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      ["post_date"]=>
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      string(239) "NCT00060502 - A Phase II, Multicenter, Randomized, Double-Blind, Placebo Controlled Study Evaluating the Efficacy and Safety of Anti-TNF a Monoclonal Antibody (Infliximab) to Treat Cancer-Related Cachexia in Subjects With Pancreatic Cancer"
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      string(192) "nct00060502-a-phase-ii-multicenter-randomized-double-blind-placebo-controlled-study-evaluating-the-efficacy-and-safety-of-anti-tnf-a-monoclonal-antibody-infliximab-to-treat-cancer-related-cach"
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      string(241) "https://dev-yoda.pantheonsite.io/clinical-trial/nct00060502-a-phase-ii-multicenter-randomized-double-blind-placebo-controlled-study-evaluating-the-efficacy-and-safety-of-anti-tnf-a-monoclonal-antibody-infliximab-to-treat-cancer-related-cach/"
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  ["project_title"]=>
  string(118) "A Retrospective Cohort Study Using Causal Inference Tools to Evaluate the Efficacy of Treatments for Pancreatic Cancer"
  ["project_narrative_summary"]=>
  string(835) "Pancreatic cancer affects nearly half a million people worldwide each year and causes almost as many deaths. Pancreatic cancer progresses quickly so researchers often measure treatment success by looking at how long patients live after diagnosis. Usually, treatments are compared using randomized clinical trials- patients are randomly assigned to one treatment or another. However, these trials are expensive and take many years to complete. We will study a new way to estimate how well treatments work. We have developed a method called Personalised Synthetic Controls, in which we use statistical models to predict how each patient would have responded to a standard treatment in comparison to the patient’s actual outcome after receiving a different treatment. This allows us to estimate which treatment benefits each individual."
  ["project_learn_source"]=>
  string(5) "other"
  ["project_learn_source_exp"]=>
  string(5) "Vivli"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(7) "Richard"
    ["last_name"]=>
    string(7) "Jackson"
    ["degree"]=>
    string(14) "PhD Statistics"
    ["primary_affiliation"]=>
    string(23) "University of Liverpool"
    ["email"]=>
    string(23) "richj23@liverpool.ac.uk"
    ["state_or_province"]=>
    string(9) "Liverpool"
    ["country"]=>
    string(14) "United Kingdom"
  }
  ["project_key_personnel"]=>
  array(1) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(7) "Kusqaum"
      ["p_pers_l_name"]=>
      string(4) "Adam"
      ["p_pers_degree"]=>
      string(18) "MSc Bioinformatics"
      ["p_pers_pr_affil"]=>
      string(23) "University of Liverpool"
      ["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(15) "NIHR Fellowship"
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1669) "Background

Pancreatic cancer is the sixth leading cause of cancer deaths worldwide and majority patients have poor outcomes. To check if new treatments are effective and due to the poor prognosis, clinical trials predominantly use Overall Survival to evaluate new therapies. Randomisation is most effective in providing a causal comparison of treatments. However, Clinical trials are costly and take long to complete.

Objective

We will perform a cohort study which will compare the outcomes of the data available from Yoda to statistical models which predicts patient performance to some control.

Study Design

For each patient in the data who is comparable to the model we predict their outcome under the synthetic control. Where patients receive the same treatment as that represented by the synthetic control we use this prediction to validate the model. Where patients receive a separate treatment, we use this treatment to estimate the efficacy of the new treatment compared to the control.

Participants

All participants will be included.

Primary Outcome Measure

Overall Survival which will be measured as the time from the start of therapy/randomisation until death by any cause. 

Statistical Analysis

We will derive efficacy using the Personalised Synthetic Controls approach. Prior to conducting the analysis, we will formalise our analysis approach using a github repository.This will allow transparency over the analytical approach and provide an audit trail for amendments made. This aims to replicate the statistical procedures followed when conducting randomised controlled trials"
  ["project_brief_bg"]=>
  string(2431) "Pancreatic cancer is the sixth leading cause of cancer deaths worldwide and majority patients have poor outcomes. Globally, cases have significantly increased over the last 30 years. With incidence rates of 496,000 and mortality rates of 466,000,  pancreatic cancer remains a public health burden. It is characterised by poor survival and an increased death rate. Pancreatic cancer is expected to become the third leading cause of cancer-related deaths in Europe as cases are continuing to rise rapidly. 



Main treatments for pancreatic cancer include surgery, radiotherapy and chemotherapy. In some cases, pancreatic cancer can be cured by surgery. However, in most cases, pancreatic cancers are diagnosed when they are too advanced, so surgery is not effective, and treatments only often focus on relieving symptoms. 



To check if new treatments are effective and due to the poor prognosis, clinical trials predominantly use Overall Survival to evaluate new therapies. Randomisation remains the tool which is most effective in providing a causal comparison of treatments. However, Clinical trials are costly, take a large period of time to complete and can be susceptible to issues of generalisability. We have developed methodology to apply causal methodology outside of the clinical trials framework. This is a process whereby we use validated statistical models to predict patients performance under some control treatment. We can then compare, for each patient, their observed response against their model predicted control response. We refer to this as ‘Personalised Synthetic Controls (PSC)’ [please seeJackson et al., 2025].



In Pancreatic Cancer, we have developed 2 models, one in the adjuvant and one in the advanced setting. Our proposal is to use the data available from Vivli and apply these synthetic controls. Where we can obtain data on patients who receive the same treatment as the synthetic control, we will use this to validate and improve our current models. Where we obtain data on alternative treatments we will use this to estimate the efficacy and explore evidence of treatment effect heterogeneity.





Prior work: 

Jackson, R., Johnson, P., Berhane, S., Kolamunnage-Dona, R., Hughes, D., Dodd, S., Neoptolemos, J., Palmer, D., & Cox, T. (2025). Estimating treatment effects using parametric models as counter-factual evidence."
  ["project_specific_aims"]=>
  string(810) "We aim to compare the effectiveness of two treatments using a counterfactual model (a model that predicts what would happen if a different scenario took place) and a cohort of patients that were treated with the experimental arm of a given treatment.



Where we obtain data for patients on treatments that differ from the synthetic controls (e.g., Treatments other than Gemcap) our Null Hypothesis is:

H0: There is no difference in Overall Survival between patients receiving Gemcitabine and any other treatment

Beyond this, we also plan to investigate the potential for treatment effect heterogeneity within the data.  Here our null hypothesis is:

H0: The measured efficacy between an experimental treatment and the control is consistent across the patient population

"
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(14) "indiv_trial_an"
    ["label"]=>
    string(25) "Individual trial analysis"
  }
  ["project_purposes"]=>
  array(3) {
    [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(76) "confirm_or_validate previously_conducted_research_on_treatment_effectiveness"
      ["label"]=>
      string(76) "Confirm or validate previously conducted research on treatment effectiveness"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(34) "research_on_clinical_trial_methods"
      ["label"]=>
      string(34) "Research on clinical trial methods"
    }
  }
  ["project_research_methods"]=>
  string(2005) "We do not specify any inclusion exclusion criteria.  We do have access to trial data (ESPAC3 and ESPAC4) but have no plans to merge data from these trials to those we obtain from YODA.  We will not aggregate or merge data and will be conducting our analyses through R (Version 4.0)



We will be using data from other studies that are available from the Vivli platform. These studies are:



NCT00417209 - A Randomized, Open Label Multi-Center Study Of Single Agent Larotaxel (XRP9881) Compared To Continuous Administration of 5-FU For The Treatment Of Patients With Advanced Pancreatic Cancer Previously Treated With A Gemcitabine-Containing Regimen

NCT01124786 - A Phase II Randomized, Open-Label, Multicenter Study Comparing CO-1.01 With Gemcitabine as First-Line Therapy in Patients With Metastatic Pancreatic Adenocarcinoma

NCT01016483 - Phase II Randomized Trial of MEK Inhibitor MSC1936369B or Placebo Combined With Gemcitabine in Metastatic Pancreas Cancer Subjects

H3E-MC-JMAD - A Phase 2 Trial of LY231514 Administered Intravenously Every 21 Days in Patients with Pancreatic Cancer

NCT00844649 - A Randomized Phase III Study of Weekly ABI-007 Plus Gemcitabine Versus Gemcitabine Alone in Patients With Metastatic Adenocarcinoma of the Pancreas

NCT01525550 - A SINGLE-ARM OPEN-LABEL INTERNATIONAL MULTI-CENTER STUDY OF THE EFFICACY AND SAFETY OF SUNITINIB MALATE (SU011248, SUTENT (REGISTERED)) IN PATIENTS WITH PROGRESSIVE ADVANCED METASTATIC WELL-DIFFERENTIATED UNRESECTABLE PANCREATIC NEUROENDOCRINE TUMORS

NCT00574275 - A Multinational, Randomized, Double-blind Study, Comparing the Efficacy of Aflibercept Once Every 2 Weeks Versus Placebo in Patients Treated With Gemcitabine for Metastatic Pancreatic Cancer

NCT00428597 -A Phase III Randomized, Double-Blind Study Of Sunitinib (SU011248, SUTENT) Versus Placebo In Patients With Progressive Advanced/Metastatic Well-Differentiated Pancreatic Islet Cell Tumors

"
  ["project_main_outcome_measure"]=>
  string(189) "The primary outcome for this analysis is Overall Survival measured as the time from the start of treatment until death by any cause.

No secondary outcome measures will be considered."
  ["project_main_predictor_indep"]=>
  string(357) "The primary outcome that we will be overall survival (measured as the time from the start of treatment until death by any cause).  A secondary analysis is recurrence free survival measured as the time from first treatment until disease recurrence or death by any cause.  The independent variable of interest is the treatment that patients were allocated to."
  ["project_other_variables_interest"]=>
  string(926) "Personalised Synthetic Controls will be applied by applying a statistical model we have developed for the survival of patients receiving Gemcitabine and Gemcitabine plus Capecitabine.  These models depend upon the following characteristics to be fitted

•	Lymph Node status

•	Resection Margin status

•	Post-Operative CA19.9

Including treatment identifiers and outcomes a minimum dataset of the above characteristics along with treatment start date, death date, date of last known follow-up (or administrative censoring) and treatment identifier are required.

Beyond this we would also be interested in the following baseline covariates for the purposes of sub-group analyses

•	Age 

•	Gender

•	Loval Invasion

•	Tumour Differentiation Status

•	Stage

•	Diabetic Status

•	Type of surgery

•	Pre-operative CA19.9

"
  ["project_stat_analysis_plan"]=>
  string(5049) "An overview of the Statistical Analysis Plan (SAP) is provided below. Please note that this may be refined prior to analysis. A final version of the SAP will be placed in a GitHub repository prior to data analysis. This is to demonstrate that the analysis intention was not altered by observing the data to add integrity to the study results.



Study Design

We plan a retrospective cohort study which applies causal inference tools to evaluate the efficacy of treatments for pancreatic cancer.  

We make use of validated statistical models which predict the response of patients to Gemcitabine therapy and will apply Bayesian methodology which compares these predictions against patients’ observed responses and uses these comparisons to derive efficacy. As the controls are synthetically generated on a patient level, we refer to this method as ‘Personalised Synthetic Controls’ (PSC).



Primary Outcome

The primary outcome for this analysis is Overall Survival measured as the time from the start of treatment until death by any cause.



Patient Groups for analysis

Analysis is planned on a ‘Intention-to-Treat’ policy retaining patients in their allocated groups irrespective of any protocol violations. Any patient who the model is appropriate for will be included in the analysis (patients may be removed if they require extrapolation beyond the support of the synthetic control model).



Descriptive Analysis

Continuous data will be summarised as median (IQR) and categorical data will be summarised as frequencies of counts with associated percentages. We will use Kaplan-Meier survival estimates to show unadjusted estimates of survival data.



Levels of Significance

As the analysis is Bayesian in form, evidence will be evaluated based on the form of posterior distributions. Results will be presented in terms of posterior means and 95% credibility intervals defined by the Highest Posterior Density.



Missing Data

Analysis will be conducted on a complete case dataset. To be applied, baseline data are required to apply the statistical models which act as the synthetic control. Only patients with a full complete dataset of baseline and outcome data will be included.



Analysis methodology

To derive efficacy, we will apply a causal inference methodology referred to as Personalised Synthetic Controls [Jackson et al., 2025]. This uses pre-defined and validated statistical models which have the ability to predict a patient’s performance under some control treatment.

Here we have 2 parametric models which will act as controls, both generated on a group of patients who received Gemcitabine therapy. In the advanced setting we have a model generated on randomised controlled trials (RCTs) which included a Gemcitabine arm (VIP, ACELERATE and GEMCAP) to provide a model generated on a combined cohort of 326 patients. In the adjuvant setting we have a model for Gemcitabine based on the combined cohort of patients from the ESPAC-3 and ESPAC-4 trials (a cohort of 1286 patients).

In both cases, models were generated using a backwards step wise procedure using Akaike’s Information Criterion (AIC). Internal validation was performed using standard measures of discrimination and calibration using a data splitting approach before re-fitting the final model on the full patient cohort.

Full details of the available models are available at https://richjjackson.github.io/mecPortal//models/pdac_gem.html and https://richjjackson.github.io/mecPortal//models/pdac_gem.html respectively.

A key aspect of the models is that the cumulative baseline hazard function is parametrically defined (in this case on the basis of flexible splines).  This allows us to obtain a personalised survival function for a new patient. The analytical procedure develops a likelihood which compares a patient’s observed response against their model estimated response. This likelihood is then evaluated in a Bayesian framework.   An R package available on CRAN (psc) has been developed to apply this methodology. 



Sample Size

Due to the complex nature of the analysis, formal sample size calculations are not possible. Based on designs of clinical trials in the same disease area, we set a HR=0.7 as a target for clinical significance. To a large part, inference depends on the precision of the posterior distribution. This precision is based on both the precision of the underlying model and the amount of experimental data available. As a guide however, previous analyses using models in the pancreatic ductal adenocarcinoma (PDAC) setting have obtained a standard error of 0.15. In the advanced setting we would expect this to be larger (approximately 0.2). 



Efficacy Parameter

The efficacy parameter of interest will be a hazard ratio (HR) comparing experimental treatments against the synthetic control.



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  ["project_timeline"]=>
  string(329) "04/03/2026-04/03/2027



A project timelines is as follows

•	Months 1- 3 Data cleaning organisation, analysis plan publication

•	Months 4 – 6 Analysis and interpretation

•	Months 7 – 9 Reporting and manuscript development

•	Months 9 – 12 Submission and Dissemination 

"
  ["project_dissemination_plan"]=>
  string(373) "We plan to publish our results to peer-reviewed journals targeted at pancreatic cancer such as Journal of Clinical Oncology/Pancreatology as well as presenting at appropriate conferences targeted at causal inference and pancreas cancer. Beyond this we are developing a web page to publicise the methodology and it’s application and will devote a web-page to this project."
  ["project_bibliography"]=>
  string(4195) "
Ghaneh, P., Kleeff, J., Halloran, C., Raraty, M., Jackson, R., Melling, J., Jones, O., Palmer, D., Cox, T., Smith, C., & others (2019). The impact of positive resection margins on survival and recurrence following resection and adjuvant chemotherapy for pancreatic ductal adenocarcinoma. Annals of surgery, 269(3), 520–529.
Neoptolemos, J., Palmer, D., Ghaneh, P., Psarelli, E., Valle, J., Halloran, C., Faluyi, O., O’Reilly, D., Cunningham, D., Wadsley, J., & others (2017). European Study Group for Pancreatic Cancer. Comparison of adjuvant gemcitabine and capecitabine with gemcitabine monotherapy in patients with resected pancreatic cancer (ESPAC-4): a multicentre, open-label, randomised, phase 3 trial. Lancet, 389(10073), 1011–1024.
Neoptolemos, J., Palmer, D., Ghaneh, P., Psarelli, E., Valle, J., Halloran, C., Faluyi, O., O’Reilly, D., Cunningham, D., Wadsley, J., & others (2017). Comparison of adjuvant gemcitabine and capecitabine with gemcitabine monotherapy in patients with resected pancreatic cancer (ESPAC-4): a multicentre, open-label, randomised, phase 3 trial. The Lancet, 389(10073), 1011–1024.
Jones, R., Psarelli, E.E., Jackson, R., Ghaneh, P., Halloran, C., Palmer, D., Campbell, F., Valle, J., Faluyi, O., O’Reilly, D., & others (2019). Patterns of recurrence after resection of pancreatic ductal adenocarcinoma: a secondary analysis of the ESPAC-4 randomized adjuvant chemotherapy trial. JAMA surgery, 154(11), 1038–1048.
Greenhalf, W., Ghaneh, P., Neoptolemos, J., Palmer, D., Cox, T., Lamb, R., Garner, E., Campbell, F., Mackey, J., Costello, E., & others (2014). Pancreatic cancer hENT1 expression and survival from gemcitabine in patients from the ESPAC-3 trial. Journal of the National Cancer Institute, 106(1), djt347.
Valle, J., Palmer, D., Jackson, R., Cox, T., Neoptolemos, J., Ghaneh, P., Rawcliffe, C., Bassi, C., Stocken, D., Cunningham, D., & others (2014). Optimal duration and timing of adjuvant chemotherapy after definitive surgery for ductal adenocarcinoma of the pancreas: ongoing lessons from the ESPAC-3 study. Journal of clinical oncology, 32(6), 504–512.
Ghaneh, P., Palmer, D., Cicconi, S., Jackson, R., Halloran, C., Rawcliffe, C., Sripadam, R., Mukherjee, S., Soonawalla, Z., Wadsley, J., & others (2023). Immediate surgery compared with short-course neoadjuvant gemcitabine plus capecitabine, FOLFIRINOX, or chemoradiotherapy in patients with borderline resectable pancreatic cancer (ESPAC5): a four-arm, multicentre, randomised, phase 2 trial. The lancet Gastroenterology & hepatology, 8(2), 157–168.
Jackson, R., & Cox, T. (2022). Kernel hazard estimation for visualisation of the effect of a continuous covariate on time-to-event endpoints. Pharmaceutical Statistics, 21(3), 514–524.
Vallabhaneni, S., Patel, S., Campbell, B., Boyle, J., Cook, A., Crosher, A., Holder, S., Jenkins, M., Ormesher, D., Rosala-Hallas, A., & others (2024). Editor’s Choice–Comparison of open surgery and endovascular techniques for juxtarenal and complex neck aortic aneurysms: The UK COMPlex AneurySm Study (UK-COMPASS)–peri-operative and midterm outcomes. European Journal of Vascular and Endovascular Surgery, 67(4), 540–553.
Palmer, D., Jackson, R., Springfeld, C., Ghaneh, P., Rawcliffe, C., Halloran, C., Faluyi, O., Cunningham, D., Wadsley, J., Darby, S., & others (2025). Pancreatic adenocarcinoma: long-term outcomes of adjuvant therapy in the ESPAC4 phase III trial. Journal of Clinical Oncology, 43(10), 1240–1253.
Jackson, R., Johnson, P., Berhane, S., Kolamunnage-Dona, R., Hughes, D., Dodd, S., Neoptolemos, J., Palmer, D., & Cox, T. (2025). Estimating treatment effects using parametric models as counter-factual evidence. BMC medical research methodology, 25(1), 91.
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    string(95) "https://yoda.yale.edu/wp-content/uploads/2025/12/YODA-Project-Protocol-2025-0876-2026-01-21.pdf"
    ["link"]=>
    string(88) "https://yoda.yale.edu/data-request/2025-0876/yoda-project-protocol-2025-0876-2026-01-21/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(42) "yoda-project-protocol-2025-0876-2026-01-21"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18443)
    ["date"]=>
    string(19) "2026-06-03 19:40:37"
    ["modified"]=>
    string(19) "2026-06-03 19:40:37"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  array(21) {
    ["ID"]=>
    int(19409)
    ["id"]=>
    int(19409)
    ["title"]=>
    string(36) "YODA Project Review - 2025-0876_site"
    ["filename"]=>
    string(38) "YODA-Project-Review-2025-0876_site.pdf"
    ["filesize"]=>
    int(1331910)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2025/12/YODA-Project-Review-2025-0876_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2025-0876/yoda-project-review-2025-0876_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(34) "yoda-project-review-2025-0876_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(18443)
    ["date"]=>
    string(19) "2026-06-03 19:40:54"
    ["modified"]=>
    string(19) "2026-06-03 19:40:54"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_highlight_button"]=>
  string(0) ""
  ["request_overridden_res"]=>
  string(1) "3"
  ["request_data_partner"]=>
  string(15) "johnson-johnson"
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(0) {
}


pi affil
array(0) {
}


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


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


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