25,594 reputation
137102
bio website biostat.mc.vanderbilt.edu/…
location Nashville, TN
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visits member for 3 years, 11 months
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I am Professor of Biostatistics and Chairman of the Department of Biostatistics at Vanderbilt University School of Medicine, Nashville TN USA. I am Associate Editor of Statistics in Medicine, a member of the Faculty of 1000 Medicine, and a member of the policy advisory board for the Journal of Clinical Epidemiology. I am a Fellow of the American Statistical Association. I am author of Regression Modeling Strategies (Springer, 2001). My specialties are development and validation of predictive models, clinical trials, observational clinical research, cardiovascular research, technology evaluation, clinical epidemiology, medical diagnostic accuracy, biomarker research, pharmaceutical safety, Bayesian methods, quantifying predictive accuracy, missing data imputation, and statistical graphics and reporting. I am a long-time user of R. In August 2014 I was given the WJ Dixon Award for Excellence in Statistical Consulting by the American Statistical Association. Among many other things, Dr Dixon was the lead developer of the first general-purpose statistical software package, BMD.


20h
comment problems in doing logistic regression with unbalanced sample, give me some references
But the magnitude of the problem is small, and ordinary maximum likelihood estimation may suffice.
Mar
26
comment How to make waffle charts in R?
All the examples seem to have a high ink:information ratio.
Mar
25
answered What model fit / predictive accuracy measure can be used to cross validate a Cox PH model with censored data?
Mar
25
answered Log likelihood function for binary classification
Mar
24
comment Getting the optimal age cut off
This will cause a great many problems and result in misleading interpretations of what are truly continuous age effects. Better is to model age as a smooth nonlinear effect using for example a regression spline.
Mar
23
awarded  Constituent
Mar
23
comment Getting the optimal age cut off
In addition, what you are saying in effect is that age has a discontinuous effect on outcome and that the decision point for one patient depends on other patients. Neither of these assumptions is correct. ROC analysis plays no role in decision making except for mass one-time decisions where utilities are unknowable.
Mar
23
answered Implementing the 0.632+ bootstrap method using the Weka Java API
Mar
18
answered The Two Cultures: statistics vs. machine learning?
Mar
17
awarded  Caucus
Mar
13
comment Terrible logistic model gives perfect results?
This is also called the Hauck-Donner effect.
Mar
13
awarded  Nice Answer
Mar
12
comment How does upsampling rare events affect the interpretation of logistic regression?
To estimate model parameters you don't want to reduce the sample size. Logistic regression works fine with a highly imbalanced $Y$ distribution.
Mar
12
comment How does upsampling rare events affect the interpretation of logistic regression?
It is confusing to see why you would do something that discards data and makes things more difficult by hurting the intercept.
Mar
9
comment Choice between different robust regressions in R
Also consider ordinal regression because it may be more robust and interpretable, and more powerful.
Mar
9
answered Schoenfeld residuals and Categorical variables with multiple categories
Mar
6
answered When should I use feature selection and when should I use dimensionality reduction techniques?
Mar
3
awarded  Enlightened
Mar
3
awarded  Nice Answer
Feb
28
comment A psychology journal banned p-values and confidence intervals; is it indeed wise to stop using them?
In symmetric situations, the standard error is a building block for a confidence interval. But in many cases the correct confidence interval is asymmetric so can't be based on a standard error at all. Some varieties of the bootstrap and back-transforming are two approaches of this type. Profile likelihood confidence intervals especially come to mind here.