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1:00 pm Registration
1:30 pm Invited Speaker: Paul Rathouz
University of Chicago
Missing Covariate Data in Matched Case-Control Studies
We consider the problem of highly-stratified or matched studies with a binary outcome that are analyzed using conditional logistic regression (CLR). We assume that data on some covariates are missing for some study participants and illustrate the problem with an example data set. Existing CLR methods for this problem involve either modeling the distribution of missing covariates or modeling the probability of data being missing. When the missingness process is modeled, a previously proposed method did not make use of data for those records with missing covariate data except in the model for the missingness. We extend this method, embedding it in a new class of estimators that use outcome and available covariate data for all study participants. We show that a particular member of this class always has better efficiency than the previously-proposed estimator. A simulation study compares these methods with respect to efficiency and robustness to model misspecification. We then present a variation on our method for the case of missingness due to drop-out in longitudinal data analyses with fixed effects models. Time permitting, we consider the approach wherein the distribution of the missing covariate is modeled. The semiparametric efficient estimator of the regression parameters is identified, and a new estimator, which reduces dependence on the model for the missing covariate, is proposed.
2:45 pm Invited Speaker: Robert Taylor
Clemson University
Consistency and Validity of Dependent Nonparametric Bootstrap Estimators
The traditional bootstrap resamples with replacement from the original sample observations to form arrays of rowwise independent and identically distributed bootstrap random variables. There are situations, for example, when sampling from finite populations, where resampling without replacement provides a more realistic bootstrap procedure and produces dependent bootstrap random variables. The desired properties of consistency and asymptotic validity are shown to hold for certain nonparametric dependent bootstrap estimators. In addition, it is shown that the smaller variation in dependent bootstrap estimators can be used to increase precision in some of the estimates even in the traditional i.i.d. setting.
4:00pm Student Paper Competition:
5:30 pm Election of Officers
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