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  string(119) "A Treatment-Conditioned Transformer for Prediction in Adult Plaque Psoriasis: an IPD Meta-Analysis of Randomized Trials"
  ["project_narrative_summary"]=>
  string(815) "People with moderate-to-severe plaque psoriasis respond differently to treatment, but trials report average results. We will analyze de-identified participant-level data from eligible randomized YODA psoriasis trials. Using information known at assignment - age, weight, psoriasis severity, previous treatment, and regimen - we will test a Transformer model to predict PASI75 at week 12 (at least a 75% reduction in severity). We will compare it with simpler models and test each on trials held out from development. We will assess accuracy, calibration, missing data, trial differences, and patient groups. This study will build generalizable evidence for psoriasis response prediction and public health; it will not make treatment recommendations or claim individual causal effects. Data will stay secure in YODA."
  ["project_learn_source"]=>
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  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(7) "Yanqing"
    ["last_name"]=>
    string(4) "Wang"
    ["degree"]=>
    string(7) "MD, PhD"
    ["primary_affiliation"]=>
    string(19) "Shandong University"
    ["email"]=>
    string(13) "yq@sdu.edu.cn"
    ["state_or_province"]=>
    string(8) "Shandong"
    ["country"]=>
    string(5) "China"
  }
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    ["label"]=>
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  }
  ["project_funding_source"]=>
  string(147) "Shandong Provincial Natural Science Foundation (ZR2025MS1247), Young Scientists Fund of the National Natural Science Foundation of China (82201383)"
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1593) "Background: Psoriasis trial averages do not directly predict individual response. Participant-level random splits can overestimate transportability.

Objective: Develop and externally evaluate a treatment-conditioned Transformer using eligible YODA psoriasis IPD, with simpler comparators and calibration assessment.

Study Design: Methodological participant-level meta-analysis of randomized psoriasis trials. The primary cohort will include baseline-randomized adults with directly observed PASI75 at week 12 within 77–91 days. NAVIGATE will be analyzed separately after delayed randomization.

Participants: Adults with protocol-defined moderate-to-severe plaque psoriasis, randomized treatment, and sufficient documentation. Exclude psoriatic arthritis, pediatric/adolescent-only cohorts, and non-harmonizable observations.

Primary Outcome Measure: Directly observed PASI75 at week 12 within the prespecified window.

Secondary Outcome Measures: PASI90 at week 16 in modern trials; other protocol-defined PASI/IGA/sPGA outcomes; continuous PASI change; and comparable exploratory outcomes.

Statistical Analysis: A small Transformer will use baseline clinical and treatment/regimen tokens. Preprocessing, tuning, and calibration will be nested within training trials. Validation will leave one complete trial out. We will report AUROC, AUPRC, Brier score, calibration, subgroup performance, and uncertainty, with penalized logistic and tree comparators. Treatment contrasts will not be treated as individual causal effects or recommendations.

"
  ["project_brief_bg"]=>
  string(2480) "Psoriasis is a chronic immune-mediated skin disease in which severity, previous treatment exposure, body size, comorbidity, and other patient characteristics may influence response to systemic therapy. Randomized trials provide high-quality comparative evidence, but their primary results are population averages [9–16]. A rigorous individual-level prediction framework should therefore be judged by transportability, calibration, and clinical transparency rather than pooled apparent accuracy alone [19–22].

The study addresses three problems. Psoriasis trials differ in regimens, eligibility criteria, endpoint timing, missing-data rules, and rescue or switching procedures. A weakly regularized Transformer can overfit a moderate clinical-trial sample. A participant-level random split can also place common trial structure in both development and test data. We will address these issues with a versioned trial-specific harmonization matrix, a small constrained architecture, nested trial-grouped development, and leave-one-trial-out external validation.

YODA already hosts psoriasis IPD evidence-synthesis and prediction projects [7,8]. We will not claim priority as the first psoriasis prediction model or the first Transformer in clinical-trial data. The falsifiable contribution is a psoriasis-only benchmark that conditions prediction on randomized treatment, separates baseline induction from delayed-randomization landmark prediction, treats endpoint timing as part of the estimand, and makes calibration, missingness, subgroup performance, and simple-model comparison central design features.

The primary analysis uses directly observed PASI75 at week 12 within a narrow prespecified window. PASI90 at week 16 is a key secondary endpoint in modern trials with suitable data. NAVIGATE is not pooled into baseline-randomized induction because its comparison follows open-label ustekinumab exposure and delayed randomization; it is analyzed separately if the released data support it.

This secondary methodological analysis does not evaluate a clinical AI intervention. Products will be a transparent harmonization protocol, reproducible prediction-model evaluation, and calibrated estimate of transportability across psoriasis trials. It will not establish clinical benefit, individual causal effects, or readiness for treatment selection. Future clinical use would require independent prospective validation and impact evaluation.

"
  ["project_specific_aims"]=>
  string(1249) "Aim 1: Harmonization. Construct a psoriasis-only, trial-aware data dictionary and analysis cohort from eligible YODA IPD, preserving original definitions, visit timing, treatment assignment, rescue/switching, and missingness provenance. The primary cohort will require baseline randomization and directly observed week-12 PASI75.

Aim 2: Prediction. Develop a small treatment-conditioned Transformer for baseline prediction and a prespecified landmark model for later response, and compare both with a clinically specified logistic model, penalized logistic regression, and a strong tree-based comparator. We hypothesize that treatment conditioning may improve prediction and calibration, but we do not assume that the Transformer will outperform simpler models.

Aim 3: Generalizability and safe interpretation. Perform leave-one-trial-out external validation, report trial-specific and pooled discrimination/calibration, audit prespecified subgroups and missingness, and conduct exploratory cross-fitted treatment contrasts only where randomized overlap and endpoint definitions support them. We will quantify uncertainty and will not interpret model outputs as validated individual causal effects or treatment recommendations.

"
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      ["label"]=>
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      ["label"]=>
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  string(1481) "Data will be requested for all eligible psoriasis randomized controlled trial records currently listed in the YODA psoriasis archive and any additional psoriasis RCT records that YODA identifies during review. The public archive snapshot identified 20 RCT-design records: NCT02203032, NCT02295865, NCT02325219, NCT03090100, NCT00106834, NCT00230529, NCT02207231, NCT02951533, NCT02905331, NCT02207244, NCT01090427, NCT01008995, NCT00747344, NCT00454584, NCT00320216, NCT01550744, NCT01483599, NCT00307437, and NCT00267969. The request is for de-identified IPD and supporting documents needed to understand variable definitions, endpoint derivation, missing-data rules, and trial conduct. Final availability and variable completeness are subject to YODA/Data Partner due diligence and the DUA.

Primary inclusion: age 18 years or older at the relevant randomization; protocol-defined moderate-to-severe plaque psoriasis; baseline randomized treatment assignment; a directly observed PASI75 assessment 77–91 days after baseline randomization; and sufficient baseline clinical data for the prespecified model. Exclude psoriatic arthritis, pediatric/adolescent-only cohorts, nonrandomized primary comparisons, trials without a released collected dataset, and observations that cannot be mapped without changing the endpoint or introducing leakage. NAVIGATE is excluded from the baseline primary cohort and considered only as a separate post-ustekinumab landmark cohort.

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  ["project_main_outcome_measure"]=>
  string(1122) "Primary outcome: directly observed PASI75, defined as at least a 75% reduction from the protocol-defined baseline PASI, assessed 77–91 days after baseline randomization. The original trial definition, denominator, missing-data rule, rescue rule, and visit date will be retained in the provenance record. A trial without an adequately documented directly observed week-12 endpoint will not be forced into the primary cohort.

Key secondary outcome: PASI90 at week 16, or a prespecified narrow window, in modern baseline-randomized trials that directly record this endpoint. Other secondary outcomes are PASI75, PASI90, PASI100, and IGA/sPGA 0/1 at their documented protocol timepoints; continuous percent change in PASI; and an explicitly time-indexed trial-defined induction response. NAVIGATE's later response after post-ustekinumab randomization will be analyzed separately. DLQI and adverse-event/discontinuation outcomes are exploratory and will be used only when definitions and ascertainment are sufficiently comparable. No secondary outcome will be used as a hidden predictor of the primary outcome.

"
  ["project_main_predictor_indep"]=>
  string(1160) "The principal predictor is randomized treatment assignment and regimen, represented by treatment arm and protocol-recorded dose, route, and schedule when harmonizable. The primary model also uses prespecified baseline clinical information: age, sex where available, weight/BMI, baseline PASI, BSA, IGA/sPGA, disease duration, previous systemic/biologic exposure, selected comorbidities, baseline DLQI or comparable quality-of-life measure, and clinically interpretable missingness indicators. A treatment token is used to estimate predictive risk conditional on the assigned regimen; it is not a claim that the model identifies an optimal treatment.

No external molecular, pharmacokinetic, pharmacodynamic, genetic, imaging, registry, or real-world data will be requested. External public information will not be used to create unapproved treatment features. Trial ID is used for splitting and evaluation strata, not as a primary inference feature. Post-randomization response, rescue, switching, discontinuation reason, or any variable recorded after the prediction cut-off is excluded from the corresponding baseline or landmark predictor set.

"
  ["project_other_variables_interest"]=>
  string(1165) "Other variables will be considered in a prespecified tiered manner: baseline symptom and quality-of-life measures; special-site involvement such as scalp, nail, or palmoplantar disease when definitions are comparable; laboratory variables only when present and interpretable across a defensible subset of trials; visit date/study week; treatment exposure and adherence descriptors needed to define the trial estimand; rescue or switching indicators for censoring and sensitivity analyses; and trial/region/era descriptors for transportability assessment. Variable definitions, units, missingness, and provenance will be retained.

The following variables will not be used in the baseline model: post-outcome measurements, future PASI/IGA values, future treatment response, rescue treatment administered after the prediction cut-off, treatment discontinuation reasons observed after the cut-off, and any variable whose derivation uses the target outcome. Race/ethnicity and sex will be described and assessed only where representation and definitions permit; subgroup reporting will include uncertainty and will not be interpreted as proof of fairness.

"
  ["project_stat_analysis_plan"]=>
  string(3851) "The primary estimand is (P(Y{12}^{75}=1\mid X0,T)), the conditional risk of directly observed PASI75 at 77–91 days after baseline randomization, where (X_0) is prespecified baseline clinical information and (T) is randomized treatment/regimen. The key secondary estimand is directly observed PASI90 at week 16 in suitable modern trials. NAVIGATE is a separate landmark estimand beginning at week-16 re-randomization.

Before outcome modeling, we will freeze the trial list, cohort rules, time windows, feature tiers, model classes, validation scheme, and metrics. Trial dictionaries will be reconciled against protocols, CRFs, SAPs, and CSRs. Transformations, mappings, imputation, and missingness indicators will be estimated within each development fold. No held-out trial will contribute to preprocessing, model selection, calibration, or threshold selection.

The baseline Transformer will use typed clinical/missingness tokens and a randomized treatment/regimen token. A landmark model will add only information observed by an early landmark. The architecture will be small and capacity constrained; external pretraining is not required. Comparators are logistic regression, penalized logistic regression, and a tree-based model. Treatment-agnostic versions and token-removal ablations will assess incremental value.

Held-out trials will be classified before outcome evaluation as having overlapping or entirely unseen treatment identities/regimens relative to the development trials. Performance will be reported separately for these strata. An unknown-treatment token will be used only as a prespecified stress-test representation; no zero-shot efficacy claim will be made.

Primary validation is leave-one-trial-out external validation. Each held-out trial is evaluated once; development preprocessing, imputation, tuning, calibration, thresholding, and selection use only the remaining trials with grouped resampling. We will report AUROC, AUPRC, Brier score, calibration-in-the-large, calibration slope, plots, trial-specific estimates, and uncertainty. Pooled summaries will state contributing trials and heterogeneity; AUROC alone will not establish usefulness.

Subgroups will be prespecified by baseline severity, weight/BMI, previous biologic exposure, age, sex where adequately represented, and reliable region/era descriptors. Reports will include size, prevalence, missingness, discrimination, calibration, and uncertainty. Sensitivity analyses will compare observed-case and documented source-trial nonresponder/treatment-failure rules. Decision curves will be exploratory only.

If treatment pairs have adequate overlap and common endpoints, cross-fitted exploratory treatment contrasts may be reported with explicit support, horizon, comparator, model, and uncertainty. They will not be called validated individual causal effects or recommendations; the primary result remains predictive performance and transportability.

No fixed power or event-count claim will be made from nominal catalogue enrollment. Before final fitting, quantify participants, events, independent trials, predictor parameters, treatment overlap, and prevalence and apply a prediction-model sample-size/overfitting assessment. Effective information will determine model capacity; if insufficient, reduce features or use a simpler model.

Before final fitting, a feasibility gate will examine independent-trial count, endpoint availability, events, treatment overlap, missingness, stability, and secure-platform computing. If unsupported, reduce features/capacity, restrict the estimand, or make the simpler model primary. Do not enlarge the architecture after poor external performance. If Python/PyTorch is unavailable, use an approved CPU implementation or obtain YODA agreement before analysis.

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      ["label"]=>
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      ["label"]=>
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  ["project_timeline"]=>
  string(1000) "The dates below are anticipated and will be updated after YODA confirms eligibility, data availability, the secure computing environment, and the DUA effective date. The work is planned to fit within a one-year access period, with an extension requested if needed.

•	Anticipated project start: 1 November 2026, conditional on approval, training, signed DUA, and data access.

•	Trial/data-dictionary and harmonization review: November–December 2026.

•	Feasibility gate and frozen SAP: January 2027.

•	Model development and internal grouped tuning: February–March 2027.

•	Leave-one-trial-out external validation and sensitivity analyses: April–May 2027.

•	Analysis completion and locked results archive: 30 June 2027.

•	Manuscript drafted: 31 July 2027.

•	First manuscript submission: 31 August 2027.

•	Results report to YODA: 30 September 2027, and at any earlier completion/DUA milestone required by YODA.

"
  ["project_dissemination_plan"]=>
  string(1237) "We plan one peer-reviewed methods/biomedical manuscript and presentations at appropriate dermatology, clinical-trials, biostatistics, or medical-AI meetings. Candidate journals will be selected for fit after results, such as JAMA Dermatology, the British Journal of Dermatology, or The Lancet Digital Health, without presuming acceptance. Reporting will follow YODA/DUA requirements, TRIPOD+AI principles, and a PROBAST+AI self-audit.

If permitted by the DUA, we will share only a data-free analysis specification, harmonization template, synthetic/toy examples, and compliant code. Raw IPD, participant-level predictions, re-identification keys, and prohibited data-derived files will not be shared. Analyses remain inside YODA unless written authorization states otherwise.

The applicant will send the abstract/manuscript to YODA at submission, include the project number, and use the partner acknowledgment required by the signed DUA. We will not issue an independent press release or make public claims of clinical utility, treatment superiority, or individual treatment selection outside the DUA/publication process. Unexpected serious safety findings will be reported promptly to the Data Partner as required.

"
  ["project_bibliography"]=>
  string(6427) "

Yale University Open Data Access (YODA) Project. Request Data. https://yoda.yale.edu/request/request-data/
Yale University Open Data Access (YODA) Project. Data Request Review Process. https://yoda.yale.edu/request/data-request-review-process/
Yale University Open Data Access (YODA) Project. Project Research Proposal Template, May 2022. https://yoda.yale.edu/wp-content/uploads/2023/08/YODA-Project-Research-Proposal-Template-May-20221.pdf
Yale University Open Data Access (YODA) Project. Data Release Procedures, January 2025. https://yoda.yale.edu/wp-content/uploads/2022/11/YODA-Project-Data-Release-Procedures-January-2025.pdf
Yale University Open Data Access (YODA) Project. J&J Available Data. https://yoda.yale.edu/jj-available-data/
Yale University Open Data Access (YODA) Project. Condition Studied: Psoriasis. https://yoda.yale.edu/condition-studied/psoriasis/
Yale University Open Data Access (YODA) Project. Data Request 2022-4894: Systemic pharmacological treatments for chronic plaque psoriasis. https://yoda.yale.edu/data-request/2022-4894/
Yale University Open Data Access (YODA) Project. Data Request 2025-0736: Predicting long-term outcomes in people with psoriasis who initially respond to systemic immunomodulatory therapy. https://yoda.yale.edu/data-request/2025-0736/
Reich K, Armstrong AW, Langley RG, et al. Guselkumab versus secukinumab for the treatment of moderate-to-severe psoriasis (ECLIPSE): results from a phase 3, randomised controlled trial. Lancet. 2019;394:831–839. doi:10.1016/S0140-6736(19)31773-8. https://pubmed.ncbi.nlm.nih.gov/31402114/
Griffiths CEM, Strober BE, van de Kerkhof P, et al. Comparison of ustekinumab and etanercept for moderate-to-severe psoriasis. N Engl J Med. 2010;362:118–128. doi:10.1056/NEJMoa0810652. https://www.nejm.org/doi/10.1056/NEJMoa0810652
Leonardi CL, Kimball AB, Papp KA, et al. Efficacy and safety of ustekinumab in patients with psoriasis: 76-week results from PHOENIX 1. Lancet. 2008;371:1665–1674. doi:10.1016/S0140-6736(08)60725-4. https://doi.org/10.1016/S0140-6736(08)60725-4
Papp KA, Langley RG, Lebwohl M, et al. Efficacy and safety of ustekinumab in patients with psoriasis: 52-week results from PHOENIX 2. Lancet. 2008;371:1675–1684. doi:10.1016/S0140-6736(08)60726-6. https://pubmed.ncbi.nlm.nih.gov/18486740/
Reich K, Armstrong AW, Foley P, et al. Efficacy and safety of guselkumab compared with adalimumab in VOYAGE 2. J Am Acad Dermatol. 2017;76:418–431. doi:10.1016/j.jaad.2016.11.042. https://pubmed.ncbi.nlm.nih.gov/28057361/
Blauvelt A, Papp KA, Griffiths CEM, et al. Efficacy and safety of guselkumab in VOYAGE 1. J Am Acad Dermatol. 2017;76:405–417. doi:10.1016/j.jaad.2016.11.047. https://pubmed.ncbi.nlm.nih.gov/28057360/
Langley RGB, Tsai T-F, Flavin S, et al. Efficacy and safety of guselkumab in patients with psoriasis who have an inadequate response to ustekinumab: NAVIGATE. Br J Dermatol. 2018;178:114–123. doi:10.1111/bjd.16065. https://pubmed.ncbi.nlm.nih.gov/28635018/
Chaudhari U, Romano P, Mulcahy LD, Dooley LT, Baker DG, Gottlieb AB. Efficacy and safety of infliximab monotherapy for plaque-type psoriasis: a randomised trial. Lancet. 2001;357:1842–1847. doi:10.1016/S0140-6736(00)04954-0. https://pubmed.ncbi.nlm.nih.gov/11410193/
YODA Project. Trial page for NCT00106834. https://yoda.yale.edu/clinical-trial/nct00106834/
YODA Project. Trial page for NCT00230529. https://yoda.yale.edu/clinical-trial/nct00230529/
Collins GS, Dhiman P, Andaur Navarro C, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378. doi:10.1136/bmj-2023-078378. https://www.bmj.com/content/385/bmj-2023-078378
Moons KGM, Damen JAAG, Kaul T, et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ. 2025;388:e082505. doi:10.1136/bmj-2024-082505. https://www.bmj.com/content/388/bmj-2024-082505
Stewart LA, Clarke M, Rovers M, et al. Preferred Reporting Items for Systematic Review and Meta-Analyses of Individual Participant Data: the PRISMA-IPD statement. BMJ. 2015;350:g7647. doi:10.1136/bmj.g7647. https://www.bmj.com/content/350/bmj.g7647
Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ. 2010;340:c221. doi:10.1136/bmj.c221. https://www.bmj.com/content/340/bmj.c221
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Advances in Neural Information Processing Systems. 2017;30. https://papers.nips.cc/paper/7181-attention-is-all-you-need
Gorishniy Y, Rubachev I, Khrulkov V, Babenko A. Revisiting deep learning models for tabular data. Advances in Neural Information Processing Systems. 2021;34:18932–18943. https://papers.nips.cc/paper_files/paper/2021/hash/9d86d83f925f2149e9edb0ac3b49229c-Abstract.html
Cruz Rivera S, Liu X, Chan A-W, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020;26:1351–1363. doi:10.1038/s41591-020-1037-7. https://www.nature.com/articles/s41591-020-1037-7
Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020;26:1364–1374. doi:10.1038/s41591-020-1034-x. https://www.nature.com/articles/s41591-020-1034-x
Riley RD, Snell KIE, Ensor J, et al. Minimum sample size for developing a multivariable prediction model: PART II — binary and time-to-event outcomes. Stat Med. 2019;38:1276–1296. doi:10.1002/sim.7992. https://pubmed.ncbi.nlm.nih.gov/30357870/
Yale University Open Data Access (YODA) Project. Condition Studied: Psoriasis, page 2. https://yoda.yale.edu/condition-studied/psoriasis/page/2/
Yale University Open Data Access (YODA) Project. Condition Studied: Psoriasis, page 3. https://yoda.yale.edu/condition-studied/psoriasis/page/3/

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