array(41) {
  ["project_status"]=>
  string(30) "approved_pending_dua_signature"
  ["project_assoc_trials"]=>
  array(1) {
    [0]=>
    object(WP_Post)#4539 (24) {
      ["ID"]=>
      int(1963)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2018-10-23 18:35:00"
      ["post_date_gmt"]=>
      string(19) "2018-10-23 18:35:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(213) "NCT01453725 - A Multicenter, Randomized, Double-blind, Placebo-controlled Study of the Effect of Golimumab Administered Subcutaneously in Subjects With Active Axial Spondyloarthritis (Also Known as MK-8259-006-02)"
      ["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(194) "nct01453725-a-multicenter-randomized-double-blind-placebo-controlled-study-of-the-effect-of-golimumab-administered-subcutaneously-in-subjects-with-active-axial-spondyloarthritis-also-known-as-mk"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-08-22 15:16:21"
      ["post_modified_gmt"]=>
      string(19) "2025-08-22 19:16:21"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(70) "https://dev-yoda.pantheonsite.io/?post_type=clinical_trial&p=1963"
      ["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(118) "Predictors of Clinical Response in Non-radiographic Axial Spondyloarthritis; a post hoc analysis of the GO-AHEAD trial"
  ["project_narrative_summary"]=>
  string(696) "Spondyloarthritis is a family of diseases that includes inflammation of the spine and commonly results in restricted motion and disability. Ankylosing spondylitis, the prototypic form of the disease, is characterized by bony changes  on X-ray. Earlier or milder disease, without X ray changes is called non-radiographic axial spondyloarthritis (nraxSpA). The goal of this work is to improve clinical outcomes and quality of life of people with nr-axSpA, through earlier disease recognition and enhanced access to effective therapies. We will perform statistical analyses to establish subject-level characteristics among persons with nr-axSpA who were in a trial of Golimumab

(NCT01453725)."
  ["project_learn_source"]=>
  string(10) "web_search"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(7) "Maureen"
    ["last_name"]=>
    string(8) "Dubreuil"
    ["degree"]=>
    string(7) "MD, MSc"
    ["primary_affiliation"]=>
    string(36) "Boston University School of Medicine"
    ["email"]=>
    string(15) "mdubreui@bu.edu"
    ["state_or_province"]=>
    string(2) "MA"
    ["country"]=>
    string(13) "United States"
  }
  ["project_key_personnel"]=>
  bool(false)
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(3) "yes"
    ["label"]=>
    string(65) "External grants or funds are being used to support this research."
  }
  ["project_funding_source"]=>
  string(34) "NIH R01 application pending review"
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1592) "Background: Spondyloarthritis is a family of diseases that includes inflammation of the spine and commonly results in restricted motion and disability. Effective treatment is commonly delayed, and this contributes to worse

outcomes.



Objective: We propose studies to establish predictors of clinical improvement (Assessment of spondyloarthritis international society 40% improvement [ASAS 40]) through a post-hoc analysis of data collected in the “GOAHEAD”

trial of golimumab in non-radiographic axial spondyloarthritis.



Study Design: We will use previously collected subject-level characteristics and MRI data to construct regression models to predict ASAS40 improvement at week 16 among subjects treated with golimumab or placebo in the GO-AHEAD Trial. For analysis of raw MRI images (baseline pelvis) we will apply a novel MRI technology predicting ASAS40 improvement. We will then compare models incorporating both baseline clinical predictors and the prediction of the MRI technology using standard model fit statistics.



Participants: All subjects treated with either golimumab or placebo in the GO-AHEAD trial



Main Outcome Measures: Assessment of spondyloarthritis international society 40% improvement (ASAS 40) at week 16



Statistical Analysis: We will construct regression models as above. We will compare the clinical model, MRI-only and the integrated models by assessing the accuracy, sensitivity, specificity and by plotting receiver operating

characteristics (ROC) for each model."
  ["project_brief_bg"]=>
  string(1485) "Spondyloarthritis is a family of diseases that includes inflammation of the spine and commonly results in restricted motion and disability. Ankylosing spondylitis, the prototypic form of the disease, is characterized by bony changes on X-ray. Earlier or milder disease, however, may have changes visible only on MRI, and has been called non-radiographic axial spondyloarthritis (nr-axSpA). The classification of nr-axSpA remains controversial in terms of the magnetic resonance Imaging (MRI) lesions that define the presence of sacroillitis. While early studies reported that bone marrow edema and erosions were specific for nr-axSpA, subsequent work found such lesions to be present in those with mechanical causes for back pain, such as healthy athletes. Therefore, the goal of this work is to construct multivariable models predicting clinical improvement, and compare these models with novel MRI-based models clinical improvement.

This application seeks to establish subject-level characteristics, including MRI patterns, among nr-axSpA trial subjects who responded to Tumor necrosis factor inhibitor (TNFi) treatment or placebo. The results of this work are expected to provide generalizable medical knowledge to inform public health through the integration into clinical practice guidelines.  The goal of this work is to improve clinical outcomes and quality of life of people with nr-axSpA, through earlier disease recognition and enhanced access to effective therapies. "
  ["project_specific_aims"]=>
  string(520) "Aim 1. To assess the ability of subject- level characteristic to predict clinical improvement among nr-axSpA subjects who were treated with golimumab or placebo in the MK-8259-006-02 (GO-AHEAD) trial.

Aim 2. To develop novel MRI image assessment techniques for use in sacroiliac joint (pelvic) MRIs among subjects with nr-axSpA who were treated with golimumab or placebo in GO-AHEAD.

Aim 3. To compare the algorithms developed in Aims 1 and 2 in predicting clinical improvement, using model fit statistics."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(14) "indiv_trial_an"
    ["label"]=>
    string(25) "Individual trial analysis"
  }
  ["project_purposes"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
  }
  ["project_research_methods"]=>
  string(199) "No exclusions. 

Summary-level results will be exported from the YODA Project platform and meta-analyzed with summary-level results from the AbbVie/adalimumab study accessed via Vivli. 

"
  ["project_main_outcome_measure"]=>
  string(588) "Primary outcome: Assessment of spondyloarthritis international society 40% improvement (ASAS 40) at week 16.



Secondary outcomes (for sensitivity analyses) will include: Assessment of spondyloarthritis international society 20% improvement (ASAS 20) achievement, Bath ankylosing spondylitis disease activity index (BASDAI) 50% improvement (ie BASDAI 50), Assessment of spondyloarthritis international society (ASAS) partial remission, and change in Spondylitis Research Consortium of Canada (SPARCC) score for MRI pelvis, which were all secondary trial outcomes in GO-AHEAD."
  ["project_main_predictor_indep"]=>
  string(61) "Treatment arm (golimumab versus placebo), intention to treat."
  ["project_other_variables_interest"]=>
  string(1210) "Age at enrollment, sex, disease duration (years), family history of spondyloarthritis, smoking status (current/prior/never), prior use of conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs),

baseline High sensitivity CRP (hsCRP) value, baseline Erythrocyte sedimentation rate (ESR) value, baseline Spondylitis Research Consortium of Canada (SPARCC) score for MRI pelvic and spine, baseline Health

assessment questionnaire- disability index (HAQ-DI), baseline Ankylosing spondylitis Disease Activity Score (ASDAS), baseline BASDAI, fulfillment of ASAS axial spondyloarthritis classification criteria at baseline (ie- clinical

versus imaging arm), determination of MRI sacroiliitis according to central MRI reader (present versus absent).

We also request raw images from baseline pelvic/sacroiliac MRIs. A novel technology will be applied to the MRI images to predict clinical improvement. In order to do this, we will require baseline pelvic MRI images (~150 images per subject), from T1 and T2 fat-suppressed semi-coronal images of the sacroiliac joints.

Study sponsor (JNJ/Janssen) has agreed to arrange for MRI image transfer and anonymization.

"
  ["project_stat_analysis_plan"]=>
  string(2000) "A novel technology will be applied to the baseline pelvis/sacroiliac joint MRI images to predict clinical improvement. In order to do this, we will require baseline pelvic MRI images (~150 images per subject), from T1 and T2 fat-suppressed semi-coronal images of the sacroiliac joints.  The novel MRI technology will generate a numeric value predicting ASAS40 improvement, which we can then compare to predictive models that use clinical variables as outlined below. 

Clinical variables at baseline (detailed in the "Other Variables of Interest" section of this application) will be used to construct regression models to predict ASAS40 improvement. We will construct models using a broad group of clinical variables from the trial datasets, with selection of variables using a random forest method. The clinical model with the best model fit statistics will be compared with MRI prediction only, and an integrated model predicting ASAS40 improvement. The integrated model will be constructed by including the MRI technology output (prediction of ASAS40 improvement) in the optimal clinical model to predict ASAS40 improvement. We will use standard meta-analytic techniques to combine models derived from the 2 trials. We will export only summary-level results (eg- odds ratios) for the variables incorporated in the model, and a weighting variable to account for differences in study size. 

We will compare the clinical model, MRI-only and the integrated models by assessing the accuracy, sensitivity, specificity and by plotting receiver operating characteristics (ROC) for each model. Confidence intervals for thesemodel performance measures will be obtained by bootstrap sampling using 100% of the test data with replacement (100 iterations). Area under the curve (AUC) will be calculated for each bootstrap iteration, using student’s t testwith 99 degrees of freedom. After generating models that include all subjects, we will additionally construct and test sex-specific models."
  ["project_software_used"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
  }
  ["project_timeline"]=>
  string(250) "The start date for this project is expected to be March 2025

Completion date for this project is expected to be February 2029

Date results reported back to YODA: September 2029

Date manuscript submitted for publication: Sept 2029"
  ["project_dissemination_plan"]=>
  string(285) "Abstracts for presentation at the American College of Rheumatology annual meetings, 2026-2029

Manuscript submission (2) to leading North American and/or European Rheumatology Journals: Arthritis Care & Research, Arthritis & Rheumatology, Annals of the Rheumatic Diseases"
  ["project_bibliography"]=>
  string(2427) "
Rudwaleit M, et al., The development of Assessment of SpondyloArthritis international Society classification criteria for axial spondyloarthritis (part II): validation and final selection. Ann Rheum Dis, 2009. 68: p. 777-83.
Maksymowych WP, L.R., Østergaard M, Pedersen SJ, Machado PM, Weber U, Bennett AN, Braun J, Burgos- Vargas R, de Hooge M, Deodhar AA, Eshed I, Jurik AG, Hermann KA, Landewé RB, Marzo-Ortega H, Navarro- Compán V, Poddubnyy D, Reijnierse M, Rudwaleit M, Sieper J, Van den Bosch FE, van der Heijde D, van der Horst-Bruinsma IE, Wichuk S, Baraliakos X, MRI lesions in the sacroiliac joints of patients with spondyloarthritis: an update of definitions and validation by the ASAS MRI working group. Ann Rheum Dis, 2019. 78(11): p. 1550-1558
Deodhar A, G.L., Kay J, Maksymowych WP, Haroon N, Landewé R, Rudwaleit M, Hall S, Bauer L, Hoepken B, de Peyrecave N, Kilgallen B, van der Heijde D., A Fifty-Two-Week, Randomized, Placebo-Controlled Trial of Certolizumab Pegol in Nonradiographic Axial Spondyloarthritis. Arthritis Rheumatol, 2016. 71(7): p. 1101-11.
Sieper J, v.d.H.D., Dougados M, Mease PJ, Maksymowych WP, Brown MA, Arora V, Pangan AL. . Ann Rheum Dis. 2013 Jun;72(6):815-22, Efficacy and safety of adalimumab in patients with non-radiographic axial spondyloarthritis: results of a randomised placebo-controlled trial (ABILITY-1). Ann Rheum Dis, 2013. 72(6): p.

815-22.
Sieper J, v.d.H.D., Dougados M, Maksymowych WP, Scott BB, Boice JA, Berd Y, Bergman G, Curtis S, Tzontcheva A, Huyck S, Weng HH. , A randomized, double-blind, placebo-controlled, sixteen-week study of subcutaneous golimumab in patients with active nonradiographic axial spondyloarthritis. Arthritis Rheumatol, 2015. 67(10): p. 2702-12.
Dietrich S, F.A., Troll M, Kühn T, Rathmann W, Peters A, Sookthai D, von Bergen M, Kaaks R, Adamski J, Prehn C, Boeing H, Schulze MB, Illig T, Pischon T, Knüppel S, Wang-Sattler R, Drogan D. , Random survival forest in practice: a method for modelling complex metabolomics data in time to event analysis. Int. J. Epidemiol., 2016. 45: p. 1406-20.
Benjamini Y, H.Y., Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B Met, 1995. 57: p. 289-300.
Brock G, D.S., Pihur V, Datta S., An R package for cluster validation. Journal of Statistical Software, 2008. 25: p.1-22.
"
  ["project_suppl_material"]=>
  bool(false)
  ["project_coi"]=>
  array(1) {
    [0]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(15096)
        ["id"]=>
        int(15096)
        ["title"]=>
        string(59) "YODA-Project-COI-Form-for-Data-Requestors-2024_Dubreuil.pdf"
        ["filename"]=>
        string(59) "YODA-Project-COI-Form-for-Data-Requestors-2024_Dubreuil.pdf"
        ["filesize"]=>
        int(20684)
        ["url"]=>
        string(108) "https://yoda.yale.edu/wp-content/uploads/2024/06/YODA-Project-COI-Form-for-Data-Requestors-2024_Dubreuil.pdf"
        ["link"]=>
        string(105) "https://yoda.yale.edu/data-request/2024-0644/yoda-project-coi-form-for-data-requestors-2024_dubreuil-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1718"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(59) "yoda-project-coi-form-for-data-requestors-2024_dubreuil-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(15094)
        ["date"]=>
        string(19) "2024-06-14 15:23:48"
        ["modified"]=>
        string(19) "2024-06-14 15:23:50"
        ["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)
  ["certification"]=>
  bool(true)
  ["search_order"]=>
  string(1) "0"
  ["project_send_email_updates"]=>
  bool(false)
  ["project_publ_available"]=>
  bool(true)
  ["project_year_access"]=>
  string(0) ""
  ["project_rep_publ"]=>
  bool(false)
  ["project_assoc_data"]=>
  array(0) {
  }
  ["project_due_dil_assessment"]=>
  bool(false)
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(15643)
    ["id"]=>
    int(15643)
    ["title"]=>
    string(28) "Data Request Approved Notice"
    ["filename"]=>
    string(32) "Data-Request-Approved-Notice.pdf"
    ["filesize"]=>
    int(195663)
    ["url"]=>
    string(81) "https://yoda.yale.edu/wp-content/uploads/2024/08/Data-Request-Approved-Notice.pdf"
    ["link"]=>
    string(77) "https://yoda.yale.edu/data-request/2024-0824/data-request-approved-notice-14/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(31) "data-request-approved-notice-14"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(15512)
    ["date"]=>
    string(19) "2024-09-27 14:56:37"
    ["modified"]=>
    string(19) "2024-09-27 14:56:37"
    ["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"]=>
  bool(false)
  ["project_highlight_button"]=>
  string(0) ""
  ["request_data_partner"]=>
  string(15) "johnson-johnson"
  ["request_overridden_res"]=>
  string(1) "3"
  ["human_research_protection_training"]=>
  bool(false)
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(0) {
}


pi affil
array(0) {
}


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


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


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