array(42) {
  ["project_status"]=>
  string(7) "ongoing"
  ["project_assoc_trials"]=>
  array(3) {
    [0]=>
    object(WP_Post)#5675 (24) {
      ["ID"]=>
      int(1939)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2022-04-27 14:41:00"
      ["post_date_gmt"]=>
      string(19) "2022-04-27 14:41:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(326) "NCT02951533 - Multicenter, Randomized, Open-Label, Efficacy Assessor-Blinded, Active Comparator-Controlled Phase 3b Study to Compare the Efficacy of Guselkumab to Fumaric Acid Esters (Fumaderm Initial/ Fumaderm) for Adult Patients With Moderate to Severe Plaque Psoriasis Who Are Candidates for and Naive to Systemic Treatment"
      ["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(193) "nct02951533-multicenter-randomized-open-label-efficacy-assessor-blinded-active-comparator-controlled-phase-3b-study-to-compare-the-efficacy-of-guselkumab-to-fumaric-acid-esters-fumaderm-initial"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2026-04-15 15:01:18"
      ["post_modified_gmt"]=>
      string(19) "2026-04-15 19:01:18"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(242) "https://dev-yoda.pantheonsite.io/clinical-trial/nct02951533-multicenter-randomized-open-label-efficacy-assessor-blinded-active-comparator-controlled-phase-3b-study-to-compare-the-efficacy-of-guselkumab-to-fumaric-acid-esters-fumaderm-initial/"
      ["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)#5676 (24) {
      ["ID"]=>
      int(1936)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2022-04-26 17:45:00"
      ["post_date_gmt"]=>
      string(19) "2022-04-26 17:45:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(278) "NCT02207244 - A Phase 3, Multicenter, Randomized, Double-blind, Placebo and Active Comparator-controlled Study Evaluating the Efficacy and Safety of Guselkumab for the Treatment of Subjects With Moderate to Severe Plaque-type Psoriasis With Randomized Withdrawal and Retreatment"
      ["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) "nct02207244-a-phase-3-multicenter-randomized-double-blind-placebo-and-active-comparator-controlled-study-evaluating-the-efficacy-and-safety-of-guselkumab-for-the-treatment-of-subjects-with-moder"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2026-04-15 14:58:52"
      ["post_modified_gmt"]=>
      string(19) "2026-04-15 18:58:52"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(243) "https://dev-yoda.pantheonsite.io/clinical-trial/nct02207244-a-phase-3-multicenter-randomized-double-blind-placebo-and-active-comparator-controlled-study-evaluating-the-efficacy-and-safety-of-guselkumab-for-the-treatment-of-subjects-with-moder/"
      ["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)#5674 (24) {
      ["ID"]=>
      int(1694)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2023-08-05 04:45:19"
      ["post_date_gmt"]=>
      string(19) "2023-08-05 04:45:19"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(236) "NCT02203032 - A Phase 3, Multicenter, Randomized, Double-blind Study to Evaluate the Efficacy and Safety of Guselkumab for the Treatment of Subjects With Moderate to Severe Plaque-type Psoriasis and an Inadequate Response to Ustekinumab"
      ["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) "nct02203032-a-phase-3-multicenter-randomized-double-blind-study-to-evaluate-the-efficacy-and-safety-of-guselkumab-for-the-treatment-of-subjects-with-moderate-to-severe-plaque-type-psoriasis-and-a"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2026-04-15 14:56:33"
      ["post_modified_gmt"]=>
      string(19) "2026-04-15 18:56:33"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(244) "https://dev-yoda.pantheonsite.io/clinical-trial/nct02203032-a-phase-3-multicenter-randomized-double-blind-study-to-evaluate-the-efficacy-and-safety-of-guselkumab-for-the-treatment-of-subjects-with-moderate-to-severe-plaque-type-psoriasis-and-a/"
      ["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(113) "Predicting long-term outcomes in people with psoriasis who initially respond to systemic immunomodulatory therapy"
  ["project_narrative_summary"]=>
  string(784) "Psoriasis is a common skin condition affecting 1 in 50 people worldwide. It causes red, scaly patches and currently has no cure. People with severe psoriasis often need tablet or injection treatments to achieve disease control (i.e. clear/nearly clear skin). Newer injectable drugs (‘biologics’) are highly effective, but not everyone maintains control long-term. We currently cannot predict who will maintain control on treatment, which means that everyone must attend regular check-ups, even when not needed, which is an inefficient use of healthcare resources. This project will develop statistical models to predict how likely someone is to keep their psoriasis under control. The results could help personalise care and allow healthcare resources to be used more effectively."
  ["project_learn_source"]=>
  string(12) "scien_public"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(17) "Weiyu (Christina)"
    ["last_name"]=>
    string(2) "Ye"
    ["degree"]=>
    string(8) "MB BChir"
    ["primary_affiliation"]=>
    string(21) "King's College London"
    ["email"]=>
    string(22) "christina.ye@kcl.ac.uk"
    ["state_or_province"]=>
    string(6) "London"
    ["country"]=>
    string(14) "United Kingdom"
  }
  ["project_key_personnel"]=>
  array(6) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(3) "Sam"
      ["p_pers_l_name"]=>
      string(6) "Norton"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(21) "King's College London"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
    [1]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(6) "Zijing"
      ["p_pers_l_name"]=>
      string(4) "Yang"
      ["p_pers_degree"]=>
      string(23) "Master of Public Health"
      ["p_pers_pr_affil"]=>
      string(21) "King's College London"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
    [2]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(5) "Nick "
      ["p_pers_l_name"]=>
      string(4) "Dand"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(21) "King's College London"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
    [3]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(7) "Satveer"
      ["p_pers_l_name"]=>
      string(5) "Mahil"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(21) "King's College London"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
    [4]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(9) "Catherine"
      ["p_pers_l_name"]=>
      string(5) "Smith"
      ["p_pers_degree"]=>
      string(2) "MD"
      ["p_pers_pr_affil"]=>
      string(21) "King's College London"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
    [5]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(5) "Marko"
      ["p_pers_l_name"]=>
      string(6) "Raseta"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(21) "King's College London"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
  }
  ["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(10) "NIHR304617"
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1700) "Background:

It is currently not possible to predict whether an individual with psoriasis will maintain clear/nearly clear skin on a given systemic treatment. As a result, management follows a one-size-fits-all approach, which may not account for individual variation or represent the most efficient use of healthcare resources.



Objective:

To externally validate prediction models developed using real-world data from the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR) using individual participant data (IPD) from clinical trials.



Study Design:

External validation of existing prediction models using IPD from relevant clinical trials.



Participants:

Adults with psoriasis who achieved clear/nearly clear skin on systemic treatment within the selected clinical trials.



Primary Outcome Measure:

Maintenance of clear/nearly clear skin on treatment, defined by:

• Psoriasis Area and Severity Index (PASI) of  ≤2 or PASI90

• Static/Investigator Physician Global Assessment (sPGA/IGA) score of 0/1

• Patient Global Assessment (PtGA) score of 0/1



Statistical Analysis:

Scoring algorithms from the BADBIR-derived prediction models will be applied to each trial dataset in turn without refitting. Model performance will be evaluated using discrimination (C-statistic/AUC) and calibration. Trial-specific estimates of model performance will be pooled using random-effects meta-analysis, with heterogeneity quantified by I². Minor recalibration may be undertaken if discrimination is adequate but calibration is poor.

"
  ["project_brief_bg"]=>
  string(2501) "It is currently not possible to predict whether an individual with psoriasis who achieves clear or nearly clear skin on a given systemic treatment will maintain this response in the longer term. As a result, disease management often follows a one-size-fits-all approach, which may not account for individual variability or represent the most efficient use of healthcare resources.



We are developing statistical models to predict the likelihood of maintaining disease control on systemic treatment among people with psoriasis, using real-world data from the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR; www.badbir.org). BADBIR is a large, ongoing, prospective observational cohort across the UK and Ireland, enrolling individuals who have started systemic immunomodulatory therapy within the previous six months. Participants are recruited from over 160 hospitals, with more than 18,000 individuals registered and over 110,000 follow-ups to date. The registry captures detailed sociodemographic, clinical, treatment, and disease severity data at six-monthly intervals for the first three years and annually thereafter.



As BADBIR reflects real-world clinical practice, it provides a highly generalisable dataset for developing and internally validating predictive models. To ensure broader applicability, we plan to externally validate the BADBIR-derived models using individual participant data from relevant clinical trials. This approach offers two key advantages. First, the clinical trial datasets include more geographically diverse populations, allowing assessment of model transportability. Second, clinical trial participants often differ systematically from real-world cohorts in baseline characteristics and comorbidity profiles (1), providing an opportunity to test the robustness and stability of model performance across distinct populations. 



This work will form the foundations in developing a clinically useful risk stratification tool to guide personalised psoriasis care. Such a tool could reduce patient anxiety, empower individuals to address modifiable risk factors, and support tailored long-term management. For example, individuals likely to sustain disease control may benefit from increased autonomy through remote monitoring or patient-initiated follow-up, while higher-risk individuals may receive adjunct therapies and closer monitoring, enabling early intervention for flares. 

"
  ["project_specific_aims"]=>
  string(551) "This project aims to use individual participant data from relevant clinical trials as an external validation cohort for prediction models trained on a different dataset (BADBIR).



Our central hypothesis is that long-term disease control (i.e. clear or nearly clear skin) in individuals with psoriasis receiving systemic immunomodulatory therapy can be predicted based on baseline patient characteristics (e.g. demographics, comorbidities, disease history) and treatment-related factors (e.g. drug class, dosage, prior therapies).

"
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(5) "other"
    ["label"]=>
    string(5) "Other"
  }
  ["project_purposes"]=>
  array(2) {
    [0]=>
    array(2) {
      ["value"]=>
      string(56) "new_research_question_to_examine_treatment_effectiveness"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
    [1]=>
    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(1866) "External validation of the BADBIR-derived prediction models will be conducted within the Vivli secure data environment. In addition to the three clinical trial datasets requested through the YODA Project (which are already loaded onto the Vivli secure data environment), eleven additional psoriasis trial datasets have been requested from the Vivli data sharing platform (NCT03051217, NCT03536884, NCT02326298, NCT02326272, NCT01695239, NCT01597245, NCT01474512, NCT03478787, NCT02684357, NCT02684370, NCT02672852). The scoring algorithms developed using prediction models derived from BADBIR real-world data will be applied to individual participant-level data from each trial separately to evaluate the predictive performance of a real-world trained model in clinical trial populations.



The primary study population will include participants with psoriasis who achieve clear or nearly clear skin on systemic immunomodulatory therapy, defined as meeting any of the following criteria: PASI90, PASI ≤2, PGA 0/1, sPGA 0/1, or IGA 0/1. Systemic therapies of interest include methotrexate, TNF inhibitors, IL-12/23 inhibitors, IL-17 inhibitors, and IL-23 inhibitors.



We will include all participants who received treatment as prescribed (per-protocol population) and who have at least one follow-up assessment after achieving clear or nearly clear skin. For these individuals, we will request all available sociodemographic, clinical, treatment, disease severity, and outcome data. Non-responders (i.e. those who do not achieve clear or nearly clear skin) will not be included. In trials with re-randomisation amongst responders, we will only include participants who achieve response and are re-randomised to continue the same treatment. Responders who are randomised to withdrawal or a different treatment will not be included.

"
  ["project_main_outcome_measure"]=>
  string(1042) "The main outcome is maintenance of disease control (i.e. clear/nearly clear skin) over time. This is defined as any of the following:

1.	Psoriasis Area and Severity Index (PASI): absolute PASI ≤2 or ≥90% reduction from baseline (PASI90)

2.	Static Physician Global Assessment (sPGA/PGA): absolute score of 0 (clear) or 1 (nearly clear)

3.	Investigator Global Assessment (IGA): absolute score of 0 (clear) or 1 (nearly clear)

4.	Patient Global Assessment (PtGA): absolute score of 0 (clear) or 1 (nearly clear)



We will also assess if the predictive model for maintaining disease control is predictive of other related outcomes, including:

1.	Quality of life, with minimal impact on quality of life defined as an absolute Dermatology Life Quality Index (DLQI) score of 0 or 1. 

2.	Depression, as measured using the change in a validated depression symptom score from baseline.

3.	Anxiety, as measured using the change in a validated anxiety symptom score from baseline.

"
  ["project_main_predictor_indep"]=>
  string(1587) "We will evaluate a range of variables as potential predictors of sustained disease control, including:

•	Sociodemographic factors

-	Age (continuous)

-	Ethnicity (categorical, e.g. White, Asian, Black etc)

-	Sex (categorical, male vs female)

-	Work status (categorical, employed vs non-employed)

-	Socioeconomic status (categorical)



•	Treatment-related factors

-	Prior treatments and current treatment, both categorised by mechanism of action



•	Disease characteristics

-	Psoriasis duration (categorical and continuous)

-	Age of onset (categorical and continuous)

-	Pre-treatment disease severity (categorical and continuous)



•	Impact on individual 

-	Dermatology Life Quality Index at baseline (continuous)



•	Lifestyle and Comorbidity 

-	Smoking status (categorical, current smoker vs never smoker vs ex-smoker)

-	BMI (categorical, obese vs non-obese, and continuous)

-	Alcohol consumption (categorical, yes vs no)

-	Cardiovascular comorbidity (categorical variable, yes vs no. Includes diabetes, hypertension, hyperlipidemia, obesity, smoking)

-	History of anxiety or depression (categorical, yes vs no)

-	Psoriatic arthritis (categorical, yes vs no)



Within each study, ‘baseline’ will be defined as the visit at which response (clear or nearly clear skin) is first achieved. Variables that are not re-measured at the response visit will be taken from the most recent assessment."
  ["project_other_variables_interest"]=>
  string(3) "N/A"
  ["project_stat_analysis_plan"]=>
  string(3802) "Study selection and rationale 

We have selected the requested clinical trial studies to serve as external validation datasets for predictive models developed using the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR). These trials provide data on maintenance of disease control after this is achieved in individuals with psoriasis receiving systemic immunomodulatory therapy. The trials capture similar outcomes and predictor variables to BADBIR, making them suitable for assessing whether our BADBIR-derived models can generalise to independent populations.



Model development and internal validation (BADBIR cohort)

Using BADBIR data, we will develop penalised logistic regression models to predict the probability of sustained disease control (defined as maintenance of clear or nearly clear skin) over the next 6 months, adjusting for treatment duration. Penalisation (using elastic net and lasso approaches) will be used to select the minimum subset of variables from sociodemographic, treatment-related, disease-specific, quality of life, and comorbidity factors (e.g., psoriatic arthritis). We will develop both drug-specific models and class-level models and compare their predictive performance. We will also explore whether additional machine learning approaches (e.g., support vector machine, random forest, extreme gradient boosting) improve model performance relative to penalised logistic regression.



Model performance will be assessed by discrimination (C-statistic) and calibration (calibration slope and calibration-in-the-large). Internal validation will be used to prevent overfitting and obtain unbiased estimates of model performance within the BADBIR population.



External validation using clinical trial data accessed through Vivli

The final models developed and internally validated using BADBIR will then undergo external validation using individual patient data from clinical trials accessed through the Vivli platform. Each clinical trial will be treated as an independent validation dataset. External validation will assess the predictive ability of all relevant prediction models for the specific therapies used in each clinical trial; for example, we will assess and compare the predictive performance of class-level and drug-specific prediction models. Prior to validation, we will compare the trial populations to the BADBIR cohort, summarise baseline characteristics for each trial separately to describe differences in population and study design. Where necessary, predictor variables in the trial datasets will be harmonised to match definitions used in BADBIR. Missing baseline clinical variables will be imputed separately within each trial using multiple imputation.



The scoring algorithm(s) derived from the BADBIR-trained models will be imported into the Vivli secure research environment in the form of R scripts and applied directly to each requested clinical trial dataset individually for external validation without refitting or modification. No raw data from BADBIR will be brought into the Vivli research environment. Model performance will be assessed within each trial using measures of discrimination (C-statistic/AUC) and calibration (calibration slope and calibration-in-the-large). Trial-specific performance estimates will then be synthesised using random-effects meta-analysis to obtain summary measures of external

validation performance while maintaining the independence of each trial. Heterogeneity in model performance across trials will be quantified using I². If primary validation demonstrates poor calibration despite good discrimination, minor recalibration (intercept and/or slope adjustment) may be considered."
  ["project_software_used"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
  }
  ["project_timeline"]=>
  string(215) "Anticipated project start date: June 2026

Analysis completion date: Aug 2027

Manuscript drafted and first submitted for publication: Oct 2027

Date results reported back to YODA: Jan 2028

"
  ["project_dissemination_plan"]=>
  string(854) "This outputs from this work will be disseminated to relevant stakeholders as follows: 



Patients and the public

We will work with patient and public involvement collaborators to produce plain language summaries and infographics, which will be shared through via our institution’s website. Through our group’s collaboration with the Psoriasis Association, our findings will also be disseminated via webinars and social media. 



Clinicians and researchers

We plan to present our findings at national and international dermatology conferences, and will also submit our work for publication in high-impact, peer-reviewed journals.



Policymakers 

Our results will inform updates of the UK national psoriasis guideline, which is led by Professor Catherine Smith (research team member).

"
  ["project_bibliography"]=>
  string(322) "

Mason KJ, Barker JNWN, Smith CH, Hampton PJ, Lunt M, McElhone K, Warren RB, Yiu ZZN, Griffiths CEM, Burden AD; BADBIR Study Group. Comparison of Drug Discontinuation, Effectiveness, and Safety Between Clinical Trial Eligible and Ineligible Patients in BADBIR. JAMA Dermatol. 2018 May 1;154(5):581-588.

"
  ["project_suppl_material"]=>
  bool(false)
  ["project_coi"]=>
  array(7) {
    [0]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(18144)
        ["id"]=>
        int(18144)
        ["title"]=>
        string(17) "YODA-COI-Form.pdf"
        ["filename"]=>
        string(17) "YODA-COI-Form.pdf"
        ["filesize"]=>
        int(19839)
        ["url"]=>
        string(66) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-COI-Form.pdf"
        ["link"]=>
        string(65) "https://yoda.yale.edu/data-request/2025-0736/yoda-coi-form-pdf-2/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2334"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(19) "yoda-coi-form-pdf-2"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(18122)
        ["date"]=>
        string(19) "2025-10-22 16:21:11"
        ["modified"]=>
        string(19) "2025-10-22 16:21:13"
        ["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(18207)
        ["id"]=>
        int(18207)
        ["title"]=>
        string(28) "Catherine-Smith-COI-form.pdf"
        ["filename"]=>
        string(28) "Catherine-Smith-COI-form.pdf"
        ["filesize"]=>
        int(24695)
        ["url"]=>
        string(77) "https://yoda.yale.edu/wp-content/uploads/2025/10/Catherine-Smith-COI-form.pdf"
        ["link"]=>
        string(74) "https://yoda.yale.edu/data-request/2025-0736/catherine-smith-coi-form-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2334"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(28) "catherine-smith-coi-form-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(18122)
        ["date"]=>
        string(19) "2025-10-28 13:13:37"
        ["modified"]=>
        string(19) "2025-10-28 13:13:44"
        ["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(18208)
        ["id"]=>
        int(18208)
        ["title"]=>
        string(12) "COI-form.pdf"
        ["filename"]=>
        string(12) "COI-form.pdf"
        ["filesize"]=>
        int(189738)
        ["url"]=>
        string(61) "https://yoda.yale.edu/wp-content/uploads/2025/10/COI-form.pdf"
        ["link"]=>
        string(58) "https://yoda.yale.edu/data-request/2025-0736/coi-form-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2334"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(12) "coi-form-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(18122)
        ["date"]=>
        string(19) "2025-10-28 13:13:38"
        ["modified"]=>
        string(19) "2025-10-28 13:13:45"
        ["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(18209)
        ["id"]=>
        int(18209)
        ["title"]=>
        string(15) "SN-YODA-COI.pdf"
        ["filename"]=>
        string(15) "SN-YODA-COI.pdf"
        ["filesize"]=>
        int(18229)
        ["url"]=>
        string(64) "https://yoda.yale.edu/wp-content/uploads/2025/10/SN-YODA-COI.pdf"
        ["link"]=>
        string(61) "https://yoda.yale.edu/data-request/2025-0736/sn-yoda-coi-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2334"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(15) "sn-yoda-coi-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(18122)
        ["date"]=>
        string(19) "2025-10-28 13:13:40"
        ["modified"]=>
        string(19) "2025-10-28 13:13:45"
        ["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(18210)
        ["id"]=>
        int(18210)
        ["title"]=>
        string(14) "COI-Zijing.pdf"
        ["filename"]=>
        string(14) "COI-Zijing.pdf"
        ["filesize"]=>
        int(23472)
        ["url"]=>
        string(63) "https://yoda.yale.edu/wp-content/uploads/2025/10/COI-Zijing.pdf"
        ["link"]=>
        string(60) "https://yoda.yale.edu/data-request/2025-0736/coi-zijing-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2334"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(14) "coi-zijing-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(18122)
        ["date"]=>
        string(19) "2025-10-28 13:13:42"
        ["modified"]=>
        string(19) "2025-10-28 13:13:45"
        ["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"
      }
    }
    [5]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(18211)
        ["id"]=>
        int(18211)
        ["title"]=>
        string(17) "Dand_YODA_COI.pdf"
        ["filename"]=>
        string(17) "Dand_YODA_COI.pdf"
        ["filesize"]=>
        int(23821)
        ["url"]=>
        string(66) "https://yoda.yale.edu/wp-content/uploads/2025/10/Dand_YODA_COI.pdf"
        ["link"]=>
        string(63) "https://yoda.yale.edu/data-request/2025-0736/dand_yoda_coi-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2334"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(17) "dand_yoda_coi-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(18122)
        ["date"]=>
        string(19) "2025-10-28 13:13:43"
        ["modified"]=>
        string(19) "2025-10-28 13:13:45"
        ["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"
      }
    }
    [6]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(18865)
        ["id"]=>
        int(18865)
        ["title"]=>
        string(11) "COI FORM MR"
        ["filename"]=>
        string(15) "COI-FORM-MR.pdf"
        ["filesize"]=>
        int(18544)
        ["url"]=>
        string(64) "https://yoda.yale.edu/wp-content/uploads/2025/10/COI-FORM-MR.pdf"
        ["link"]=>
        string(57) "https://yoda.yale.edu/data-request/2025-0736/coi-form-mr/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1885"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(11) "coi-form-mr"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(18122)
        ["date"]=>
        string(19) "2026-03-05 14:40:06"
        ["modified"]=>
        string(19) "2026-03-05 14:40:06"
        ["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(114) "External validation of existing prediction models using individual participant data from relevant clinical trials."
  ["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(19057)
    ["id"]=>
    int(19057)
    ["title"]=>
    string(47) "YODA Project Due Diligence Assessment 2025-0736"
    ["filename"]=>
    string(51) "YODA-Project-Due-Diligence-Assessment-2025-0736.pdf"
    ["filesize"]=>
    int(127725)
    ["url"]=>
    string(100) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-Project-Due-Diligence-Assessment-2025-0736.pdf"
    ["link"]=>
    string(93) "https://yoda.yale.edu/data-request/2025-0736/yoda-project-due-diligence-assessment-2025-0736/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(47) "yoda-project-due-diligence-assessment-2025-0736"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18122)
    ["date"]=>
    string(19) "2026-04-07 20:06:37"
    ["modified"]=>
    string(19) "2026-04-07 20:06:37"
    ["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_title_link"]=>
  array(21) {
    ["ID"]=>
    int(19058)
    ["id"]=>
    int(19058)
    ["title"]=>
    string(46) "YODA Project Protocol - 2025-0736 - 2026-03-05"
    ["filename"]=>
    string(46) "YODA-Project-Protocol-2025-0736-2026-03-05.pdf"
    ["filesize"]=>
    int(134938)
    ["url"]=>
    string(95) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-Project-Protocol-2025-0736-2026-03-05.pdf"
    ["link"]=>
    string(88) "https://yoda.yale.edu/data-request/2025-0736/yoda-project-protocol-2025-0736-2026-03-05/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(42) "yoda-project-protocol-2025-0736-2026-03-05"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18122)
    ["date"]=>
    string(19) "2026-04-07 20:06:58"
    ["modified"]=>
    string(19) "2026-04-07 20:06:58"
    ["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(19059)
    ["id"]=>
    int(19059)
    ["title"]=>
    string(36) "YODA Project Review - 2025-0736_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2025-0736_site.pdf"
    ["filesize"]=>
    int(1331893)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2025/10/YODA-Project-Review-2025-0736_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2025-0736/yoda-project-review-2025-0736_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(34) "yoda-project-review-2025-0736_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18122)
    ["date"]=>
    string(19) "2026-04-07 20:07:15"
    ["modified"]=>
    string(19) "2026-04-07 20:07:15"
    ["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_data_partner"]=>
  string(15) "johnson-johnson"
  ["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(1) {
  [0]=>
  string(7) "tremfya"
}


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


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