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  ["project_title"]=>
  string(94) "Early Prediction of Treatment Response in Rheumatoid Arthritis Using Patient-Reported Outcomes"
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  string(766) "Rheumatoid arthritis (RA) is a long-term autoimmune disease that can cause pain, fatigue, disability, and poor quality of life. Although many treatments are available, patients often must try several medications before finding one that works. This study will use existing RA clinical trial data to test whether symptoms reported by patients soon after starting treatment can predict later response. We will study changes in pain, physical function, fatigue, and quality of life during the first 12 weeks, then use statistical models and machine learning to predict disease activity after Week 52, measured by DAS28. The results may help doctors identify effective treatments earlier, reduce time spent on ineffective therapies, and support more personalized RA care."
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    ["first_name"]=>
    string(6) "Jiawei"
    ["last_name"]=>
    string(4) "Zhou"
    ["degree"]=>
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    ["primary_affiliation"]=>
    string(43) "University of North Carolina at Chapel Hill"
    ["email"]=>
    string(14) "zhoujw@unc.edu"
    ["state_or_province"]=>
    string(2) "NC"
    ["country"]=>
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    ["label"]=>
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  string(1635) "Background: Rheumatoid arthritis (RA) treatment response varies widely, and many patients try several therapies before achieving disease control. Patient-reported outcomes (PROs), such as pain and physical function, reflect patients’ symptoms and daily functioning and may provide early signals of later treatment response.

Objective: To determine whether PRO trajectories during the first 12 weeks of treatment can predict RA treatment response measured by DAS28 at Week 52.

Study Design: This study will conduct a secondary analysis of individual participant data from approximately 10 RA clinical trials available through YODA. PRO data, including physical function measured by HAQ-DI and pain measured by VAS, fatigue (if applicable) will be modeled over the first 12 weeks and used to predict later disease activity.

Participants: Adults with RA in trials with DAS28-CRP or DAS28-ESR assessed beyond Week 48, preferably Week 52, and early physical function and pain PROs.

Primary and Secondary Outcome Measure(s): The primary outcome is prediction accuracy for DAS28, including continuous change from baseline and categorical response. Secondary outcomes include identification of thresholds in PROs associated with DAS28 response.

Statistical Analysis: Nonlinear mixed-effect models will estimate patient-specific PRO trajectories and reduce noise. These trajectories, plus baseline clinical and demographic factors, will train and validate machine learning models. Performance will be assessed using discrimination, calibration, and prediction error, with sensitivity analyses by DAS28 type."
  ["project_brief_bg"]=>
  string(2645) "Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects approximately 1.3 million adults in the United States and is associated with pain, fatigue, impaired physical function, reduced quality of life, and substantial healthcare burden. [1] Although many disease-modifying antirheumatic drugs are available, treatment response varies widely across patients. As a result, many patients must try multiple therapies before achieving adequate disease control. Approximately one in five patients with RA cycles through therapies without substantial benefit, highlighting the urgent need for precision medicine in RA. [2]



Patient-reported outcomes (PROs) provide direct information on symptoms and daily functioning that matter to patients, including pain, physical function, fatigue, and quality of life. Because PROs are noninvasive and can be collected frequently, including remotely, they may reveal early signals of treatment response before later clinical assessments. However, PRO data are often noisy and multidimensional, and traditional analyses commonly rely on single follow-up scores or total scale scores. This approach may miss important symptom trajectories and symptom-level patterns that could help predict later response. [3]



This project will use individual participant data from approximately 10 RA clinical trials available through YODA to determine whether PRO trajectories during the first 12 weeks of treatment can predict later treatment response measured by DAS28 at or beyond Week 48, preferably Week 52. We will model early trajectories of physical function and pain, and fatigue when available, using nonlinear mixed-effect models to separate meaningful symptom patterns from random variation. [4] These patient-specific trajectories will then be combined with baseline demographic and clinical characteristics to train and validate machine learning models for predicting later DAS28 outcomes.



The information gained from this work will materially enhance generalizable scientific and medical knowledge by identifying whether early patient-reported symptom patterns can serve as practical predictors of RA treatment response. If successful, this approach could support earlier identification of ineffective therapies, reduce delays in treatment optimization, and inform future precision-medicine strategies in RA. Because PROs can be collected outside clinic visits, this work may also advance scalable, patient-centered monitoring approaches that improve clinical trial design and public health decision-making for chronic inflammatory diseases.



"
  ["project_specific_aims"]=>
  string(1441) "Aim 1. Develop NLME-informed machine learning models to predict DAS28 outcomes at Week 52 using longitudinal PRO trajectories and baseline patient characteristics.

    We will characterize individual-level longitudinal trajectories of key PRO measures, including pain and physical function, using nonlinear mixed-effects modeling. Fatigue will be modeled if applicable. Individual-level PRO trajectory features derived from these models, together with patient demographics and baseline clinical characteristics, will be evaluated as predictors of DAS28 outcomes at Week 52 using machine learning algorithms. We hypothesize that integrating model-derived PRO trajectory features with baseline patient characteristics will improve prediction of long-term DAS28 response compared with baseline characteristics alone.



Aim 2. Identify clinically meaningful PRO change thresholds associated with DAS28 treatment response.

    We will determine the magnitude and timing of changes in PRO measures that are associated with clinically meaningful DAS28 responses at Week 52. Statistical analyses will evaluate threshold values of improvement in pain and physical function that best discriminate responders from non-responders. We hypothesize that specific early changes in PROs are associated with favorable DAS28 outcomes and may serve as interpretable indicators of treatment effectiveness in RA clinical trials.

"
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  string(1806) "Data Source: This study will use de-identified individual participant-level data from rheumatoid arthritis clinical trials available through the YODA Project. Analyses will be conducted within the secure YODA platform using trial datasets and supporting documents, including protocols and clinical study reports when available. No external participant-level datasets will be pooled with YODA data.

Trial Inclusion Criteria: Trials will be eligible if they: (1) are available through the YODA Project; (2) include DAS28-CRP or DAS28-ESR assessments beyond Week 48, preferably at Week 52; and (3) include PRO measures for physical function and pain before or by Week 12, such as HAQ-DI and pain VAS.

Participant Inclusion Criteria: Eligible participants will include randomized rheumatoid arthritis trial participants with baseline DAS28 assessment and sufficient follow-up DAS28 data to define Week 52 or post-Week 48 outcomes. Participants must also have baseline and at least one additional PRO assessment before or by Week 12 for both pain and physical function domains.

Exclusion Criteria: Trials without clear published literature evidence of DAS28 and PRO data availability will be excluded. Participants may be excluded for major protocol deviations, inadequate follow-up, substantial missing longitudinal data, or assessment schedules that cannot be harmonized across trials.

Pooling Plan: Eligible trials will be harmonized using common definitions for DAS28 measures, PRO variables, treatment assignment, assessment time points, and available baseline demographic and clinical variables (specified in the Other Variables section). Trial-level differences will be addressed by incorporating study membership and treatment arm into the statistical models.



"
  ["project_main_outcome_measure"]=>
  string(1288) "Primary Outcome Measure: The primary outcome will be the predictive performance of NLME-informed machine learning models using early PRO trajectories to predict DAS28 outcomes beyond Week 48, preferably at Week 52. Performance will be assessed using AUROC for categorical DAS28 response and MAE/RMSE for continuous DAS28 change from baseline. Strong predictive performance will be defined as AUROC ≥0.85 for categorical response and prediction of continuous DAS28 change with RMSE ≤1.0 DAS28 unit or ≥20% lower prediction error compared with baseline-only models.



Secondary Outcome Measures: Secondary outcomes will include: (1) identification of early changes in pain and physical function associated with favorable Week 52 DAS28 outcomes; (2) determination of PRO change thresholds associated with later DAS28 response categories; and (3) comparison of baseline-only models versus models incorporating longitudinal PRO trajectory features, with improvement assessed using AUROC, prediction error, calibration, and cross-validated performance.



Exploratory analyses may evaluate fatigue-related PROs when available. Any modifications required due to data harmonization or feasibility considerations will be documented in resulting publications.

"
  ["project_main_predictor_indep"]=>
  string(1025) "The main independent variables will be early longitudinal PRO trajectories for pain and physical function measured from baseline through Week 12. Pain VAS scores and HAQ-DI item-level responses will be treated as ordinal variables in the nonlinear mixed effect (NLME) models to characterize PRO trajectories.

The NLME models will generate individual-level PRO trajectory parameters, such as baseline status, rate of change, magnitude of early improvement, and model-derived random effects. These trajectory parameters will be treated as continuous predictors in machine learning models to predict Week 52 or post-Week 48 DAS28 treatment response.

For PRO threshold identification, composite PRO scores, such as total HAQ-DI and pain VAS change from baseline, may be analyzed as continuous variables to identify clinically meaningful improvement thresholds. Sensitivity analyses may also retain ordinal coding of item-level or response-level PRO data to evaluate the robustness of identified thresholds. 

"
  ["project_other_variables_interest"]=>
  string(1440) "Other variables will include available baseline demographic and clinical characteristics used to describe the study population and support covariate adjustment or comparator prediction models. Demographic variables may include age, sex, race/ethnicity, body weight, body mass index (BMI), geographic region, and disease duration. Clinical variables may include tender joint count, swollen joint count, C-reactive protein (CRP) or erythrocyte sedimentation rate (ESR), rheumatoid factor status, anti-cyclic citrullinated peptide (anti-CCP) antibody status, prior biologic or targeted synthetic disease-modifying antirheumatic drug (DMARD) use, concomitant methotrexate or corticosteroid use, treatment assignment, liver function measures, kidney function measures, and relevant comorbidities when available.



Continuous variables, such as age, body mass index, disease duration, laboratory values, and joint counts, will generally be analyzed as continuous variables and summarized using means, standard deviations, medians, and interquartile ranges. Categorical variables, such as sex, race/ethnicity, serostatus, treatment arm, and prior medication use, will be analyzed as categorical variables. To reduce re-identification risk, selected demographic variables, such as age, may be transformed into categorical variables. Trial membership will be included to account for between-study differences in pooled analyses.

"
  ["project_stat_analysis_plan"]=>
  string(2591) "All analyses will be conducted within the secure YODA Project platform using de-identified individual participant-level data from eligible rheumatoid arthritis trials. Variables will be harmonized across studies, including Disease Activity Score in 28 joints (DAS28), patient-reported outcomes (PROs), treatment assignment, assessment time points, and available baseline demographic and clinical variables. Statistical analyses will be performed using R/RStudio. Nonlinear mixed-effects (NLME) models will be built using Monolix, with the user providing installation and licensing as needed, and machine learning models will be implemented in Python.



Descriptive analyses will summarize DAS28 outcomes, PRO measures, and baseline demographic and clinical variables overall and by study. Continuous variables will be summarized using means, standard deviations, medians, and interquartile ranges; categorical variables will be summarized using frequencies and percentages. Baseline characteristics, DAS28 distributions, and PRO profiles will be compared across studies to assess between-study differences.



Multivariable analyses will evaluate population heterogeneity across studies. Baseline demographic and clinical variables will be compared across trials using regression-based approaches to identify cross-study differences requiring adjustment in pooled analyses.



For longitudinal PRO modeling, NLME models will characterize pain and physical function trajectories from baseline through Week 12. Pain VAS scores and HAQ-DI scores will be treated as ordinal variables when appropriate. Baseline demographic and clinical variables will be examined as prespecified covariates. Covariates will be retained based on clinical relevance, improvement in model fit, and prespecified covariate tests with two-sided P values <0.05.



Linear regression models will assess DAS28 change from baseline at Week 52, and logistic regression models will assess categorical DAS28 response status at Week 52. Predictors will include baseline demographic and clinical variables and NLME-derived PRO trajectory parameters. Spearman correlations will assess associations between model-predicted PRO change at Week 12 and DAS28 outcomes at Week 52 to support threshold identification.



All statistical tests will be two-sided, and P values <0.05 will be considered statistically significant. Sensitivity analyses may evaluate results by study, treatment arm, DAS28 type, and alternative definitions of Week 52 DAS28 outcomes.

"
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      ["label"]=>
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      ["label"]=>
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  ["project_timeline"]=>
  string(756) "The anticipated project start date is March 2027, pending YODA Project approval and execution of the Data Use Agreement. Data access, review of study documentation, and variable harmonization are expected to be completed by May 2027. Descriptive analyses and development of nonlinear mixed-effects models are expected to be completed by October 2027. Machine learning analyses, threshold identification, sensitivity analyses, and interpretation of results are expected to be completed by December 2028.



A manuscript draft is expected to be completed by January 2028, with first submission for publication by February 2028. Final results will be reported back to the YODA Project by March 2028, within the 12-month data access period.

"
  ["project_dissemination_plan"]=>
  string(916) "Anticipated products include at least one peer-reviewed manuscript, conference abstract(s), and poster/oral presentation(s). The target audience includes rheumatologists, clinical pharmacologists, trialists, quantitative scientists, and regulatory stakeholders interested in RA, patient-reported outcomes, and long-term treatment response.



We plan to submit the completed work to RA-focused journals such as Annals of the Rheumatic Diseases, Arthritis & Rheumatology, Rheumatology, Seminars in Arthritis and Rheumatism, Clinical Rheumatology, or The Journal of Rheumatology. Depending on the modeling focus, CPT: Pharmacometrics & Systems Pharmacology may also be suitable. We also plan to present this work at American Society for Clinical Pharmacology and Therapeutics (ASCPT) and/or American Conference of Pharmacometrics (ACoP) to reach clinical pharmacology and pharmacometrics audiences."
  ["project_bibliography"]=>
  string(371) "
References:

[1] Ciofoaia, Elena I., et al. Therapeutic advances in musculoskeletal disease 14 (2022): 1759720X221137127.

[2] Van der Elst, Kristien, et al. RMD open 6.1 (2020).

[3] Zhou, Jiawei, et al. Clinical Cancer Research 31.9 (2025): 1580-1586.

[4] Zhou, Jiawei, et al. Journal of Cachexia, Sarcopenia and Muscle 16.6 (2025): e70150.
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