Joseph Lipscomb

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Expert panels are playing an increasingly important role in U.S. health policy decision making. A fundamental issue in these applications is how to synthesize the judgments of individual experts into a group judgment. In this paper we propose an approach to synthesis based on Bayesian hierarchical models, and apply it to the problem of determining physician(More)
OBJECTIVE The economic costs of hyperglycemia are substantial. Early detection would allow management to prevent or delay development of diabetes and diabetes-related complications. We investigated the economic justification for screening for pre-diabetes/diabetes. RESEARCH DESIGN AND METHODS We projected health system and societal costs over 3 years for(More)
Datasets characterized by highly non-Gaussian distributions pose interesting challenges for prediction and comparison goals. Health care expenditure data is a common example where point masses and severe skewness often complicate analyses. Parametric approaches can improve efficiency characteristics of estimators but may sacrifice robustness in the process.(More)
This paper introduces the concept of categorizing the amount and speed of a discounting procedure in order to generate well-characterized families of procedures for use in social project evaluation. Exponential discounting isolates the concepts of amount and speed into a single parameter that must be disaggregated in order to characterize nonconstant rate(More)
OBJECTIVE Although screening for diabetes and prediabetes is recommended, it is not clear how best or whom to screen. We therefore compared the economics of screening according to baseline risk. RESEARCH DESIGN AND METHODS Five screening tests were performed in 1,573 adults without known diabetes--random plasma/capillary glucose, plasma/capillary glucose(More)
Weinstein and Martin Weitzman — on an earlier draft at a seminar at Resources for the Future. Ken-neth Arrow provided valuable written comments. (through Order No. 263-MK-610514) provided partial support for preparation of this paper. Conclusions and opinions expressed are those of the authors and not of the U.S. government.
s A major limitation of making inference about treatment effect based on observational data from a non-randomized study designs is the treatment selection bias, in which the baseline characteristics of the population under one treatment could dramatically differ from the other one. If not handled properly, such sources of heterogeneity will introduce(More)
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