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      string(333) "NCT00094458 - Multicenter, Randomized, Double-Blind, Active Controlled Trial Comparing REMICADE® (infliximab) and REMICADE plus Azathioprine to Azathioprine in the Treatment of Patients with Crohn's Disease Naive to both Immunomodulators and Biologic Therapy (Study of Biologic and Immunomodulator Naive Patients in Crohn's Disease)"
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  ["project_title"]=>
  string(131) "Dynamic Prediction of Net Clinical Benefit During Biologic Therapy in Inflammatory Bowel Disease Using Participant-Level Trial Data"
  ["project_narrative_summary"]=>
  string(742) "Biologic therapies can improve ulcerative colitis and Crohn's disease, but patients and clinicians often face a harder question after treatment has begun: is this patient still likely to have more benefit than harm? This study will use de-identified participant-level data from completed randomized trials available through the YODA Project to build dynamic models that update this probability as symptoms, endoscopy, laboratory tests, treatment exposure, and safety events accumulate. Ulcerative colitis and Crohn's disease will be modeled separately, then compared within a common inflammatory bowel disease framework. The work may improve how trial evidence is translated into monitoring decisions during induction and maintenance therapy."
  ["project_learn_source"]=>
  string(12) "scien_public"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(6) "Weimin"
    ["last_name"]=>
    string(2) "Xu"
    ["degree"]=>
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    ["primary_affiliation"]=>
    string(64) "Xinhua Hospital, Shanghai Jiaotong University School of Medicine"
    ["email"]=>
    string(17) "xwmgodmin@163.com"
    ["state_or_province"]=>
    string(8) "Shanghai"
    ["country"]=>
    string(5) "China"
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      ["p_pers_l_name"]=>
      string(5) "Zhang"
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      string(4) "M.S."
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      string(64) "Xinhua Hospital, Shanghai Jiaotong University School of Medicine"
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      string(4) "Peng"
      ["p_pers_l_name"]=>
      string(2) "Du"
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      string(64) "Xinhua Hospital, Shanghai Jiaotong University School of Medicine"
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    ["label"]=>
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  }
  ["project_funding_source"]=>
  string(69) "National Natural Science Foundation of China (No. 82570638, 82470549)"
  ["project_date_type"]=>
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  ["property_scientific_abstract"]=>
  string(1617) "Background: Biologic therapies have changed the treatment of ulcerative colitis (UC) and Crohn's disease (CD), yet follow-up decisions still rely on separate measures of symptoms, biomarkers, endoscopy, and adverse events. A useful model should estimate net clinical benefit, not response alone.

Objective: To develop and validate dynamic prediction models estimating favorable net clinical benefit during biologic therapy in UC and CD.

Study Design: Participant-level meta-analysis of completed randomized trials available through the YODA Project. UC and CD will be modeled separately, then compared within a common IBD framework.

Participants: Adults with moderate-to-severe UC or CD in selected biologic trials, with baseline data and at least one post-baseline efficacy or laboratory assessment.

Primary and Secondary Outcome Measure(s): The primary outcome is favorable net clinical benefit at a prespecified horizon: clinical/endoscopic benefit without serious adverse event, serious infection, AE-related discontinuation, or clinically meaningful laboratory abnormality. Secondary outcomes include clinical remission, endoscopic response or remission, discontinuation, safety events, laboratory abnormalities, and quality of life.

Statistical Analysis: Landmark dynamic prediction models will use information available up to each landmark to predict later net benefit. Models will be compared with baseline-only and efficacy-only models, assessed by discrimination, calibration, Brier score, and decision curve analysis, and validated by trial-stratified validation.

"
  ["project_brief_bg"]=>
  string(2632) "Moderate-to-severe inflammatory bowel disease (IBD) often requires long-term biologic therapy[1]. Randomized trials of infliximab, golimumab, ustekinumab, and other advanced therapies have established efficacy in UC and CD [2-7]. In practice, however, the clinical question changes after treatment begins. The relevant decision is no longer simply whether a drug works on average. It is whether the individual patient, with his or her evolving symptoms, laboratory profile, treatment exposure, and adverse events, is still likely to obtain more benefit than harm from continuing therapy.

Most secondary analyses of IBD trial data have examined single domains: clinical response, endoscopic healing, biomarker trajectories, placebo response, drug concentration, concomitant medication, age, sex, or disease extent [8]. These studies have been valuable, but the evidence remains fragmented. A patient who improves clinically but develops a serious infection, stops treatment because of an adverse event, or develops a clinically relevant laboratory abnormality does not represent the same clinical state as a patient who improves without such burden. Likewise, a delayed responder with stable laboratory values may deserve a different interpretation from an early non-responder with rising inflammatory markers and early safety concerns.

Dynamic prediction models offer a way to use follow-up information as it arrives [9]. They have been applied in oncology, cardiovascular medicine, and other chronic diseases to update risk estimates from longitudinal markers. IBD trials are well suited to this approach because they collect repeated clinical activity scores, patient-reported symptoms, biomarkers, drug exposure, concomitant medications, and adverse events at protocol-defined visits. The proposed study will use these data to estimate a clinically interpretable probability of net clinical benefit rather than an isolated probability of response.

This work will create generalizable scientific knowledge in three ways. First, it will provide disease-specific dynamic models for UC and CD using participant-level randomized trial data. Second, it will quantify the added value of safety, laboratory, and treatment-exposure information beyond baseline covariates and efficacy measures alone. Third, it will clarify whether the structure of benefit-risk prediction is shared across UC and CD or remains disease-specific. The results may inform future treat-to-target strategies, trial enrichment, monitoring schedules, and individualized continuation decisions during induction and maintenance therapy.

"
  ["project_specific_aims"]=>
  string(1208) "Aim 1: To define and harmonize a disease-specific net clinical benefit outcome for UC and CD trials of biologic therapy. Net benefit will combine evidence of clinical or endoscopic improvement with absence of major safety burden, adverse-event-related discontinuation, and clinically meaningful laboratory abnormality.

Aim 2: To develop dynamic prediction models for favorable net clinical benefit in UC and CD. Models will use prespecified baseline factors and time-updated information from symptoms, disease activity scores, laboratory markers, safety events, concomitant medication, and treatment exposure.

Aim 3: To compare full benefit-risk models with baseline-only and efficacy-only models, and to evaluate model performance by discrimination, calibration, Brier score, and decision curve analysis using trial-stratified validation.

We hypothesize that early longitudinal benefit-risk information will predict later net clinical benefit more accurately than baseline information alone. We further hypothesize that adding safety, laboratory, and treatment-exposure data will improve clinical utility beyond models based only on symptoms, biomarkers, or endoscopic response.

"
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    ["value"]=>
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    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
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  ["project_purposes"]=>
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      ["value"]=>
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      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
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      ["value"]=>
      string(49) "new_research_question_to_examine_treatment_safety"
      ["label"]=>
      string(49) "New research question to examine treatment safety"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
    [3]=>
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      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
  }
  ["project_research_methods"]=>
  string(1682) "The study will use de-identified participant-level data from completed randomized clinical trials available through the YODA Project. No external participant-level data will be used.

Requested UC trials: NCT02407236, NCT00036439, NCT00096655, NCT00487539, NCT00488774, and NCT00488631. These trials include ustekinumab, infliximab, or golimumab induction and/or maintenance therapy in moderate-to-severe UC.

Requested CD trials: NCT01369329, NCT01369342, NCT01369355, NCT03464136, NCT00207662, and NCT00094458. These trials include ustekinumab, infliximab, adalimumab, azathioprine-comparator, or combination-treatment arms in moderate-to-severe CD, including induction, maintenance, and active-comparator settings.

Inclusion criteria for the analytic sample will be: adult participants enrolled in the requested trials; protocol-defined UC or CD; baseline demographic and disease activity data; randomized treatment assignment; and at least one post-baseline efficacy, safety, or laboratory assessment. Both active and comparator arms will be retained when needed for calibration, trial-specific benchmarking, and treatment-assignment adjustment. The main biologic-treated profile will focus on participants assigned to biologic therapy.

Exclusion criteria will be: pediatric participants; participants without any post-baseline follow-up; participants with missing treatment assignment; and records that cannot be harmonized to the prespecified disease-specific outcome or landmark structure. Trials or variables that lack compatible documentation will be excluded from the affected analysis, with the reason documented before outcome modeling.

"
  ["project_main_outcome_measure"]=>
  string(1686) "The primary outcome is favorable net clinical benefit at the prespecified decision horizon. In maintenance trials, the main horizon will be the protocol-defined end of maintenance. In induction-only trials, the analogous end-of-induction net benefit outcome will be used in secondary and sensitivity analyses.

Favorable net clinical benefit will be defined as evidence of disease benefit without major safety or treatment burden. The efficacy component will be defined using trial-specific, harmonized measures. For UC, this will include clinical response or remission by Mayo or partial Mayo criteria and, when available, endoscopic improvement or remission. For CD, this will include clinical response or remission by CDAI-based criteria and, when available, endoscopic response or remission by SES-CD or trial-defined endoscopic criteria. The safety-burden component will require no serious adverse event, no serious infection, no adverse-event-related treatment discontinuation, and no clinically meaningful laboratory abnormality during the relevant risk window.

Secondary outcomes will include each component of the composite outcome: clinical remission; clinical response; endoscopic improvement or remission; histologic improvement or remission in UC trials with histology; serious adverse events; serious or clinically relevant infections; adverse-event-related discontinuation; all-cause treatment discontinuation; prespecified laboratory abnormalities; and improvement in IBDQ or other health-related quality-of-life measures. Sensitivity analyses will use narrower and broader definitions of net clinical benefit to test the robustness of the findings.

"
  ["project_main_predictor_indep"]=>
  string(1553) "The main predictors are prespecified dynamic benefit-risk information sets available at each landmark time. These will be organized into clinically interpretable domains rather than selected by blind stepwise procedures.

The baseline domain will include age, sex, race or region if available, body weight or BMI, smoking status, disease duration, disease extent or location, baseline disease activity, prior biologic exposure or failure, concomitant corticosteroid use, concomitant immunomodulator use, and baseline laboratory markers.

The longitudinal efficacy domain will include changes from baseline and current values in disease activity measures. UC measures will include Mayo score, partial Mayo score, stool frequency, rectal bleeding, physician global assessment, and Mayo endoscopic score where available. CD measures will include CDAI, abdominal pain, stool frequency, general well-being, and SES-CD or endoscopic components where available.

The laboratory domain will include current value, change from baseline, and trajectory features for CRP, albumin, hemoglobin, leukocyte count, neutrophils, lymphocytes, platelets, liver enzymes, bilirubin, and creatinine where available.

The safety and exposure domain will include cumulative adverse events, infection events, serious adverse events, new laboratory abnormalities, dose received, dose delay or deviation, drug accountability or exposure records, concomitant steroid changes, and serum drug concentration or pharmacokinetic variables when available.

"
  ["project_other_variables_interest"]=>
  string(1527) "Other variables will be used to describe the study population, harmonize trial differences, and adjust for prespecified clinical structure. Trial identifier, treatment assignment, biologic class, dose regimen, induction versus maintenance phase, visit window, and follow-up time will be retained in all analysis datasets.

Disease-specific variables will include UC extent when available and CD location, behavior, perianal disease, baseline stricturing or penetrating complications, prior intestinal surgery, and prior treatment failure or intolerance. Concomitant medications will include corticosteroids, immunomodulators, 5-aminosalicylates, antibiotics, and other protocol-relevant therapies.

Safety variables will be grouped using trial-reported adverse event coding and standardized categories where feasible: serious adverse events, infections, serious infections, infusion or injection reactions, gastrointestinal adverse events, hepatobiliary abnormalities, hematologic abnormalities, and renal laboratory abnormalities. Laboratory values will be analyzed both continuously and as clinically meaningful abnormality indicators when reference ranges or protocol definitions allow.

Patient-reported and quality-of-life variables, including IBDQ and related domains, will be used as secondary outcomes or predictors depending on trial availability. Missingness indicators and visit-window indicators will be retained to support transparent evaluation of differential measurement across trials.

"
  ["project_stat_analysis_plan"]=>
  string(4822) "All analyses will be conducted within the secure YODA Project platform using de-identified participant-level data. UC and CD will be analyzed separately because the core disease activity instruments and endoscopic endpoints differ. A secondary cross-disease comparison will examine whether predictor domains contribute similarly across the two diseases.

First, trial documentation, protocols, case report forms, data specifications, and analysis datasets will be reviewed to construct harmonized analytic files. Visit windows will be prespecified around clinically meaningful landmarks, including baseline, early induction, end induction, early maintenance, and end maintenance where available. Trial-specific outcome definitions will be mapped to common UC and CD efficacy components. Safety outcomes will be harmonized by seriousness, infection status, discontinuation relationship, and laboratory abnormality category.

Descriptive analyses will summarize participant characteristics, treatment arms, baseline disease severity, biomarker distributions, concomitant medications, follow-up patterns, and crude rates of efficacy, safety, discontinuation, and net clinical benefit. Continuous variables will be summarized by median and interquartile range or mean and standard deviation as appropriate; categorical variables will be summarized by counts and percentages. Patterns of missingness will be described by trial, visit, disease, and treatment arm.

The primary modeling approach will be landmark dynamic prediction. At each landmark, only information observed up to that time will be used to predict favorable net clinical benefit at the relevant future horizon. Candidate landmarks will be aligned to trial schedules, such as weeks 2, 4, 6, 8, 14, and later maintenance visits when available. Landmark supermodels will use pooled logistic regression or flexible binary regression with trial fixed effects or random intercepts, treatment assignment, and prespecified predictor domains. Continuous predictors will be modeled on their natural scale with restricted cubic splines or clinically meaningful transformations when needed. No blind stepwise variable selection will be used. If regularization is required for stability, it will be applied within prespecified predictor domains and reported as a sensitivity analysis.

Model comparison will follow a staged structure: baseline-only model; baseline plus efficacy model; baseline plus efficacy and laboratory model; and full benefit-risk model including efficacy, laboratory, safety, concomitant medication, and treatment-exposure information. This comparison will estimate the added value of safety and exposure information beyond response prediction alone.

Model performance will be evaluated by discrimination, calibration, overall accuracy, and clinical utility. Discrimination will be assessed with AUROC or time-dependent AUC as appropriate. Overall performance will be assessed with Brier score. Calibration will be examined by calibration intercept, calibration slope, and graphical calibration across predicted-risk strata. Decision curve analysis will estimate the net benefit of using the dynamic model across plausible threshold probabilities for treatment continuation or intensified monitoring [10]. Internal-external validation will be performed by leaving out one trial at a time for validation when the number of compatible trials permits; bootstrap optimism correction will be used as a complementary approach.

Secondary analyses will evaluate individual outcome components and alternative net-benefit definitions. These will include stricter definitions requiring endoscopic improvement, broader definitions based on clinical benefit alone without serious safety burden, and models excluding low-frequency safety events. Subgroup analyses will be prespecified by disease, biologic class, prior biologic exposure or failure, baseline severity, concomitant corticosteroid use, age group, and induction versus maintenance setting.

Missing covariate data will be handled using multiple imputation within disease and, where possible, within trial or compatible trial groups. Longitudinal models will use all available repeated measurements under standard missing-at-random assumptions. Primary outcome missingness will be handled according to trial conventions where available; sensitivity analyses will compare complete-case, non-responder, and imputed-outcome approaches. Bayesian joint models using longitudinal disease activity and laboratory markers may be fitted as a secondary analysis in trials with sufficient repeated measurements. These models will be used to assess whether jointly modeled trajectories produce materially different dynamic predictions from the landmark approach.

"
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      ["label"]=>
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  ["project_timeline"]=>
  string(1045) "Months 0-2 after data access: review trial documentation, protocols, data specifications, and case report forms; create harmonized UC and CD data dictionaries; define landmark windows and outcome algorithms.

Months 3-4: construct analytic datasets; complete descriptive analyses; finalize the primary and sensitivity definitions of net clinical benefit before outcome modeling.

Months 5-7: fit UC and CD landmark dynamic prediction models; compare baseline-only, efficacy-only, laboratory-extended, and full benefit-risk models.

Months 8-9: complete validation, calibration, decision curve analysis, subgroup analyses, and sensitivity analyses, including joint modeling where feasible.

Months 10-11: prepare tables, figures, and reproducible code; draft the manuscript.

Month 12: submit the manuscript for peer-reviewed publication and report results back to the YODA Project. If additional validation or journal revision requires more time, an extension will be requested according to YODA procedures.

"
  ["project_dissemination_plan"]=>
  string(924) "The primary product will be a peer-reviewed manuscript reporting the development and validation of dynamic net clinical benefit models for UC and CD. Suitable target journals include Clinical Gastroenterology and Hepatology, Journal of Crohn's and Colitis, and Inflammatory Bowel Diseases. Results may also be submitted as an abstract to Digestive Disease Week, European Crohn’s and Colitis Organization and Asian Crohn’s and Colitis Organization

The intended audience includes gastroenterologists, IBD clinical trialists, clinical epidemiologists, statisticians, and researchers developing treat-to-target or precision-medicine strategies. Findings will be reported to the YODA Project according to data-use requirements. If permitted, analysis code that does not contain participant-level data will be shared publicly with simulated or synthetic example data to improve transparency and reproducibility.

"
  ["project_bibliography"]=>
  string(1954) "

Turner D, Ricciuto A, Lewis A, et al. STRIDE-II: An update on the Selecting Therapeutic Targets in Inflammatory Bowel Disease initiative of the International Organization for the Study of IBD. Gastroenterology. 2021;160(5):1570-1583.
Rutgeerts P, Sandborn WJ, Feagan BG, et al. Infliximab for induction and maintenance therapy for ulcerative colitis. N Engl J Med. 2005;353:2462-2476.
Sandborn WJ, Feagan BG, Marano C, et al. Subcutaneous golimumab induces clinical response and remission in patients with moderate-to-severe ulcerative colitis. Gastroenterology. 2014;146:85-95.
Sands BE, Sandborn WJ, Panaccione R, et al. Ustekinumab as induction and maintenance therapy for ulcerative colitis. N Engl J Med. 2019;381:1201-1214.
Hanauer SB, Feagan BG, Lichtenstein GR, et al. Maintenance infliximab for Crohn’s disease: the ACCENT I randomised trial. Lancet. 2002;359:1541-1549.
Feagan BG, Sandborn WJ, Gasink C, et al. Ustekinumab as induction and maintenance therapy for Crohn’s disease. N Engl J Med. 2016;375:1946-1960.
Sands BE, Irving PM, Hoops T, et al. Ustekinumab versus adalimumab for induction and maintenance therapy in biologic-naive patients with moderately to severely active Crohn’s disease: a multicentre, randomised, double-blind, parallel-group, phase 3b trial. Lancet. 2022;399(10342):2200-2211.
Zheng J, Zhang X, Zhang L, Li L, Chen M, Chen R, Zhang S. Serum Albumin and Its Trajectory Are Associated With Therapeutic Outcomes in Ulcerative Colitis. Clin Gastroenterol Hepatol. 2025;23(10):1808-1816.
Rizopoulos D, Molenberghs G, Lesaffre EMEH. Dynamic predictions with time-dependent covariates in survival analysis using joint modeling and landmarking. Biom J. 2017;59:1261-1276.
Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26:565-574.

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