The early, primary and secondary efficacy success rates were 94.8%, 98.5% and 91.6%, respectively, for the device group, and 96.1%, 100% and 89.0% for the surgical group (all p > 0.05). The complication rate was 7.2% for the device group and 24.0% for the surgical group (p < 0.001). The mean length of hospital stay was 1.0 +/- 0.3 day for the device group and 3.4 +/- 1.2 days for the surgical group (p < 0.001). Mortality was 0% for both groups. The early, primary and secondary efficacy success rates for surgical versus. device closure of ASD were not statistically different; however, the complication rate was lower and the length of hospital stay was shorter for device closure than for surgical repair. Appropriate patient selection is an important factor for successful device closure. Transcatheter closure of secundum ASD using the ASO is a safe and effective alternative to surgical repair.

A traditional way to design a binary response experiment is to design the experiment to be most efficient for a best guess of the parameter values. A design which is optimal for a best guess however may not be efficient for parameter values close to that best guess. We propose designs which formally account for the prior uncertainty in the parameter values. A design for a situation where the best guess has substantial uncertainty attatched to it is very different from a design for a situation where approximate values of the parameters are known.We derive a general theory for concave design criteria for non-linear models and then apply the theory to logistic regression. Two numerical algorithms are described, an algorithm using the Nelder and Mead version of the simplex algorithm, which requires that the number of design points be specified, and an algorithm like the Wynn-Federov algorithm for linear design. The Wynn-Federov algorithm is much the slower of the two. Designs found by the Nelder-Mead algorithm are examined for a range of prior distributions and a range of criteria. The theoretical results are used to verify that the designs are indeed optimal~ A general finding is that as the uncertainty in the prior distribution increases so does the number of support points in the optimal design.

Traditional discussions of goodness-of-fit tests for multinomial data consider asymptotic chi-squared properties under the assumption that all expected cell frequencies become large. However, this condition is not always satisfied and other asymptotic theories must be considered. For testing a specified simple hypothesis, Morris gave conditions for the asymptotic normality of the Pearson and likelihood ratio statistics when both the sample size and number of cells become large (even if •the expected cell frequencies remain small). Monte Carlo techniques are used to examine the applicability of the normal approximations for moderate sample sizes with moderate numbers of cells.

No abstract

Traditional discussions of goodness-of-fit tests for multinomial data consider asymptotic chi-squared properties under the assumption that all expected cell frequencies become large. However, this condition is not always satisfied and other asymptotic theories must be considered. For testing a specified simple hypothesis, Morris gave conditions for the asymptotic normality of the Pearson and likelihood ratio statistics when both the sample size and number of cells become large (even if •the expected cell frequencies remain small). Monte Carlo techniques are used to examine the applicability of the normal approximations for moderate sample sizes with moderate numbers of cells.