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  ["project_title"]=>
  string(87) "rEVealIng Drug Effects when applyiNg psychomeTrics In schizophreniA triaLs (EVIDENTIAL)"
  ["project_narrative_summary"]=>
  string(741) "The EVIDENTIAL project evaluates how advanced statistical and psychometric models can improve interpretation of schizophrenia trial outcomes. Current trials often rely on composite scales like the Positive and Negative Syndrome Scale (PANSS) and the Brief Psychiatric Rating Scale (BPRS), typically analyzed as total scores, which may obscure variation across symptom domains (positive, negative, general). Using individual participant data from completed trials shared via the Vivli platform, the study will apply advanced psychometric analyses to refine symptom structures and re-run meta-analyses. The goal is to assess whether using advanced psychometrics alter the effects of antipsychotic medication compared with conventional methods."
  ["project_learn_source"]=>
  string(12) "scien_public"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(5) "Frank"
    ["last_name"]=>
    string(5) "Doyle"
    ["degree"]=>
    string(3) "PhD"
    ["primary_affiliation"]=>
    string(47) "RSCI University of Medicine and Health Sciences"
    ["email"]=>
    string(15) "FDoyle4@rcsi.ie"
    ["state_or_province"]=>
    string(6) "Dublin"
    ["country"]=>
    string(7) "Ireland"
  }
  ["project_key_personnel"]=>
  array(3) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(4) "Timo"
      ["p_pers_l_name"]=>
      string(6) "Schurr"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(36) "Royal College of Surgeons in Ireland"
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      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
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      string(5) "Fiona"
      ["p_pers_l_name"]=>
      string(6) "Boland"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(36) "Royal College of Surgeons in Ireland"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [2]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(5) "David"
      ["p_pers_l_name"]=>
      string(5) "Byrne"
      ["p_pers_degree"]=>
      string(3) "PhD"
      ["p_pers_pr_affil"]=>
      string(36) "Royal College of Surgeons in Ireland"
      ["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(69) "Marie Skłodowska-Curie Actions (MSCA); Grant agreement ID: 101209817"
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1727) "Background

Schizophrenia causes major clinical and societal burden, yet antipsychotic effect sizes remain modest. Efficacy is usually measured with PANSS or BPRS total scores, despite concerns about their psychometric validity and sensitivity. Advanced methods—confirmatory factor analysis (CFA), item response theory (IRT) and network analysis (NA)—may reduce measurement error and change estimated effects. These approaches have not been systematically applied to individual participant data (IPD) across trials.



Objective

To test whether advanced psychometric methods applied to PANSS/BPRS data change antipsychotic effect estimates, and whether effects differ across symptom domains or by sex.



Study Design

Secondary analysis of anonymized IPD from randomized Phase II–IV schizophrenia trials obtained via YODA and Vivli.



Participants

Adults (≥18) with schizophrenia, schizoaffective, or related psychotic disorders with baseline and endpoint PANSS or BPRS assessments.



Outcomes

Primary: standardized mean differences (SMDs) for PANSS total change and corresponding psychometric changes (CFA factor scores, IRT theta scores, NA netscores).

Secondary: PANSS Positive/Negative/Disorganization/General domains, BPRS total, domain-specific factor scores, response/remission rates, and psychometrically derived SMD differences.



Statistical Analysis

CFA, IRT and NA will define model structures and generate factor, theta and net scores. Mixed-effects IPD meta-analyses will estimate treatment effects for raw totals and psychometric outcomes, with moderator analyses for sex and other covariates."
  ["project_brief_bg"]=>
  string(2986) "Mental health problems affect 1 in 6 people across the EU (~84 million individuals) and 

account for 4% of EU GDP, underscoring the societal and economic urgency of effective interventions [1,2].  Schizophrenia, among the most severe psychiatric illnesses, affects ~1 in 300 and remains highly burdensome, with 70–90% unemployment and ~15 years reduced life expectancy [1,5]. Treatment nonresponse persists in ~20–30% of patients despite modern antipsychotics; sex differences in treatment response, stigma tied to ethnicity, and misperceptions about schizophrenia further hinder outcomes [3,4]. Meanwhile, trial effect sizes have not increased over time and remain moderate (SMD ≈ 0.47) [6,7].



Efficacy in schizophrenia trials is typically measured by PANSS (gold standard) and BPRS clinician-rated scales [8,9,10]. Yet, their psychometric properties and capacity to detect treatment effects remain debated [10–12]. Advanced psychometric/statistical methods – FA, IRT, and NA - can reduce measurement error (e.g., by removing non-performing items, or relying on psychometrically-derived scores) and may increase detectable effects [13–15]. A recent line of work suggests 10–15% larger effects when such methods are applied in depression [13,15,20], but no comprehensive multi-method application has yet been undertaken across schizophrenia trials [21].



EVIDENTIAL will combine IPD from schizophrenia trials and apply FA/IRT/NA to provide psychometrically-derived outcomes scores, then re-estimate treatment effects. We will:



•	Establish best-fitting psychometric structures for PANSS/BPRS across trials

•	Compare original vs. psychometrically-derived outcomes in IPD meta-analyses

•	Explore sex (and where available/feasible, drug class/dose/region/ethnicity/diagnosis) as moderators/invariance factors



Going beyond the state-of-the-art & ambition 

In treatment of schizophrenia, developments in psychosocial and pharmacological interventions are mainly based on the assumptions regarding the structure of symptoms, which are assessed by questionnaires, and assumed to be a total score for trial analysis [16].

•	PANSS/BPRS psychometrics have been examined for factor structures [17,18]; recent LVM/NA applications yield mixed/contradictory findings and often lack sufficiently large, high-quality datasets [12,19].

•	IPD across multiple RCTs addresses power and quality gaps; antidepressant IPD shows 10–15% effect increases after psychometric optimization [13, 20].

•	Novelty in schizophrenia: No integrated FA/IRT/NA application across interventional schizophrenia IPD to date [21].

•	Ambition: Apply multi-method psychometrics at scale; explicitly model sex and, where available/feasible, drug class/dose/region/ethnicity/disease type - to inform measurement-invariant outcomes and clinical applicability for future trials.

"
  ["project_specific_aims"]=>
  string(1680) "Aim

Determine whether applying state-of-the-art psychometric analyses to IPD from randomized trials of antipsychotics in Schizophrenia/Schizoaffective disorder/Psychotic disorders yields quantitatively or qualitatively important differences relative to the original trial results. A priori, we will consider differences important if psychometrically informed analyses produce (i) a ≥10% relative change in the estimated treatment effect and/or an absolute change in Cohen’s d (SMD) of ≥0.10, and/or (ii) a qualitative change in inference, including a change in effect direction, statistical conclusion, or whether effects meet established clinical change benchmarks for PANSS/BPRS.



Objectives

1.	Determine the optimum psychometric models (FA, IRT and NA) for PANSS/BPRS in randomized trials (e.g., domain structures and item functioning by method)

2.	Ascertain whether applying these methods moderates the efficacy if schizophrenia treatment studies, in terms of increasing or decreasing effect sizes

3.	Explore whether psychometric approaches reveal clinically meaningful qualitative differences, e.g. across symptom domains or sex (measurement invariance; domain-specific response)



Hypotheses

1.	The optimum psychometric models for PANSS/BPRS will differ from the original total scoring conventions, and each other. 

2.	Psychometric analysis will lead to an altered antipsychotic treatment efficacy estimate due to the change in error measurement versus conventional total/subscale scores.

3.	Psychometric analysis will identify qualitatively different models by important subgroup (e.g. sex)."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
  }
  ["project_purposes"]=>
  array(4) {
    [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"
    }
    [3]=>
    array(2) {
      ["value"]=>
      string(5) "other"
      ["label"]=>
      string(5) "Other"
    }
  }
  ["project_purposes_exp"]=>
  string(60) "optimizing psychometric approaches in Schizophrenia research"
  ["project_research_methods"]=>
  string(431) "Beside YODA, we will also use Vivli (https://search.vivli.org/) as data source.



Inclusion

•	Phase II–IV, 

•	RCTs

•	adult Schizophrenia, Schizoaffective Disorders, Psychotic Disorders

•	PANSS/BPRS at baseline and endpoint; item-level IPD preferred



Exclusion

•	Phase I/PK-only

•	Pediatric

•	trials lacking standardized PANSS/BPRS.

"
  ["project_main_outcome_measure"]=>
  string(1619) "Primary Outcome Measures

1.	Psychometrically-derived latent symptom domain scores

•	Using CFA, IRT, and NA, psychometrically-derived models of schizophrenia symptoms will be generated separately for baseline and outcome (endpoint) timepoints.

•	These models will produce scores representing key latent symptom domains (e.g., positive symptoms, negative symptoms, disorganization/general psychopathology, and affective symptom domains).

•	These latent scores constitute the primary psychometric outcomes.



2.	Psychometrically-derived difference outcome

•	The difference between SMDs based on raw change scores and SMDs based on psychometrically-derived change scores will be reported, reflecting the degree to which advanced psychometric modelling alters estimated treatment effects.



Secondary Outcome Measures

1.	SMDs for raw (original) total score change

•	SMDs calculated using conventional PANSS or BPRS total score (baseline to endpoint).



2.	SMDs for psychometrically-derived change scores

•	The primary meta-analytic outcome will be the SMD representing change in the psychometrically-derived latent symptom scores from baseline to endpoint across intervention and control groups.



3.	Remission-related outcomes

•	Remission rates and absolute risk reduction (ARR) will be calculated using

(a)	conventional remission definitions based on PANSS or BPRS total score thresholds, and

(b)	corresponding thresholds based on psychometrically-derived scores.



"
  ["project_main_predictor_indep"]=>
  string(1874) "The independent variable in this study is the type of psychometric model applied to the PANSS and BPRS data. Several advanced modelling frameworks will be implemented, including:



•	Confirmatory Factor Analysis (CFA) to estimate predefined (weighted) latent symptom domains

•	(Multidimensional) Item Response Theory ([M]-IRT) to model item-level properties and derive weighted latent scores that account for differential item functioning and non-linear item difficulty

•	Network Analysis (NA) to capture symptom interdependencies, generating (weighted) network-derived scores based on centrality and connectivity patterns.



Each of these modelling approaches will produce a distinct psychometrically-derived scoring solution, which will be computed separately for both baseline and endpoint data. For each instrument (PANSS and BPRS), the resulting scores will represent refined latent symptom estimates that potentially more accurately reflect underlying psychopathological dimensions (e.g., positive, negative, disorganization/general, affective).



These psychometrically-derived scores will function as alternative, model-informed outcome measures, which will be directly compared with conventional raw total scores used in the original clinical trials. Change from baseline to endpoint will be calculated for both score types and expressed as SMDs.



This procedure mirrors established methods for evaluating the impact of psychometric refinement: SMDs derived from raw totals will be compared to SMDs derived from latent/model-based scores. This comparison will determine whether advanced modelling techniques meaningfully alter the magnitude or interpretation of treatment effects, thereby testing the extent to which psychometric sophistication influences clinical outcome estimation.

"
  ["project_other_variables_interest"]=>
  string(526) "Demographics:

•	Age

•	Sex: Male, Female, Other / not specified

•	Ethnicity



Study-level: 

•	country/region (Europe, North America, other)

•	phase (II - IV)

•	endpoint week (e.g., baseline, week 4, 6, 8 etc.)

•	inpatient/outpatient (if available)



Clinical: 

•	baseline/follow-up PANSS/BPRS totals and item profiles

•	Drug class/type/dose

•	concomitant meds (if available)

•	type of diagnosis

"
  ["project_stat_analysis_plan"]=>
  string(2520) "Data management: 



•	Assign each study a unique ID; maintain arm and visit mappings.

•	Build two harmonized item-level datasets: PANSS and BPRS (baseline & endpoint), retaining essential covariates (age, sex, arm, dose, country/region, diagnosis).

•	Conduct complete-case analyses primarily; apply FIML within CFA/IRT or multiple imputation (sensitivity) where appropriate.



Psychometric modelling:



•	Dimensionality: parallel analysis will be applied to PANSS and BPRS

o	EFA – CFA

o	IRT; consider Mokken when ambiguous

o	NA

•	Fit: AIC/BIC, RMSEA/SRMR  0.95 (as feasible for scale length/distribution). Distribution checks (e.g., Henze–Zirkler) to pick robust estimators

•	Measurement invariance (sex), differential item functioning (loadings/thresholds/R²/intercepts)

•	Factor scores: CFA (e.g., lavPredict), MIRT (expected.test; unstandardize as needed)

•	symptoms with particular nodes of centrality will be assessed within NA



Modelling change and calculating effect sizes



Primary (continuous) analyses:



•	IPD mixed-effects models predicting endpoint scores with arm as predictor and baseline as covariate; random intercepts for study (and arm).

o	Model 1: raw total/subscale outcomes (e.g., PANSS total).

o	Model 2: psychometric factor-score outcomes (e.g., PANSS latent domains).

o	Model 3/4: Models 1/2 + covariates (e.g. sex)

•	Transform estimates to Cohen’s d (SMD) for comparability.

•	Per-trial effect sizes computed similarly; combine via random-effects meta-analysis.

•	Effect of interest per trial: SMD(factor) − SMD(raw); pool with random-effects meta-analysis.

•	If possible, meta-regression for potential moderators (sex; and where available/feasible, drug class/dose/region/ethnicity/diagnosis).



Secondary (binary) analyses - Remission:

•	Define remission using standard PANSS/BPRS thresholds; derive psychometric analogs using proportional thresholds of factor-score maxima.

•	Mixed-effects logistic models (IPD) for remission (raw vs. psychometrically-derived), with and without covariates; compute absolute risk differences.

•	Per-trial remission differences pooled via random-effects meta-analysis; potential moderators as above.



Reproducible analysis scripts; open-science sharing where allowed.

"
  ["project_software_used"]=>
  array(2) {
    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(7) "rstudio"
      ["label"]=>
      string(7) "RStudio"
    }
  }
  ["project_timeline"]=>
  string(500) "Project Start Date: 01. January 2026

Data Access and Harmonization Initiated: January/February 2026

Protocol Drafted and First Submitted: March 2026

Psychometric Modelling Phase: March - June 2026

Meta-analytic Modelling and Sensitivity Analyses: July - September 2026

Manuscript Drafted and First Submitted: October 2026

Results Reported Back to the YODA Project: December 2026

Optional Extension Period (if required): January - December 2027

"
  ["project_dissemination_plan"]=>
  string(1545) "Four publications are planned to disseminate the findings from the EVIDENTIAL project, alongside one open-access study protocol describing the aims, hypotheses, and methods in detail. Working titles and target journals are listed below.



1.	A protocol for evaluating psychometric and meta-analytic approaches in schizophrenia clinical trials using PANSS and BPRS outcome measures.

Target Journal: BMC Psychiatry / BJPsych Open or European Psychiatry.



2.	Psychometric evaluation of PANSS and BPRS structures across randomized clinical trials: comparing factor analysis, item response theory and network models pre- and post-treatment.

Target Journal: Schizophrenia Bulletin / Psychological Medicine or European Psychiatry.



3.	Comparing total-score versus psychometrically refined outcomes in antipsychotic trials: An individual participant data meta-analysis.

Target Journal: The Lancet Psychiatry / Journal of Clinical Psychopharmacology or European Psychiatry.



4.	Applying modern psychometric modelling to improve treatment effect estimation in schizophrenia clinical trials. (This will potentially be split into two papers for PANSS / BPRS each)

Target Journal: Journal of Psychiatric Research / Journal of Clinical Epidemiology or European Psychiatry.



We will also present project results at international conferences to reach both clinical and methodological audiences – these presentations will mirror the planned journal outputs above."
  ["project_bibliography"]=>
  string(7275) "

OECD/EU (2018). Health at a Glance: Europe 2018: State of Health in the EU Cycle. Paris: OECD Publishing. https://doi.org/10.1787/health_glance_eur-2018-en
European Commission (2023). Communication on a comprehensive approach to mental health (COM/2023/298 final). Brussels: European Commission. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52023DC0298&utm
Abel, K. M., Drake, R., & Goldstein, J. M. (2010). Sex differences in schizophrenia. International Review of Psychiatry, 22(5), 417–428. https://doi.org/10.3109/09540261.2010.515205
Schwartz, R. C., & Blankenship, D. M. (2014). Racial disparities in psychotic disorder diagnosis: A review of empirical literature. World Journal of Psychiatry, 4(4), 133–140. https://doi.org/10.5498/wjp.v4.i4.133
Jauhar, S., Johnstone, M., & McKenna, P. J. (2022). Schizophrenia. The Lancet, 399(10323), 473–486. https://doi.org/10.1016/S0140-6736(21)01730-X
The Lancet (Editorial) (2024). Time for renewed optimism for schizophrenia? The Lancet, 403(10422), 117. https://doi.org/10.1016/S0140-6736(24)00047-3
Leucht, S., Leucht, C., Huhn, M., et al. (2017). Sixty years of placebo-controlled antipsychotic drug trials in acute schizophrenia: Systematic review, Bayesian meta-analysis, and meta-regression of efficacy predictors. American Journal of Psychiatry, 174(10), 927–942. https://doi.org/10.1176/appi.ajp.2017.16121358
Kay, S. R., Fiszbein, A., & Opler, L. A. (1987). The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophrenia Bulletin, 13(2), 261–276. https://doi.org/10.1093/schbul/13.2.261
Overall, J. E., & Gorham, D. R. (1962). The Brief Psychiatric Rating Scale. Psychological Reports, 10(3), 799–812. https://doi.org/10.2466/pr0.1962.10.3.799
European Medicines Agency (EMA) (2012/2013). Guideline on clinical investigation of medicinal products, including depot preparations, in the treatment of schizophrenia – Revision 1 (EMA/CHMP/40072/2010 Rev.1). Legal effective date 01/04/2013. https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-clinical-investigation-medicinal-products-including-depot-preparations-treatment-schizophrenia-revision-1_en.pdf
Baandrup, L., Lublin, H., Glenthøj, B. Y., et al. (2022). Scalability of the Positive and Negative Syndrome Scale in first-episode schizophrenia assessed by Rasch models. Acta Psychiatrica Scandinavica, 146(1), 21–35. https://doi.org/10.1111/acps.13434
Siafis, S., Samara, M., Bighelli, I., et al. (2024). Relapse in clinically stable adult patients with schizophrenia or schizoaffective disorder: Scale-derived criteria and evidence-based thresholds. The Lancet Psychiatry, 11(1), 36–46. https://doi.org/10.1016/S2215-0366(23)00364-4
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Doyle, F., Byrne, D., Carney, R. M., et al. (2023). The effects of advanced factor analysis approaches on outcomes in randomised trials for depression: Protocol for secondary analysis of individual participant data. BJPsych Open, 9(5), e157. https://doi.org/10.1192/bjo.2023.544
Messinger, J. W., Trémeau, F., Antonius, D., et al. (2011). Avolition and expressive deficits capture negative symptom phenomenology: Implications for DSM-5 and schizophrenia research. Clinical Psychology Review, 31(1), 161–168. https://doi.org/10.1016/j.cpr.2010.09.002
Shafer, A., & Dazzi, F. (2019). Meta-analysis of the Positive and Negative Syndrome Scale (PANSS) factor structure. Journal of Psychiatric Research, 115, 113–120. https://doi.org/10.1016/j.jpsychires.2019.05.008
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Byrne, D., Doyle, F., et al. (2024). The effects of advanced psychometric approaches on trial effect sizes: Secondary analysis of individual participant data (depression). Psychiatry Research, 339, 116057. https://doi.org/10.1016/j.psychres.2024.116057
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Saraceno, B., Levav, I., & Kohn, R. (2005). The public health aspects of schizophrenia and related disorders. World Psychiatry, 4(3), 181–185. PMC1414773.

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