Smith Center Fellowships and Other Education
The first of its kind in Boston, the Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology places the Division of Cardiovascular Medicine and BIDMC at the forefront of a field that will only become more critical in the years ahead as health care reform places a greater emphasis on providing high-quality, cost-effective care that is centered on the patient.
The Smith Center Fellowship
The Smith Center offers a fellowship training program for researchers interested in studying cardiovascular outcomes. The fellowship provides early career cardiologists with an opportunity to develop the expertise and skills needed to advance their portfolio in outcomes research.
The fellow will undertake formal training at Harvard T.H. Chan School of Public Health in research methodology, develop novel research ideas, apply for grants, conduct research and publish their findings. They will be expected to submit abstracts at national and international conferences, and develop relationships within the broader research community focusing on this specialized field of research in both academia and industry.
Statistical Analysis Software
As part of our research mission, we are making some statistical software available to the research community. The following text files are R programs that can be used to perform statistical analyses for the study of heterogeneity of treatment effect using data from randomized clinical trials. The statistical theories behind these programs are described in the following publications:
- Shen C, Li X, Jeong J. Estimation of treatment effect in a sub-population: an empirical Bayes approach. Journal of Biopharmaceutical Statistics. 2016;26:507-518.
- Taft L, Shen C. A non-parametric statistical test of null treatment effect in sub-populations. Journal of Biopharmaceutical Statistics. 2019;15:1-17.
Test_main.R is the main function to detect heterogeneity of treatment effect.
Test_functions.R includes all functions needed for Test_main.R.
Effect_estimation_main.R is the main function to estimate treatment effect in a subgroup.
Effect_estimation_functions.R includes all functions needed for Effect_estimation_main.R.
If you have feedback or questions regarding these programs, please email Statistical Director, Dr. Changyu Shen.
The resources below allow our cardiovascular medicine fellows to build strong conceptual foundations in statistical inference, gain familiarity with the principles of basic statistical analysis tools, and apply these principles within three major areas: randomized trials, observational studies, and prediction.
These resources focus on the philosophy of statistical inference and its applications to different analytical situations in the real world during the design and analysis stages of studies. The educational focus is on concepts (“what” and “why”) instead of implementation (“how”).
- Basic statistical concepts I (population, sampling, parameters, statistics, hypothesis testing, estimation, sampling distribution) Lecture Slides
- Basic statistical tools (univariate, bivariate and multivariate; parametric versus non-parametric) Lecture Slides
- Randomized trials (rationale, design, sample size, analysis) Lecture Slides
- Randomized trials (benefit-risk trade-off) Lecture Slides
- Observational studies (confounding, potential outcomes, design) Lecture Slides
- Causal inference (propensity score, outcome regression, matching, instrumental variable) Lecture Slides
- Predictions (prediction model, model fitting, bias-variance trade-off) Lecture Slides
- Predictions (metrics of performance, estimation of performance) Lecture Slides