We propose a multi-phase program of research to develop, validate, apply and disseminate methods for estimating and communicating individual level heterogeneity in effectiveness and safety (for a range of patient/clinician identified outcomes of interest) for non-randomized comparative effectiveness research. The research program will translate theory to practice, and produce a template for clinical decision support tools that could be used to facilitate personalized medicine and informed decision making while addressing the personal characteristics, conditions and preferences of individual patients. Recent innovations in statistical methods for analyzing randomized clinical trials involve prediction of individual patient probabilities of experiencing benefit and/or harm. During the K99 phase of the award, I will work with my mentors and collaborators as we develop methods for non-randomized comparative effectiveness research which allow us to predict heterogeneous individual level treatment effects based on a constellation of patient characteristics. During this time, I will participate in the Postdoctoral Medical Informatics Research Training program at Harvard Medical School with a focus on training related to clinical and population health informatics. I will also be an active participant in education and training on topics such as patient/stakeholder engagement through the Brigham and Women's Hospital (BWH) Center for Patient Centered Comparative Effectiveness Research (PCERC). During the R00 phase of the award, I will solicit input from patient and physician members of the Patient and Family Advisory Councils (PFAC) at BWH PCERC regarding choices between alternative lipid lowering therapeutic strategies. I will work with them to identify safety and effectiveness outcomes that are priority concerns for the stakeholder groups they represent and understand what they expect from personalized treatment information. We will apply validated methods for estimating individual level treatment heterogeneity in studies using large, diverse healthcare databases. These studies will be designed to address the patient and provider identified priority questions. There will be continuous input from patients and providers as I work on developing clinical decision support tools designed to communicate relevant evidence, using metrics that healthcare consumers understand. I will use the skills developed through the Medical Informatics Training Program and collaborate with members of the Clinical Informatics Department at BWH to implement and test an evidence-based clinical decision support tool that is compatible with the hospital informatics infrastructure. This tool could be used during the office visit to help physicians and patients discuss the expected risks and benefits that are particular to each patient at a ?critical moment?, during the office visit when they the treatment decision is being made.

Public Health Relevance

We propose a multi-phase program of research to develop, validate, apply and disseminate methods for predicting individual level heterogeneity in effectiveness and safety (for a range of patient/clinician identified outcomes of interest) for observational comparative effectiveness research. The proposed program of research will translate theory to practice, and produce a template for clinical decision support tools that could be used to facilitate personalized medicine and help patients make informed healthcare decisions based on their personal characteristics, conditions and preferences. The decision aids could be used during the office visit to help physicians and patients discuss expected risks and benefits that are particular to each patient at a ?critical moment?, when the treatment decision is being made. As more evidence on patient identified priority topics is generated, the template would be readily adaptable for communicating new evidence to patients and their providers.

Agency
National Institute of Health (NIH)
Institute
Agency for Healthcare Research and Quality (AHRQ)
Type
Research Transition Award (R00)
Project #
5R00HS022193-05
Application #
9233920
Study Section
Special Emphasis Panel (NSS)
Program Officer
Willis, Tamara
Project Start
2015-03-01
Project End
2018-02-28
Budget Start
2017-03-01
Budget End
2018-02-28
Support Year
5
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
Tian, Lu; Fu, Haoda; Ruberg, Stephen J et al. (2018) Efficiency of two sample tests via the restricted mean survival time for analyzing event time observations. Biometrics 74:694-702
Wang, Shirley V; Maro, Judith C; Baro, Elande et al. (2018) Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic. Epidemiology 29:895-903
Zhou, Meijia; Wang, Shirley V; Leonard, Charles E et al. (2017) Sentinel Modular Program for Propensity Score-Matched Cohort Analyses: Application to Glyburide, Glipizide, and Serious Hypoglycemia. Epidemiology 28:838-846
Franklin, Jessica M; Dejene, Sara; Huybrechts, Krista F et al. (2017) A Bias in the Evaluation of Bias Comparing Randomized Trials with Nonexperimental Studies. Epidemiol Methods 6:
Wang, Shirley V; Rogers, James R; Jin, Yinzhu et al. (2017) Use of electronic healthcare records to identify complex patients with atrial fibrillation for targeted intervention. J Am Med Inform Assoc 24:339-344
Kim, Dae Hyun; Uno, Hajime; Wei, Lee-Jen (2017) Restricted Mean Survival Time as a Measure to Interpret Clinical Trial Results. JAMA Cardiol 2:1179-1180
Desai, Rishi J; Glynn, Robert J; Wang, Shirley et al. (2016) Performance of Disease Risk Score Matching in Nested Case-Control Studies: A Simulation Study. Am J Epidemiol 183:949-57
Wang, Shirley V; Franklin, Jessica M; Glynn, Robert J et al. (2016) Prediction of rates of thromboembolic and major bleeding outcomes with dabigatran or warfarin among patients with atrial fibrillation: new initiator cohort study. BMJ 353:i2607
Hallas, Jesper; PottegÄrd, Anton; Wang, Shirley et al. (2016) Persistent User Bias in Case-Crossover Studies in Pharmacoepidemiology. Am J Epidemiol 184:761-769
Wang, S V; Verpillat, P; Rassen, J A et al. (2016) Transparency and Reproducibility of Observational Cohort Studies Using Large Healthcare Databases. Clin Pharmacol Ther 99:325-32

Showing the most recent 10 out of 14 publications