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      string(278) "NCT02257736 - A Phase 3 Randomized, Placebo-controlled Double-blind Study of JNJ-56021927 in Combination With Abiraterone Acetate and Prednisone Versus Abiraterone Acetate and Prednisone in Subjects With Chemotherapy-naive Metastatic Castration-resistant Prostate Cancer (mCRPC)"
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  ["project_title"]=>
  string(185) "Development of a clinical decision-support algorithm based on Network Meta-Analysis and Decision Curve Analysis for patients with metastatic castration-resistant prostate cancer (mCRPC)"
  ["project_narrative_summary"]=>
  string(2064) "Prostate cancer (PC) is one of the most common cancers affecting men. In Europe, it's particularly widespread, with a significant number of new cases every year. In 2018 alone, there were 450,000 cases of prostate cancer diagnosed, making up over 11% of all cancer diagnoses and about 22% of male cancer cases. When initially diagnosed, most men have a localized form of the disease. However, some cases progress to a more advanced stage called metastatic castration-resistant prostate cancer (mCRPC), which unfortunately has a worse outlook.



There are several treatments available for mCRPC, but deciding which one to use can be tricky for doctors. Previous studies proving the effectiveness of these treatments often compared them to either a "fake treatment" (called "placebo") or an ineffective treatment. Also, there is a lack of studies specifically looking at the best order to use these treatments in. Another complication is that some treatments used to be given later on, but are now also approved for use earlier in the disease. This means doctors have a lot to consider when deciding on treatment for mCRPC.



Our research aims to help clinicians make better decisions for their patients with mCRPC. We are going to analyze data from many different clinical trials to see which first-line treatments are the most effective. This analysis, called a network meta-analysis (NMA), will help us understand which treatments work best in real-world situations. By asking "Which first-line therapy offers the highest treatment efficacy in patients with mCRPC?" we hope to provide clear guidance for doctors.



To do this, we're gathering data from many clinical trials and using advanced statistical methods to compare the treatments. We'll then use this information to create personalized recommendations for individual patients, using a method called decision curve analysis (DCA). This way, clinicians can tailor their treatment plans to each patient's unique situation, ultimately improving care for men with mCRPC."
  ["project_learn_source"]=>
  string(5) "other"
  ["project_learn_source_exp"]=>
  string(46) "Vivli Center for Global Clinical Research Data"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(5) "Marie"
    ["last_name"]=>
    string(5) "Wosny"
    ["degree"]=>
    string(8) "MSc, BSc"
    ["primary_affiliation"]=>
    string(24) "University of St. Gallen"
    ["email"]=>
    string(27) "mariejohanna.wosny@unisg.ch"
    ["state_or_province"]=>
    string(10) "St. Gallen"
    ["country"]=>
    string(11) "Switzerland"
  }
  ["project_key_personnel"]=>
  array(1) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(5) "Janna"
      ["p_pers_l_name"]=>
      string(8) "Hastings"
      ["p_pers_degree"]=>
      string(13) "PhD, MSc, BSc"
      ["p_pers_pr_affil"]=>
      string(43) "School of Medicine, University of St.Gallen"
      ["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(5327) "Background: Prostate cancer is one of the most frequent cancer types in men. As for many cancers, the challenge for clinicians is to find the most suitable treatment modalities for each patient. This challenge is particularly pronounced in the case of advanced disease stages, such as metastatic castration-resistant prostate cancer (mCRPC), characterized by a less favorable prognosis. Despite medical advancements and various treatment options, clinicians often face difficulties in selecting the optimal treatment regime as well as sequence. The reasons for this encompass challenges caused by the absence of medical evidence from prospective clinical trials that investigate the sequencing of different treatments. Additionally, concerns emerge due to the up-front use of treatments for earlier cancer stages, leading to decreased effectiveness if cancer advances to later stages, primarily due to cross-resistance between agents, necessitating changes in therapy sequencing.



Objective: The objective of this study is to explore how technology can improve the selection of prostate cancer treatment by providing insights into the development and evaluation of an algorithm that functions as a comprehensive decision-support tool. The algorithm aims to rank mCRPC treatment options based on their relative effectiveness, leveraging current medical evidence from clinical trials and published literature, while also incorporating patient characteristics to facilitate personalized decision-making.  The ultimate goal is to integrate this algorithm into a prototype web-based decision support application and evaluate its performance, focusing on both accuracy and efficiency. 





Study Design: This is accomplished through the execution of a systematic review of clinical trials, which is followed by a network meta-analysis (NMA) in order to indirectly compare the relative effect of available treatments for mCRPC patients. Subsequently, an extended decision curve analysis (DCA) will be conducted, integrating individual patient data, and ultimately enabling treatment recommendations on the individual patient level.



Participants: Male patients with diagnosed metastatic castration-resistant prostate cancer





Primary and Secondary Outcome Measure(s): 

Our primary outcome measure is treatment efficacy, specifically overall survival, which is defined as the duration from randomization to death due to any cause. Effect measures include median survival and the hazard ratio. 

Our secondary outcome measure is progression-free survival, which refers to the time from randomization to disease progression or death, with the effect measure being the hazard ratio. This encompasses: 

a) Radiographic progression-free survival (rPFS): the duration from randomization to the occurrence of first radiographic progression or death. 

b) Failure-free survival (FFS): the duration from randomization to the onset of first clinical, radiographic, or biochemical progression, or death.





Statistical Analysis: 

We will perform a pairwise and network meta-analysis, along with decision curve analysis, utilizing hazard ratio (HR) and 95% Confidence Interval (CI) for overall survival and progression-free survival in mCRPC patients as treatment effect measures. Cox proportional hazards regression models, stratified by trial, will be applied to the entire participant dataset, resulting in trial-specific estimates of treatment effect (log HR), variance, and covariances, where applicable. Direct pairwise treatment effect estimates will be calculated, and trial-specific log hazard ratios will be pooled for pairwise meta-analysis. We will perform frequentist NMA, assuming equal heterogeneity across comparisons, with a fixed between-study covariance structure. This structure is necessary for networks lacking pairwise comparisons between all treatments. Our analysis aims to identify the most effective interventions for mCRPC in terms of OS and PFS, utilizing R software and the "netmeta" package. The random-effects model will account for expected clinical and methodological diversity. Interventions will be ranked using P-scores, and network and forest plots will illustrate treatment effects. We will assess evidence certainty with the GRADE framework. Treatment effect rankings will be derived from previous NMAs, supplemented by patient information from corresponding RCTs. The multivariable DCA will be conducted using the R software using the package “dcurves” and the extended  DCA methodology, first introduced by Chalkou et al .in 2023, will be performed, allowing the evolution of the clinical usefulness of a personalized prediction model that aims at recommending a treatment among many possible options according to individual patient characteristics. For this, we will need to define threshold values for each included treatment, determining the minimum risk difference compared with a control that will make a treatment worth considering. Subsequently, we will plot the net benefit per strategy across a plausible range of threshold values to identify the most clinically useful strategy for mCRPC patients, ultimately enabling decision-making between different treatment options."
  ["project_brief_bg"]=>
  string(2392) "Prostate cancer (PC) ranks among the most frequently diagnosed cancers in men (Siegel et al., 2020). In Europe, PC is a highly prevalent type of cancer, marked by a significant incidence and mortality (Ferlay et al., 2018). In 2018, it accounted for 450,000 cases, encompassing 11.5% of all cancer diagnoses and 21.8% of male cancer cases. Most men newly diagnosed with PC experience a localized disease, however, some patients progress to metastatic castration-resistant prostate cancer (mCRPC), a condition characterized by a poorer prognosis. Several standard treatments for mCRPC exist, however, As these treatments have become part of routine clinical practice, clinicians often face the challenge of determining the optimal selection and sequence (Maurice Dror et al., 2021). The landmark studies demonstrating their efficacy often had control arms involving either a placebo or minimally effective therapy, and there is a lack of prospective clinical trials specifically investigating the sequencing of these treatments (Maurice Dror et al., 2021). An additional challenge arises from the earlier up-front use of treatment regimens such as docetaxel and androgen receptor pathway inhibitors with ADT, that are now also approved for patients with metastatic hormone-sensitive PC (Sweeney et al., 2015). This cancer type thus presents challenges for oncology decision-makers at present and therefore will be addressed in our research study. To support clinicians with the treatment selection for mCRPC patients to provide the best care, we will conduct a network meta-analysis (NMA) of the existing evidence from clinical trials to assess the first-line treatments for mCRPC are the most effective. To address this, we performed a comprehensive systematic review of clinical trials available from the ClinicalTrials.gov database, followed by a Bayesian NMA (Rouse et al., 2017). Following the meta-analysis, an extended decision curve analysis (DCA) will be conducted to evaluate the potential for evidence-based personalized recommendations for individual patients (Chalkou et al., 2023). Overall, our study seeks to provide invaluable insights to aid clinicians in enhancing their decision-making processes concerning patients with mCRPC. Employing a rigorous analysis of data from a multitude of clinical trials, we aim to ascertain the most efficacious first-line treatment strategy."
  ["project_specific_aims"]=>
  string(1146) "The objective of this project is to address these challenges by developing an algorithm designed to function as a comprehensive decision-support tool. This will be achieved through the conduction of a network meta-analysis (NMA), using the existing evidence from clinical trials to assess the first-line treatments for mCRPC. Following this, an extended decision curve analysis (DCA) will be conducted, enabling the integration of patient data for enhanced decision-making at the level of the individual cancer patient. The research question to be answered is "Which first-line therapy offers the highest treatment efficacy in patients with mCRPC?” 



The hypothesis we aim to evaluate asserts that the algorithm, designed as a comprehensive decision support tool and employing network meta-analysis (NMA) along with extended decision curve analysis (DCA), will effectively identify the first-line therapy demonstrating the highest treatment efficacy for patients with metastatic castration-resistant prostate cancer (mCRPC). This process is expected to facilitate more informed treatment decisions at the individual patient level."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
  }
  ["project_purposes"]=>
  array(2) {
    [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"
    }
  }
  ["project_software_used"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
  }
  ["project_research_methods"]=>
  string(2947) "Data Sources:

We will use IPD from the YODA Project and other IPD from clinical trials that have not been listed in the YODA database. 

Other studies from which request IPD and their respective data contributors are: 

NCT01949337 (Alliance NCTN), NCT02288247 (Clinical Study Data Request Platform), NCT01288911 (Clinical Study Data Request Platform), NCT03511664 (Clinical Study Data Request Platform), NCT01605227 (Company E-Mail), NCT04446117 (Company E-Mail), NCT00065442 (Company E-Mail), NCT02975934 (Company E-Mail), NCT03834506 (Company Platform), NCT01519414 (Individual), NA (Individual), NCT02933801 (Individual), NCT00988208 (Project Data Sphere), NCT00634647 (Project Data Sphere), NCT02407054 (Vivli), NCT03072238 (Vivli), NCT00683475 (Vivli), NCT01664923 (Vivli), NCT00417079 (Vivli), NCT00519285 (Vivli), NCT00554229 (Vivli), NCT03732820 (Vivli), NCT01193244 (Vivli), NCT01193257 (Vivli), NCT00974311 (Vivli), NCT01212991 (Vivli), NCT03395197 (Vivli), NCT00699751 (Vivli), NCT01308567 (Vivli), NCT02485691 (Vivli), NCT02987543 (Vivli).

The individual participant data (IPD) analysis will be conducted at the University of St. Gallen School of Medicine in Switzerland.





Inclusion/Exclusion Criteria:

Our study will encompass male patients diagnosed with metastatic castration-resistant prostate cancer (mCRPC) who were participants in clinical trials evaluating the efficacy of interventions for mCRPC treatment. We will consider patients from diverse countries and cultural backgrounds; however, inclusion criteria will be limited to trials and reports published in the English language. The treatments under scrutiny for mCRPC, including various dosages and combinations, encompass but are not confined to: Androgen Receptor Pathway Inhibitors (ARPI): Abiraterone acetate, Enzalutamide, Apalutamide, Darolutamide, administered in conjunction with Androgen Deprivation Therapy (ADT), such as Goserelin, chemotherapy, including Docetaxel, Cabazitaxel, immunotherapy such as Pembrolizumab, Sipuleucel-T, PARP (Poly (ADP-ribose) polymerase) inhibitors including Olaparib, Rucaparib and targeted radiation therapy as for example Radium-223 dichloride, Lutetium-177 PSMA.



Patients diagnosed with non-metastatic or non-castration-resistant prostate cancer (mCRPC) will be excluded from the study. Additionally, clinical trials or reports lacking published results available in the English language, as well as those without clear documentation of interventions and outcomes specifically related to mCRPC treatment, will be excluded. Participants presenting with comorbidities or conditions that could potentially confound the evaluation of mCRPC treatment efficacy will also be excluded. Furthermore, clinical trials with inadequate documentation or insufficient follow-up periods to adequately assess treatment efficacy for mCRPC will be excluded from the analysis."
  ["project_main_outcome_measure"]=>
  string(741) "Our primary outcome measure is treatment efficacy, specifically overall survival, which is defined as the duration from randomization to death due to any cause. Effect measures include median survival and the hazard ratio. 

Our secondary outcome measure is progression-free survival, which refers to the time from randomization to disease progression or death, with the effect measure being the hazard ratio. This encompasses: 

a) Radiographic progression-free survival (rPFS): the duration from randomization to the occurrence of first radiographic progression or death. 

b) Failure-free survival (FFS): the duration from randomization to the onset of first clinical, radiographic, or biochemical progression, or death."
  ["project_main_predictor_indep"]=>
  string(230) "In our study, the main predictor variable is the type of intervention or treatment modality, categorized into cytotoxic therapies, radiotherapy, and immunotherapy, among others, for metastatic castration-resistant prostate cancer."
  ["project_other_variables_interest"]=>
  string(524) "Initial treatment for mHSPC with ADT vs ADT + ARPI vs. Triple-therapy.



Best treatment response in the mHSPC setting: PSA nadir < 0.2 ug/l at 6 – 9 months vs higher.



Primary metastatic vs metachronous at diagnosis of metastatic disease.



Location of metastases (bone only vs lymph node only vs bone and lymph node vs visceral).



Patients with known DNA damage repair pathway deficiency (DDR deficiency).



Histologic variants (e.g. small cell), etc."
  ["project_stat_analysis_plan"]=>
  string(4034) "We will perform a pairwise and network meta-analysis, along with decision curve analysis, utilizing hazard ratio (HR) and 95% Confidence Interval (CI) for overall survival and progression-free survival in mCRPC patients as treatment effect measures. Cox proportional hazards regression models, stratified by trial, will be applied to the entire participant dataset, resulting in trial-specific estimates of treatment effect (log HR), variance, and covariances, where applicable. Direct pairwise treatment effect estimates will be calculated and trial-specific log hazard ratios will be pooled for pairwise meta-analysis.

To screen relevant clinical trials from clinicaltrials.gov, a CVS dataset was exported and transformed into an XML file, capturing pertinent details. Predefined eligibility criteria guided the manual screening process, conducted by two independent reviewers, ensuring trials targeting mCRPC patients with therapeutic interventions and focusing on treatment efficacy were included. Discrepancies were resolved through collaborative discussion or consultation with a third author.

NMA will be performed in a frequentist, multivariate framework assuming equal heterogeneity for all comparisons (i.e. a between‐study covariance structure (variance‐covariance matrix) proportional to unknown parameter tau- squared). It is necessary to make an assumption regarding the between‐study covariance structure for a network without pairwise comparisons between all treatments of interest. NMA provides treatment effecWe will conduct a frequentist NMA to indirectly compare the different interventions on the primary and secondary outcomes to investigate the relatively most effective mCRPC interventions for improving OS and PFS. All analyses will be performed using the R software (version 4.3.2) and the R-package “netmeta” which is based on graph theory methodology to model the relative treatment effects of multiple treatments under a frequentist framework. Due to anticipated clinical and methodological diversity among the identified studies, resulting in potential statistical heterogeneity, the random-effects (RE) model implemented in this package accommodates such variability by assuming a constant heterogeneity variance across each comparison within the network. The interventions will be ranked based on efficacy using P-scores, which measure the certainty that one treatment is better than another treatment, averaged over all competing treatments. To illustrate the network geometry, we will create network plots including all interventions, and forest plots to present the NMA treatment effects including the respective direct and indirect treatment effects of comparisons with the reference intervention. of the network. Finally, two authors will appraise the certainty of the evidence for each outcome by applying the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) framework. The ranking of treatment effects for mCRPC patients will be obtained from the previously conducted NMA. Additionally, the respective patient information from the included RCTs used in the NMA will be obtained from the corresponding clinical investigators. The multivariable DCA will be conducted using the R software using the package “dcurves” and the extended  DCA methodology, first introduced by Chalkou et al .in 2023, will be performed, allowing the evolution of the clinical usefulness of a personalized prediction model that aims at recommending a treatment among many possible options according to individual patient characteristics. For this, we will need to define threshold values for each included treatment, determining the minimum risk difference compared with a control that will make a treatment worth considering.



If any minor inconsistencies or missing data are present in the requested individual patient data, we will include the data in analyses and pursue sensitivity analyses to test the robustness of results included in these data."
  ["project_timeline"]=>
  string(615) "The analysis of patient-level data from clinical trials is poised to commence approximately two and a half months from now, projected to initiate between May and September 2024. This phase is slated for completion by the end of the year 2024, with meticulous attention required due to the diverse origins of data from various trials, platforms, and sponsors necessitating thorough collation and organization. Notably, the initial milestone of our systematic review of clinical trials has been achieved, with our current focus directed towards the ongoing Network Meta-Analysis, expected to be finalized in May 2024."
  ["project_dissemination_plan"]=>
  string(961) "We intend to submit our research findings to journals focused on Genitourinary Oncology, targeting the scientific community within this research field, including clinicians, medical oncologists, urologists, as well as researchers.

The peer-reviewed journals we are considering for submission are:

•	Journal of Clinical Oncology

•	European Urology

•	Clinical Cancer Research

•	Journal of the National Cancer Institute

•	npj Precision Oncology

•	European Journal of Cancer

•	European Urology Oncology

•	American Journal of Urology

•	ESMO Open

•	European Urology Focus

Additionally, we are exploring potential conference proceedings for presenting our results, with consideration given to events such as the European Society for Medical Oncology (ESMO), the American Society of Clinical Oncology (ASCO), and the American Association for Cancer Research (AACR)."
  ["project_bibliography"]=>
  string(1589) "
Chalkou, K., Vickers, A. J., Pellegrini, F., Manca, A., & Salanti, G. (2023). Decision Curve Analysis for Personalized Treatment Choice between Multiple Options. Medical Decision Making, 43(3), 337–349. https://doi.org/10.1177/0272989X221143058
Ferlay, J., Colombet, M., Soerjomataram, I., Dyba, T., Randi, G., Bettio, M., Gavin, A., Visser, O., & Bray, F. (2018). Cancer incidence and mortality patterns in Europe: Estimates for 40 countries and 25 major cancers in 2018. European Journal of Cancer (Oxford, England: 1990), 103, 356–387. https://doi.org/10.1016/j.ejca.2018.07.005
Maurice Dror, C., Chi, K. N., & Khalaf, D. J. (2021). Finding the optimal treatment sequence in metastatic castration-resistant prostate cancer—A narrative review. Translational Andrology and Urology, 10(10), 3931–3945. https://doi.org/10.21037/tau-20-1341
Rouse, B., Chaimani, A., & Li, T. (2017). Network meta-analysis: An introduction for clinicians. Internal and Emergency Medicine, 12(1), 103–111. https://doi.org/10.1007/s11739-016-1583-7
Sweeney, C. J., Chen, Y.-H., Carducci, M., Liu, G., Jarrard, D. F., Eisenberger, M., Wong, Y.-N., Hahn, N., Kohli, M., Cooney, M. M., Dreicer, R., Vogelzang, N. J., Picus, J., Shevrin, D., Hussain, M., Garcia, J. A., & DiPaola, R. S. (2015). Chemohormonal Therapy in Metastatic Hormone-Sensitive Prostate Cancer. The New England Journal of Medicine, 373(8), 737–746. https://doi.org/10.1056/NEJMoa1503747
"
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        string(89) "https://yoda.yale.edu/wp-content/uploads/2024/03/SV_57KskaKADT3U9Aq-R_2dVarfb9yzRhofn.pdf"
        ["link"]=>
        string(86) "https://yoda.yale.edu/data-request/2024-0372/sv_57kskakadt3u9aq-r_2dvarfb9yzrhofn-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1741"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(40) "sv_57kskakadt3u9aq-r_2dvarfb9yzrhofn-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(14394)
        ["date"]=>
        string(19) "2024-03-20 09:26:59"
        ["modified"]=>
        string(19) "2024-03-20 09:27:00"
        ["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(14398)
        ["id"]=>
        int(14398)
        ["title"]=>
        string(40) "SV_57KskaKADT3U9Aq-R_3OlkeujL1894Lqs.pdf"
        ["filename"]=>
        string(40) "SV_57KskaKADT3U9Aq-R_3OlkeujL1894Lqs.pdf"
        ["filesize"]=>
        int(26677)
        ["url"]=>
        string(89) "https://yoda.yale.edu/wp-content/uploads/2024/03/SV_57KskaKADT3U9Aq-R_3OlkeujL1894Lqs.pdf"
        ["link"]=>
        string(86) "https://yoda.yale.edu/data-request/2024-0372/sv_57kskakadt3u9aq-r_3olkeujl1894lqs-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1741"
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        ["status"]=>
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        ["uploaded_to"]=>
        int(14394)
        ["date"]=>
        string(19) "2024-03-21 12:12:37"
        ["modified"]=>
        string(19) "2024-03-21 12:12:39"
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        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
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      }
    }
  }
  ["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"]=>
  array(1) {
    [0]=>
    array(2) {
      ["publication_link"]=>
      array(3) {
        ["title"]=>
        string(79) "*Request originally approved but subsequently withdrawn: insufficient resources"
        ["url"]=>
        string(86) "http://*Request originally approved but subsequently withdrawn: insufficient resources"
        ["target"]=>
        string(6) "_blank"
      }
      ["publication_doi"]=>
      string(0) ""
    }
  }
  ["project_assoc_data"]=>
  array(0) {
  }
  ["project_due_dil_assessment"]=>
  bool(false)
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(14716)
    ["id"]=>
    int(14716)
    ["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/2024/04/Data-Request-Approved-Notice.pdf"
    ["link"]=>
    string(77) "https://yoda.yale.edu/data-request/2024-0400/data-request-approved-notice-10/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(2) "20"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(31) "data-request-approved-notice-10"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(14453)
    ["date"]=>
    string(19) "2024-04-25 15:38:15"
    ["modified"]=>
    string(19) "2024-04-25 15:38:15"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
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    string(3) "pdf"
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    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"
  ["human_research_protection_training"]=>
  bool(false)
  ["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(6) "zytiga"
  [1]=>
  string(7) "erleada"
}


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


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