Zusammenfassungen

Ohne Zusammenfassung German translations by Monika Lütke-Daldrup.     

A modified random-effect procedure for combining risk difference in sets of 2×2 tables from clinical trials

Summary Meta-analyses of sets of clinical trials often combine risk differences from several 2×2 tables according to a random-effects model. The DerSimonian-Laird random-effects procedure, widely used for estimating the populaton mean risk difference, weights the risk difference from each primary study inversely proportional to an estimate of its variance (the sum of the between-study variance and the conditional within-study variance). Because those weights are not independent of the risk differences, however, the procedure sometimes exhibits bias and unnatural behavior. The present paper proposes a modified weighting scheme that uses the unconditional within-study variance to avoid this source of bias. The modified procedure has variance closer to that available from weighting by ideal weights when such weights are known. We studied the modified procedure in extensive simulation experiments using situations whose parameters resemble those of actual studies in medical research. For comparison we also included two unbiased procedures, the unweighted mean and a sample-size-weighted mean; their relative variability depends on the extent of heterogeneity among the primary studies. An example illustrates the application of the procedures to actual data and the differences among the results. This research was supported by Grant HS 05936 from the Agency for Health Care Policy and Research to Harvard University.     

Fast retraction of evidence in a probabilistic expert system

We present and justify a propagation algorithm to facilitate the simultaneous calculation, for every node in a probabilistic exper system of the distribution of the associated random quantity, conditional on all the evidence obtained about the remaining nodes.     

Sexual identity in the ghetto

The more things change, the worse they get.Presented April 18, 1991, Duke University, at the Department of Psychiatry's Grand Rounds, this paper condenses, integrates and updates the author's earlier publication. Acknowledgement and thanks for funding and professional sponsorship of the four research studies this paper draws from are selectively due to the Eugene and Agnes E. Meyer Foundation, the Edgar Stern Family Fund, The National Institute for Mental Health, the Baltimore-Washington Institute for Psychoanalysis and the Department of Psychiatry, Children's Hospital National Medical Center.     

AN EFFICIENCY EXPLANATION FOR WHY FIRMS SECOND SOURCE

Firms facing research costs and demand uncertainty may engage in second-sourcing, in which potential suppliers agree to pool production facilities. I show how sellers and buyers both can benefit from the practice. Second-sourcing allows firms to meet a wider range of possible rates of demand and often to supply a given rate of demand at a lower total cost than under non-cooperation. Buyers benefit through a reduced probability of stock-outs and frequently a lower purchase price. Semiconductor industry data are found to be consistent with the paper's predictions.     

Estimation of parameters for rounded data from non-normal distributions

This paper is concerned with how standard estimation procedures perform in terms of eficiency for non-normal rounded data. Previous research has shown that the loss in eficiency due to rounding normal data is small. However, evidence from the non-normal distribution considered in this paper suggests, if rounding is coarse or the distribution is very skewed the loss in efficiency due to rounding can be considerable.