array(41) {
  ["request_overridden_res"]=>
  string(1) "3"
  ["project_status"]=>
  string(7) "ongoing"
  ["project_assoc_trials"]=>
  array(1) {
    [0]=>
    object(WP_Post)#4688 (24) {
      ["ID"]=>
      int(1845)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2019-12-12 12:23:00"
      ["post_date_gmt"]=>
      string(19) "2019-12-12 12:23:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(257) "NCT01715285 - A Randomized, Double-blind, Comparative Study of Abiraterone Acetate Plus Low-Dose Prednisone Plus Androgen Deprivation Therapy (ADT) Versus ADT Alone in Newly Diagnosed Subjects With High-Risk, Metastatic Hormone-naive Prostate Cancer (mHNPC)"
      ["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) "nct01715285-a-randomized-double-blind-comparative-study-of-abiraterone-acetate-plus-low-dose-prednisone-plus-androgen-deprivation-therapy-adt-versus-adt-alone-in-newly-diagnosed-subjects-with-h"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-04-30 16:02:44"
      ["post_modified_gmt"]=>
      string(19) "2025-04-30 20:02:44"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(242) "https://dev-yoda.pantheonsite.io/clinical-trial/nct01715285-a-randomized-double-blind-comparative-study-of-abiraterone-acetate-plus-low-dose-prednisone-plus-androgen-deprivation-therapy-adt-versus-adt-alone-in-newly-diagnosed-subjects-with-h/"
      ["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(106) "The Impact of Metformin on Survival Outcomes in Patients with Comorbid Cancer and Type 2 Diabetes Mellitus"
  ["project_narrative_summary"]=>
  string(852) "This study will investigate whether the metformin can help people who have both cancer and type 2 diabetes live longer. Many previous studies have suggested this benefit, but they may have been influenced by a statistical problem called "immortal time bias" - which may lead to incorrect research conclusions.

We will combine data from eight different studies involving various cancer types. When conducting statistical analysis, we will account for the timing of when patients began metformin treatment, helping us avoid the immortal time bias problem.

This research is important because it will clarify whether metformin has real benefits for cancer patients with diabetes. The findings may guide doctors in making better treatment decisions and highlight the importance of using correct methods to avoid bias in future medical studies."
  ["project_learn_source"]=>
  string(5) "other"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(3) "Bin"
    ["last_name"]=>
    string(4) "Peng"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(28) "Chongqing Medical University"
    ["email"]=>
    string(19) "pengbin@cqmu.edu.cn"
    ["state_or_province"]=>
    string(9) "Chongqing"
    ["country"]=>
    string(5) "China"
  }
  ["project_key_personnel"]=>
  array(5) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(6) "Jiawei"
      ["p_pers_l_name"]=>
      string(4) "Zhou"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(28) "Chongqing Medical University"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [1]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(5) "Lijia"
      ["p_pers_l_name"]=>
      string(5) "Cheng"
      ["p_pers_degree"]=>
      string(26) "Bachelor of Medical Degree"
      ["p_pers_pr_affil"]=>
      string(28) "Chongqing Medical University"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [2]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(7) "Linghao"
      ["p_pers_l_name"]=>
      string(2) "Ni"
      ["p_pers_degree"]=>
      string(13) "Master degree"
      ["p_pers_pr_affil"]=>
      string(28) "Chongqing Medical University"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
    [3]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(5) "Baolu"
      ["p_pers_l_name"]=>
      string(3) "Yue"
      ["p_pers_degree"]=>
      string(26) "Bachelor of Medical Degree"
      ["p_pers_pr_affil"]=>
      string(28) "Chongqing Medical University"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
    [4]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(5) "Xiwei"
      ["p_pers_l_name"]=>
      string(4) "Wang"
      ["p_pers_degree"]=>
      string(26) "Bachelor of Medical Degree"
      ["p_pers_pr_affil"]=>
      string(28) "Chongqing Medical University"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
  }
  ["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(1232) "Background: Metformin is widely used for type 2 diabetes and may confer survival benefits in cancer patients with type 2 diabetes (T2DM). However, prior observational studies are susceptible to immortal time bias(ITB), which could distort the true treatment effect.

Objective: To evaluate the association between metformin use and survival outcomes in cancer patients with T2DM while rigorously controlling for immortal time bias using time-dependent statistical methods.

Study Design: This study will be conducted as a pooled individual participant data (IPD) meta-analysis of eight completed cancer clinical trials.

Participants: Adult cancer patients with a documented diagnosis of T2DM (either at baseline or during trial follow-up) from the included studies.

Primary Outcome: Overall survival (OS).

Secondary Outcomes: Progression-free survival (PFS), Disease-free survival (DFS).

Statistical Analysis: Time-dependent Cox proportional hazards models will be employed to control for immortal time bias. Stratified analysis will be conducted by trial. Heterogeneity will be assessed using I² and Q tests, and study-specific estimates will be pooled using inverse-variance meta-analysis."
  ["project_brief_bg"]=>
  string(3047) "Type 2 diabetes mellitus (T2DM) is a common condition that affects hundreds of millions of people worldwide. It occurs when the body does not use insulin properly, leading to high blood sugar. Insulin is a hormone made by the pancreas that helps move sugar from the blood into the body’s cells, where it can be used for energy. Many people with T2DM take metformin, a safe and affordable medication. Metformin helps lower blood sugar by reducing sugar production in the liver, slowing sugar absorption from food, and improving how the body responds to insulin. Because of these benefits, it is one of the most widely prescribed drugs for diabetes.



In recent years, researchers have also discovered that metformin may have health benefits beyond diabetes. It may help with obesity (excess weight that increases the risk of illness) by reducing fat storage, lowering appetite, and increasing energy use. It may also help protect against heart disease by reducing inflammation (the body’s reaction to injury or stress) and stress on blood vessels.



There is also interest in whether metformin might improve outcomes for people who have both cancer (uncontrolled cellular growth) and diabetes. Many patients live with both conditions, which makes treatment decisions more complex. Some studies have suggested that people with cancer who take metformin may live longer, but these studies may be misleading because of a problem called immortal time bias. Immortal time bias happens when researchers incorrectly count a period of time during which patients could not have had the outcome (for example, death) as time spent on treatment, making the treatment seem more effective than it may really be.



We will study the effect of metformin on cancer outcomes while carefully accounting for this potential bias. To do this, we will use data from clinical trials, which are high-quality research studies where patient care and reporting are carefully monitored. These data are more reliable than routine medical records because they include exact dates for diagnosis, treatments, and medication use, as well as regular follow-up information. This allows us to more accurately examine when patients started taking metformin and how this relates to their cancer outcomes.

We will carry out a secondary analysis, which means we will use data that have already been collected in cancer clinical trials. We will combine individual patient data from multiple trials to study the true relationship between metformin use and survival in cancer patients with diabetes. We will also test statistical methods that can reduce the impact of immortal time bias, so that our results are not exaggerated or misleading.



This research is important because it will clarify whether metformin has real benefits for cancer patients with diabetes. The findings may guide doctors in making better treatment decisions and highlight the importance of using correct methods to avoid bias in future medical studies."
  ["project_specific_aims"]=>
  string(715) "This study aims to utilize clinical data from various cancers and apply statistical methods to control immortal time bias, thereby assessing whether the reported survival benefit of metformin in cancer patients is distorted by immortal time bias and evaluate the actual impact of metformin on the prognosis of cancer patients.



In this study, there is a Hypothesis : After adjusting for immortal time bias, metformin may have a protective effect on the prognosis of cancer patients.



This study highlights the impact of immortal time bias in previous research and evaluates the actual role of metformin in cancer prognosis by controlling for immortal time bias in detailed data analysis."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
  }
  ["project_purposes"]=>
  array(3) {
    [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"
    }
  }
  ["project_research_methods"]=>
  string(1039) "Data Source: Individual participant data (IPD) from eight completed cancer clinical trials were requested for this study. Among these, six trials were requested from the Vivli platform (e.g., NCT00378690, NCT01085136, NCT00312013, NCT03088540, NCT01566721, NCT02402712.), one from the YODA Project platform(e.g., NCT01715285.), and one from Project Data Sphere(e.g., NCT00457691.). All data will be pooled and analyzed on the Vivli platform.



Inclusion Criteria: All trial participants with a documented diagnosis of T2DM at any timepoint, including: (1) Pre-existing T2DM at trial baseline (enrollment); (2) Newly diagnosed T2DM during the trial follow-up period.



Diagnosis will be identified via medical history records and/or the initiation of anti-diabetic medication.

Exclusion Criteria: (1) Patients with missing or ambiguous data regarding the date of metformin initiation will be excluded; (2) Patients with a diagnosis of Type 1 diabetes mellitus (if identifiable in the data) will be excluded."
  ["project_main_outcome_measure"]=>
  string(747) "The primary outcome of this study was overall survival (OS), Secondary outcomes included progression-free survival (PFS) and disease-free survival (DFS).



OS refers to the time from diagnosis of cancer to the first occurrence of death from any cause. PFS is defined as the time from the initiation of the protocol-specified study treatment (as defined in each original clinical trial) to the first occurrence of disease progression or death from any cause. DFS is defined for patients who have undergone curative-intent treatment (e.g., surgical resection). It is the time from the date of complete resection (or from the initiation of adjuvant therapy) to tumor recurrence, metastasis, new tumor occurrence, or death from any cause."
  ["project_main_predictor_indep"]=>
  string(505) "The main variable in this study is metformin use, assessed at different time points, including baseline use (prior and current) documented in the medication history, and post-treatment use recorded in the concomitant medication data.



The exposure in this study is defined as a time-varying variable. Specifically, subjects will be classified as belonging to the unexposed group until the precise date of metformin initiation, at which point they will be reclassified into the exposed group."
  ["project_other_variables_interest"]=>
  string(1176) "In addition to the use of metformin at different time points and survival endpoints, this study also requires several covariate information, such as demographic and clinical confounding factors. For demographic data, variables include age, gender, geographic region, and race. For clinically relevant confounding factors, records of comorbidities(included diabetes, cerebrovascular disease, peripheral vascular disease, renal disease, and cardiopulmonary disease), post-diagnostic use of concomitant medications(statins, metformin, non-aspirin antiplatelets, non-metformin antidiabetics, non-steroidal anti-inflammatory drugs, beta-blockers, angiotensin converting enzyme inhibitors, angiotensin receptor blockers, calcium channel blockers, and diuretics), Eastern Cooperative Oncology Group performance status(ECOG), tumor histologic type, number of tumor sites, tumor type, molecular subtype, tumor stage, metastatic status at diagnosis, sites of metastasis, number of metastatic sites, timing of metastasis, treatment regimen, body mass index (BMI), smoking history, alcohol consumption, malnutrition, age of diagnosis, Charlson comorbidity index (CCI score), among others."
  ["project_stat_analysis_plan"]=>
  string(2724) "For data management, patients with missing records of metformin initiation time point will be excluded from the analysis. Regarding missing baseline data, multiple imputation(MI) will be used to handle missing values in covariates under the assumption that data are missing at random (MAR). 



For statistical analysis, to ensure the structural integrity and independence of the studies, a stratified Cox model will be used, with each study treated as a separate stratum and we will individually analyze each study. Kaplan-Meier methods will be used to draw the survival curve. Cox proportional hazards model will be employed to assess the association between metformin use and overall survival (OS) , progression-free survival (PFS) or disease-free survival (DFS) reporting hazard ratios (HRs) and their 95% confidence intervals (CI). To assess the impact of immortal time bias on the study results and explore the true effect of metformin use on the prognosis of cancer patients, a time-dependent Cox proportional hazards model will be employed. The metformin exposure status will be defined as a time-varying covariate (i.e., patients were coded as nonusers before metformin initiation, then recoded to users on the date when metformin was started). The association between metformin use and OS as well as progression-free survival PFS will be evaluated, and the hazard ratio (HRs) and its 95% confidence interval (CI) will be reported.



This model will simultaneously adjust for clinical confounding factors, such as age, gender, smoking history, alcohol consumption, tumor type, molecular subtype, tumor histological type, tumor stage, treatment plan, BMI, concomitant drugs, and modify the adjusted confounding factors based on different clinical trial records. All variables that were adjusted in the model were time-fixed covariates. The only time dependent variable in the model was exposure to metformin.



To address between-study heterogeneity, the inverse-variance weighting method will be applied to pool the hazard ratio (HR) estimates from individual studies, yielding a pooled overall effect estimate. We will first perform subgroup analyses stratified by tumor type, followed by an overall pooled analysis.Following the assessment of heterogeneity among studies through I² and Q tests, the appropriate statistical model was selected: if heterogeneity was low (I² < 50%), a fixed-effects model was applied; if substantial heterogeneity was observed (I² ≥ 50%), a random-effects model was employed. Results from both models are presented for comparison.



All statistical analyses will be conducted using R software provided by the Vivli platform."
  ["project_software_used"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
  }
  ["project_timeline"]=>
  string(255) "Anticipated Project Start Date: 2026/1/1

Analysis completion date: 2026/4/30

Manuscript Draft Completed: 2026/6/15

Manuscript First Submitted for Publication: 2026/07/30

Date Results Reported Back to the YODA Project: 2026/12/1"
  ["project_dissemination_plan"]=>
  string(509) "This study aims to utilize clinical data from various cancers and apply statistical methods to control immortal time bias, thereby assessing whether the reported survival benefit of metformin in cancer patients is distorted by immortal time bias and evaluate the actual impact of metformin on the prognosis of cancer patients.

We plan to complete this study within 1 year, and our target journal for publication is Diabetes, Obesity and Metabolism or Journal of Clinical Endocrinology & Metabolism."
  ["project_bibliography"]=>
  string(4132) "

Vrinzen C E J, Delfgou L, Stadhouders N, et al. A systematic review and multilevel regression analysis reveals the comorbidity prevalence in cancer[J]. Cancer Research, 2023, 83(7): 1147-1157.
Zhu D, Ding R, Ma Y, et al. Comorbidity in lung cancer patients and its association with hospital readmission and fatality in China[J]. BMC Cancer, 2021, 21(1): 557.
Pernicova I, Korbonits M. Metformin–mode of action and clinical implications for diabetes and cancer. Nat Rev Endocrinol. 2014 Mar;10(3):143-56.
Lv Z, Guo Y. Metformin and Its Benefits for Various Diseases. Front Endocrinol (Lausanne). 2020 Apr 16;11:191.
Kheniser KG, Kashyap SR, Kasumov T. A systematic review: the appraisal of the effects of metformin on lipoprotein modification and function. Obes Sci Pract. 2019 Jan 7;5(1):36-45.
Wang Z, Lu C, Zhang K, et al. Metformin combining PD-1 inhibitor enhanced anti-tumor efficacy in STK11 mutant lung cancer through AXIN-1-dependent inhibition of STING ubiquitination[J]. Frontiers in Molecular Biosciences, 2022, 9: 780200.
Ling S, Xie H, Yang F, et al. Metformin potentiates the effect of arsenic trioxide suppressing intrahepatic cholangiocarcinoma: roles of p38 MAPK, ERK3, and mTORC1[J]. Journal of Hematology and Oncology, 2017, 10(1): 59.
Hua Y, Zheng Y, Yao Y, et al.Zhuang A. Metformin and cancer hallmarks: shedding new lights on therapeutic repurposing. J Transl Med. 2023 Jun 21;21(1):403.
Li Y, Liu X, Lv W, et al.Metformin use correlated with lower risk of cardiometabolic diseases and related mortality among US cancer survivors: evidence from a nationally representative cohort study. BMC Med. 2024 Jun 26;22(1):269.
Tarhini Z, Manceur K, Magne J, et al. The effect of metformin on the survival of colorectal cancer patients with type 2 diabetes mellitus. Sci Rep. 2022 Jul 20;12(1):12374.
Garcia C, Yao A, Camacho F, et al. A SEER-Medicare analysis of the impact of metformin on overall survival in ovarian cancer. Gynecol Oncol. 2017 Aug;146(2):346-350.
Löfling LL, Støer NC, Andreassen BK, et al. Low-dose aspirin, statins, and metformin and survival in patients with breast cancers: a Norwegian population-based cohort study. Breast Cancer Res. 2023 Aug 30;25(1):101.
Kaur P, Berchuck A, Chase A, et al. Metformin use and survival in people with ovarian cancer: A population-based cohort study from British Columbia, Canada. Neoplasia. 2024 Oct;56:101026.
Yadav K, Lewis RJ. Immortal Time Bias in Observational Studies. JAMA. 2021 Feb 16;325(7):686-687.
Yu OHY, Suissa S. Metformin and Cancer: Solutions to a Real-World Evidence Failure. Diabetes Care. 2023 May 1;46(5):904-912.
Suissa S. Immortal time bias in observational studies of drug effects[J]. Pharmacoepidemiology and Drug Safety, 2007, 16(3): 241-249.
Suissa S. Immortal time bias in pharmaco-epidemiology[J]. American Journal of Epidemiology, 2008, 167(4): 492-499.
Suissa S, Azoulay L. Metformin and the risk of cancer: time-related biases in observational studies[J]. Diabetes Care, 2012, 35(12): 2665-2673.
Karim M E, Gustafson P, Petkau J, et al. Comparison of statistical approaches for dealing with immortal time bias in drug effectiveness studies[J]. American Journal of Epidemiology, 2016, 184(4): 325-335.
Maringe C, Benitez Majano S, Exarchakou A, et al. Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data[J]. International Journal of Epidemiology, 2020, 49(5): 1719-1729.
Duchesneau E D, Jackson B E, Webster-Clark M, et al. The timing, the treatment, the question: comparison of epidemiologic approaches to minimize immortal time bias in real-world data using a surgical oncology example[J]. Cancer Epidemiology, Biomarkers & Prevention: A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology, 2022, 31(11): 2079-2086.

"
  ["project_suppl_material"]=>
  bool(false)
  ["project_coi"]=>
  array(6) {
    [0]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(17992)
        ["id"]=>
        int(17992)
        ["title"]=>
        string(15) "Pengbin-COI.pdf"
        ["filename"]=>
        string(15) "Pengbin-COI.pdf"
        ["filesize"]=>
        int(20346)
        ["url"]=>
        string(64) "https://yoda.yale.edu/wp-content/uploads/2025/09/Pengbin-COI.pdf"
        ["link"]=>
        string(61) "https://yoda.yale.edu/data-request/2025-0672/pengbin-coi-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2215"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(15) "pengbin-coi-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17952)
        ["date"]=>
        string(19) "2025-10-01 01:05:02"
        ["modified"]=>
        string(19) "2025-10-01 01:05:03"
        ["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(17993)
        ["id"]=>
        int(17993)
        ["title"]=>
        string(18) "ZhouJiawei-COI.pdf"
        ["filename"]=>
        string(18) "ZhouJiawei-COI.pdf"
        ["filesize"]=>
        int(20142)
        ["url"]=>
        string(67) "https://yoda.yale.edu/wp-content/uploads/2025/10/ZhouJiawei-COI.pdf"
        ["link"]=>
        string(64) "https://yoda.yale.edu/data-request/2025-0672/zhoujiawei-coi-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2215"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(18) "zhoujiawei-coi-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17952)
        ["date"]=>
        string(19) "2025-10-01 13:11:58"
        ["modified"]=>
        string(19) "2025-10-01 13:12:00"
        ["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"
      }
    }
    [2]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(17957)
        ["id"]=>
        int(17957)
        ["title"]=>
        string(16) "ChengLijia-2.pdf"
        ["filename"]=>
        string(16) "ChengLijia-2.pdf"
        ["filesize"]=>
        int(21089)
        ["url"]=>
        string(65) "https://yoda.yale.edu/wp-content/uploads/2025/09/ChengLijia-2.pdf"
        ["link"]=>
        string(62) "https://yoda.yale.edu/data-request/2025-0672/chenglijia-2-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2215"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(16) "chenglijia-2-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17952)
        ["date"]=>
        string(19) "2025-09-20 09:38:57"
        ["modified"]=>
        string(19) "2025-09-20 09:39:04"
        ["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(17958)
        ["id"]=>
        int(17958)
        ["title"]=>
        string(15) "NiLinghao-1.pdf"
        ["filename"]=>
        string(15) "NiLinghao-1.pdf"
        ["filesize"]=>
        int(20015)
        ["url"]=>
        string(64) "https://yoda.yale.edu/wp-content/uploads/2025/09/NiLinghao-1.pdf"
        ["link"]=>
        string(61) "https://yoda.yale.edu/data-request/2025-0672/nilinghao-1-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2215"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(15) "nilinghao-1-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17952)
        ["date"]=>
        string(19) "2025-09-20 09:38:58"
        ["modified"]=>
        string(19) "2025-09-20 09:39:04"
        ["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(17959)
        ["id"]=>
        int(17959)
        ["title"]=>
        string(14) "YueBaolu-1.pdf"
        ["filename"]=>
        string(14) "YueBaolu-1.pdf"
        ["filesize"]=>
        int(20977)
        ["url"]=>
        string(63) "https://yoda.yale.edu/wp-content/uploads/2025/09/YueBaolu-1.pdf"
        ["link"]=>
        string(60) "https://yoda.yale.edu/data-request/2025-0672/yuebaolu-1-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2215"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(14) "yuebaolu-1-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17952)
        ["date"]=>
        string(19) "2025-09-20 09:39:00"
        ["modified"]=>
        string(19) "2025-09-20 09:39:04"
        ["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(17960)
        ["id"]=>
        int(17960)
        ["title"]=>
        string(13) "WangXiwei.pdf"
        ["filename"]=>
        string(13) "WangXiwei.pdf"
        ["filesize"]=>
        int(20834)
        ["url"]=>
        string(62) "https://yoda.yale.edu/wp-content/uploads/2025/09/WangXiwei.pdf"
        ["link"]=>
        string(59) "https://yoda.yale.edu/data-request/2025-0672/wangxiwei-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "2215"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(13) "wangxiwei-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(17952)
        ["date"]=>
        string(19) "2025-09-20 09:39:02"
        ["modified"]=>
        string(19) "2025-09-20 09:39:04"
        ["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_learn_source_exp"]=>
  string(0) ""
  ["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(19131)
    ["id"]=>
    int(19131)
    ["title"]=>
    string(47) "YODA Project Due Diligence Assessment 2025-0672"
    ["filename"]=>
    string(51) "YODA-Project-Due-Diligence-Assessment-2025-0672.pdf"
    ["filesize"]=>
    int(106311)
    ["url"]=>
    string(100) "https://yoda.yale.edu/wp-content/uploads/2025/09/YODA-Project-Due-Diligence-Assessment-2025-0672.pdf"
    ["link"]=>
    string(93) "https://yoda.yale.edu/data-request/2025-0672/yoda-project-due-diligence-assessment-2025-0672/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(47) "yoda-project-due-diligence-assessment-2025-0672"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(17952)
    ["date"]=>
    string(19) "2026-04-17 14:38:09"
    ["modified"]=>
    string(19) "2026-04-17 14:38:09"
    ["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"
  }
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(19132)
    ["id"]=>
    int(19132)
    ["title"]=>
    string(46) "YODA Project Protocol - 2025-0672 - 2025-10-01"
    ["filename"]=>
    string(46) "YODA-Project-Protocol-2025-0672-2025-10-01.pdf"
    ["filesize"]=>
    int(145148)
    ["url"]=>
    string(95) "https://yoda.yale.edu/wp-content/uploads/2025/09/YODA-Project-Protocol-2025-0672-2025-10-01.pdf"
    ["link"]=>
    string(88) "https://yoda.yale.edu/data-request/2025-0672/yoda-project-protocol-2025-0672-2025-10-01/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(42) "yoda-project-protocol-2025-0672-2025-10-01"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(17952)
    ["date"]=>
    string(19) "2026-04-17 14:38:38"
    ["modified"]=>
    string(19) "2026-04-17 14:38:38"
    ["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(19133)
    ["id"]=>
    int(19133)
    ["title"]=>
    string(36) "YODA Project Review - 2025-0672_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2025-0672_site.pdf"
    ["filesize"]=>
    int(1315556)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2025/09/YODA-Project-Review-2025-0672_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2025-0672/yoda-project-review-2025-0672_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(34) "yoda-project-review-2025-0672_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(17952)
    ["date"]=>
    string(19) "2026-04-17 14:40:39"
    ["modified"]=>
    string(19) "2026-04-17 14:40:39"
    ["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(0) ""
}
data partner
array(1) {
  [0]=>
  string(0) ""
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(0) {
}


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


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