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  ["project_title"]=>
  string(79) "Heterogeneous treatment effects in Ankylosing spondylitis: an IPD meta-analysis"
  ["project_narrative_summary"]=>
  string(634) "Patients with ankylosing spondylitis respond variably to therapy. Identifying the right drug improves outcomes and avoids ineffective treatment. Estimating heterogeneous treatment effects (HTEs) or conditional average treatment effects (CATEs) from large datasets enables personalized therapy. We propose using a phenomapping-based tool to visualize treatment effects and identify HTE drivers. This allows mapping patients into dissimilarity spaces and predicting drug response with machine learning. Insights into subphenotypes may further guide studies on mechanisms such as pharmacokinetics, pharmacodynamics, or cytokine profiles."
  ["project_learn_source"]=>
  string(12) "scien_public"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(9) "Zhi-Qiang"
    ["last_name"]=>
    string(5) "Zhong"
    ["degree"]=>
    string(2) "MD"
    ["primary_affiliation"]=>
    string(28) "Shanghai Changzheng Hospital"
    ["email"]=>
    string(16) "570866847@qq.com"
    ["state_or_province"]=>
    string(8) "Shanghai"
    ["country"]=>
    string(5) "China"
  }
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    ["value"]=>
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    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
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  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1523) "Background: Ankylosing spondylitis (AS) exhibits significant heterogeneity in disease manifestation and treatment response, with approximately 30-50% of patients showing inadequate response to current therapies. Despite established treatment benefits, evidence suggests improvements are primarily driven by small patient subgroups.

Objective: To estimate and visualize individualized treatment effects of TNF inhibitors (TNFi) versus placebo in AS patients, and identify phenotypically similar subgroups with differential treatment responses.

Study Design: Secondary analysis of randomized controlled trials data using machine learning approaches, including phenotype mapping through uniform manifold approximation and projection (UMAP) and individualized hazard ratio calculations.

Participants: Adult patients diagnosed with active ankylosing spondylitis by modified New York criteria, randomized to either TNFi (adalimumab, certolizumab, etanercept, golimumab, or infliximab) or placebo control groups.

Primary and Secondary Outcome Measure(s): Primary outcome is achieving ASDAS Major Improvement (decrease ≥2.0 units) at week 12. Secondary outcomes include ASDAS-CII, ASDAS-ID, ASAS20, ASAS40, ASAS5/6, ASAS-PR, and BASDAI 50 at week 12.

Statistical Analysis: We will implement phenotype mapping using Gower's distance, individualized hazard ratio calculations through weighted Cox regression, and XGBoost prediction model development with SHAP analysis for feature interpretation."
  ["project_brief_bg"]=>
  string(3182) "Ankylosing spondylitis (AS) is highly heterogeneous in terms of manifestation[1] and response to standard treatment[2]. Approximately 1 in 2 to 3 patients did not respond to current therapy adequately[3], one of the main reasons underlying drug switching or discontinuation[4], which in turn is associated with higher health-care costs[5], more rapid structural bone damage[[4]][6] and lower rate of response in subsequent trial of targeted therapy[4]. On the other hand, we should be aware that the benefit were largely seen in only small patient group that were driving the direction of the overall treatment effect, especially regarding radiographical progression[7], where individual data were presented in the form of cumulative probability[8].  And ‘spontaneous’  improvements were also observed, although less likely in the placebo-treated group[9]. These may potentially lead to over- or under-treatment at the individual level, despite established benefit and safety in the overall study group. Society recommendations based on these evidence[[10]][11] represented the ‘trial and error’ reality in AS pharmacotherapy. Understanding the heterogenous treatment effects (HTEs) and key factors driving HTEs is essential for managing complex diseases including AS, and also the implementation of prognostic and predictive enrichment, the key strategies that empowered precison medicine[Enrichment Strategies for Clinical Trials]. Although previous investigations have already revealed a long list of both clinical[[12]][13] and genetical factors(reviewed in [14]) that predict response to TNFi (Tumor necrosis factor alpha inhibitors). These studies either ignored or lacked the placebo-control group, which is necessary for inferring the counterfactual response and the “true” treatment effect. For example, baseline BASFI score, a component in calculating widely-used relative response measures such as ASAS 20, ASAS 40, is intrinsically associated with chance of improvement, independent of treatment. Ignoring placebo effect or spontaneous improvement of disease activity, which was not uncommon in AS[9] can significantly bias the results. In short, these studies failed to target the causal effect. Meanwhile, when planning the present project, we are aware of the analysis by Wang et al[12], based on similar topic and collection of requested data source. Their study also ignored the placebo group, the information of which is invaluable in the setting of RCT, as already mentioned. In the present project, we will instead estimate the causal effect of the treatment[15] leveraging the individual-participant-level data from RCTs comparing the efficacy of  TNFi versus placebo. The primary outcome is decrease of Ankylosing Spondylitis Disease Activity Score (ASDAS) ≥ 2.0 unit vs. baseline (Major improvement) at week 12. Since this target have been demonstrated to be associated with long-term remission[16], radiographical progression[17]. Other important secondary outcomes include ASDAS-Clinically important improvement (ASDAS-CII), i.e.,decrease ASDAS ≥ 1.1 units vs baseline, ASAS20, ASAS40, each of which have been associated with prognosis[18]. "
  ["project_specific_aims"]=>
  string(421) "1.Estimate and visualize individualized hazard ratios of achieving ASDAS-MI at week 12 in all included participants in the similarity space.

2.Define phenotypically similar subgroup of patients for whom the benefit of TNFi over placebo was expected to be more pronounced, less or equal.

3.Construct and cross-validate a medical risk prediction model mapping patients' baseline characteristics to their HTEs."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
  }
  ["project_purposes"]=>
  array(2) {
    [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"
    }
  }
  ["project_research_methods"]=>
  string(593) "Inclusion criteria:

	1) Adult patients diagnosed with active (defined per the trial inclusion criteria) ankylosing spondylitis by the modified New York criteria.

	2) Participants randomized to either TNFi (adalimumab, certolizumab, etanercept, golimumab, or infliximab) or placebo control-treated group.

No additional exclusion criteria will be applied as long as inclusion criteria was met.

Additionally,  studies from the Vivli platform with the following NCT ID  will be used: NCT00195819, NCT00085644, NCT00478660, NCT01114880, NCT00667355, NCT00939003.

"
  ["project_main_outcome_measure"]=>
  string(150) "Primary outcome: Achieving ASDAS-MI at week 12

Secondary outcomes: ASDAS-CII, ASDAS-ID, ASAS20, ASAS40, ASAS5/6, ASAS-PR, BASDAI 50 at week 12."
  ["project_main_predictor_indep"]=>
  string(840) "Since we will use machine learning approach, all clinically relevant baseline characteristics shared by all the included RCT with data missingness less than 10% will be candidate predictor variable after data-preprocessing(exclusion of variable with high collinearity) and feature selection:

-Demographical:

Ethnicity(Caucasian, Asian, etc.), age, gender, body mass index, weight, smoking status (current, former or never smokers)

-AS-related: HLA-B27 status (positive or negative), time since first diagnosis (disease duration), time since first symptom, medication history including prior use of TNFi, sulfasalazine, comorbidities including uveitis, enteritis or psoriasis

-Baseline disease assessment: ASDAS, BASDAI, BASFI 

-Laboratory results: C-reactive protein level,  erythrocyte sedimentation rate"
  ["project_other_variables_interest"]=>
  string(24) "No additional variables."
  ["project_stat_analysis_plan"]=>
  string(1095) "1. Data preprocessing:  we will perform exploratory data analysis to check the integrity, consistence, distribution, missingness and outliers of provided data. 

2. Phenomapping: We will follow the algorithm described by Oikonomou et al[33881513] to map each participant into a high-dimensional manifold. Briefly, based on the baseline characteristics, the Gower's distance/dissimilarity of any pair of participants will be calculated and visualized by 

uniform manifold approximation and projection (UMAP) . 

3.Individualized hazard ratios (iHR) calculation and visualization: As exemplified by Oikonomou et al's work[33881513,36307193], weighted Cox regression will be used to estimate iHR within the 

neighborhood of each individual patients. In this way, the overall distribution of iHR can be assess intuitively. 

4.Construction and validation of risk prediction model: XGBoost prediction model will be trained and tested. Feature Model agnostic SHAP (Shapley Additive exPlanations) for each baseline characteristics will be presented as SHAP summary plot."
  ["project_software_used"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(7) "rstudio"
      ["label"]=>
      string(7) "RStudio"
    }
  }
  ["project_timeline"]=>
  string(233) "1.Data preprocessing and exploratory data analysis: 1 month

2.Main analysis: 2~3 months

3.Result Interpretation and manuscript drafting: 1~2 months

4.Result dissemination (submission and peer reviews): 2~6 months"
  ["project_dissemination_plan"]=>
  string(239) "I plan to include this work in part of my PhD dissertation if permission are approved. At the same time, I'll seek to publish the results in peer-reviewed rheumatology journals such as Arthritis Research&Therapy, Rheumatology(Oxford). "
  ["project_bibliography"]=>
  string(8461) "

[1] El Maghraoui A. Extra-articular manifestations of ankylosing spondylitis: Prevalence, characteristics and therapeutic implications. European Journal of Internal Medicine 2011;22:554–60. https://doi.org/10.1016/j.ejim.2011.06.006.


[2] Maneiro JR, Souto A, Salgado E, Mera A, Gomez-Reino JJ. Predictors of response to TNF antagonists in patients with ankylosing spondylitis and psoriatic arthritis: systematic review and meta-analysis. RMD Open 2015;1:e000017. https://doi.org/10.1136/rmdopen-2014-000017.


[3] Ørnbjerg LM, Brahe CH, Askling J, Ciurea A, Mann H, Onen F, et al. Treatment response and drug retention rates in 24 195 biologic-naïve patients with axial spondyloarthritis initiating TNFi treatment: routine care data from 12 registries in the EuroSpA collaboration. Annals of the Rheumatic Diseases 2019;78:1536–44. https://doi.org/10.1136/annrheumdis-2019-215427.


[4] Glintborg B, Østergaard M, Krogh NS, Tarp U, Manilo N, Loft AGR, et al. Clinical response, drug survival and predictors thereof in 432 ankylosing spondylitis patients after switching tumour necrosis factor α inhibitor therapy: results from the Danish nationwide DANBIO registry. Annals of the Rheumatic Diseases 2013;72:1149–55. https://doi.org/10.1136/annrheumdis-2012-201933.


[5] Yi E, Dai D, Piao OW, Zheng JZ, Park Y. Health care utilization and cost associated with switching biologics within the first year of biologic treatment initiation among patients with ankylosing spondylitis. Journal of Managed Care & Specialty Pharmacy 2021;27:27–36. https://doi.org/10.18553/jmcp.2020.19433.


[6] Creemers MCW, Franssen MJAM, van ’t Hof MA, Gribnau FWJ, Putte LBA van de, Riel PLCM van. Assessment of outcome in ankylosing spondylitis: an extended radiographic scoring system. Annals of the Rheumatic Diseases 2005;64:127–9. https://doi.org/10.1136/ard.2004.020503.


[7] Torgutalp M, Rios Rodriguez V, Dilbaryan A, Proft F, Protopopov M, Verba M, et al. Treatment with tumour necrosis factor inhibitors is associated with a time-shifted retardation of radiographic spinal progression in patients with axial spondyloarthritis. Annals of the Rheumatic Diseases 2022;81:1252–9. https://doi.org/10.1136/annrheumdis-2022-222324.


[8] Landewé R, Heijde D van der. Radiographic progression depicted by probability plots: Presenting data with optimal use of individual values. Arthritis & Rheumatism 2004;50:699–706. https://doi.org/10.1002/art.20204.


[9] Wei JC-C, Zhang L-J, Huang J-X. Placebo responses in ankylosing spondylitis patients worldwide: variations and possible explanations. Expert Review of Clinical Immunology 2020;16:447–50. https://doi.org/10.1080/1744666x.2020.1748500.


[10] Ramiro S, Nikiphorou E, Sepriano A, Ortolan A, Webers C, Baraliakos X, et al. ASAS-EULAR recommendations for the management of axial spondyloarthritis: 2022 update. Annals of the Rheumatic Diseases 2023;82:19–34. https://doi.org/10.1136/ard-2022-223296.


[11] Ward MM, Deodhar A, Gensler LS, Dubreuil M, Yu D, Khan MA, et al. 2019 Update of the American College of Rheumatology/Spondylitis Association of America/Spondyloarthritis Research and Treatment Network Recommendations for the Treatment of Ankylosing Spondylitis and Nonradiographic Axial Spondyloarthritis. Arthritis & Rheumatology 2019;71:1599–613. https://doi.org/10.1002/art.41042.


[12] Wang R, Dasgupta A, Ward MM. Predicting Probability of Response to Tumor Necrosis Factor Inhibitors for Individual Patients With Ankylosing Spondylitis. JAMA Network Open 2022;5:e222312. https://doi.org/10.1001/jamanetworkopen.2022.2312.


[13] Arends S, Brouwer E, Veer E van der, Groen H, Leijsma MK, Houtman PM, et al. Baseline predictors of response and discontinuation of tumor necrosis factor-alpha blocking therapy in ankylosing spondylitis: a prospective longitudinal observational cohort study. Arthritis Research & Therapy 2011;13. https://doi.org/10.1186/ar3369.


[14] Ortolan A, Cozzi G, Lorenzin M, Galozzi P, Doria A, Ramonda R. The genetic contribution to drug response in spondyloarthritis: A systematic literature review. Frontiers in Genetics 2021;12. https://doi.org/10.3389/fgene.2021.703911.


[15] Little RJ, Rubin DB. Causal Effects in Clinical and Epidemiological Studies Via Potential Outcomes: Concepts and Analytical Approaches. Annual Review of Public Health 2000;21:121–45. https://doi.org/10.1146/annurev.publhealth.21.1.121.


[16] Sieper J, Heijde D van der, Dougados M, Brown LS, Lavie F, Pangan AL. Early response to adalimumab predicts long-term remission through 5 years of treatment in patients with ankylosing spondylitis. Annals of the Rheumatic Diseases 2012;71:700–6. https://doi.org/10.1136/annrheumdis-2011-200358.


[17] Ramiro S, Heijde D van der, Tubergen A van, Stolwijk C, Dougados M, Bosch F van den, et al. Higher disease activity leads to more structural damage in the spine in ankylosing spondylitis: 12-year longitudinal data from the OASIS cohort. Annals of the Rheumatic Diseases 2014;73:1455–61. https://doi.org/10.1136/annrheumdis-2014-205178.


[18] Molnar C, Scherer A, Baraliakos X, Hooge M de, Micheroli R, Exer P, et al. TNF blockers inhibit spinal radiographic progression in ankylosing spondylitis by reducing disease activity: results from the Swiss Clinical Quality Management cohort. Annals of the Rheumatic Diseases 2018;77:63–9. https://doi.org/10.1136/annrheumdis-2017-211544.


[19] Oikonomou EK, Van Dijk D, Parise H, Suchard MA, Lemos J de, Antoniades C, et al. A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST). European Heart Journal 2021;42:2536–48. https://doi.org/10.1093/eurheartj/ehab223.

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