Book Review

Quantitative Methods in Law represents the efforts of one legal scholar to apply mathematical probability and statistics to the solution of a wide range of legal problems. Michael O. Finkelstein has republished in book form a collection of his articles, beginning with his most famous and most widely cited: the application of mathematical probability to jury discrimination cases. After leading the reader through a series of fascinating applications of statistical problem solving to an impressively wide range of legal situations, the book concludes with the final words of one of the most engaging battles among legal scholars in recent years: the exchange between Michael Finkelstein and Laurence Tribe on the use of Bayes\u27 theorem in a criminal trial to assist the jury in integrating probabilistic evidence with nonnumerical testimony

Let the Damages Fit the Wrong: An Immodest Proposal for Reforming Personal Injury Damages

Rather than comment on the wisdom of piecemeal reform, this article questions the premise of compensatory damages and takes the position that make-whole recovery is an unnecessary consequence of liability and does not necessarily achieve just results...I propose that civil damages should fit the wrong.6 Compensatory damages should abandon the make-whole premise and be measured by three factors: the degree of the wrongfulness of the tort, the severity of the harm, and the extent to which the risky conduct was directed at the plaintiff—which I call connectedness

Book Review: The Burden of Brown. by Raymond Wolters.

Book review: The Burden of Brown. By Raymond Wolters. Knoxville: The University of Tennessee Press. 1984. Pp. 346. Reviewed by: Elaine W. Shoben

Book Review: The Burden of Brown. by Raymond Wolters.

Book review: The Burden of Brown. By Raymond Wolters. Knoxville: The University of Tennessee Press. 1984. Pp. 346. Reviewed by: Elaine W. Shoben

Estimates of Information Growth in Longitudinal Clinical Trials

In group sequential clinical trials, it is necessary to estimate the amount of information present at interim analysis times relative to the amount of information that would be present at the final analysis. If only one measurement is made per individual, this is often the ratio of sample sizes available at the interim and final analyses. However, as discussed by Wu and Lan (1992), when the statistic of interest is a change over time, as with longitudinal data, such an approach overstates the information. In this paper, we discuss other problems that can result in overestimating the information, such as heteroscedasticity and correlated observations. We demonstrate that when using an inefficient estimator on unbalanced data, the true information growth can be nonmonotonic across interim analyses

Using Trends in Biometric Data to Predict Interest in Enrolling in an Employer-Sponsored National Diabetes Prevention Program Focusing on Diet and Exercise: A Retrospective Cohort Study

Background: Evidence-based lifestyle programs including the Diabetes Prevention Program can delay an individual’s risk of developing type 2 diabetes. Identifying which individuals are less likely to enroll in these programs and tailoring recruitment approaches to encourage participation among those with perceived barriers is an effective strategy to increase engagement in health promotion. This study aimed to identify the pre-enrollment differences in biometric trends between individuals with prediabetes who did and did not express interest in free worksite diabetes prevention programs.Subjects and Method: This retrospective cohort study was conducted among individuals in the Midwest enrolled in a private insurance plan from 2011 to 2014. Data was combined from annual biometric screenings and a health survey. Demographic characteristics were summarized for the study population (n=2,066). The dependent variable for this study was interest in the DPP, while the independent variables included body mass index, waist circumference, body weight, lipid measurements, and blood pressure. Linear mixed models with random intercepts were used to compare bio-metric trajectories for body mass index, waist circumference, body weight, lipid measurements (triglycerides and cholesterol), and blood pressure for the two groups.Results: No differences were observed in biometric trends for those who did and did not choose to enroll in the free worksite program.Conclusion: Examining pre-enrollment biometric trend data is a relatively novel approach to evaluating engagement in health programs. More research is needed to understand how this information can be used to identify an individual’s interest in enrolling in health programming

Discussions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/32164/1/0000219.pd

Change of Art

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