array(41) {
  ["project_status"]=>
  string(7) "ongoing"
  ["project_assoc_trials"]=>
  array(3) {
    [0]=>
    object(WP_Post)#5742 (24) {
      ["ID"]=>
      int(17174)
      ["post_author"]=>
      string(4) "1885"
      ["post_date"]=>
      string(19) "2025-04-29 16:20:54"
      ["post_date_gmt"]=>
      string(19) "2025-04-29 20:20:54"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(214) "NCT02277444 - A Multicenter, Open-Label Trial of Intravenous Golimumab, a Human Anti-TNFα Antibody, in Pediatric Subjects With Active Polyarticular Course Juvenile Idiopathic Arthritis Despite Methotrexate Therapy"
      ["post_excerpt"]=>
      string(0) ""
      ["post_status"]=>
      string(7) "publish"
      ["comment_status"]=>
      string(6) "closed"
      ["ping_status"]=>
      string(6) "closed"
      ["post_password"]=>
      string(0) ""
      ["post_name"]=>
      string(195) "nct02277444-a-multicenter-open-label-trial-of-intravenous-golimumab-a-human-anti-tnf%ce%b1-antibody-in-pediatric-subjects-with-active-polyarticular-course-juvenile-idiopathic-arthritis-despite-me"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2026-01-06 09:59:31"
      ["post_modified_gmt"]=>
      string(19) "2026-01-06 14:59:31"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(60) "https://yoda.yale.edu/?post_type=clinical_trial&p=17174"
      ["menu_order"]=>
      int(0)
      ["post_type"]=>
      string(14) "clinical_trial"
      ["post_mime_type"]=>
      string(0) ""
      ["comment_count"]=>
      string(1) "0"
      ["filter"]=>
      string(3) "raw"
    }
    [1]=>
    object(WP_Post)#5743 (24) {
      ["ID"]=>
      int(1707)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2018-08-06 10:22:00"
      ["post_date_gmt"]=>
      string(19) "2018-08-06 10:22:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(205) "NCT01230827 - A Study of the Safety and Efficacy of CNTO 148 (Golimumab) in Children With Juvenile Idiopathic Arthritis (JIA) and Multiple Joint Involvement Who Have Poor Response to Methotrexate (GO KIDS)"
      ["post_excerpt"]=>
      string(0) ""
      ["post_status"]=>
      string(7) "publish"
      ["comment_status"]=>
      string(6) "closed"
      ["ping_status"]=>
      string(6) "closed"
      ["post_password"]=>
      string(0) ""
      ["post_name"]=>
      string(192) "nct01230827-a-study-of-the-safety-and-efficacy-of-cnto-148-golimumab-in-children-with-juvenile-idiopathic-arthritis-jia-and-multiple-joint-involvement-who-have-poor-response-to-methotrexate-go"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-05-14 15:18:43"
      ["post_modified_gmt"]=>
      string(19) "2025-05-14 19:18:43"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(241) "https://dev-yoda.pantheonsite.io/clinical-trial/nct01230827-a-study-of-the-safety-and-efficacy-of-cnto-148-golimumab-in-children-with-juvenile-idiopathic-arthritis-jia-and-multiple-joint-involvement-who-have-poor-response-to-methotrexate-go/"
      ["menu_order"]=>
      int(0)
      ["post_type"]=>
      string(14) "clinical_trial"
      ["post_mime_type"]=>
      string(0) ""
      ["comment_count"]=>
      string(1) "0"
      ["filter"]=>
      string(3) "raw"
    }
    [2]=>
    object(WP_Post)#5744 (24) {
      ["ID"]=>
      int(1111)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2014-09-22 10:26:00"
      ["post_date_gmt"]=>
      string(19) "2014-09-22 10:26:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(212) "NCT00036374 - A Randomized, Double-Blind Trial of Anti-TNF Chimeric Monoclonal Antibody (Infliximab) in Combination With Methotrexate for the Treatment of Patients With Polyarticular Juvenile Rheumatoid Arthritis"
      ["post_excerpt"]=>
      string(0) ""
      ["post_status"]=>
      string(7) "publish"
      ["comment_status"]=>
      string(6) "closed"
      ["ping_status"]=>
      string(6) "closed"
      ["post_password"]=>
      string(0) ""
      ["post_name"]=>
      string(195) "nct00036374-a-randomized-double-blind-trial-of-anti-tnf-chimeric-monoclonal-antibody-infliximab-in-combination-with-methotrexate-for-the-treatment-of-patients-with-polyarticular-juvenile-rheumato"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-05-14 16:07:52"
      ["post_modified_gmt"]=>
      string(19) "2025-05-14 20:07:52"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(244) "https://dev-yoda.pantheonsite.io/clinical-trial/nct00036374-a-randomized-double-blind-trial-of-anti-tnf-chimeric-monoclonal-antibody-infliximab-in-combination-with-methotrexate-for-the-treatment-of-patients-with-polyarticular-juvenile-rheumato/"
      ["menu_order"]=>
      int(0)
      ["post_type"]=>
      string(14) "clinical_trial"
      ["post_mime_type"]=>
      string(0) ""
      ["comment_count"]=>
      string(1) "0"
      ["filter"]=>
      string(3) "raw"
    }
  }
  ["project_title"]=>
  string(147) "Estimating heterogeneous treatment effects of biologic and targeted synthetic disease modifying drugs (DMARDs) from multiple clinical trials in JIA"
  ["project_narrative_summary"]=>
  string(843) "Due to the heterogeneous nature of JIA, the treatment effect of biologic DMARDs (bDMARDs) and targeted synthetic DMARDs (tsDMARDs) differs in different subgroups of patients. Using data from multiple RCTs can help to improve the power from a single RCT in detecting HTE. One issue is that participants of RCTs may differ from trial to trial and may not be representative of the general patient population. RWD extracted from EHR can augment trial population as an external control to understand treatment effect in general patient population who would otherwise not eligible for a randomized clinical trial. Leveraging on both RCT and RWD, the study is aimed to understand the heterogeneity in treatment effect of DMARDs for treating JIA. Such knowledge will help support patient-centered treatment decisions and caring for children with JIA. "
  ["project_learn_source"]=>
  string(9) "colleague"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(3) "Bin"
    ["last_name"]=>
    string(5) "Huang"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(45) "Cincinnati Children's Hospital Medical Center"
    ["email"]=>
    string(19) "bin.huang@cchmc.org"
    ["state_or_province"]=>
    string(2) "OH"
    ["country"]=>
    string(13) "United States"
  }
  ["project_key_personnel"]=>
  array(4) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(7) "Yuxiang"
      ["p_pers_l_name"]=>
      string(2) "Li"
      ["p_pers_degree"]=>
      string(2) "MS"
      ["p_pers_pr_affil"]=>
      string(45) "Cincinnati Children's Hospital Medical Center"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [1]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(6) "Sandra"
      ["p_pers_l_name"]=>
      string(6) "Andorf"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(45) "Cincinnati Children's Hospital Medical Center"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
    [2]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(4) "Chen"
      ["p_pers_l_name"]=>
      string(4) "Chen"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(45) "Cincinnati Children's Hospital Medical Center"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [3]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(7) "Shixuan"
      ["p_pers_l_name"]=>
      string(4) "Wang"
      ["p_pers_degree"]=>
      string(13) "PhD Candidate"
      ["p_pers_pr_affil"]=>
      string(45) "Cincinnati Children's Hospital Medical Center"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
  }
  ["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_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1632) "Background: Due to the heterogeneous nature of the JIA population, the treatment effect of bDMARDs and tsDMARDs differs in different subgroups of patients. Using data from multiple RCTs can help to improve analytical power from a single RCT in detecting HTE.1 Many flexible non-parametric methods have been proposed to study heterogeneity in treatment effects.2–10

Objective: Estimate CATE of bDMARDs and tsDMARDs in comparison to NSAID and conventional DMARD control for patients with JIA using IPD from multiple clinical trials and RWD extracted from EHR database.

Study design:  This IPD meta comparative effectiveness analyses will follow the trial selection protocol as pre-registered in PROSPERO14,15. The eligible trials are those that investigated currently used treatments indicated for non-systemic JIA with ACR Pediatric JIA criteria or cJADAS1016 as outcomes. 

Participants: all participants from the requested trials.

Primary and secondary outcome measure: the primary outcomes are ACR Pediatric JIA criteria for efficacy and the incidence of SAE for safety. The secondary outcome is the cJADAS10,16 for efficacy and the AEs of special interests for safety. 

Statistical analysis: We will apply and compare several non-parametric approaches including those studied in the recently published literatures.11 Specifically, we will apply causal forest, BART, Gaussian process estimating CATEs of DMARDs. Causal inference using S, T, and X-learner7 will also be used. The RWD will be further designed to match the RCT design and used as historical control and historical treatment data. "
  ["project_brief_bg"]=>
  string(2792) "JIA encompasses multiple heterogeneous chronic inflammatory arthritic conditions of childhood onset that neither have a known etiology nor a cure.18 The treatment of JIA have dramatically improved by bDMARDs in the past two decades with numerous RCTs showing superior response comparing to background placebo control or cDMARD.19 Targeted synthetic disease-modifying antirheumatic drugs (tsDMARDs) is a new class of medication that offers a convenient alternative to other treatments. Despite many advanced highly effective treatment options, nearly half of the children with JIA remain suffer from active JIA. Due to the heterogeneous nature of JIA population, better understanding of heterogeneous treatment effect will help inform better treatment strategies among the many different treatment options. 

RCT is the gold standard to study treatment effects. However, a single RCT is often powered to detect ATE at the trial level and lacks enough power to estimate heterogeneous treatment effects.1 It was found that sample size should be at least four times larger for a trial to detect an effect moderator compared to a trial power to detect ATE. In addition, participants of RCTs may not necessarily represent the general clinical population.12 To overcome these issues, researchers have developed methods and frameworks to explore treatment effect heterogeneity using data from multiple RCTs and evaluate the treatment effect in a target population.20–22 The conventional parametric methods requires a prior knowledge on the parametric relationship between treatment effect and covariates, which are rarely known. Non-parametric methods allow for more flexible approach to model the complex relationships and have been extensively studied in simulation studies using IPD from multiple RCTs under potential outcome framework.11 In addition, one must take care to control type I error. We plan to apply and compare the methods for evaluating CATEs of bDMARDs from eligible trials. Further, we will augment the trial data with RWD collected from large patient registries and from electronic health records (EHRs) collected during routine clinical care from a large pediatric rheumatology clinic in US. The EHRs have been curated, harmonized and validated for non-systematic JIA patients cared for over more than 10 years of span. This RWD provided a sample of general patient sample, which may or may not be captured in the randomized trial samples. While the ACR responses used in the clinical trials are not available, the dataset captured cJADAS10, lab measures, medications and patient demographic and disease characteristics during the routine clinical care. The RWD can be designed to match the trial design, augment trial data both for control and bDMARD treatment.

"
  ["project_specific_aims"]=>
  string(925) "Leveraging on both RCT and RWD, the overarching goal of this study is to support more personalized decision-making and improve health outcomes of JIA by better understanding the heterogeneity in treatment effect of bDMARDs and tsDMARDs for JIA. The project has two specific aims: 

Aim 1. Estimate CATE and explore HTE of different bDMARDs and tsDMARDs by applying and comparing nonparametric causal inference methods using data from multiple clinical trials for patients with non-active systemic JIA; and

Aim 2. Further estimate CATE and explore HTE of different bDMARDs and tsDMARDs from multiple randomized clinical trials by borrowing information obtained from RWD and estimate average treatment effect (ATE) of these DMARDs in a JIA patient population at a US Pediatric Rheumatology Clinic. Power prior methods including covariate-based and outcome-dependent borrowing will be applied and compared. 

"
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(5) "other"
    ["label"]=>
    string(5) "Other"
  }
  ["project_purposes"]=>
  array(4) {
    [0]=>
    array(2) {
      ["value"]=>
      string(22) "participant_level_data"
      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(56) "participant_level_data_meta_analysis_from_yoda_and_other"
      ["label"]=>
      string(69) "Meta-analysis using data from the YODA Project and other data sources"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(34) "research_on_clinical_trial_methods"
      ["label"]=>
      string(34) "Research on clinical trial methods"
    }
  }
  ["project_research_methods"]=>
  string(643) "Inclusion/Exclusion criteria:  we will include data from patients who have been diagnosed with JIA and received treatment indicated for JIA including bDMARD, tsDMARD, conventional DMARD (cDMARD), and NSAID. We will exclude patients who have active systemic JIA (sJIA). The sJIA is excluded because non-systemic and systemic JIA present different disease entities.23

Data source: the individual patient data (IPD) from the requested trials meeting study inc/exc criteria will be obtained from the Vivli and YODA. RWD data collected from patient registries and from EHR during routine clinical care will be included in the study. 

"
  ["project_main_outcome_measure"]=>
  string(1278) "The primary efficacy outcome measures are the ACR Pediatric criteria for JIA at 16+/-4 weeks. The ACR Pedi criteria will be categorized as ACR Pedi 30, ACR Pedi 50, ACR Pedi 70 defined as measuring a minimal 30%, 50%, 70% improvement from baseline in three of six core criteria, respectively. The six core criteria include physician global assessment of disease activity, parent/patient global assessment of overall well-being, functional ability, number of joints with active arthritis, number of joints with limited range of motion, and erythrocyte sedimentation rate (ESR). The secondary efficacy outcome measure is the cJADAS10 which can be derived based on physician’s global rating, patient reported overall wellbeing and number of active joint counts truncated at 10.16 This is a widely adopted point-of-care outcome measure which can be derived from the RCT data. 

The primary safety outcome measure is the incidence rate of serious adverse events reported during the study period of the trials, which is defined as the number of events reported per person-time exposure. The secondary safety outcomes are the incidence rate of adverse events of special interests including skin or UTI infections, gastrointestinal issues, headache, nausea, and fatigue.

"
  ["project_main_predictor_indep"]=>
  string(1433) "The main exposure is the treatment intervention categorized into bDMARD, tsDMARD usage vs. control treatment of other conventional approaches. The bDMARD, tsDMARD when used in combination with cDMARD will be considered as a combination treatment group. The sensitivity analyses will consider separate analyses by class of bDMARD mechanisms. The control group will include all other types of conventional treatment, including cDMARD with or without NSAID. Steroid and/or joint injection are considered as concurrent medication and will be adjusted for. For the randomized withdrawal study, both data from the open-lead-in and the randomization phase will be used. 

The main covariates are baseline covariates, i.e., age, gender, race, duration of JIA, JIA subtypes, onset age, age at diagnosis, treatment assignment, disease activities, such as loss of joint range of motion, active joint count, physician global evaluation, patient wellbeing score, child health assessment questionnaire, ESR, C-reactive protein (CRP), rheumatoid factor (RF) positivity, antinuclear antibody (ANA) positivity, concurrent medications, and previous treatment history of DMARD. Trial level features including the start and end date, study sites, design features such as trial inclusion/exclusion criteria, follow-up time period, trial phase, and trial type (single arm, parallel arm, random withdrawal, registry) will also be considered. 

"
  ["project_other_variables_interest"]=>
  string(5) "None."
  ["project_stat_analysis_plan"]=>
  string(4431) "We will apply the non-parametric causal inference method for estimating HTEs for bDMARDs and tsDMARDs compared to the control treatment, as well as among different mechanism of action. The IPD data from each trial will first be analyzed following the workflow to assess treatment effect heterogeneity for clinical trials (WATCH) framework for estimating CATEs.24 Then the trial level CATEs will be synthesized following recent literatures11,17. By pooling IPD from multiple trials, the increased sample size allows the models to detect interaction between treatment and covariates that would be too weak to identify using data from a single trial. For estimating trial-level CATE, honest causal forest, X-learner, and T-learner with tree-based methods will be used, given their well proven properties as causal learners. The summary results will present variable importance (both as prognostic variable and treatment effect modifier variable), and treatment effect as a function of the top three important variables. For each trial, we will also identify subgroups of patients who are expected to have better treatment effect than averaged treatment effect by applying the approach in Sivaganisan et al25 based on Bayesian’s decision theory. This approach accounts for complexity of a subgroup and the variances of CATE estimations from a non-parametric model to identify subgroups with elevated ATE. The sensitivity analyses will consider separate analyses by mechanism of action, i.e., tumor necrosis factor (TNF) inhibitor and Janus Kinase Inhibitor (JAKi) inhibitor, or in combination with cDMARD. It’s possible that some classes of DMARDs might be represented by a single trial. As a result, the estimates for those classes might be unstable and the results should be interpreted with caution. 

For the specific aim 2, we will consider the RWD obtained from routine clinical care. We will use our well curated, harmonized and validated database that was extracted from EHR from a large single center pediatric rheumatology clinic over 10 years span. The RWD will be further designed to match the RCT design and used as 1) historical control and 2) historical treatment data. This approach provides more information on the control and treatment arm resulting in more accurate point estimates and increased statistical power compared to replying solely on clinical trial data. The WATCH framework will be modified for this RWD data, by applying causal inference method, such as inverse propensity score weighting (IPTW).  The HTE analyses results from trial and observational studies will be combined by discounting evidence from the RWD, using power prior, including propensity score based IPTW power prior and the outcome-based power prior methods previously proposed for historical borrowing.26 Latest reviews on power prior suggest that covariate-based borrowing lead to more biased results than outcome-dependent borrowing.26 We will apply both to the data for comparison. The analyses results will be summarized by presenting CATE as function of important covariates. In addition, the ATE of these DMARDs will be estimated in the real-world population using data from eligible trials by applying causal methods in Dahabreh et al.27 The ATE observed in one population may not be the same as the ATE for the same treatment in a different population if the treatment effect is highly heterogeneous. Given the rigor of data collection in RCT, we do not expect to encounter missing data at the baseline. However, we expect to encounter missing data to loss-to-follow-up, protocol violation or early withdrawal. These missing data when differentially distributed between treatment arm and have known effect on outcomes could raise concerns over intercurrent event (ICE). We will follow the ICH E9(R1) recommended strategies for handling missing data due to ICE. If the missing data is deemed missing at random, we will use multiple imputation technique to handle missing data in the request data. 



All analysis will be conducted in the US on the Vivli secure research environment using data from clinical trials, then those models will be exported and updated externally using RWD. The models we plan to export include Classification and Regression Tree, Generalized Linear Model, Random Forest, Bayesian Additive Regression Trees, and Causal Forest. No training data will be exported via these models."
  ["project_software_used"]=>
  array(2) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(7) "rstudio"
      ["label"]=>
      string(7) "RStudio"
    }
  }
  ["project_timeline"]=>
  string(214) "Anticipated project start date: March 2026

Analysis completion date: March 2027

Date results reported back to the YODA Project: March 2027

Manuscript submission date:  June 2027



"
  ["project_dissemination_plan"]=>
  string(335) "The study will be disseminated through 1-2 scientific publications in Journals in medicine such as Arthritis & Rheumatology. The work will also be presented at national conferences such as Annual Meeting of American College of Rheumatology. The target audience of this study is researchers and clinicians in Pediatric Rheumatology."
  ["project_bibliography"]=>
  string(6755) "

Fleiss J. Design and Analysis of Clinical Experiments. Wiley; 2011.
Green DP, Kern HL. Modeling heterogeneous treatment effects in survey experiments with Bayesian additive regression trees. Public Opinion Quarterly. 2012;76(3):491-511. doi:10.1093/poq/nfs036
Athey S, Imbens G. Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences. 2016;113(27):7353-7360. doi:10.1073/pnas.1510489113
Roy J, Lum KJ, Daniels MJ. A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome. Biostatistics. 2017;18(1):32-47. doi:10.1093/biostatistics/kxw029
Wager S, Athey S. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association. 2018;113(523):1228-1242. doi:10.1080/01621459.2017.1319839
Athey S, Tibshirani J, Wager S. Generalized random forests. The Annals of Statistics. 2019;47(2):1148-1178. doi:10.1214/18-AOS1709
Künzel SR, Sekhon JS, Bickel PJ, Yu B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proceedings of the National Academy of Sciences. 2019;116(10):4156-4165. doi:10.1073/pnas.1804597116
Hahn PR, Murray JS, Carvalho CM. Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion). Bayesian Analysis. 2020;15(3):965-1056. doi:10.1214/19-BA1195
Nie X, Wager S. Quasi-oracle estimation of heterogeneous treatment effects. Biometrika. 2021;108(2):299-319. doi:10.1093/biomet/asaa076
Kennedy EH. Towards optimal doubly robust estimation of heterogeneous causal effects. arXiv. Preprint posted online August 21, 2023. doi:10.48550/arXiv.2004.14497
Brantner CL, Nguyen TQ, Tang T, Zhao C, Hong H, Stuart EA. Comparison of Methods that Combine Multiple Randomized Trials to Estimate Heterogeneous Treatment Effects. arXiv. Preprint posted online November 15, 2023. doi:10.48550/arXiv.2303.16299
Green AK, Trivedi N, Hsu JJ, Yu NL, Bach PB, Chimonas S. Despite The FDA’s Five-Year Plan, Black Patients Remain Inadequately Represented In Clinical Trials For Drugs. Health Aff (Millwood). 2022;41(3):368-374. doi:10.1377/hlthaff.2021.01432
Huang B, Qiu T, Chen C, et al. Timing matters: Real-world effectiveness of early combination of biologic and conventional synthetic disease-modifying antirheumatic drugs for treating newly diagnosed polyarticular course juvenile idiopathic arthritis. RMD Open. 2020;6(1). doi:10.1136/rmdopen-2019-001091
Huang B, Andorf S, Lovell D, et al. Synthesize evidence of effectiveness and safety profiles of disease modifying anti-arthritis drugs (DMARD) in Treating Children with Juvenile Idiopathic Arthritis (JIA): Meta Analyses of the aggregate and individual patient data and subgroup effect. PROSPERO 2023 CRD42023402840. Accessed June 27, 2024. https://www.crd.york.ac.uk/PROSPERO/view/CRD42023402840
Li Y, Huang B, Andorf S, Yue X, Lovell DJ, Brunner HI. Comparative Effectiveness and Safety of the JAK Inhibitors and Biologic Disease-Modifying Antirheumatic Drugs in Treating Children With Nonsystemic Juvenile Idiopathic Arthritis: A Bayesian Meta-Analysis of Randomized Controlled Trials. ACR Open Rheumatol. 2025;7(2):e11788. doi:10.1002/acr2.11788
Consolaro A, Giancane G, Schiappapietra B, et al. Clinical outcome measures in juvenile idiopathic arthritis. Pediatr Rheumatol Online J. 2016;14:23. doi:10.1186/s12969-016-0085-5
Tan X, Chang CCH, Zhou L, Tang L. A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources. Proc Mach Learn Res. 2022;162:21013-21036.
Martini A, Lovell DJ, Albani S, et al. Juvenile idiopathic arthritis. Nat Rev Dis Primers. 2022;8(1):1-18. doi:10.1038/s41572-021-00332-8
Shenoi S, Horneff G, Aggarwal A, Ravelli A. Treatment of non-systemic juvenile idiopathic arthritis. Nat Rev Rheumatol. 2024;20(3):170-181. doi:10.1038/s41584-024-01079-8
Debray TPA, Moons KGM, van Valkenhoef G, et al. Get real in individual participant data (IPD) meta-analysis: a review of the methodology. Res Synth Methods. 2015;6(4):293-309. doi:10.1002/jrsm.1160
Liu Y, Schnitzer ME, Wang G, et al. Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patientdata meta-analysis. Stat Methods Med Res. 2022;31(4):689-705. doi:10.1177/09622802211046383
Seo M, White IR, Furukawa TA, et al. Comparing methods for estimating patient-specific treatment effects in individual patient data meta-analysis. Stat Med. 2021;40(6):1553-1573. doi:10.1002/sim.8859
Bridges JM, Mellins ED, Cron RQ. Recent progress in the treatment of non-systemic juvenile idiopathic arthritis. Fac Rev. 2021;10:23. doi:10.12703/r/10-23
Sechidis K, Sun S, Chen Y, et al. WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors. Pharmaceutical Statistics. 2025;24(2):e2463. doi:10.1002/pst.2463
Sivaganesan Siva, Müller P, Huang B. Subgroup finding via Bayesian additive regression trees. Statistics in Medicine. 2017;36(15):2391-2403. doi:10.1002/sim.7276
Chen MH, Guan Z, Lin M, Sun M. Power Priors for Leveraging Historical Data: Looking Back and Looking Forward. Journal of Data Science. 2024;23(1):1-30. doi:10.6339/24-JDS1161
Dahabreh IJ, Robertson SE, Petito LC, Hernán MA, Steingrimsson JA. Efficient and robust methods for causally interpretable meta-analysis: Transporting inferences from multiple randomized trials to a target population. Biometrics. 2023;79(2):1057-1072. doi:10.1111/biom.13716

"
  ["project_suppl_material"]=>
  array(3) {
    [0]=>
    array(1) {
      ["suppl_file"]=>
      array(21) {
        ["ID"]=>
        int(18712)
        ["id"]=>
        int(18712)
        ["title"]=>
        string(72) "YODA-Project-Research-Proposal_20260130_Data-harmonisation-strategy.docx"
        ["filename"]=>
        string(72) "YODA-Project-Research-Proposal_20260130_Data-harmonisation-strategy.docx"
        ["filesize"]=>
        int(17197)
        ["url"]=>
        string(121) "https://yoda.yale.edu/wp-content/uploads/2026/02/YODA-Project-Research-Proposal_20260130_Data-harmonisation-strategy.docx"
        ["link"]=>
        string(118) "https://yoda.yale.edu/data-request/2025-0344/yoda-project-research-proposal_20260130_data-harmonisation-strategy-docx/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1825"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(72) "yoda-project-research-proposal_20260130_data-harmonisation-strategy-docx"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17262)
        ["date"]=>
        string(19) "2026-02-02 21:49:22"
        ["modified"]=>
        string(19) "2026-02-02 21:49:22"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(71) "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(59) "vnd.openxmlformats-officedocument.wordprocessingml.document"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
    [1]=>
    array(1) {
      ["suppl_file"]=>
      array(21) {
        ["ID"]=>
        int(18714)
        ["id"]=>
        int(18714)
        ["title"]=>
        string(31) "full_list_study_yoda_Vivli.docx"
        ["filename"]=>
        string(31) "full_list_study_yoda_Vivli.docx"
        ["filesize"]=>
        int(16808)
        ["url"]=>
        string(80) "https://yoda.yale.edu/wp-content/uploads/2026/02/full_list_study_yoda_Vivli.docx"
        ["link"]=>
        string(79) "https://yoda.yale.edu/data-request/2025-0344/full_list_study_yoda_vivli-docx-2/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1825"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(33) "full_list_study_yoda_vivli-docx-2"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17262)
        ["date"]=>
        string(19) "2026-02-02 21:49:22"
        ["modified"]=>
        string(19) "2026-02-02 21:49:22"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(71) "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(59) "vnd.openxmlformats-officedocument.wordprocessingml.document"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
    [2]=>
    array(1) {
      ["suppl_file"]=>
      array(21) {
        ["ID"]=>
        int(18818)
        ["id"]=>
        int(18818)
        ["title"]=>
        string(46) "YODA-Project-Research-Proposal_20260130-2.docx"
        ["filename"]=>
        string(46) "YODA-Project-Research-Proposal_20260130-2.docx"
        ["filesize"]=>
        int(77711)
        ["url"]=>
        string(95) "https://yoda.yale.edu/wp-content/uploads/2026/02/YODA-Project-Research-Proposal_20260130-2.docx"
        ["link"]=>
        string(92) "https://yoda.yale.edu/data-request/2025-0344/yoda-project-research-proposal_20260130-2-docx/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1825"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(46) "yoda-project-research-proposal_20260130-2-docx"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17262)
        ["date"]=>
        string(19) "2026-02-25 19:24:07"
        ["modified"]=>
        string(19) "2026-02-25 19:24:07"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(71) "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(59) "vnd.openxmlformats-officedocument.wordprocessingml.document"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
  }
  ["project_coi"]=>
  array(5) {
    [0]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(17266)
        ["id"]=>
        int(17266)
        ["title"]=>
        string(10) "COI-BH.pdf"
        ["filename"]=>
        string(10) "COI-BH.pdf"
        ["filesize"]=>
        int(20294)
        ["url"]=>
        string(59) "https://yoda.yale.edu/wp-content/uploads/2025/05/COI-BH.pdf"
        ["link"]=>
        string(56) "https://yoda.yale.edu/data-request/2025-0344/coi-bh-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1825"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(10) "coi-bh-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17262)
        ["date"]=>
        string(19) "2025-05-12 00:11:07"
        ["modified"]=>
        string(19) "2025-05-12 00:11:09"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
    [1]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(17355)
        ["id"]=>
        int(17355)
        ["title"]=>
        string(16) "COI-YL_CCHMC.pdf"
        ["filename"]=>
        string(16) "COI-YL_CCHMC.pdf"
        ["filesize"]=>
        int(20135)
        ["url"]=>
        string(65) "https://yoda.yale.edu/wp-content/uploads/2025/05/COI-YL_CCHMC.pdf"
        ["link"]=>
        string(62) "https://yoda.yale.edu/data-request/2025-0344/coi-yl_cchmc-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1825"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(16) "coi-yl_cchmc-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17262)
        ["date"]=>
        string(19) "2025-05-20 22:21:04"
        ["modified"]=>
        string(19) "2025-05-20 22:21:06"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
    [2]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(17354)
        ["id"]=>
        int(17354)
        ["title"]=>
        string(10) "COI_SA.pdf"
        ["filename"]=>
        string(10) "COI_SA.pdf"
        ["filesize"]=>
        int(20281)
        ["url"]=>
        string(59) "https://yoda.yale.edu/wp-content/uploads/2025/05/COI_SA.pdf"
        ["link"]=>
        string(56) "https://yoda.yale.edu/data-request/2025-0344/coi_sa-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1825"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(10) "coi_sa-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17262)
        ["date"]=>
        string(19) "2025-05-20 22:21:03"
        ["modified"]=>
        string(19) "2025-05-20 22:21:06"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
    [3]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(17351)
        ["id"]=>
        int(17351)
        ["title"]=>
        string(12) "COI_Chen.pdf"
        ["filename"]=>
        string(12) "COI_Chen.pdf"
        ["filesize"]=>
        int(19741)
        ["url"]=>
        string(61) "https://yoda.yale.edu/wp-content/uploads/2025/05/COI_Chen.pdf"
        ["link"]=>
        string(58) "https://yoda.yale.edu/data-request/2025-0344/coi_chen-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1825"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(12) "coi_chen-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17262)
        ["date"]=>
        string(19) "2025-05-20 22:20:58"
        ["modified"]=>
        string(19) "2025-05-20 22:21:06"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
    [4]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(17352)
        ["id"]=>
        int(17352)
        ["title"]=>
        string(20) "COI_Shixuan_yoda.pdf"
        ["filename"]=>
        string(20) "COI_Shixuan_yoda.pdf"
        ["filesize"]=>
        int(20108)
        ["url"]=>
        string(69) "https://yoda.yale.edu/wp-content/uploads/2025/05/COI_Shixuan_yoda.pdf"
        ["link"]=>
        string(66) "https://yoda.yale.edu/data-request/2025-0344/coi_shixuan_yoda-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1825"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(20) "coi_shixuan_yoda-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17262)
        ["date"]=>
        string(19) "2025-05-20 22:21:00"
        ["modified"]=>
        string(19) "2025-05-20 22:21:06"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
  }
  ["data_use_agreement_training"]=>
  bool(true)
  ["human_research_protection_training"]=>
  bool(true)
  ["certification"]=>
  bool(true)
  ["project_study_design_exp"]=>
  string(76) "Meta-analysis of IPD from multiple clinical trials and RWD from EHR database"
  ["request_data_partner"]=>
  string(15) "johnson-johnson"
  ["search_order"]=>
  string(1) "0"
  ["project_send_email_updates"]=>
  bool(false)
  ["project_publ_available"]=>
  bool(true)
  ["project_year_access"]=>
  string(4) "2026"
  ["project_rep_publ"]=>
  bool(false)
  ["project_assoc_data"]=>
  array(0) {
  }
  ["project_due_dil_assessment"]=>
  array(21) {
    ["ID"]=>
    int(19404)
    ["id"]=>
    int(19404)
    ["title"]=>
    string(47) "YODA Project Due Diligence Assessment 2025-0344"
    ["filename"]=>
    string(51) "YODA-Project-Due-Diligence-Assessment-2025-0344.pdf"
    ["filesize"]=>
    int(156148)
    ["url"]=>
    string(100) "https://yoda.yale.edu/wp-content/uploads/2025/05/YODA-Project-Due-Diligence-Assessment-2025-0344.pdf"
    ["link"]=>
    string(93) "https://yoda.yale.edu/data-request/2025-0344/yoda-project-due-diligence-assessment-2025-0344/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(47) "yoda-project-due-diligence-assessment-2025-0344"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(17262)
    ["date"]=>
    string(19) "2026-06-03 19:34:56"
    ["modified"]=>
    string(19) "2026-06-03 19:34:56"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(19405)
    ["id"]=>
    int(19405)
    ["title"]=>
    string(46) "YODA Project Protocol - 2025-0344 - 2026-02-25"
    ["filename"]=>
    string(46) "YODA-Project-Protocol-2025-0344-2026-02-25.pdf"
    ["filesize"]=>
    int(189549)
    ["url"]=>
    string(95) "https://yoda.yale.edu/wp-content/uploads/2025/05/YODA-Project-Protocol-2025-0344-2026-02-25.pdf"
    ["link"]=>
    string(88) "https://yoda.yale.edu/data-request/2025-0344/yoda-project-protocol-2025-0344-2026-02-25/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(42) "yoda-project-protocol-2025-0344-2026-02-25"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(17262)
    ["date"]=>
    string(19) "2026-06-03 19:35:14"
    ["modified"]=>
    string(19) "2026-06-03 19:35:14"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  array(21) {
    ["ID"]=>
    int(19406)
    ["id"]=>
    int(19406)
    ["title"]=>
    string(36) "YODA Project Review - 2025-0344_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2025-0344_site.pdf"
    ["filesize"]=>
    int(1315606)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2025/05/YODA-Project-Review-2025-0344_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2025-0344/yoda-project-review-2025-0344_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(34) "yoda-project-review-2025-0344_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(17262)
    ["date"]=>
    string(19) "2026-06-03 19:35:31"
    ["modified"]=>
    string(19) "2026-06-03 19:35:31"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_highlight_button"]=>
  string(0) ""
  ["request_overridden_res"]=>
  string(1) "3"
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(2) {
  [0]=>
  string(7) "simponi"
  [1]=>
  string(8) "remicade"
}


num of trials
array(1) {
  [0]=>
  string(1) "3"
}


res
array(1) {
  [0]=>
  string(1) "3"
}