Exploratory randomized controlled trial evaluating the impact of a waiting list control design

BACKGROUND Employing waiting list control designs in psychological and behavioral intervention research may artificially inflate intervention effect estimates. This exploratory randomized controlled trial tested this proposition in a study employing a brief intervention for problem drinkers, one domain of research in which waiting list control designs are used. METHODS All participants (N = 185) were provided with brief personalized feedback intervention materials after being randomly allocated either to be told that they were in the intervention condition and that this was the intervention or to be told that they were in the waiting list control condition and that they would receive access to the intervention in four weeks with this information provided in the meantime. RESULTS A total of 157 participants (85%) were followed-up after 4 weeks. Between-group differences were found in one of four outcomes (proportion within safe drinking guidelines). An interaction was identified between experimental manipulation and stage of change at study entry such that participant change was arrested among those more ready to change and told they were on the waiting list. CONCLUSIONS Trials with waiting list control conditions may overestimate treatment effects, though the extent of any such bias appears likely to vary between study populations. Arguably they should only be used where this threat to valid inference has been carefully assessed.During the conduct of this research, John Cunningham was supported as the Canada Research Chair on Brief Interventions for Addictive Behaviours. Kypros Kypri is supported by a National Health & Medical Research Council Senior Research Fellowship (APP1041867) and a Senior Brawn Fellowship from the University of Newcastle Jim McCambridge is supported by a Wellcome Trust Research Career Development fellowship in Basic Biomedical Science (WT086516MA)

The Impact of asking about interest in free nicotine patches on smoker's stated intent to change: real effect or artefact of question ordering?

INTRODUCTION Stage of change questions are often included on general population surveys to assess the proportion of current smokers intending to quit. The current study reported on a methodological experiment to establish whether participant's self-reported stage of change can be influenced by asking about interest in free nicotine patches immediately prior to asking about intent to change. METHODS As part of an ongoing random digit dialing survey, a randomized half of participants were asked if they would be interested in receiving nicotine patches to help them quit smoking prior to being asked whether they intended to quit smoking in the next 6 months and 30 days. RESULTS Participants who were first asked about interest in free nicotine patches were more likely to rate themselves as in preparation for change (asked first = 33%; not asked first = 19%), and less likely to rate themselves as in the precontemplation stage of change (asked first = 34%; not asked first = 47%), compared with participants who were not asked about their interest in free nicotine patches prior to being asked about their stage of change (P < .001). CONCLUSIONS There are several possible explanations of the results. It is possible that offers of free nicotine patches increases smokers intentions to quit, at least temporarily. Alternatively, smokers being asked about interest in free nicotine patches may expect that the researchers would like to hear about people intending to quit, and respond accordingly.This research is funded by the Canadian Institutes of Health Research (CIHR) grant #: MOP 111209

Preconditioning Kernel Matrices

The computational and storage complexity of kernel machines presents the primary barrier to their scaling to large, modern, datasets. A common way to tackle the scalability issue is to use the conjugate gradient algorithm, which relieves the constraints on both storage (the kernel matrix need not be stored) and computation (both stochastic gradients and parallelization can be used). Even so, conjugate gradient is not without its own issues: the conditioning of kernel matrices is often such that conjugate gradients will have poor convergence in practice. Preconditioning is a common approach to alleviating this issue. Here we propose preconditioned conjugate gradients for kernel machines, and develop a broad range of preconditioners particularly useful for kernel matrices. We describe a scalable approach to both solving kernel machines and learning their hyperparameters. We show this approach is exact in the limit of iterations and outperforms state-of-the-art approximations for a given computational budget