The Wisconsin Chapter of the ASA
The Division of Biostatistics at MCW
and Visit Milwaukee
proudly present
Our Annual Meeting Program
May 31, 2024
The Baird Center
401 W. Kilbourn Avenue
Milwaukee, WI 53203
This event is free (except for parking)!
Parking available at 501 W. Kilbourn Ave.
Schedule
- 1:00pm - 1:05pm: Introductory remarks
- 1:05pm - 2:00pm: "Bayesian hospital mortality rate estimation: Calibration and standardization for public reporting" by Edward George (Universal Furniture Professor Emeritus, Dept. of Statistics, The Wharton School of the University of Pennsylvania)
- 2:05pm - 3:00pm: "Multidimensional monotonicity discovery via mBART" by Robert McCulloch (Professor of Mathematical and Statistical Sciences, Arizona State University)
- 3:00pm - 3:30pm: Social Break
- 3:30pm - 4:25pm: "Moving to a world beyond p < 0.05" by Ronald Wasserstein (Executive Director of the American Statistical Association)
- 4:25pm - 5:00pm: Q&A with panel of speakers
- 5:00pm - 6:30pm: Happy hour
1:05 - 2:00 Edward George, Universal Furniture Professor Emeritus
Department of Statistics, The Wharton School of the University of Pennsylvania
"Bayesian Hospital Mortality Rate Estimation: Calibration and
Standardization for Public Reporting"
(Joint work with Veronika Rockova, Paul Rosenbaum, Ville Satopaa and
Jeffrey Silber). Bayesian models are increasingly fit to large
administrative data sets and then used to make individualized
recommendations. In particular, Medicare's Hospital Compare (MHC)
webpage provides information to patients about specific hospital
mortality rates for a heart attack or Acute Myocardial Infarction
(AMI). MHC's recommendations have been based on a random-effects
logit model with a random hospital indicator and patient risk
factors. Except for the largest hospitals, these recommendations or
predictions are not individually checkable against data, because data
from smaller hospitals are too limited. Before individualized Bayesian
recommendations, people derived general advice from empirical studies
of many hospitals, e.g., prefer hospitals of type 1 to type 2 because
the observed mortality rate is lower at type 1 hospitals. Here we
calibrate these Bayesian recommendation systems by checking, out of
sample, whether their predictions aggregate to give correct general
advice derived from another sample. This process of calibrating
individualized predictions against general empirical advice leads to
substantial revisions in the MHC model for AMI mortality; revisions
that hierarchically incorporate information about hospital volume,
nursing staff, medical residents, and the hospital's ability to
perform cardiovascular procedures. And for the ultimate purpose of
meaningful public reporting, predicted mortality rates must then be
standardized to adjust for patient-mix variation across hospitals.
Such standardization can be accomplished with counterfactual mortality
predictions for any patient at any hospital. It is seen that indirect
standardization, as currently used by MHC, fails to adequately control
for differences in patient risk factors and systematically
underestimates mortality rates at the low volume hospitals. As a
viable alternative, we propose a full population direct
standardization which yields correctly calibrated mortality rates
devoid of patient-mix variation.
2:05 - 3:00 Robert McCulloch, Professor of Mathematical and Statistical Sciences
Arizona State University
"Multidimensional Monotonicity Discovery via mBART"
(Joint work with Hugh Chipman, Edward George and Thomas Shively). For
the discovery of a regression relationship between y and x (a vector
of p potential predictors), the flexible nonparametric nature of
Bayesian Additive Regression Trees (BART) allows for a much richer set
of possibilities than restrictive parametric approaches. To exploit
the potential monotonicity of the predictor effects, we introduce
monotonic BART (mBART), a constrained version of BART that
incorporates monotonicity with a multivariate basis of monotone
trees. When the relationship between y and x can be safely assumed to
be monotone in a particular subset of the predictors, mBART can be
used to constrain BART over those predictors to yield (i) function
estimates that are smoother and more interpretable, (ii) better
out-of-sample predictive performance and (iii) less post-data
uncertainty. However, when such monotonicity assumptions are
unavailable, mBART can still be deployed within a higher dimensional
predictor space to estimate the Jordan decomposition of the underlying
regression function into its monotone components. Deployed in this
way and coupled with variable selection, mBART provides a new approach
for the simultaneous discovery of both the increasing and decreasing
effect regions of all the predictors.
3:00 - 3:30 Social Break
3:30 - 4:25 Ronald Wasserstein, Executive Director
American Statistical Association
"Moving to a World Beyond p<0.05"
For nearly a hundred years, the concept of "statistical significance"
has been fundamental to statistics and to science. And for nearly that
long, it has been controversial and misused as well. In a completely
non-technical (and generally humorous) way, ASA Executive Director Ron
Wasserstein will explain this controversy, and say why he and others
have called for an end to the use of statistical significance as means
of determining the worth of scientific results. He will talk about why
this change is so hard for the scientific community to make, but why
it is good for science and for statistics and will point to alternate
approaches. Please note: Dr. Wasserstein will be speaking in his
capacity as an individual researcher and not in his role as Executive
Director.
4:15 - 5:00 Q&A with Panel of Speakers
5:00 - 6:30 Happy Hour
------------------------------
Rodney Sparapani, Associate Professor of Biostatistics
Vice President for the Wisconsin Chapter of the ASA
Institute for Health and Equity, Division of Biostatistics
Medical College of Wisconsin, Milwaukee Campus
------------------------------