Research Program

1
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 Grays 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