Conditional moving linear regression: Modeling the recruitment process for ALLHAT

Effective recruitment is a prerequisite for successful execution of a clinical trial. ALLHAT, a large hypertension treatment trial (N = 42,418), provided an opportunity to evaluate adaptive modeling of recruitment processes using conditional moving linear regression. Our statistical modeling of recruitment, comparing Brownian and fractional Brownian motion, indicates that fractional Brownian motion combined with moving linear regression is better than classic Brownian motion in terms of higher conditional probability of achieving a global recruitment goal in 4-week ahead projections. Further research is needed to evaluate how recruitment modeling can assist clinical trialists in planning and executing clinical trials.     

Neutral faces in context: Their emotional meaning and their function

We report two studies which attempt to explain why some researchers found that neutral faces determine judgments of recognition as strongly as expressions of basic emotion, even through discrepant contextual information. In the first study we discarded the possibility that neutral faces could have an intense but undetected emotional content: 60 students' dimensional ratings showed that 10 neutral faces were perceived as less emotional than 10 emotional expressions. In Study 2 we tested whether neutral faces can convey strong emotional messages in some contexts: 128 students' dimensional ratings on 36 discrepant combinations of neutral faces or expressions with contextual information were more predictable from expressions when the contextual information consisted of common, everyday situations, but were more predictable from neutral faces when the context was an uncommon, extreme situation. In line with our hypothesis, we discuss these paradoxical findings as being caused by the salience of neutral faces in some particular contexts.This research was conducted as a part of the first author's doctoral dissertation, and was supported by a grant (PS89-022) of the Spanish DGICyT. We thank David Weston for his help in preparing the text. We also thank two anonymous reviewers for their valuable comments on a previous draft of this article.