Recently announced Precision Medicine a fantastic mission to bring all the research institutions country wide to collaborate together and holistically solve the civilization’s most complex and pressing problem Cancer, employing genomics while engaging science in an integrative discipline approach.

While the Precision Medicine mission is grand and certainly requires much attention and focus; that many new tools are now available for medical research such as complex algorithms in the areas of cognitive science (data mining, deep learning, etc), bigdata processing, cloud computing, etc; **we also need efforts to arrest redundant spend and grants. **

Speaking of precision medicine such waste what an irony.

**The White House Hosts a Precision Medicine Initiative Summit**

Grand Initiative Redundant Research Grants for Same Methods

**$1,399,997** :- Study Description: * We propose to develop Bayesian double-robust causal inference methods *that are accurate, vigorous, and efficient for evaluating the clinical effectiveness of ATSs, utilizing electronic health records and registry studies, through working closely with our stakeholder advisory panel. The proposed “PCATS” R package will allow easy application of our methods without requiring R programming skills. We will assess clinical effectiveness of the expert-recommended ATSs for the pJIA patient population using a multicenter new-patient registry study design. The study outcomes are clinical responses and the health-related quality of life after a year of treatment.

**$832,703** :- * Bayesian statistical approach in contrary try to use present as well as historical trial data in a combined framework and can provide better precision for CER. *Bayesian methods also flexible in capturing subjecting prior opinion about multiple treatment options and tend to be robust. Despite these advantages, the Bayesian method for CER is underused and underdeveloped (see

*PCORI Methodology Report*, pg. 64, 2013). The primary reasons being a lack of understanding about the role, the lack of methodological development, and the unavailability of easy-to-use software to design and conduct such analysis.

**$839,943** :- * We propose to use a method of analysis called Bayes method, in which data on the frequency of a disease in a population is combined with data taken from an individual patient* (for example, the result of a diagnostic test) to calculate the chance that the patient has the disease given his or her test result. Clinicians currently use Bayes method when screening patients for disease, but we believe the utility of this methodology extends far beyond its current use.

**$535,277** Specific Aims:

- To encourage
:**Bayesian analysis of HTE**

- To develop recommendations on how to study HTE using Bayesian statistical models
- To develop a user-friendly, free, validated software for Bayesian methods for HTE analysis

2. To develop recommendations about the choice of treatment effect scale for the assessment of HTE in PCOR. The main products of this study will be:

- recommendations or guidance on how to do Bayesian analysis of HTE in PCOR
- software to do the Bayesian methods
- recommendations or guidance on choosing appropriate treatment effect scale for HTE analysis in PCOR, and
- demonstration of our products using data from large comparative effectiveness trials.