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Research Program. Lisa A. Weissfeld Professor and Associate Chair Dept. of Biostatistics. Methodology. Survival Analysis

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research program

Research Program

Lisa A. Weissfeld

Professor and Associate Chair

Dept. of Biostatistics

methodology
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
collabarotive
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)
other research funding
Other Research funding
  • Cancer training grant: funds two students and has funding for one postdoc
critical care medicine
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.
critical care medicine6
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.
critical care medicine7
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.

critical care medicine8
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.

slide9
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
slide10
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).
slide11
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.
slide12
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.
slide13
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.
slide14
PET
  • Statistical Issues (ctd.)
    • Assessment of “best” parameter settings for voxel-based analyses.
    • Analysis of repeated PIB scans using a voxel-based approach