plans.txt

Sent to Kathy Hartmann 2015-06-09 for NCATS X02 proposal to host a
regional Clinical and Translational Scientist Summer Institute that
will feature a scientific focus as well as a career development
component for approximately 20-25 participants (local and traveling
from other institutions). Since the first institute would not be here
at VU until 2017, it is not crucial that a description of our interest
be exactly what ends up being taught. Rather this seems a chance to
advertise, very briefly where we have unique strengths. I can think of
ideal content from each of your areas.
-----------------------------------------------------------

Frank Harrell Jr, Chair of the Department of Biostatistics, Vanderbilt School of Medicine, along with some of his colleagues, have created a course and comprehensive notes entitled Biostatistics for Biomedical Research.  This is an intensive 3-5 day course that can also be given as a regular semester course.  The purpose of the course is to expose biomedical researchers to modern biostatistical methods, highlighting those methods that make fewer assumptions, including nonparametric statistics and robust statistical measures.  In addition to covering traditional estimation and inferential techniques, the course includes several components that have been increasingly important in the past few years, including challenges of high-dimensional data analysis, modeling for observational treatment comparisons, analysis of differential treatment effect (heterogeneity of treatment effect), statistical methods for biomarker research, medical diagnostic research, and methods for reproducible research.


Course notes are available at http://biostat.mc.vanderbilt.edu/ClinStat.

 

----------------------------- Twitter Suggestions for Webcast Topics

It's intended both for consumers of research (understand study design,
stat methods, stat interpretation) and those doing research (slightly
more so for those who work with statisticians).  Hope that
statisticians also enjoy it.


Covariate adjustment in RCTs and writing stat plans for same
Prediction and decision making in medicine
graphics
interactions
power calculation
RCT Table
Most common errors in RCT interpretation

Confounders, variable selection, multicollinearity, the use of data
transformation, inferences vs prediction

simulation based power calculations and use of AUC/C-statistic to assess predictive utility of models (esp how NOT to interpret them/misuse).

measurement error and misclassifiction in covariates


you might want to feature studies in the news at that moment and give verdicts on their methods, and conclusions...


https://t.co/3sqpRjjYqo