University of Wisconsin-Madison

About: University of Wisconsin-Madison is a education organization based out in Madison, Wisconsin, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 108707 authors who have published 237594 publications receiving 11883575 citations.

A Group Process Model for Problem Identification and Program Planning

Abstract: This article sets forth a group process approach useful for practicing administrators charged with a program development task. More specifically, meeting formats are suggested for involving the fol...

Low-barrier hydrogen bonds and enzymic catalysis

Abstract: Formation of a short (less than 2.5 angstroms), very strong, low-barrier hydrogen bond in the transition state, or in an enzyme-intermediate complex, can be an important contribution to enzymic catalysis. Formation of such a bond can supply 10 to 20 kilocalories per mole and thus facilitate difficult reactions such as enolization of carboxylate groups. Because low-barrier hydrogen bonds form only when the pKa's (negative logarithm of the acid constant) of the oxygens or nitrogens sharing the hydrogen are similar, a weak hydrogen bond in the enzyme-substrate complex in which the pKa's do not match can become a strong, low-barrier one if the pKa's become matched in the transition state or enzyme-intermediate complex. Several examples of enzymatic reactions that appear to use this principle are presented.

Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome

Abstract: This paper proposes a simple technique for assessing the range of plausible causal con- clusions from observational studies with a binary outcome and an observed categorical covariate. The technique assesses the sensitivity of conclusions to assumptions about an unobserved binary covariate relevant to both treatment assignment and response. A medical study of coronary artery disease is used to illustrate the technique. Inevitably, the results of clinical studies are subject to dispute. In observational studies, one basis for dispute is obvious: since patients were not assigned to treatments at random, patients at greater risk may be over-represented in some treatment groups. This paper proposes a method for assess- ing the sensitivity of causal conclusions to an unmeasured patient characteristic relevant to both treatment assignment and response. Despite their limitations, observational studies will continue to be a valuable source of information, and therefore it is prudent to develop appropriate methods of analysis for them. Our sensitivity analysis consists of the estimation of the average effect of a treatment on a binary outcome variable after adjustment for observed categorical covariates and an unobserved binary covariate u, under several sets of assumptions about u. Both Cornfield et al. (1959) and Bross (1966) have proposed guidelines for determining whether an unmeasured binary covariate having specified properties could explain all of the apparent effect of a treatment, that is, whether the treatment effect, after adjustment for u could be zero. Our method has two advantages: first, Cornfield et al. (1959) and Bross (1966) adjust only for the unmeasured binary covariate u, whereas we adjust for measured covariates in addition to the unmeasured covariate u. Second, Cornfield et al. (1959) and Bross (1966, 1967) only judge whether the effect of the treatment could be zero having adjusted for u, where Cornfield et al. (1959) employ an implicit yet extreme assumption about u. In contrast, we provide actual estimates of the treatment effect adjusted for both u and the observed categorical covariates under any assumption about u. In principle, the ith of the N patients under study has both a binary response r1i that would have resulted if he had received the new treatment, and a binary response ro0 that would have resulted if he had received the control treatment. In this formulation, treatment effects are comparisons of r1i and roi, such as r1i - roi. Since each patient receives only one treatment, either rli or ro0 is observed, but not both, and therefore comparisons of rli and roi imply some degree of speculation. Treatment effects defined as comparisons of the two potential responses, r1i and roi, of individual patients are implicit in Fisher's (1953) randomization test of the sharp null