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  ["project_title"]=>
  string(130) "Predicting and Validating Individual Treatment Response in ADHD: A Precision Medicine Project Using Randomized Clinical Trial Data"
  ["project_narrative_summary"]=>
  string(815) "ADHD affects millions globally, yet treatment decisions often rely on trial-and-error due to variable individual responses. This study has two aims. First, we validate three existing models for children predicting treatment response or risk to develop ADHD. It is of interest to consolidate existing results on a different population. Furthermore, it will be checked if these models provide high quality predictions which can be used in clinical decision making. The second part will use a large clinical trial to develop a prediction model to decide on the best choice between two alternatives for the first line treatment of children (Methylphenidate HCl or Atomoxetine). We will analyze demographic, clinical, and neurological data to predict treatment outcomes, such as symptom improvement and quality of life. "
  ["project_learn_source"]=>
  string(5) "other"
  ["project_learn_source_exp"]=>
  string(82) "YODA is a partner of the MSCA DN SHARE-CTD Project financed by the European Union."
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(6) "Ulrich"
    ["last_name"]=>
    string(8) "Mansmann"
    ["degree"]=>
    string(14) "Professor, PhD"
    ["primary_affiliation"]=>
    string(20) "University of Munich"
    ["email"]=>
    string(26) "ulrich.mansmann@med.lmu.de"
    ["state_or_province"]=>
    string(7) "Bavaria"
    ["country"]=>
    string(7) "Germany"
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      ["p_pers_f_name"]=>
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      ["p_pers_l_name"]=>
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      ["p_pers_degree"]=>
      string(3) "MSc"
      ["p_pers_pr_affil"]=>
      string(37) "Medical Faculty, University of Munich"
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      ["requires_data_access"]=>
      string(3) "yes"
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  ["project_ext_grants"]=>
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    ["value"]=>
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    ["label"]=>
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  ["property_scientific_abstract"]=>
  string(1463) "Background: ADHD treatment responses vary widely, but no externally validated models predict individual outcomes. Precision medicine can optimize treatment by tailoring it to patient characteristics.

Objective: Develop and validate prediction models to identify optimal first-line ADHD treatments (Methylphenidate HCl vs. Atomoxetine) for children and external validation of three prediction models for children, using clinical trial data.

Study Design: Individual trial analysis with discovery and validation datasets.

Participants: Children diagnosed with ADHD (DSM-IV, ADHD-RS ≥ 24, CGI-S ≥ “moderately ill”) from two YODA RCTs (NCT00866996, NCT00799487, NCT00799409).

Primary and Secondary Outcome Measure(s): Primary: ADHD Rating Scale (ADHD-RS) total score. Secondary: Clinical Global Impression-Improvement (CGI-I), Quality of Life (QoL) measures, cognitive performance scores, serious adverse events (SAEs), treatment discontinuation, common side effects.

Statistical Analysis: Model development is based on hierarchically nested cross-validation or bootstrap sampling to validate the best model selected from different modelling approaches like LASSO, Elastic Net, SVM, and Decision Trees, with k-fold cross-validation and hyperparameter tuning. Model performance evaluated via AUC-ROC and calibration; External model validation of already published models uses AUC-ROC, calibration and decision curve analysis."
  ["project_brief_bg"]=>
  string(1136) "ADHD, impacting approximately 5% of children and 3% of adults globally, contributes significantly to disease burden and healthcare costs. While neurobiological research highlights subcortical volume reductions associated with ADHD, dopamine pathway alterations as causal mechanism, and genetic risk loci associated with ADHD. These findings have not translated into clinical strategies. Current ADHD treatment relies on RCT evidence, but individual responses vary, and no validated models predict optimal treatment. 

Current ADHD treatment needs prediction models to stratify patients regarding risk of ADHD worsening or treatment response. The project will at the one hand externally validate existing prediction tools to assess their clinical usefulness and will also try to develop a new model to support the choice of first-line treatment for children.

This work hopefully will advance precision psychiatry, addressing a gap where only 7% of ADHD prediction models are externally validated. It will enhance clinical decision-making, improve patient outcomes, and inform public health by optimizing resource allocation."
  ["project_specific_aims"]=>
  string(455) "1. Develop a prediction model for identifying the appropriate first-line treatment (Methylphenidate HCl vs. Atomoxetine) for children with ADHD by utilizing discovery and validation datasets from clinical trials of Methylphenidate HCl and Atomoxetine (NCT00866996).

2. Validate three specific prediction model (LASSO and classification trees) regarding treatment response of risk of worsening for children using NCT00799487 and NCT00799409.

"
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    ["label"]=>
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      ["value"]=>
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      ["label"]=>
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      ["label"]=>
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    [2]=>
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      ["label"]=>
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      ["label"]=>
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  ["project_research_methods"]=>
  string(167) "Data Source: YODA Project trials: NCT00866996, NCT00799487, NCT00799409. No external data sources.

We will not apply additional exclusion or inclusion criteria."
  ["project_main_outcome_measure"]=>
  string(1000) "i.	Primary Outcome: The main outcome will be the total score on the ADHD Rating Scale (ADHD-RS).

ii.	Efficacy Outcome

Clinical Global Impression - Improvement (CGI-I): A physician-rated scale measuring overall improvement in ADHD symptoms.

Quality of Life (QoL) Measures: Parent- or child-reported improvements in daily functioning, emotional well-being, and social interactions.

Cognitive Performance Scores: If available, measures of attention, working memory, or executive function from cognitive tests.

iii.	Safety Outcome

Serious Adverse Events (SAEs): Events such as hospitalization, severe side effects, or other medical complications.

Treatment Discontinuation: The number of patients who stopped treatment due to side effects or lack of effectiveness.

Common Side Effects: Monitoring issues like insomnia, appetite loss, mood swings, or increased heart rate.

Changes: No planned changes unless unexpected data limitations arise."
  ["project_main_predictor_indep"]=>
  string(1008) "Prediction of best first-line alternative: We will include a large set of predictors (along with the treatment variable), prioritizing those identified in the relevant literature, particularly as outlined in the systematic review by Salazar de Pablo et al. The coding of the predictors will also follow the rules established in the community and applied in the specific research. Key predictors should include demographics (e.g., age, sex, ethnicity), clinical assessments (e.g., ADHD symptom severity, treatment response), laboratory parameters (e.g., blood biomarkers, hormone levels), family medical history (e.g., ADHD or psychiatric disorders in relatives), comorbidities (e.g., anxiety, depression, learning disabilities, or other conditions), neurological or brain-related data (e.g., EEG, MRI), and chemical factors such as drug resistance variables.

For the external validation of the three previously published models, we will use the original coding provided by the authors of these models."
  ["project_other_variables_interest"]=>
  string(217) "We would like to use the wide range of variables provided by the trials of interest. We have no prespecified hypothesis. Variables of interest will be determined after having defined the  respective prediction models."
  ["project_stat_analysis_plan"]=>
  string(1677) "As described above this project has two different statistical approaches.

The development and internal validation of a prediction model to identify the best first-line treatment option will follow a three-level hierarchical framework (as previously described), incorporating cross-validation and bootstrap sampling. These sampling strategies are used to assess the robustness of the modelling outcomes under different resampling mechanisms. To investigate treatment–patient interactions, we will use models from various methodological families, including LASSO, elastic net, SVM and model-based recursive partitioning trees (Seibold et al., model4you).

In the external validation component, we will apply three previously published prediction models (as provided by the original authors) to YODA trial data (NCT00799487 and NCT00799409). The models will be evaluated using multiple metrics of predictive performance and clinical utility, including area under the curve (AUC), calibration, decision curve analysis, and mutual information.

For the external validation, we conducted a sample size calculation to determine the number of subjects required to reject the null hypothesis (AUC = 0.5) at a two-sided significance level of 0.05 with 80% power, assuming an alternative hypothesis of AUC = 0.65. This calculation yielded a required sample size of 250 subjects, based on Example 8.3 from Pepe (2003), The Statistical Evaluation of Medical Tests for Classification and Prediction, Oxford University Press. 

A detailed statistical analysis plan will be made publicly available on the Open Science Framework (OSF) by the end of this year.

"
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      ["label"]=>
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      ["label"]=>
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  string(503) "The project will last maximally 18 months (M1–M18), with specific tasks assigned to each month. We aim to finish the main research and model development by month 12 (M12). We would like to submit the results for publication at month 13. In order to be able to respond for reviewer commentaries we would need additional time until Month 18.  Months 13 to 18 (M13–M18) will focus on making any needed corrections after having submitted the final paper and the final results (also to the YODA project)."
  ["project_dissemination_plan"]=>
  string(910) "The target audience includes researchers, academics, clinicians, healthcare professionals, and patients. We aim to disseminate the study findings through publications in high-impact open access journals such as BMC Medicine, PLOS Medicine, Scientific Reports, Frontiers in Psychiatry, and Journal of Medical Internet Research. Additionally, results will be presented at relevant national and international conferences in psychiatry, digital health, and machine learning in medicine.To increase real-world impact, we will work closely with clinicians to include their insights and support knowledge sharing (LMU department of child psychiatry, Prof. Dr. Gerd Schulte-Körne). Additionally, to ensure transparency and reproducibility, we will make the source code and analytical methods publicly available in a repository (e.g., SHARE-CTD website and GitHub, OSF), encouraging further research and collaboration."
  ["project_bibliography"]=>
  string(1858) "
Faraone, S. V., Gomeni, R., Hull, J. T., Busse, G. D., Melyan, Z., O’Neal, W., … & Nasser, A. (2021). Early response to SPN-812 (viloxazine extended-release) can predict efficacy outcome in pediatric subjects with ADHD: a machine learning post-hoc analysis of four randomized clinical trials. Psychiatry Research, 296, 113664.
Lavigne, J. V., Hopkins, J., Ballard, R. J., Gouze, K. R., Ariza, A. J., & Martin, C. P. (2024). A Precision Mental Health Model for Predicting Stability of 4-year-olds’ Attention Deficit/Hyperactivity Disorder Symptoms to Age 6 Diagnostic Status. Academic Pediatrics, 24(3), 433–441.
Salazar de Pablo, G., Iniesta, R., Bellato, A., Caye, A., Dobrosavljevic, M., Parlatini, V., … & Cortese, S. (2024). Individualized prediction models in ADHD: a systematic review and meta-regression. Molecular Psychiatry, 1–9.
Seibold, H., Zeileis, A., & Hothorn, T. (2019). model4you: an R package for personalised treatment effect estimation. Journal of Open Research Software, 7(1).
Setyawan, J., Yang, H., Cheng, D., Cai, X., Signorovitch, J., Xie, J., & Erder, M. H. (2015). Developing a risk score to guide individualized treatment selection in attention deficit/hyperactivity disorder. Value in Health, 18(6), 824–831.
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    ["filename"]=>
    string(38) "YODA-Project-Review-2025-0244_site.pdf"
    ["filesize"]=>
    int(1315583)
    ["url"]=>
    string(87) "https://yoda.yale.edu/wp-content/uploads/2025/04/YODA-Project-Review-2025-0244_site.pdf"
    ["link"]=>
    string(80) "https://yoda.yale.edu/data-request/2025-0244/yoda-project-review-2025-0244_site/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(4) "1885"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(34) "yoda-project-review-2025-0244_site"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(17085)
    ["date"]=>
    string(19) "2025-08-11 16:08:12"
    ["modified"]=>
    string(19) "2025-08-11 16:08:12"
    ["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"
  ["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(1) {
  [0]=>
  string(8) "concerta"
}


num of trials
array(1) {
  [0]=>
  string(1) "3"
}


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