array(43) {
  ["project_status"]=>
  string(7) "ongoing"
  ["project_assoc_trials"]=>
  array(6) {
    [0]=>
    object(WP_Post)#5844 (24) {
      ["ID"]=>
      int(1756)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2018-12-20 10:25:00"
      ["post_date_gmt"]=>
      string(19) "2018-12-20 10:25:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(214) "NCT00236379 - A Six-month, Double-blind, Randomized, International, Multicenter Trial to Evaluate the Glucoregulatory Effects of Risperidone and Olanzapine in Subjects With Schizophrenia or Schizoaffective Disorder"
      ["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) "nct00236379-a-six-month-double-blind-randomized-international-multicenter-trial-to-evaluate-the-glucoregulatory-effects-of-risperidone-and-olanzapine-in-subjects-with-schizophrenia-or-schizoaffe"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-05-16 16:38:38"
      ["post_modified_gmt"]=>
      string(19) "2025-05-16 20:38:38"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(243) "https://dev-yoda.pantheonsite.io/clinical-trial/nct00236379-a-six-month-double-blind-randomized-international-multicenter-trial-to-evaluate-the-glucoregulatory-effects-of-risperidone-and-olanzapine-in-subjects-with-schizophrenia-or-schizoaffe/"
      ["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)#5849 (24) {
      ["ID"]=>
      int(1199)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2014-09-25 11:40:00"
      ["post_date_gmt"]=>
      string(19) "2014-09-25 11:40:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(261) "NCT00391222 - A Randomized, Double Blind, Placebo and Active Controlled Parallel Group Study to Evaluate the Efficacy and Safety of Risperidone Long-acting Injectable (LAI) for the Prevention of Mood Episodes in the Treatment of Subjects With Bipolar I Disorder"
      ["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) "nct00391222-a-randomized-double-blind-placebo-and-active-controlled-parallel-group-study-to-evaluate-the-efficacy-and-safety-of-risperidone-long-acting-injectable-lai-for-the-prevention-of-mood"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-10-22 13:29:51"
      ["post_modified_gmt"]=>
      string(19) "2025-10-22 17:29:51"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(242) "https://dev-yoda.pantheonsite.io/clinical-trial/nct00391222-a-randomized-double-blind-placebo-and-active-controlled-parallel-group-study-to-evaluate-the-efficacy-and-safety-of-risperidone-long-acting-injectable-lai-for-the-prevention-of-mood/"
      ["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)#5845 (24) {
      ["ID"]=>
      int(1496)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2016-03-31 12:53:00"
      ["post_date_gmt"]=>
      string(19) "2016-03-31 12:53:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(274) "NCT00490971 - A Randomized, Double-Blind, Active- and Placebo-controlled, Parallel-group, Multicenter Study to Evaluate the Efficacy and Safety of Extended-Release Paliperidone as Maintenance Treatment After an Acute Manic or Mixed Episode Associated With Bipolar I Disorder"
      ["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) "nct00490971-a-randomized-double-blind-active-and-placebo-controlled-parallel-group-multicenter-study-to-evaluate-the-efficacy-and-safety-of-extended-release-paliperidone-as-maintenance-treatmen"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-10-22 12:16:37"
      ["post_modified_gmt"]=>
      string(19) "2025-10-22 16:16:37"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(242) "https://dev-yoda.pantheonsite.io/clinical-trial/nct00490971-a-randomized-double-blind-active-and-placebo-controlled-parallel-group-multicenter-study-to-evaluate-the-efficacy-and-safety-of-extended-release-paliperidone-as-maintenance-treatmen/"
      ["menu_order"]=>
      int(0)
      ["post_type"]=>
      string(14) "clinical_trial"
      ["post_mime_type"]=>
      string(0) ""
      ["comment_count"]=>
      string(1) "0"
      ["filter"]=>
      string(3) "raw"
    }
    [3]=>
    object(WP_Post)#5846 (24) {
      ["ID"]=>
      int(1422)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2015-10-27 09:50:00"
      ["post_date_gmt"]=>
      string(19) "2015-10-27 09:50:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(159) "NCT00078039 - Trial Evaluating Three Fixed Dosages of Paliperidone Extended-Release (ER) Tablets and Olanzapine in the Treatment of Patients With Schizophrenia"
      ["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(155) "nct00078039-trial-evaluating-three-fixed-dosages-of-paliperidone-extended-release-er-tablets-and-olanzapine-in-the-treatment-of-patients-with-schizophrenia"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-10-22 12:19:30"
      ["post_modified_gmt"]=>
      string(19) "2025-10-22 16:19:30"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(204) "https://dev-yoda.pantheonsite.io/clinical-trial/nct00078039-trial-evaluating-three-fixed-dosages-of-paliperidone-extended-release-er-tablets-and-olanzapine-in-the-treatment-of-patients-with-schizophrenia/"
      ["menu_order"]=>
      int(0)
      ["post_type"]=>
      string(14) "clinical_trial"
      ["post_mime_type"]=>
      string(0) ""
      ["comment_count"]=>
      string(1) "0"
      ["filter"]=>
      string(3) "raw"
    }
    [4]=>
    object(WP_Post)#5848 (24) {
      ["ID"]=>
      int(1392)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2015-10-20 10:53:00"
      ["post_date_gmt"]=>
      string(19) "2015-10-20 10:53:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(296) "NCT00077714 - A Randomized, Double-blind, Placebo- and Active-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 2 Fixed Dosages of Paliperidone Extended Release Tablets and Olanzapine, With Open-label Extension, in the Treatment of Patients With Schizophrenia"
      ["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) "nct00077714-a-randomized-double-blind-placebo-and-active-controlled-parallel-group-dose-response-study-to-evaluate-the-efficacy-and-safety-of-2-fixed-dosages-of-paliperidone-extended-release-ta"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-10-22 12:22:57"
      ["post_modified_gmt"]=>
      string(19) "2025-10-22 16:22:57"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(242) "https://dev-yoda.pantheonsite.io/clinical-trial/nct00077714-a-randomized-double-blind-placebo-and-active-controlled-parallel-group-dose-response-study-to-evaluate-the-efficacy-and-safety-of-2-fixed-dosages-of-paliperidone-extended-release-ta/"
      ["menu_order"]=>
      int(0)
      ["post_type"]=>
      string(14) "clinical_trial"
      ["post_mime_type"]=>
      string(0) ""
      ["comment_count"]=>
      string(1) "0"
      ["filter"]=>
      string(3) "raw"
    }
    [5]=>
    object(WP_Post)#5847 (24) {
      ["ID"]=>
      int(1395)
      ["post_author"]=>
      string(4) "1363"
      ["post_date"]=>
      string(19) "2015-10-20 11:15:00"
      ["post_date_gmt"]=>
      string(19) "2015-10-20 11:15:00"
      ["post_content"]=>
      string(0) ""
      ["post_title"]=>
      string(301) "NCT00083668 - A Randomized, Double-blind, Placebo- and Active-controlled, Parallel-group, Dose-response Study to Evaluate the Efficacy and Safety of 3 Fixed Dosages of Paliperidone Extended Release (ER) Tablets and Olanzapine, With Open-label Extension, in the Treatment of Patients With Schizophrenia"
      ["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(192) "nct00083668-a-randomized-double-blind-placebo-and-active-controlled-parallel-group-dose-response-study-to-evaluate-the-efficacy-and-safety-of-3-fixed-dosages-of-paliperidone-extended-release-e"
      ["to_ping"]=>
      string(0) ""
      ["pinged"]=>
      string(0) ""
      ["post_modified"]=>
      string(19) "2025-10-22 12:20:47"
      ["post_modified_gmt"]=>
      string(19) "2025-10-22 16:20:47"
      ["post_content_filtered"]=>
      string(0) ""
      ["post_parent"]=>
      int(0)
      ["guid"]=>
      string(241) "https://dev-yoda.pantheonsite.io/clinical-trial/nct00083668-a-randomized-double-blind-placebo-and-active-controlled-parallel-group-dose-response-study-to-evaluate-the-efficacy-and-safety-of-3-fixed-dosages-of-paliperidone-extended-release-e/"
      ["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(102) "Causal actionable prediction for early cardiometabolic intervention in psychosis – targeting obesity"
  ["project_narrative_summary"]=>
  string(591) "Psychosis is a mental illness where individuals interpret or perceive reality differently, often experiencing muddled thoughts or seeing/hearing things others do not. People with psychosis die up to 15 years earlier than those without it, mainly due to preventable health issues like heart disease, stroke, and diabetes. Obesity is a major factor in these health issues and is more common in people with psychosis, partly because antipsychotic medications cause weight gain. My research aims to understand and address early weight gain to prevent long-term obesity and related health issues."
  ["project_learn_source"]=>
  string(5) "other"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(6) "Samuel"
    ["last_name"]=>
    string(8) "Leighton"
    ["degree"]=>
    string(45) "PhD, MRCPsych, MRCP(UK), MBChB, BMedSci(Hons)"
    ["primary_affiliation"]=>
    string(21) "University of Glasgow"
    ["email"]=>
    string(29) "samuel.leighton@glasgow.ac.uk"
    ["state_or_province"]=>
    string(7) "Glasgow"
    ["country"]=>
    string(14) "United Kingdom"
  }
  ["project_key_personnel"]=>
  array(4) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(6) "Rajeev"
      ["p_pers_l_name"]=>
      string(10) "Krishnadas"
      ["p_pers_degree"]=>
      string(29) "MBBS, MD, PhD, FRCPsych, FRCP"
      ["p_pers_pr_affil"]=>
      string(23) "University of Cambridge"
      ["p_pers_scop_id"]=>
      string(11) "21743072300"
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [1]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(4) "Fani"
      ["p_pers_l_name"]=>
      string(10) "Deligianni"
      ["p_pers_degree"]=>
      string(19) "PhD, MSc, MSc, MEng"
      ["p_pers_pr_affil"]=>
      string(21) "University of Glasgow"
      ["p_pers_scop_id"]=>
      string(10) "6506096408"
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [2]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(5) "I Lam"
      ["p_pers_l_name"]=>
      string(5) "Leong"
      ["p_pers_degree"]=>
      string(2) "MS"
      ["p_pers_pr_affil"]=>
      string(23) "University of Cambridge"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [3]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(6) "Morven"
      ["p_pers_l_name"]=>
      string(5) "Baker"
      ["p_pers_degree"]=>
      string(4) "MBBS"
      ["p_pers_pr_affil"]=>
      string(23) "University of Edinburgh"
      ["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_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1638) "Background

People with psychosis are three times more likely to develop obesity, increasing their risk of diabetes, cardiovascular disease and death. Antipsychotics, prescribed to most people with psychosis, cause weight gain. I hypothesise that early weight gain during the first few weeks of treatment is critical for the development of obesity and its consequences. 

Objective

I aim to develop causal actionable prediction models model the individualised treatment effect and predict counterfactuals, incorporating the intervention into the model and updating dynamically. 

Study Design

In work package 1, I will delineate a critical period of early antipsychotic-induced weight gain.  In work package 2, I will co-design, develop and validate causal actionable prediction models.

Participants

Retrospective study of individual participant data of all double blind RCTs of olanzapine versus other less metabolically perturbing antipsychotic (also higher versus lower dose olanzapine) in psychotic disorders available via the Vivli and Yoda clinical trial data sharing platforms. I will validate the models in electronic healthcare record data from NHS Scotland.

Primary Outcome

I will model multiple cardiometabolic outcomes including weight/obesity, glucose and cholesterol at multiple time points from 12 weeks to 1 year.

Statistical Analysis

Work package 1 will use causal mediation analysis. Work package 2 will use causal prediction modelling to model the individualised treatment effect and predict counterfactuals, the different potential outcomes for"
  ["project_brief_bg"]=>
  string(1943) "Psychosis is a mental illness where individuals interpret or perceive reality differently, often experiencing muddled thoughts or seeing/hearing things others do not. People with psychosis die up to 15 years earlier than those without it, mainly due to preventable health issues like heart disease, stroke, and diabetes. Obesity is a major factor in these health issues and is more common in people with psychosis, partly because antipsychotic medications cause weight gain. This weight gain starts early in treatment and may be related to differences in the immune system or how the body's cells respond to insulin, a hormone that helps control blood sugar levels. My research aims to understand and address early weight gain to prevent long-term obesity and related health issues. Currently, people with psychosis are offered the same standard treatment options for managing obesity, if any are offered at all. Precision medicine could offer a solution by tailoring treatments for individuals using prediction models. A prediction model is a set of rules that forecasts an individual’s risk of something happening in the future based on a set of risk factors. However, current models do not focus on including risk factors that are causes of what they predict. Just because two things often happen together, like more people wearing sunglasses and increased ice-cream sales, does not mean one causes the other; they may share a common cause, like sunny weather. Additionally, currently treatments are recommended based on how they work on average across the population. However, we know that every individual responds to treatment differently. My research will focus on creating prediction models based on causes and will account for individual differences in treatment responses. I aim to improve how healthcare professionals help people with psychosis choose their treatments for obesity and to advance the field of prediction modelling."
  ["project_specific_aims"]=>
  string(1118) "My research proposal aim is to develop Causal Actionable Prediction Models. These are based on causation, model individualised treatment effect (ITE) and predict counterfactuals (the different potential outcomes forecast for a specific individual with their unique characteristics following an intervention), incorporating the proposed intervention into the model and updating dynamically.



I aim to test the following hypotheses: 

1. Obesity and its cardiometabolic consequences in psychosis are causally mediated by early antipsychotic-induced weight gain. 

2. Early antipsychotic-induced adipose tissue accumulation causally mediates later obesity by driving inflammation and preexisting insulin resistance. 

3. Causal actionable prediction can model ITE and predict counterfactuals for early interventions for antipsychotic-induced obesity using RCT data. 

4. Causal actionable prediction models will generalise real-world patients in routine clinical practice. 



Work Package 1 will test hypotheses 1 & 2. Work Package 2 will test hypotheses 3 & 4. "
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(5) "other"
    ["label"]=>
    string(5) "Other"
  }
  ["project_purposes"]=>
  array(4) {
    [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(59) "preliminary_research_to_be_used_as_part_of_a_grant_proposal"
      ["label"]=>
      string(59) "Preliminary research to be used as part of a grant proposal"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
    [3]=>
    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(595) "Retrospective study of individual participant data of all double blind RCTs of olanzapine versus other less metabolically perturbing antipsychotics or other psychotropic (also higher versus lower dose olanzapine) in psychotic disorder available via the Vivli clinical trial data sharing platform. Vivli also hosts data from Yoda platform and separate approval is required. No exclusion criteria.



I also have access to observational electronic healthcare record data from NHS Scotland for patients with early psychosis which will be used to validate my findings in real-world data."
  ["project_main_outcome_measure"]=>
  string(428) "For work package 1 causal mediation analysis, I will model multiple cardiometabolic outcomes including weight/obesity, glucose and cholesterol at 1 year. 



For work package 2 causal actionable prediction model development, I will predict weight/obesity at serial time points including 12 weeks and 1 year. I will validate models built using randomised trial data in observational electronic healthcare record data."
  ["project_main_predictor_indep"]=>
  string(608) "Work Package 1 - causal mediation analysis:

The exposure will be antipsychotic choice (olanzapine versus less metabolically perturbing antipsychotic) or early antipsychotic-induced adipose tissue accumulation specifically (using change in weight or waist size as proxies).

The mediators will be weight gain,  inflammation and insulin resistance at 12 weeks.



Work Package 2:

I will derive the individualised treatment effect (ITE) of the trial intervention (olanzapine versus less metabolically perturbing antipsychotic or other psychotropic, dose reduction of olanzapine)."
  ["project_other_variables_interest"]=>
  string(711) "Work Package 1:

I will adjust the mediation analysis for all available mediator-outcome confounders (e.g. sex, age, ethnicity, comorbidities etc). When the intervention/exposure of interest is not randomised, additional adjustment is required for exposure-outcome confounders and exposure-mediator confounders. I will conduct doubly robust analysis also using propensity score adjustment.



Work Package 2:

I will also derive individualised treatment effects for other possible interventions (e.g. weight reduction, smoking status) derived from the RCT data. In addition, any relevant confounding variables (e.g. sex, age, ethnicity, comorbidities etc) will be included in the models."
  ["project_stat_analysis_plan"]=>
  string(3650) "Work Package 1:



Using PROCESS in R, I will obtain bootstrapped confidence intervals for the natural indirect and direct effects, total effect, and test for moderation of the mediators’ effects by antipsychotic.(Hayes, 2022). I will also model other cardiometabolic outcomes, including blood pressure, glucose, and cholesterol. Using RCT data, the intervention is unconfounded and hence its influence on outcomes is causal, requiring adjustment only for mediator-outcome confounders. For sensitivity analyses, I will exclude obese patients at baseline and compare effects by stage of psychotic illness. When the intervention is not randomised, additional adjustment is required for exposure-outcome confounders and exposure-mediator confounders. I will conduct doubly robust analysis also using propensity score adjustment. Further, EHR data allows much longer follow up to capture serious cardiometabolic consequences of early weight gain including diabetes, myocardial infarction, stroke and death. For these binary outcomes, I will model the cause specific hazard of developing the outcome given the competing risk of death before its development using Cox proportional hazard regression. This will be conducted using the ‘mediator’ R package.(Gerke & Creed, 2020) Sensitivity analysis will be conducted as above and by alternative antipsychotic agents. 



Hernán argues that power calculations are not required for causal analysis in existing datasets because causal effects are not binary signals that are either statistically significant or not.(Hernan, 2022)



Work Package 2:

Using AI and machine learning methodology together with best practice statistical approaches is in line with the NICE position statement on use of AI in evidence generation.(National Institute for Health and Care Excellence (NICE), 2024) There is limited published research modelling ITE and predicting counterfactuals in clinical populations. The application of these methodologies is a key goal and central training aspect of my proposed fellowship. Several methodologies exist including: ‘DoWhy’ Python library;(Sharma & Kiciman, 2020) Generative Adversarial Nets for inference of Individualized Treatment Effects’ in Python;(Yoon et al., 2018) Bayesian Additive Regression Trees using ‘bartCause’ in R;(Hill, 2011) conventional regression with treatment-covariate interactions to model treatment effect heterogeneity.(Hoogland et al., 2021) 



I will consider overall prediction model performance at internal and external validation in terms of discrimination, calibration and clinical usefulness with confidence intervals. The concordance-statistic for benefit will assess the performance for predicting ITE.(van Klaveren et al., 2018) Missing data will be handled by multiple imputation. Understanding uncertainty around risk prediction estimates is crucial to critically evaluate models and impacts shared decision-making – I will explore methods using bootstrap and Bayesian approaches.(Riley et al., 2025) 



For prediction model development, the suggested sample size, given a 50% prevalence of obesity in multi-episode psychosis and 10 predictor parameters for a c-statistic of 0.8 is 385 patients, 193 with obesity.(Riley et al., 2020) For external validation, the suggested sample size is 13,873, with 6,937 events.(Riley et al., 2024) Development and validation in smaller samples is possible with lower precision. As above, power calculations are not required for causal analysis (i.e., the ITE and counterfactuals) in existing datasets.(Hernan, 2022)"
  ["project_software_used"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
  }
  ["project_timeline"]=>
  string(282) "Timeline:

Apply for study data from Vivli  and Yoda: 1st May 25 to 31st September 25

Data Analysis - 1st October 25 to 31st March 26

Write Up: 1st April 26 to 31st September 26

Deadline to submit for publication and Report Back Results: 31st September 26"
  ["project_dissemination_plan"]=>
  string(1285) "My academic output will be published in peer reviewed journals (potential target journals include the BJPsych, Schizophrenia Bulletin and Lancet Psychiatry), which will be disseminated through the university. I will involve patient and public involvement (PPI) colleagues in writing and review to ensure what is disseminated also represents patient perspectives. Papers and computer code will be made open access as per University Open Access policies.  Prior to publication, unpublished data will be presented at relevant scientific conferences and via preprint servers including medRxiv and bioRxiv. There has been very little work incorporating causality into prediction modelling in clinical research and none that I am aware of in psychiatry, I expect this research proposal to generate multiple high-impact publications. 



I also aim to disseminate the findings of the research more broadly through regular feedback to the first episode psychosis services in Glasgow and Cambridge and to patients and the public through interaction with mental health charity organisations, via the NHS Research Scotland Mental Health Network and through social media, including my Twitter/X profile. I will approach media outlets with published findings to inform the wider public."
  ["project_bibliography"]=>
  string(7403) "
Alderman, J. E., Palmer, J., Laws, E., McCradden, M. D., Ordish, J., Ghassemi, M., Pfohl, S. R., Rostamzadeh, N., Cole-Lewis, H., Glocker, B., Calvert, M., Pollard, T. J., Gill, J., Gath, J., Adebajo, A., Beng, J., Leung, C. H., Kuku, S., Farmer, L. A.,…Liu, X. (2025). Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations. Lancet Digital Health, 7(1), e64-e88. https://doi.org/10.1016/S2589-7500(24)00224-3

Astle, W. J., Elding, H., Jiang, T., Allen, D., Ruklisa, D., Mann, A. L., Mead, D., Bouman, H., Riveros-Mckay, F., Kostadima, M. A., Lambourne, J. J., Sivapalaratnam, S., Downes, K., Kundu, K., Bomba, L., Berentsen, K., Bradley, J. R., Daugherty, L. C., Delaneau, O.,…Soranzo, N. (2016). The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease. Cell, 167(5), 1415-1429 e1419. https://doi.org/10.1016/j.cell.2016.10.042

Baneu, P., Vacarescu, C., Dragan, S. R., Cirin, L., Lazar-Hocher, A. I., Cozgarea, A., Faur-Grigori, A. A., Crisan, S., Gaita, D., Luca, C. T., & Cozma, D. (2024). The Triglyceride/HDL Ratio as a Surrogate Biomarker for Insulin Resistance. Biomedicines, 12(7). https://doi.org/10.3390/biomedicines12071493

Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., Astle, D. E., Auyeung, B., Ayub, M., Bae, J., Ball, G., Baron-Cohen, S., Beare, R., Bedford, S. A., Benegal, V.,…Alexander-Bloch, A. F. (2022). Brain charts for the human lifespan. Nature, 604(7906), 525-533. https://doi.org/10.1038/s41586-022-04554-y

Gerke, T., & Creed, J. (2020). ‘mediator’ R package. In GitHub. https://github.com/GerkeLab/mediator

Gianfrancesco, M. A., & Goldstein, N. D. (2021). A narrative review on the validity of electronic health record-based research in epidemiology. BMC Med Res Methodol, 21(1), 234. https://doi.org/10.1186/s12874-021-01416-5

Hammerton, G., & Munafo, M. R. (2021). Causal inference with observational data: the need for triangulation of evidence. Psychol Med, 51(4), 563-578. https://doi.org/10.1017/S0033291720005127

Hayes, A. F. (2022). Introduction to Mediation, Moderation, and Conditional Process Analysis – A Regression-Based Approach (3rd ed.). Guilford Press.

Hemingway, H., Croft, P., Perel, P., Hayden, J. A., Abrams, K., Timmis, A., Briggs, A., Udumyan, R., Moons, K. G., Steyerberg, E. W., Roberts, I., Schroter, S., Altman, D. G., Riley, R. D., & Group, P. (2013). Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. BMJ, 346, e5595. https://doi.org/10.1136/bmj.e5595

Hernan, M. A. (2022). Causal analyses of existing databases: no power calculations required. J Clin Epidemiol, 144, 203-205. https://doi.org/10.1016/j.jclinepi.2021.08.028

Hill, J. L. (2011). Bayesian Nonparametric Modeling for Causal Inference. Journal of Computational and Graphical Statistics, 20(1), 217-240. https://doi.org/10.1198/jcgs.2010.08162

Hoogland, J., IntHout, J., Belias, M., Rovers, M. M., Riley, R. D., E. Harrell F, J., Moons, K. G. M., Debray, T. P. A., & Reitsma, J. B. (2021). A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint. Stat Med, 40(26), 5961-5981. https://doi.org/10.1002/sim.9154

Jiralerspong, T., Chen, X., More, Y., Shah, V., & Bengio, Y. (2024). Efficient Causal Graph Discovery Using Large Language Models. arXiv preprint arXiv:2402.01207v1. https://doi.org/10.48550/arXiv.2402.01207

National Institute for Health and Care Excellence (NICE). (2024). Use of AI in evidence generation: NICE position statement. https://www.nice.org.uk/about/what-we-do/our-research-work/use-of-ai-in-evidence-generation–nice-position-statement

Perry, B. I., Burgess, S., Jones, H. J., Zammit, S., Upthegrove, R., Mason, A. M., Day, F. R., Langenberg, C., Wareham, N. J., Jones, P. B., & Khandaker, G. M. (2021). The potential shared role of inflammation in insulin resistance and schizophrenia: A bidirectional two-sample mendelian randomization study. PLoS Med, 18(3), e1003455. https://doi.org/10.1371/journal.pmed.1003455

Pillinger, T., McCutcheon, R. A., Vano, L., Mizuno, Y., Arumuham, A., Hindley, G., Beck, K., Natesan, S., Efthimiou, O., Cipriani, A., & Howes, O. D. (2020). Comparative effects of 18 antipsychotics on metabolic function in patients with schizophrenia, predictors of metabolic dysregulation, and association with psychopathology: a systematic review and network meta-analysis. Lancet Psychiatry, 7(1), 64-77. https://doi.org/10.1016/S2215-0366(19)30416-X

Riley, R. D., Collins, G. S., Kirton, L., Snell, K. I., Ensor, J., Whittle, R., Dhiman, P., van Smeden, M., Liu, X., Alderman, J., Nirantharakumar, K., Manson-Whitton, J., Westwood, A. J., Cazier, J. B., Moons, K. G. M., Martin, G. P., Sperrin, M., Denniston, A. K., Harrell, F. E., Jr., & Archer, L. (2025). Uncertainty of risk estimates from clinical prediction models: rationale, challenges, and approaches. BMJ, 388, e080749. https://doi.org/10.1136/bmj-2024-080749

Riley, R. D., Ensor, J., Snell, K. I. E., Harrell, F. E., Jr., Martin, G. P., Reitsma, J. B., Moons, K. G. M., Collins, G., & van Smeden, M. (2020). Calculating the sample size required for developing a clinical prediction model. BMJ, 368, m441. https://doi.org/10.1136/bmj.m441

Riley, R. D., Snell, K. I. E., Archer, L., Ensor, J., Debray, T. P. A., van Calster, B., van Smeden, M., & Collins, G. S. (2024). Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study. BMJ, 384, e074821. https://doi.org/10.1136/bmj-2023-074821

Sharma, A., & Kiciman, E. (2020). DoWhy: An end-to-end library for causal inference. arXiv preprint arXiv:2011.04216.

Steyerberg, E. W., & Harrell, F. E., Jr. (2016). Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol, 69, 245-247. https://doi.org/10.1016/j.jclinepi.2015.04.005

Tennant, P. W. G., Murray, E. J., Arnold, K. F., Berrie, L., Fox, M. P., Gadd, S. C., Harrison, W. J., Keeble, C., Ranker, L. R., Textor, J., Tomova, G. D., Gilthorpe, M. S., & Ellison, G. T. H. (2021). Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. Int J Epidemiol, 50(2), 620-632. https://doi.org/10.1093/ije/dyaa213

van Klaveren, D., Steyerberg, E. W., Serruys, P. W., & Kent, D. M. (2018). The proposed ‘concordance-statistic for benefit’ provided a useful metric when modeling heterogeneous treatment effects. J Clin Epidemiol, 94, 59-68. https://doi.org/10.1016/j.jclinepi.2017.10.021

Yoon, J., Jordon, J., & Van Der Schaar, M. (2018). GANITE: Estimation of individualized treatment effects using generative adversarial nets. International conference on learning representations,

Zhu, J., Li, J., Richards, A. M., Chan, M. Y., & Tai, B. C. (2025). Accounting for extent of non-compliance when estimating treatment effects on an ordinal outcome in randomized clinical trials. BMC Med Res Methodol, 25(1), 52. https://doi.org/10.1186/s12874-025-02493-6
"
  ["project_suppl_material"]=>
  bool(false)
  ["project_coi"]=>
  array(5) {
    [0]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(15462)
        ["id"]=>
        int(15462)
        ["title"]=>
        string(19) "YodaCoILeighton.pdf"
        ["filename"]=>
        string(19) "YodaCoILeighton.pdf"
        ["filesize"]=>
        int(21262)
        ["url"]=>
        string(68) "https://yoda.yale.edu/wp-content/uploads/2024/08/YodaCoILeighton.pdf"
        ["link"]=>
        string(65) "https://yoda.yale.edu/data-request/2024-0776/yodacoileighton-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1864"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(19) "yodacoileighton-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(15461)
        ["date"]=>
        string(19) "2024-08-08 11:20:31"
        ["modified"]=>
        string(19) "2024-08-08 11:20:33"
        ["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(15490)
        ["id"]=>
        int(15490)
        ["title"]=>
        string(17) "CoIDeligianni.pdf"
        ["filename"]=>
        string(17) "CoIDeligianni.pdf"
        ["filesize"]=>
        int(20434)
        ["url"]=>
        string(66) "https://yoda.yale.edu/wp-content/uploads/2024/08/CoIDeligianni.pdf"
        ["link"]=>
        string(63) "https://yoda.yale.edu/data-request/2024-0776/coideligianni-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1864"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(17) "coideligianni-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(15461)
        ["date"]=>
        string(19) "2024-08-15 09:47:28"
        ["modified"]=>
        string(19) "2024-08-15 09:47:31"
        ["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(15491)
        ["id"]=>
        int(15491)
        ["title"]=>
        string(18) "Krishnadas_COI.pdf"
        ["filename"]=>
        string(18) "Krishnadas_COI.pdf"
        ["filesize"]=>
        int(20821)
        ["url"]=>
        string(67) "https://yoda.yale.edu/wp-content/uploads/2024/08/Krishnadas_COI.pdf"
        ["link"]=>
        string(64) "https://yoda.yale.edu/data-request/2024-0776/krishnadas_coi-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1864"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(18) "krishnadas_coi-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(15461)
        ["date"]=>
        string(19) "2024-08-15 09:47:30"
        ["modified"]=>
        string(19) "2024-08-15 09:47:31"
        ["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(18059)
        ["id"]=>
        int(18059)
        ["title"]=>
        string(11) "COI - Leong"
        ["filename"]=>
        string(13) "COI-Leong.pdf"
        ["filesize"]=>
        int(20136)
        ["url"]=>
        string(62) "https://yoda.yale.edu/wp-content/uploads/2024/08/COI-Leong.pdf"
        ["link"]=>
        string(55) "https://yoda.yale.edu/data-request/2024-0776/coi-leong/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1885"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(9) "coi-leong"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(15461)
        ["date"]=>
        string(19) "2025-10-08 15:40:32"
        ["modified"]=>
        string(19) "2025-10-08 15:40:32"
        ["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(20059)
        ["id"]=>
        int(20059)
        ["title"]=>
        string(11) "COI - Baker"
        ["filename"]=>
        string(13) "COI-Baker.pdf"
        ["filesize"]=>
        int(32942)
        ["url"]=>
        string(62) "https://yoda.yale.edu/wp-content/uploads/2024/08/COI-Baker.pdf"
        ["link"]=>
        string(55) "https://yoda.yale.edu/data-request/2024-0776/coi-baker/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1885"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(9) "coi-baker"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(15461)
        ["date"]=>
        string(19) "2026-09-18 16:13:36"
        ["modified"]=>
        string(19) "2026-09-18 16:13:36"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
  }
  ["data_use_agreement_training"]=>
  bool(true)
  ["certification"]=>
  bool(true)
  ["project_learn_source_exp"]=>
  string(103) "Vivli asked me to request access to Yoda trials in parallel and they will be uploaded to Vivli platform"
  ["project_study_design_exp"]=>
  string(63) "Causal mediation analysis and causal prediction modelling study"
  ["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(20398)
    ["id"]=>
    int(20398)
    ["title"]=>
    string(47) "YODA Project Due Diligence Assessment 2024-0776"
    ["filename"]=>
    string(51) "YODA-Project-Due-Diligence-Assessment-2024-0776.pdf"
    ["filesize"]=>
    int(113899)
    ["url"]=>
    string(100) "https://yoda.yale.edu/wp-content/uploads/2024/08/YODA-Project-Due-Diligence-Assessment-2024-0776.pdf"
    ["link"]=>
    string(93) "https://yoda.yale.edu/data-request/2024-0776/yoda-project-due-diligence-assessment-2024-0776/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(47) "yoda-project-due-diligence-assessment-2024-0776"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(15461)
    ["date"]=>
    string(19) "2026-11-04 20:31:51"
    ["modified"]=>
    string(19) "2026-11-04 20:31:51"
    ["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(20399)
    ["id"]=>
    int(20399)
    ["title"]=>
    string(42) "YODA Project Protocol 2024-0776 - 26-09-18"
    ["filename"]=>
    string(44) "YODA-Project-Protocol-2024-0776-26-09-18.pdf"
    ["filesize"]=>
    int(144140)
    ["url"]=>
    string(93) "https://yoda.yale.edu/wp-content/uploads/2024/08/YODA-Project-Protocol-2024-0776-26-09-18.pdf"
    ["link"]=>
    string(86) "https://yoda.yale.edu/data-request/2024-0776/yoda-project-protocol-2024-0776-26-09-18/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(40) "yoda-project-protocol-2024-0776-26-09-18"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(15461)
    ["date"]=>
    string(19) "2026-11-04 20:32:21"
    ["modified"]=>
    string(19) "2026-11-04 20:32:21"
    ["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(20400)
    ["id"]=>
    int(20400)
    ["title"]=>
    string(36) "YODA Project Review - 2024-0776_SITE"
    ["filename"]=>
    string(38) "YODA-Project-Review-2024-0776_SITE.pdf"
    ["filesize"]=>
    int(1315606)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2024/08/YODA-Project-Review-2024-0776_SITE.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2024-0776/yoda-project-review-2024-0776_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(34) "yoda-project-review-2024-0776_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(15461)
    ["date"]=>
    string(19) "2026-11-04 20:32:41"
    ["modified"]=>
    string(19) "2026-11-04 20:32:41"
    ["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"
  ["human_research_protection_training"]=>
  bool(false)
  ["project_funding_source"]=>
  string(591) "I have applied for a post-doctoral fellowship from a the Chief Scientist Office Scotland (a Scottish government agency) and also from UK Research and Innovation (a UK government agency) both with a start date of 1st October 2025. I will supplement the fellowship funding with an existing personal grant from the NHS Greater Glasgow & Clyde Endowment Funding Award for my £14,762 costs for access to the West of Scotland Safe Haven first episode psychosis electronic healthcare record patient data and for the analysis platform (Project Reference: GN24MH456 - awarded 14th January 2025)."
  ["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(3) {
  [0]=>
  string(9) "risperdal"
  [1]=>
  string(16) "risperdal-consta"
  [2]=>
  string(6) "invega"
}


num of trials
array(1) {
  [0]=>
  string(1) "6"
}


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