Showing papers by "Donald B. Rubin published in 1984"

Reducing Bias in Observational Studies Using Subclassification on the Propensity Score

Abstract: The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Previous theoretical arguments have shown that subclassification on the propensity score will balance all observed covariates. Subclassification on an estimated propensity score is illustrated, using observational data on treatments for coronary artery disease. Five subclasses defined by the estimated propensity score are constructed that balance 74 covariates, and thereby provide estimates of treatment effects using direct adjustment. These subclasses are applied within sub-populations, and model-based adjustments are then used to provide estimates of treatment effects within these sub-populations. Two appendixes address theoretical issues related to the application: the effectiveness of subclassification on the propensity score in removing bias, and balancing properties of propensity scores with incomplete data.

Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician

Abstract: A common reaction among applied statisticians is that the Bayesian statistician's energies in an applied problem must be directed at the a priori elicitation of one model specification from which an optimal design and all inferences follow automatically by applying Bayes's theorem to calculate conditional distributions of unknowns given knowns. I feel, however, that the applied Bayesian statistician's tool-kit should be more extensive and include tools that may be usefully labeled frequency calculations. Three types of Bayesianly justifiable and relevant frequency calculations are presented using examples to convey their use for the applied statistician.

Difficulties with Regression Analyses of Age-Adjusted Rates

Abstract: A common type of observational study compares population rates in several regions having differing policies in an effort to assess the effects of those policies. In many studies, particularly in public health and epidemiology, age-adjusted rates are regressed on predictor variables to give a covariance-adjusted estimate of effect; this estimate is shown to be generally biased for the appropriate regression coefficient. For familiar models, the analysis of crude rates with age as a covariate can lead to unbiased estimates, and therefore can be preferable. Several other regression methods are also considered.

Comment: Estimating the Effects Caused by Treatments

Abstract: (1984). Comment: Estimating the Effects Caused by Treatments. Journal of the American Statistical Association: Vol. 79, No. 385, pp. 26-28.

Sensitivity of Bayes Inference with Data-Dependent Stopping Rules

Abstract: It is sometimes argued that Bayesian inference is unaffected by data-dependent stopping rules. Although this can be true in a formal sense, it is likely that there will be heightened sensitivity to prior assumptions when data-dapendent rules are used rather than stopping rules that do not depend on the data. That is, there is an interaction between violations of prior assumptions and data-dependent stopping rules such that the violations have more severe consequences in repeated practice when data-dependent rules are used. We illustrate this fact in a simple example where 95% intervals are created using a flat prior when in fact the correct prior is normal with positive prior precision ρ. The coverage probabilities of the nominal 95% intervals are less tightly concentrated around .95 when data-dependent stopping rules are used, and the effect becomes stronger as ρ becomes larger.

Comment: Assessing the Fit of Logistic Regressions Using the Implied Discriminant Analysis

Abstract: (1984). Comment: Assessing the Fit of Logistic Regressions Using the Implied Discriminant Analysis. Journal of the American Statistical Association: Vol. 79, No. 385, pp. 79-80.