1. Lisa A. Weissfeld Professor and Associate Chair Dept. of Biostatistics
Research Program
Lisa A. Weissfeld
Professor and Associate Chair
Dept. of Biostatistics

2. Methodology Survival Analysis
Spline-based extensions of the Cox proportional hazards model: development of estimators for correlated outcome data, estimation of survival curve, goodness-of-fit, tests of proportionality
Correlated outcome data: copula approach
Missing data: copula approach, pattern mixture model, estimating equation approach

3. Collabarotive
Critical Care Medicine: sepsis research, health services research (2 GSRs funded)
Obesity and Nutrition Research Center: behavioral trials, metabolic studies, PET studies (2 GSRs funded)
Positron Emission Tomography: Pittsburgh compound B, late life depression (2 GSRs funded)

4. Other Research funding
Cancer training grant: funds two students and has funding for one postdoc

5. Critical Care Medicine
Spline based extensions of the Cox proportional hazards model based on Gray’s model.
Application: transplant data and ICU data.
Properties of model: does not require that proportionality assumption holds.

6. Critical Care Medicine
Missing and/or truncated data
Examples: inflammatory marker data has a lower limit of detection. Most “normal” samples are at this lower limit.
Development of statistical methodology: modeling techniques for accounting for the truncation of the outcome variable in the repeated measures setting, modeling techniques that account for truncation when the variable is a covariate, modeling techniques that allow for the inclusion of multiple correlated inflammatory markers.

7. Critical Care Medicine
Missing and/or truncated data (ctd.)
Organ Failure assessment: how to handle large amounts of missing data. Examine the impact of “filling in” missing values on analyses.
Informative censoring: how do you account for informative censoring in a repeated measures analysis.

8. Critical Care Medicine
Quality of Life Analyses
Estimation of quality adjusted survival: methods in this area are different from those in cancer where there are discrete states. Missing data is also a problem with this type of data.

9. ONRC
Missing data
This is a big issue in behavioral intervention studies.
In the area of pediatric obesity, the problem is further confounded by the fact that the subjects are growing over the course of the study.
Received attention in the medical literature with an editorial in the New England Journal of Medicine

10. ONRC Missing data (ctd.)
Also a problem in smoking cessation where individuals often miss visits.
Appears as a different problem in metabolic studies, where you may sample a small portion of a large cohort (outcome-dependent sampling).

11. ONRC Definition of outcomes
Problem in pediatric obesity where many of the subjects recruited are > 95th percentile of body mass index. Need good definition of weight loss for individuals in this category.

12. PET
Development of methods for the analysis of a new ligand, Pittsburgh Compound B (PIB), which binds with amyloid
Development of discrimination rules for a diagnosis of Alzheimers disease and mild cognitive impairment from PIB results.

13. PET Statistical Issues
Development of voxel-based methods for the analysis of PIB data, particularly across modalities. Currently, there are no methods that are computationally feasible.
Development of summary measures that can be readily used to discriminate diagnosis categories.

14. PET Statistical Issues (ctd.)
Assessment of “best” parameter settings for voxel-based analyses.
Analysis of repeated PIB scans using a voxel-based approach