Showing papers by "Donald B. Rubin published in 1985"

Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score

Abstract: Matched sampling is a method for selecting units from a large reservoir of potential controls to produce a control group of modest size that is similar to a treated group with respect to the distribution of observed covariates. We illustrate the use of multivariate matching methods in an observational study of the effects of prenatal exposure to barbiturates on subsequent psychological development. A key idea is the use of the propensity score as a distinct matching variable.

The Bias Due to Incomplete Matching

Abstract: Observational studies comparing groups of treated and control units are often used to estimate the effects caused by treatments. Matching is a method for sampling a large reservoir of potential controls to produce a control group of modest size that is ostensibly similar to the treated group. In practice, there is a trade-off between the desires to find matches for all treated units and to obtain matched treated-control pairs that are extremely similar to each other. We derive expressions for the bias in the average matched pair difference due to the failure to match all treated units--incomplete matching, and the failure to obtain exact matches--inexact matching. A practical example shows that the bias due to incomplete matching can be severe, and moreover, can be avoided entirely by using an appropriate multivariate nearest available matching algorithm, which, in the example, leaves only a small residual bias due to inexact matching.

Estimation and Hypothesis Testing in Finite Mixture Models

Abstract: SUMMARY Finite mixture models are a useful class of models for application to data. When sample sizes are not large and the number of underlying densities is in question, likelihood ratio tests based on joint maximum likelihood estimation of the mixing parameter, X, and the parameter of the underlying densities, 0, are problematical. Our approach places a prior distribution on X and estimates 0 by maximizing the likelihood of the data given 0 with X integrated out. Advantages of this approach, computational issues using the EM algorithm and directions for further work are discussed. The technique is applied to two examples.

Discussion of “on State Education Statistics”: A Difficulty with Regression Analyses of Regional Test Score Averages

Abstract: The Department of Education’s table “State Education Statistics” reports mean test scores by state and mean resource inputs by state. The means are calculated from quite different groups of students, a process we callinconsistent aggregation. We investigate the bias in regression coefficients caused by inconsistent aggregation, first using theoretical calculations, and then by artificially aggregating data from the High School and Beyond sample.

Isoelectric focusing and immunofixation of cerebrospinal fluid oligoclonal IgG in neurological disorders.

Abstract: Coded samples of cerebrospinal fluid (CSF) from 112 patients with a variety of neurologic disorders were investigated for oligoclonal IgG bands by isoelectric focusing followed by immunofixation. All 16 patients with probable multiple sclerosis had oligoclonal bands. All four patients with paraneoplastic syndromes having multifocal nervous system involvement also had bands, whereas five patients with lupus, three having multifocal nervous system involvement, did not show any oligoclonal bands in the CSF. This technique is highly sensitive in detecting bands in MS, whereas other disorders that produce demyelination showed various levels of sensitivity.