Graduate Student ENAR Presentations
March 15, 2011 @ 3:30 pm - 4:30 pmLocation: 701 Blockley Hall
Biostatistics
Elizabeth Handorf
Title: A sensitivity analysis for the treatment effect on cost with unmeasured
confounding
Abstract: Observational studies can be used to compare costs for treatment
alternatives, but the estimated treatment effect is subject to bias from
confounding. Even after adjustment for all known covariates, the results may
still be subject to bias from unmeasured confounders. It is therefore advisable
to assess the sensitivity of the treatment effect to various hypothesized
unmeasured confounders. In some cases, closed-form relationships exist between
the true and observed treatment effects. The investigator may use this
relationship to adjust the estimated effect and corresponding confidence
intervals for hypothesized distributions of the unknown confounder. We show how
this adjustment can be used with cost models, and derive the adjustment for
confounders that follow the Poisson and Gamma distributions. We assess the
performance of the adjustment for cost data using simulation studies assuming a
range of potential distributions of the confounder, and apply it to costs
derived from SEER-Medicare for a stage II/III muscle-invasive bladder cancer
cohort. We evaluate the costs for radical cystectomy versus combined
radiation/chemotherapy, and find that the significance of the treatment effect
is insensitive to unmeasured Bernoulli and Gamma confounders.
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Victoria Gamerman
Poster Title: Parametric and Non-Parametric Methods for Estimating Conditional
Survival
Abstract: There is an extensive body of literature in clinical oncology
on conditional survival (CS) and on differential CS both over time and between
groups. These papers focus on estimates of five-year CS, for example, for
increasing patient survival time post-diagnosis. The statistical properties of
estimators of CS, required for appropriate statistical inference, have not been
studied. In this study, we compare the statistical properties of CS estimates
using non-parametric and parametric methods. Non-parametric CS estimators are
obtained from the survival distribution estimated using the Kaplan-Meier method
and the parametric estimators are based on maximum likelihood theory assuming
an underlying Weibull distribution. We developed estimators for the variances
and covariances among the CS estimates required for multivariate analysis.
Finally, we use our proposed methodology to evaluate changes in CS over time
for patients with melanoma using survival data from the National Cancer
Institute's Surveillance, Epidemiology and End Results (SEER) registry.
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Lola Luo
Poster Title:
Comparison of Non-linear vs. linear models in
detecting disease-modifying effects in Alzheimer's Trials
Abstract: Alzheimer's is a brain disease that
causes problems with memory, thinking and behavior. Currently,
drugs that available on the markets for people who suffer
from Alzheimer's disease only approved explicitly to treat the symptoms
but not
the underlying disease progression. This presentation will compare
non-linear vs. linear models in detecting
the disease-modifying(DM) effect in Alzheimer's trials. A nonlinear
model which is adopted from
Ploeger B. and Holford N.(1) is used to generate the data. Non-linear
models are from Ploeger B.
and Holford N. (1) and Bhattaram V. et al (2). Linear, cLDA, model is
from Liang and Zeger,(4). A delayed-start trial is used and three
types of dropout rates are also implemented in combination with three
different
sample sizes. Simulation results
show that non-linear models generally perform better in terms of power
and bias
in the estimate of the DM effect but may nevertheless have elevated
type I
error rates. They may also fail to
converge, even when the assumed model is correct with starting
parameters set
to true values. The cLDA model,
although typically biased, achieves near-nominal type I error rates, is
simple
to implement, and has no problems with convergence. Thus the cLDA model
appears to be a good candidate for the
detection of DM effects.
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Matthew White
Poster Title: Adjustment for Measurement Error in Evaluating Diagnostic Markers
Abstract: Repeated measurements of a biomarker vary within a subject due to
measurement error. Sources of measurement error include variability within
individual over time and variability in the test. A naive approach ignores the
error, biasing the sensitivity and specificity of the test and giving the
erroneous impression that the biomarker is not effective. We propose
bias-correction approaches for estimating sensitivity, specificity as well as
positive and negative predictive values when the test is subject to measurement
error. We derive their asymptotic properties. We then perform simulations to
compare our approaches to naive approaches in estimates of sensitivity,
specificity as well as positive and negative predictive values. The proposed
methods have broad biomedical applications (e.g., renal disease, Alzheimer's
disease) and are illustrated using a biomarker study in Alzheimer's disease.
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Matthew Davis
Poster Title:
Using SAS for Calculation of Prentice Constraints for GEE Analysis of Binary Data
Abstract: It is well known that in GEE analysis of binary data, that the correlations should satisfy additional constraints. We describe the constraints in general and present simplified versions for a logistic model. We then demonstrate our SAS macro that can be used to calculate the constraints. We recommend routine application of this macro after implementation of PROC GENMOD in GEE.
