential monitoring framework that robustly incorporates external data in Bayesian adaptive designs; and (ii) evaluate the power gains and type I error inflation comparing against traditional designs with and without borrowing.
" ["project_study_design"]=> array(2) { ["value"]=> string(8) "meth_res" ["label"]=> string(23) "Methodological research" } ["project_purposes"]=> array(3) { [0]=> array(2) { ["value"]=> string(59) "preliminary_research_to_be_used_as_part_of_a_grant_proposal" ["label"]=> string(59) "Preliminary research to be used as part of a grant proposal" } [1]=> array(2) { ["value"]=> string(37) "develop_or_refine_statistical_methods" ["label"]=> string(37) "Develop or refine statistical methods" } [2]=> array(2) { ["value"]=> string(34) "research_on_clinical_trial_methods" ["label"]=> string(34) "Research on clinical trial methods" } } ["project_software_used"]=> array(2) { ["value"]=> string(86) "not_analyzing_participant_level_data__plan_to_use_another_secure_data_sharing_platform" ["label"]=> string(92) "I am not analyzing participant-level data / plan to use another secure data sharing platform" } ["project_software_used_exp"]=> string(43) "We will use RStudio via the Vivli platform." ["project_research_methods"]=> string(629) "To keep our example realistic, the inclusion/exclusion criteria will be the same as that for each individual trial's.

Trial data will not be pooled. Rather, an informative prior for the pediatric trial parameters will be created based on the corresponding adult trial (i.e., the adult trial in RA that has the same treatment(s)).

Use of the Vivli Secure Research Environment is simply because Vivli offers additional trials of interest, and it will be convenient to analyze each adult/pediatric trial-pair in one environment. The Vivli trials that will be requested are NCT01007435 and NCT00988221." ["project_main_outcome_measure"]=> string(1080) "As this is for methodological research, we must conduct exploratory data analysis before we decide on the outcome. However, the current plan is to use components of the ACR scores.

ACR score is a scale to measure change in rheumatoid arthritis symptoms. It is named after the American College of Rheumatology. The ACR score is more often used in clinical trials than in doctor patient-relationships, as it allows a common standard between researchers.

Different degrees of improvement are referred to as ACR20, ACR50, ACR70. ACR20 was initially proposed with ACR scoring, measuring a 20% improvement on a scale of 28 intervals. ACR50 and ACR70 were later proposed, corresponding to 50% and 70% improvements.

The Rheumatoid Arthritis Severity Scale (RASS) is based on sections of the ACR scoring system.

The 2010 ACR / EULAR Rheumatoid Arthritis Classification Criteria, which includes anti-CCP testing, has been developed to focus on early disease, and on features that are associated with persistent or erosive disease." ["project_main_predictor_indep"]=> string(239) "We will consider each adult/pediatric trial as a pair. Each trial-pair will have the same exposure (control vs. exploratory treatment). For our study, the independent variable will be precisely the same as each trial-pair.

" ["project_other_variables_interest"]=> string(36) "Age, sex, race, prior use of therapy" ["project_stat_analysis_plan"]=> string(635) "This project is for statistical methods development. Specifically, we aim to use Bayesian nonparametric techniques to borrow information from adults to pediatrics. We will compare our approach against other existing priors, such as the power prior, commensurate prior, and potentially propensity score integrated versions of these priors.

Using these approaches as well as our own, we will compare point estimation (posterior mean, mode, and/or quantiles), uncertainty (posterior standard deviation), and interval estimation (credible intervals). The goal is to see how our approach differs from the previous approaches." ["project_timeline"]=> string(155) "Start date: January 2024
Analysis completion date: December 2024
Results reported to Yoda: March 2025
Manuscript submitted: April 2025" ["project_dissemination_plan"]=> string(227) "We will publish our findings in biostatistics methods journals such as Journal of the American Statistical Association, Biostatistics, and Biometrics. The target audience is statisticians working in pediatric clinical research." ["project_bibliography"]=> string(5523) "
  1. T. Smith, P. R. Williamson, and M. W. Beresford, “Methodology of clinical trials for rare diseases,” Best practice & research Clinical rheumatology, vol. 28, no. 2, pp. 247–262, 2014.
  2. A. Weintraub and C. E. Breland, “Challenges, benefits, and factors to enhance recruitment and inclusion of children in pediatric dental research,” International Journal of Paediatric Dentistry, vol. 25, no. 5, pp. 310–316, 2015.
  3. Welzel, C. Winskill, N. Zhang, A. Woerner, and M. Pfister, “Biologic disease modifying antirheumatic drugs and janus kinase inhibitors in paediatric rheumatology–what we know and what we do not know from randomized controlled trials,” Pediatric Rheumatology, vol. 19, no. 1, pp. 1–17, 2021.
  4. Afshar, A. Lodha, A. Costei, and N. Vaneyke, “Recruitment in pediatric clinical trials: an ethical perspective,” The Journal of Urology, vol. 174, no. 3, pp. 835–840, 2005.
  5. Laventhal, B. A. Tarini, and J. Lantos, “Ethical issues in neonatal and pediatric clinical trials,” Pediatric Clinics, vol. 59, no. 5, pp. 1205–1220, 2012.
  6. Sammons and E. Starkey, “Ethical issues of clinical trials in children,” Paediatrics and child health, vol. 26, no. 3, pp. 95–98, 2016.
  7. M. Nelson, “The use of pediatric extrapolation to avoid unnecessary pediatric clinical trials,” The American Journal of Bioethics, vol. 20, no. 4, pp. 114–116, 2020.
  8. I. Brunner, L. E. Schanberg, Y. Kimura, A. Dennos, D. O. Co, R. A. Colbert, R. C. Fuhlbrigge, E. Goldmuntz, D. J. Kingsbury, C. Patty-Resk, et al., “New medications are needed for children with juvenile idiopathic arthritis,” Arthritis & Rheumatology, vol. 72, no. 11, pp. 1945–1951, 2020.
  9. Singh, V. D. Ivaturi, J. Penzenstadler, T. Liu, J. Chen, A. Marathe, P. Ji, R. Glaser, N. Nikolov, and C. Sahajwalla, “Response similarity assessment between polyarticular juvenile idiopathic arthritis and adult rheumatoid arthritis for biologics,” Clinical Pharmacology & Therapeutics, vol. 110, no. 1, pp. 98–107, 2021.
  10. H. Leu, N. J. Shiff, M. Clark, K. Bensley, K. G. Lomax, K. Berezny, R. M. Nelson, H. Zhou, and Z. Xu, “Intravenous golimumab in patients with polyarticular juvenile idiopathic arthritis and juvenile psoriatic arthritis and subcutaneous ustekinumab in patients with juvenile psoriatic arthritis: Extrapolation of data from studies in adults and adjacent pediatric populations,” Pediatric Drugs, vol. 24, no. 6, pp. 699–714, 2022.
  11. E.Schanberg, L.Mulugeta, B.Akinlade, H.I.Brunner, J.Chen, R.A.Colbert, V.Delgaizo, M.R.Gastonguay, R. Glaser, L. Imundo, et al., “Therapeutic development in polyarticular course juvenile idiopathic arthritis: Extrapolation, dose selection, and clinical trial design,” Arthritis & Rheumatology, vol. 75, no. 10, pp. 1856– 1866, 2023.
  12. Zhang, Y. Wang, M. Khurana, H. C. Sachs, H. Zhu, G. J. Burckart, J. Alexander, L. P. Yao, and J. Wang, “Exposure–response assessment in pediatric drug development studies submitted to the us food and drug administration,” Clinical Pharmacology & Therapeutics, vol. 108, no. 1, pp. 90–98, 2020.
  13. Triaille, P. Quartier, L. De Somer, P. Durez, B. Lauwerys, P. Verschueren, P. C. Taylor, and C. Wouters, “Patternsanddeterminantsofresponsetonoveltherapiesinjuvenileandadult-onsetpolyarthritis,” Rheumatology, p. kead490, 2023.
  14. G. Ibrahim and M.-H. Chen, “Power prior distributions for regression models,” StatisticalScience, pp. 46–60, 2000.
  15. Schmidli, S. Gsteiger, S. Roychoudhury, A. O’Hagan, D. Spiegelhalter, and B. Neuenschwander, “Robust meta-analytic-predictive priors in clinical trials with historical control information,” Biometrics, vol. 70, no. 4, pp. 1023–1032, 2014.
  16. P. Hobbs, D. J. Sargent, and B. P. Carlin, “Commensurate priors for incorporating historical information in clinical trials using general and generalized linear models,” Bayesian Analysis (Online), vol. 7, no. 3, p. 639, 2012.
  17. Ruperto, D. J. Lovell, R. Cuttica, N. Wilkinson, P. Woo, G. Espada, C. Wouters, E. D. Silverman, Z. Balogh, M. Henrickson, et al., “A randomized, placebo-controlled trial of infliximab plus methotrexate for the treatment of polyarticular-course juvenile rheumatoid arthritis,” Arthritis & Rheumatism: Official Journal of the American College of Rheumatology, vol. 56, no. 9, pp. 3096–3106, 2007.
  18. I. Brunner, N. Ruperto, Z. Zuber, C. Keane, O. Harari, A. Kenwright, P. Lu, R. Cuttica, V. Keltsev, R. M. Xavier, et al., “Efficacy and safety of tocilizumab in patients with polyarticular-course juvenile idiopathic arthritis: results from a phase 3, randomised, double-blind withdrawal trial,” Annals of the rheumatic diseases, vol. 74, no. 6, pp. 1110–1117, 2015.
  19. R. Burmester, W. F. Rigby, R. F. Van Vollenhoven, J. Kay, A. Rubbert-Roth, R. Blanco, A. Kadva, and S. Dimonaco, “Tocilizumab combination therapy or monotherapy or methotrexate monotherapy in methotrexatenaive patients with early rheumatoid arthritis: 2-year clinical and radiographic results from the randomised, placebo-controlled function trial,” Annals of the rheumatic diseases, vol. 76, no. 7, pp. 1279–1284, 2017.
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2023-5446

Research Proposal

Project Title: Bayesian nonparametric methods for pediatric extrapolation

Scientific Abstract: Background: The challenges in pediatric drug development include recruitment difficulties and ethical concerns due to the vulnerable nature of the population. To mitigate these issues, it has been proposed to leverage external data (e.g., from a similar adult trial) in order to reduce the recruitment burden necessary to power a pediatric study. However, leveraging this information is controversial since the pediatric and adult populations are clearly heterogeneous. In the case of polyarticular juvenile idiopathic arthritis (PJIA), borrowing information from adult trials in rheumatoid arthritis (RA) has been proposed. Although pharmacokentic / pharmacodynamic studies have shown that the response to treatments between the PJIA and RA populations can be similar, borrowing information from two different disease areas can result in substantially inflated type I error rates, complicating clinical and regulatory decision making.

Objective: To develop Bayesian nonparametric methods to borrow information from adults in pediatric studies, with an application to PJIA.

Study Design: We will demonstrate our novel methodology on real clinical trial examples. Pairs of adult RA / pediatric PJIA studies will be identified, and analysis will be conducted using the proposed methodology. We will compare our approach with existing approaches such as the power prior, meta-analytic predictive prior, and commensurate prior.

Participants: Our study population consists of children diagnosed with PJIA. We will borrow information from adults with RA to increase the precision of our estimates.

Primary and Secondary Outcome Measure(s): We will determine the outcomes after the methodology is developed based upon which outcome(s) are appropriate to borrow from adults.

Statistical Analysis : We will develop novel statistical methodology to enable robust borrowing from pediatrics to adults in PJIA trials.

Brief Project Background and Statement of Project Significance: Pediatric drug development poses a unique set of challenges ranging from recruitment difficulties of a small population[1–3] to ethical issues of experimenting on a vulnerable population that cannot provide informed consent.[1,4–7] In the case of polyarticular juvenile idiopathic arthritis (PJIA), a rare autoimmune disease that causes children to have arthritis in five or more joints, experts have recently called for more treatments, citing a failure of patients to achieve remission based on currently approved drugs.[8] Many have proposed leveraging adult trial data in rheumatoid arthritis (RA) in PJIA trial design.[9–12]
Bayesian dynamic borrowing (BDB) methods, which can result in increased power to detect a treatment effect, have emerged as a promising approach to extrapolate information from adults to pediatrics, resulting in a lower sample size to power an effect. However, incorporating adult data is controversial as the populations are not homogeneous, which can jeopardize decision making. On the other hand, not allowing for such borrowing presents a deterrent for sponsors to conduct pediatric trial, impeding the availability of drugs available for children. A workshop hosted between key stakeholders and FDA, coming to the conclusion that polyarticular course JIAs may be more similar to adult diseases than other pediatric arthritides, specifically mentioned Bayesian approaches to borrow information from adults in PJIA clinical trials.[11]
However, the dose-response relationships between and within PJIA and RA are heterogeneous.[13] It thus seems unreasonable to borrow information from adults to children using simple parametric models failing to account for this heterogeneity. Specifically, the amount of information borrowed may be too much (yielding substantial bias and inflating type I error rates) or too little (resulting in a lack of precision and insufficient power). Unfortunately, most external data priors currently existing, including the power prior,[14] robust meta-analtyic predictive prior,[15] and commensurate prior,[16] have been developed only for parametric models and conduct blanket discounting of the external data set. There is thus an unmet need for methods that can (i) flexibly model the the response of PJIA to a given treatment and (ii) robustly borrow information from adults, discounting adult trial participants who are less relevant for the pediatric study in a data-driven fashion.
Our overarching goal is to develop Bayesian nonparametric dynamic borrowing (BNPDB) methods for the design and analysis of pediatric clinical trials that leverage external data. Our proposed approach approach allows for observation-specific discounting of the external data, where the degree of discounting is data-driven, depending on how well a given participant fits to the pediatric trial data. This makes the framework more robust than existing approaches and could result in substantial efficiency gains when only a subset of the adult population is relevant for the pediatric trial data (perhaps due to disease subtypes).

Specific Aims of the Project: Aim 1: Develop a BNPDB framework to borrow information from adult trials in a pediatric study (no confounders). We will: (i) use BNP approaches to make model-free assumptions about the outcomes in the pediatric and adult trial data; (ii) use mixture modeling to estimate the probability that an adult trial participant has the same density as a pediatric participant; and (iii) evaluate the power gains and type I error inflation compared to traditional borrowing approaches.
Aim 2: Develop a BNP approach to incorporate adult and pediatric real-world data in pediatric clinical trials (where confounding is a concern). We will: (i) use BNP approaches and propensity score methods to conduct unbiased, model-free inference on the treatment effect of the pediatric trial and real-world data sets; (ii) use mixture modeling to estimate the probability that an individual from an external data set has the same outcome distribution as the pediatric population; and (iii) evaluate the power gains and type I error inflation under the proposed approach.
Aim 3: Develop an adaptive design for pediatric trials borrowing information from other trials and/or realworld data. We will: (i) use our BNPDB approaches to develop a sequential monitoring framework that robustly incorporates external data in Bayesian adaptive designs; and (ii) evaluate the power gains and type I error inflation comparing against traditional designs with and without borrowing.

Study Design: Methodological research

What is the purpose of the analysis being proposed? Please select all that apply.: Preliminary research to be used as part of a grant proposal Develop or refine statistical methods Research on clinical trial methods

Software Used: I am not analyzing participant-level data / plan to use another secure data sharing platform

Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study: To keep our example realistic, the inclusion/exclusion criteria will be the same as that for each individual trial's.

Trial data will not be pooled. Rather, an informative prior for the pediatric trial parameters will be created based on the corresponding adult trial (i.e., the adult trial in RA that has the same treatment(s)).

Use of the Vivli Secure Research Environment is simply because Vivli offers additional trials of interest, and it will be convenient to analyze each adult/pediatric trial-pair in one environment. The Vivli trials that will be requested are NCT01007435 and NCT00988221.

Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study: As this is for methodological research, we must conduct exploratory data analysis before we decide on the outcome. However, the current plan is to use components of the ACR scores.

ACR score is a scale to measure change in rheumatoid arthritis symptoms. It is named after the American College of Rheumatology. The ACR score is more often used in clinical trials than in doctor patient-relationships, as it allows a common standard between researchers.

Different degrees of improvement are referred to as ACR20, ACR50, ACR70. ACR20 was initially proposed with ACR scoring, measuring a 20% improvement on a scale of 28 intervals. ACR50 and ACR70 were later proposed, corresponding to 50% and 70% improvements.

The Rheumatoid Arthritis Severity Scale (RASS) is based on sections of the ACR scoring system.

The 2010 ACR / EULAR Rheumatoid Arthritis Classification Criteria, which includes anti-CCP testing, has been developed to focus on early disease, and on features that are associated with persistent or erosive disease.

Main Predictor/Independent Variable and how it will be categorized/defined for your study: We will consider each adult/pediatric trial as a pair. Each trial-pair will have the same exposure (control vs. exploratory treatment). For our study, the independent variable will be precisely the same as each trial-pair.

Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: Age, sex, race, prior use of therapy

Statistical Analysis Plan: This project is for statistical methods development. Specifically, we aim to use Bayesian nonparametric techniques to borrow information from adults to pediatrics. We will compare our approach against other existing priors, such as the power prior, commensurate prior, and potentially propensity score integrated versions of these priors.

Using these approaches as well as our own, we will compare point estimation (posterior mean, mode, and/or quantiles), uncertainty (posterior standard deviation), and interval estimation (credible intervals). The goal is to see how our approach differs from the previous approaches.

Narrative Summary: The challenges in pediatric drug development include recruitment difficulties and ethical concerns due to the vulnerable nature of the population. To mitigate these issues, it has been proposed to leverage external data (e.g., from a similar adult trial) in order to reduce the recruitment burden necessary to power a pediatric study. However, leveraging this information is controversial since the pediatric and adult populations are clearly heterogeneous. In the case of polyarticular juvenile idiopathic arthritis (PJIA), borrowing information from adult trials in rheumatoid arthritis (RA) has been proposed. Although pharmacokentic / pharmacodynamic studies have shown that the response to treatments between the PJIA and RA populations can be similar, borrowing information from two different disease areas can result in substantially inflated type I error rates, complicating clinical and regulatory decision making.
Bayesian dynamic borrowing (BDB) methods have been a promising approach to leverage adult data in pediatric studies. However, most of these approaches rely on simple parametric models. PJIA and RA are notably heterogeneous, both within and between the diseases. Thus, assumptions from parametric models may be untenable for these disease areas. As a result, the amount of borrowing could be too little (resulting in a loss of statistical power) or too much (resulting in inflated type I error rates). Moreover, traditional BDB approaches conduct uniform discounting of the external data. Considering the heterogeneity of the populations, it is crucial to discount less relevant individuals in the external data set(s) more than pertinent individuals.
Our overarching objective is to decrease the duration of pediatric trials where an established adult therapy is being tested while minimizing type I error inflation. We will develop model-free borrowing techniques of external data via: incorporating adult clinical trial data in a pediatric study (Aim 1); incorporating real-world data in a pediatric study (Aim 2); and development of Bayesian adaptive designs incorporating sequential monitoring (i.e., stopping early due to efficacy or futility) while borrowing information from adult trials and/or RWD (Aim 3). By protecting against model misspecification, our proposed approach will have superior power and/or type I error properties compared to currently existing approaches. We will demonstrate our proposed approach using real pediatric clinical trial data. Our strategy has the potential to dramatically shift the conduct of pediatric clinical trials where it is suitable to borrow information from external data.

Project Timeline: Start date: January 2024
Analysis completion date: December 2024
Results reported to Yoda: March 2025
Manuscript submitted: April 2025

Dissemination Plan: We will publish our findings in biostatistics methods journals such as Journal of the American Statistical Association, Biostatistics, and Biometrics. The target audience is statisticians working in pediatric clinical research.

Bibliography:

  1. T. Smith, P. R. Williamson, and M. W. Beresford, “Methodology of clinical trials for rare diseases,” Best practice & research Clinical rheumatology, vol. 28, no. 2, pp. 247–262, 2014.
  2. A. Weintraub and C. E. Breland, “Challenges, benefits, and factors to enhance recruitment and inclusion of children in pediatric dental research,” International Journal of Paediatric Dentistry, vol. 25, no. 5, pp. 310–316, 2015.
  3. Welzel, C. Winskill, N. Zhang, A. Woerner, and M. Pfister, “Biologic disease modifying antirheumatic drugs and janus kinase inhibitors in paediatric rheumatology–what we know and what we do not know from randomized controlled trials,” Pediatric Rheumatology, vol. 19, no. 1, pp. 1–17, 2021.
  4. Afshar, A. Lodha, A. Costei, and N. Vaneyke, “Recruitment in pediatric clinical trials: an ethical perspective,” The Journal of Urology, vol. 174, no. 3, pp. 835–840, 2005.
  5. Laventhal, B. A. Tarini, and J. Lantos, “Ethical issues in neonatal and pediatric clinical trials,” Pediatric Clinics, vol. 59, no. 5, pp. 1205–1220, 2012.
  6. Sammons and E. Starkey, “Ethical issues of clinical trials in children,” Paediatrics and child health, vol. 26, no. 3, pp. 95–98, 2016.
  7. M. Nelson, “The use of pediatric extrapolation to avoid unnecessary pediatric clinical trials,” The American Journal of Bioethics, vol. 20, no. 4, pp. 114–116, 2020.
  8. I. Brunner, L. E. Schanberg, Y. Kimura, A. Dennos, D. O. Co, R. A. Colbert, R. C. Fuhlbrigge, E. Goldmuntz, D. J. Kingsbury, C. Patty-Resk, et al., “New medications are needed for children with juvenile idiopathic arthritis,” Arthritis & Rheumatology, vol. 72, no. 11, pp. 1945–1951, 2020.
  9. Singh, V. D. Ivaturi, J. Penzenstadler, T. Liu, J. Chen, A. Marathe, P. Ji, R. Glaser, N. Nikolov, and C. Sahajwalla, “Response similarity assessment between polyarticular juvenile idiopathic arthritis and adult rheumatoid arthritis for biologics,” Clinical Pharmacology & Therapeutics, vol. 110, no. 1, pp. 98–107, 2021.
  10. H. Leu, N. J. Shiff, M. Clark, K. Bensley, K. G. Lomax, K. Berezny, R. M. Nelson, H. Zhou, and Z. Xu, “Intravenous golimumab in patients with polyarticular juvenile idiopathic arthritis and juvenile psoriatic arthritis and subcutaneous ustekinumab in patients with juvenile psoriatic arthritis: Extrapolation of data from studies in adults and adjacent pediatric populations,” Pediatric Drugs, vol. 24, no. 6, pp. 699–714, 2022.
  11. E.Schanberg, L.Mulugeta, B.Akinlade, H.I.Brunner, J.Chen, R.A.Colbert, V.Delgaizo, M.R.Gastonguay, R. Glaser, L. Imundo, et al., “Therapeutic development in polyarticular course juvenile idiopathic arthritis: Extrapolation, dose selection, and clinical trial design,” Arthritis & Rheumatology, vol. 75, no. 10, pp. 1856– 1866, 2023.
  12. Zhang, Y. Wang, M. Khurana, H. C. Sachs, H. Zhu, G. J. Burckart, J. Alexander, L. P. Yao, and J. Wang, “Exposure–response assessment in pediatric drug development studies submitted to the us food and drug administration,” Clinical Pharmacology & Therapeutics, vol. 108, no. 1, pp. 90–98, 2020.
  13. Triaille, P. Quartier, L. De Somer, P. Durez, B. Lauwerys, P. Verschueren, P. C. Taylor, and C. Wouters, “Patternsanddeterminantsofresponsetonoveltherapiesinjuvenileandadult-onsetpolyarthritis,” Rheumatology, p. kead490, 2023.
  14. G. Ibrahim and M.-H. Chen, “Power prior distributions for regression models,” StatisticalScience, pp. 46–60, 2000.
  15. Schmidli, S. Gsteiger, S. Roychoudhury, A. O’Hagan, D. Spiegelhalter, and B. Neuenschwander, “Robust meta-analytic-predictive priors in clinical trials with historical control information,” Biometrics, vol. 70, no. 4, pp. 1023–1032, 2014.
  16. P. Hobbs, D. J. Sargent, and B. P. Carlin, “Commensurate priors for incorporating historical information in clinical trials using general and generalized linear models,” Bayesian Analysis (Online), vol. 7, no. 3, p. 639, 2012.
  17. Ruperto, D. J. Lovell, R. Cuttica, N. Wilkinson, P. Woo, G. Espada, C. Wouters, E. D. Silverman, Z. Balogh, M. Henrickson, et al., “A randomized, placebo-controlled trial of infliximab plus methotrexate for the treatment of polyarticular-course juvenile rheumatoid arthritis,” Arthritis & Rheumatism: Official Journal of the American College of Rheumatology, vol. 56, no. 9, pp. 3096–3106, 2007.
  18. I. Brunner, N. Ruperto, Z. Zuber, C. Keane, O. Harari, A. Kenwright, P. Lu, R. Cuttica, V. Keltsev, R. M. Xavier, et al., “Efficacy and safety of tocilizumab in patients with polyarticular-course juvenile idiopathic arthritis: results from a phase 3, randomised, double-blind withdrawal trial,” Annals of the rheumatic diseases, vol. 74, no. 6, pp. 1110–1117, 2015.
  19. R. Burmester, W. F. Rigby, R. F. Van Vollenhoven, J. Kay, A. Rubbert-Roth, R. Blanco, A. Kadva, and S. Dimonaco, “Tocilizumab combination therapy or monotherapy or methotrexate monotherapy in methotrexatenaive patients with early rheumatoid arthritis: 2-year clinical and radiographic results from the randomised, placebo-controlled function trial,” Annals of the rheumatic diseases, vol. 76, no. 7, pp. 1279–1284, 2017.