Missing data in randomized clinical trials for weight loss: scope of the problem, state of the field, and performance of statistical methods.

BACKGROUND: Dropouts and missing data are nearly-ubiquitous in obesity randomized controlled trails, threatening validity and generalizability of conclusions. Herein, we meta-analytically evaluate the extent of missing data, the frequency with which various analytic methods are employed to accommodate dropouts, and the performance of multiple statistical methods. METHODOLOGY/PRINCIPAL FINDINGS: We searched PubMed and Cochrane databases (2000-2006) for articles published in English and manually searched bibliographic references. Articles of pharmaceutical randomized controlled trials with weight loss or weight gain prevention as major endpoints were included. Two authors independently reviewed each publication for inclusion. 121 articles met the inclusion criteria. Two authors independently extracted treatment, sample size, drop-out rates, study duration, and statistical method used to handle missing data from all articles and resolved disagreements by consensus. In the meta-analysis, drop-out rates were substantial with the survival (non-dropout) rates being approximated by an exponential decay curve (e(-lambdat)) where lambda was estimated to be .0088 (95% bootstrap confidence interval: .0076 to .0100) and t represents time in weeks. The estimated drop-out rate at 1 year was 37%. Most studies used last observation carried forward as the primary analytic method to handle missing data. We also obtained 12 raw obesity randomized controlled trial datasets for empirical analyses. Analyses of raw randomized controlled trial data suggested that both mixed models and multiple imputation performed well, but that multiple imputation may be more robust when missing data are extensive. CONCLUSION/SIGNIFICANCE: Our analysis offers an equation for predictions of dropout rates useful for future study planning. Our raw data analyses suggests that multiple imputation is better than other methods for handling missing data in obesity randomized controlled trials, followed closely by mixed models. We suggest these methods supplant last observation carried forward as the primary method of analysis.

Specialized motor-driven dusp1 expression in the song systems of multiple lineages of vocal learning birds.

Mechanisms for the evolution of convergent behavioral traits are largely unknown. Vocal learning is one such trait that evolved multiple times and is necessary in humans for the acquisition of spoken language. Among birds, vocal learning is evolved in songbirds, parrots, and hummingbirds. Each time similar forebrain song nuclei specialized for vocal learning and production have evolved. This finding led to the hypothesis that the behavioral and neuroanatomical convergences for vocal learning could be associated with molecular convergence. We previously found that the neural activity-induced gene dual specificity phosphatase 1 (dusp1) was up-regulated in non-vocal circuits, specifically in sensory-input neurons of the thalamus and telencephalon; however, dusp1 was not up-regulated in higher order sensory neurons or motor circuits. Here we show that song motor nuclei are an exception to this pattern. The song nuclei of species from all known vocal learning avian lineages showed motor-driven up-regulation of dusp1 expression induced by singing. There was no detectable motor-driven dusp1 expression throughout the rest of the forebrain after non-vocal motor performance. This pattern contrasts with expression of the commonly studied activity-induced gene egr1, which shows motor-driven expression in song nuclei induced by singing, but also motor-driven expression in adjacent brain regions after non-vocal motor behaviors. In the vocal non-learning avian species, we found no detectable vocalizing-driven dusp1 expression in the forebrain. These findings suggest that independent evolutions of neural systems for vocal learning were accompanied by selection for specialized motor-driven expression of the dusp1 gene in those circuits. This specialized expression of dusp1 could potentially lead to differential regulation of dusp1-modulated molecular cascades in vocal learning circuits.

Delay activity of saccade-related neurons in the caudal dentate nucleus of the macaque cerebellum.

The caudal dentate nucleus (DN) in lateral cerebellum is connected with two visual/oculomotor areas of the cerebrum: the frontal eye field and lateral intraparietal cortex. Many neurons in frontal eye field and lateral intraparietal cortex produce "delay activity" between stimulus and response that correlates with processes such as motor planning. Our hypothesis was that caudal DN neurons would have prominent delay activity as well. From lesion studies, we predicted that this activity would be related to self-timing, i.e., the triggering of saccades based on the internal monitoring of time. We recorded from neurons in the caudal DN of monkeys (Macaca mulatta) that made delayed saccades with or without a self-timing requirement. Most (84%) of the caudal DN neurons had delay activity. These neurons conveyed at least three types of information. First, their activity was often correlated, trial by trial, with saccade initiation. Correlations were found more frequently in a task that required self-timing of saccades (53% of neurons) than in a task that did not (27% of neurons). Second, the delay activity was often tuned for saccade direction (in 65% of neurons). This tuning emerged continuously during a trial. Third, the time course of delay activity associated with self-timed saccades differed significantly from that associated with visually guided saccades (in 71% of neurons). A minority of neurons had sensory-related activity. None had presaccadic bursts, in contrast to DN neurons recorded more rostrally. We conclude that caudal DN neurons convey saccade-related delay activity that may contribute to the motor preparation of when and where to move.