Volume 2, Issue 1

There has been growing concern about ghostwriting practices in peer-reviewed biomedical journals. As a result, various proposals have been put forth to abolish or prevent the practice of ghostwriting. In this article, we review the issues underlying ghostwriting in medical research; highlight the shared responsibilities of the pharmaceutical industry and other trial sponsors, medical centers and journal editors; and propose a roadmap for an effective policy on the ethical, transparent and reliable communication of results of clinical research.

Vote-tabulation audits can be used to collect evidence that the set of winners of an election (the outcome) according to the machine count is correct — that it agrees with the outcome that a full hand count of the audit trail would show. The strength of evidence is measured by the p-value of the hypothesis that the machine outcome is wrong. Smaller p-values are stronger evidence that the outcome is correct. Most states that have election audits of any kind require audit samples stratified by county for contests that cross county lines. Previous work on p-values for stratified samples based on the largest weighted overstatement of the margin used upper bounds that can be quite weak. Sharper p-values can be found by solving a 0-1 knapsack problem. For example, the 2006 U.S. Senate race in Minnesota was audited using a stratified sample of 2–8 precincts from each of 87 counties, 202 precincts in all. Earlier work (Stark 2008b) found that the p-value was no larger than 0.042. We show that it is no larger than 0.016: much stronger evidence that the machine outcome was correct. We also give algorithms for choosing how many batches to draw from each stratum to reduce the counting burden. In the 2006 Minnesota race, a stratified sample about half as large — 109 precincts versus 202 — would have given just as small a p-value if the observed maximum overstatement were the same. This would require drawing 11 precincts instead of 8 from the largest county, and 1 instead of 2 from the smallest counties. We give analogous results for the 2008 U.S. House of Representatives contests in California.

The Early Aberration Reporting System (EARS) is used by some local health departments (LHDs) to monitor emergency room and clinic data for disease outbreaks. Using actual chief complaint data from local public health clinics, we evaluate how EARS—both the baseline system distributed by the CDC and two variants implemented by one LHD—perform at locally detecting the 2009 influenza A H1N1 pandemic. We also compare the EARS methods to a CUSUM-based method. We find that the baseline EARS system performed poorly in comparison to one of the LHD variants and the CUSUM-based method. These results suggest that changes in how syndromes are defined can substantially improve EARS performance. The results also show that incorporating algorithms that use more historical data will improve EARS performance for routine surveillance by local health departments.

The chronic widespread misuse of statistics is usually inadvertent, not intentional. We find cautionary examples in a series of recent papers by Christakis and Fowler that advance statistical arguments for the transmission via social networks of various personal characteristics, including obesity, smoking cessation, happiness, and loneliness. Those papers also assert that such influence extends to three degrees of separation in social networks. We shall show that these conclusions do not follow from Christakis and Fowler's statistical analyses. In fact, their studies even provide some evidence against the existence of such transmission. The errors that we expose arose, in part, because the assumptions behind the statistical procedures used were insufficiently examined, not only by the authors, but also by the reviewers. Our examples are instructive because the practitioners are highly reputed, their results have received enormous popular attention, and the journals that published their studies are among the most respected in the world. An educational bonus emerges from the difficulty we report in getting our critique published. We discuss the relevance of this episode to understanding statistical literacy and the role of scientific review, as well as to reforming statistics education.

The clear association between population density and partisan preference in elections suggests that redistricting plans would be better aligned with principles of partisan fairness if there were a deliberate effort to balance population density across legislative districts. To balance population density without sacrificing geometric compactness, we define a density-variation/compactness (DVC) measure that can serve as a one-number summary of a proposed redistricting plan. After analyzing voter registration data from California to guide the choice of a specific DVC measure, we evaluate its performance in both actual and hypothetical redistricting plans using election data from Texas during the past decade. Using a well-established political-science model of the relationship between legislative representation and the proportion of votes received, higher DVC scores corresponded to estimates of partisan bias with smaller magnitude across a range of redistricting scenarios; meanwhile, contrary to expectations that reduced partisan bias would be accompanied by reduced electoral responsiveness, there was no discernible pattern between DVC scores and estimates of electoral responsiveness. Although there are apt to be multiple considerations in choosing a redistricting plan, we discuss how the use of DVC measures could provide a check on attempts to introduce partisan bias into the redistricting process.

This editorial explores statistical methods to improve reproducibility and provide a more reliable foundation for trust in scientific research.

While research using cross country data and analysis is rife with statistical problems, the best papers do provide valuable insight. This commentary illustrates how an excellent paper on democratization provides a useful “stability threshold” that helps to understand current events in the Middle East, especially the Western intervention in Libya.

This note comments on the article by Alemayehu and Levenstein. It expands a bit on the work and suggests other directions the work could go in.

Alemayehu and Levenstein offer an industry perspective on ghoswriting. They propose some general principles to prevent the types of egregious cases that have been reported, but more specifics are needed to understand how to combat this serious problem.