A Correction has been published for this article in http://www3.interscience.wiley.com/cgi-bin/abstract/77001962/START The identification of changes in the recent trend is an important issue in the analysis of cancer mortality and incidence data. We apply a joinpoint regression model to describe such continuous changes and use the grid‐search method to fit the regression function with unknown joinpoints assuming constant variance and uncorrelated errors. We find the number of significant joinpoints by performing several permutation tests, each of which has a correct significance level asymptotically. Each p‐value is found using Monte Carlo methods, and the overall asymptotic significance level is maintained through a Bonferroni correction. These tests are extended to the situation with non‐constant variance to handle rates with Poisson variation and possibly autocorrelated errors. The performance of these tests are studied via simulations and the tests are applied to U.S. prostate cancer incidence and mortality rates. Copyright © 2000 John Wiley & Sons, Ltd.

Infection with the obligate intracellular protozoan Leishmania is thought to be initiated by direct parasitization of macrophages, but the early events following transmission to the skin by vector sand flies have been difficult to examine directly. Using dynamic intravital microscopy and flow cytometry, we observed a rapid and sustained neutrophilic infiltrate at localized sand fly bite sites. Invading neutrophils efficiently captured Leishmania major (L.m.) parasites early after sand fly transmission or needle inoculation, but phagocytosed L.m. remained viable and infected neutrophils efficiently initiated infection. Furthermore, neutrophil depletion reduced, rather than enhanced, the ability of parasites to establish productive infections. Thus, L.m. appears to have evolved to both evade and exploit the innate host response to sand fly bite in order to establish and promote disease.

In a mathematical approach to hypothesis tests, we start with a clearly defined set of hypotheses and choose the test with the best properties for those hypotheses. In practice, we often start with less precise hypotheses. For example, often a researcher wants to know which of two groups generally has the larger responses, and either a t-test or a Wilcoxon-Mann-Whitney (WMW) test could be acceptable. Although both t-tests and WMW tests are usually associated with quite different hypotheses, the decision rule and p-value from either test could be associated with many different sets of assumptions, which we call perspectives. It is useful to have many of the different perspectives to which a decision rule may be applied collected in one place, since each perspective allows a different interpretation of the associated p-value. Here we collect many such perspectives for the two-sample t-test, the WMW test and other related tests. We discuss validity and consistency under each perspective and discuss recommendations between the tests in light of these many different perspectives. Finally, we briefly discuss a decision rule for testing genetic neutrality where knowledge of the many perspectives is vital to the proper interpretation of the decision rule.