Patient Centered Adaptive Treatment Strategies Using Bayesian Causal Inference

Patient Centered Adaptive Treatment Strategies for Juvenile Idiopathic Arthritis Using Bayesian Causal Inference

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.

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".

Participants should meet the following inclusion criteria:

1. Age ≤19 years at baseline
2. Diagnosed with ployarticular course of juvenile idiopathic arthritis (pcJIA) follow the operational case definition of pcJIA presented in Table 2 of the journal article published by Ringold et al. (2014).
3. Meet new patient definition, i.e. diagnosed with pcJIA no more than 6 month at the first clinical encounter captured in the database
4. Taken DMARDs no more than 9 months after diagnosed with pcJIA

Participants will be excluded if they meet the following exclusion criteria:

1. Systematic JIA patients according to the ILAR code
2. Patients with comorbidities of inflammatory bowel disease (IBD), celiac disease, and trisomy 21.