Patient Centered Adaptive Treatment Strategies Using Bayesian Causal Inference
Condition: Juvenile Idiopathic Arthritis
MeSH terms: Arthritis; Arthritis, Juvenile
Study type: Observational

Summary: 
      The best treatment plan for Juvenile Idiopathic Arthritis (JIA) is often complicated.
      Patients and clinicians often don't know what is the best treatment strategy for a given
      patient at a given time. The purpose of this study is to develop a method to analyze data in
      situations where the treatment and disease state change over time. The researchers will
      develop a web-based package that will use the methods developed in this study. The package
      will be easy to use and allow dissemination of the methods to the public.
    

Detailed description: 
      During routine clinical care, patients and physicians are often confronted with the following
      questions: "Given my (my child's) responses to the previous treatments, what is the best
      treatment option for me (my child)?" (by a patient/parent) and "What treatment should we
      recommend to patients who fail to respond to the first (or second) line of treatment?" (by a
      physician). Both questions are at the heart of patient centered outcomes research and
      clinical care, yet answers to these questions are seriously hindered by the lack of adequate
      analytic methods that appropriately take into account the fact that treatments, as well as
      the determinants of a treatment decision, vary over time during the course of the disease.
      Case in point: despite many medication options, polyarticular Juvenile Idiopathic Arthritis
      (pJIA) is often refractory, and requires better adaptive treatment strategies (ATS). Three
      ATS were recommended by a panel of experts for pJIA patients, but they need adequate analysis
      methods to evaluate and identify better ATS using observational data. Motivated by our
      patient-centered questions, and rigorously designed to evaluate the clinical effectiveness of
      patient centered adaptive treatment strategies (PCATS), the proposed method development will
      directly address: "development and dissemination of methods for adequate analysis of data in
      cases where the treatment/exposure varies over time", an area of interest identified by
      PCORI.

      Accomplishing the proposed study will provide much needed double robust Bayesian causal
      inference methods that take the challenges of analyzing large registry and electronic health
      records including model uncertainty, large dimensional covariates and the unmeasured
      confounders, into account. A web-based userfriendly analytic computational package will be
      developed to allow easy application of the proposed methods. These developments will: 1)
      immediately offer methods and computational tools for evaluating clinical effectiveness and
      informing optimal ATS, 2) in the near future, enable shared-decision making tools for
      identifying optimal PCATS at the point-of-care, and 3) eventually enable a rapid learning
      system that will facilitate optimal PCATS. This study will have an immediate impact to
      children and stakeholders of JIA and a long-term broad impact to many chronically ill
      patients. Successful completion of this project will significantly move PCORI closer to its
      mission of helping "people make better informed healthcare decisions and improve healthcare
      delivery and outcomes".
    

Observational model: Cohort
Time perspective: Retrospective
Sampling method: Non-Probability Sample
Study population: 
        Children less or equal to 19 years of age that have been diagnosed with ployarticular
        course of juvenile idiopathic arthritis.
      
Keywords: JIA; Arthritis; pcJIA; PCATS