Treating neuropsychiatric symptoms in dementia with Lewy bodies: a randomized controlled-trial.

Sensitivity to psychotropic medications presents a therapeutic challenge when treating neuropsychiatric symptoms in patients with dementia with Lewy bodies (DLB). We compared under randomized, double-blinded conditions the tolerability and efficacy of citalopram and risperidone in the treatment of behavioral and psychotic symptoms in patients with DLB and Alzheimer disease (AD). Thirty-one participants with DLB and 66 with AD hospitalized for behavioral disturbance were treated under randomized, double-blind conditions with citalopram or risperidone for up to 12 weeks. Neuropsychiatric symptoms were assessed with the nursing home version of the Neuropsychiatric Inventory (NPI) and the Clinical Global Impression of Change (CGIC). Side effects were measured using the UKU Side Effect Rating Scale. A significantly higher proportion of participants with DLB (68%) than with AD (50%) discontinued the study prematurely. Discontinuation rates were comparable in DLB participants treated with citalopram (71%) or risperidone (65%). However, participants with DLB randomized to risperidone experienced a higher overall burden of side effects. Scores on the NPI and the CGIC worsened in DLB participants and improved in those with AD. Most patients with behavioral disturbances or psychosis associated with DLB tolerate citalopram or risperidone poorly and do not seem to benefit from either medication.

Comparisons of methods for multiple hypothesis testing in neuropsychological research.

Hypothesis testing with multiple outcomes requires adjustments to control Type I error inflation, which reduces power to detect significant differences. Maintaining the prechosen Type I error level is challenging when outcomes are correlated. This problem concerns many research areas, including neuropsychological research in which multiple, interrelated assessment measures are common. Standard p value adjustment methods include Bonferroni-, Sidak-, and resampling-class methods. In this report, the authors aimed to develop a multiple hypothesis testing strategy to maximize power while controlling Type I error. The authors conducted a sensitivity analysis, using a neuropsychological dataset, to offer a relative comparison of the methods and a simulation study to compare the robustness of the methods with respect to varying patterns and magnitudes of correlation between outcomes. The results lead them to recommend the Hochberg and Hommel methods (step-up modifications of the Bonferroni method) for mildly correlated outcomes and the step-down minP method (a resampling-based method) for highly correlated outcomes. The authors note caveats regarding the implementation of these methods using available software.

Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes.

BACKGROUND: Gene expression data frequently contain missing values, however, most down-stream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures x time series data. We then introduce two complementary selection schemes for determining the most appropriate imputation method for any given data set. RESULTS: We found that the optimal imputation algorithms (LSA, LLS, and BPCA) are all highly competitive with each other, and that no method is uniformly superior in all the data sets we examined. The success of each method can also depend on the underlying "complexity" of the expression data, where we take complexity to indicate the difficulty in mapping the gene expression matrix to a lower-dimensional subspace. We developed an entropy measure to quantify the complexity of expression matrixes and found that, by incorporating this information, the entropy-based selection (EBS) scheme is useful for selecting an appropriate imputation algorithm. We further propose a simulation-based self-training selection (STS) scheme. This technique has been used previously for microarray data imputation, but for different purposes. The scheme selects the optimal or near-optimal method with high accuracy but at an increased computational cost. CONCLUSION: Our findings provide insight into the problem of which imputation method is optimal for a given data set. Three top-performing methods (LSA, LLS and BPCA) are competitive with each other. Global-based imputation methods (PLS, SVD, BPCA) performed better on mcroarray data with lower complexity, while neighbour-based methods (KNN, OLS, LSA, LLS) performed better in data with higher complexity. We also found that the EBS and STS schemes serve as complementary and effective tools for selecting the optimal imputation algorithm.

A double-blind comparison of citalopram and risperidone for the treatment of behavioral and psychotic symptoms associated with dementia.

OBJECTIVE: To compare citalopram and risperidone for the treatment of psychotic symptoms and agitation associated with dementia, with a priori hypotheses that risperidone would be more efficacious for psychosis and citalopram for agitation. METHODS: A 12-week randomized, controlled trial in nondepressed patients with dementia hospitalized because of behavioral symptoms (N = 103) was conducted at the University of Pittsburgh Medical Center. Participants were consecutively recruited on an inpatient unit if they had at least one moderate to severe target symptom (aggression, agitation, hostility, suspiciousness, hallucinations, or delusions). Once they improved sufficiently, they were discharged to nursing homes, personal care homes, or residential homes for continued treatment. Planned pre-post and mixed model analyses of the main outcome measures of Neurobehavioral Rating Scale and Side Effect Rating Scale at baseline and at weekly/biweekly intervals were conducted. RESULTS: Completion rates did not differ for citalopram and risperidone (overall completion rate: 44%). Agitation symptoms (aggression, agitation, or hostility) and psychotic symptoms (suspiciousness, hallucinations, or delusions) decreased in both treatment groups but the improvement did not differ significantly between the two groups. There was a significant increase in side effect burden with risperidone but not with citalopram such that the two groups differed significantly. CONCLUSION: No statistical difference was found in the efficacy of citalopram and risperidone for the treatment of either agitation or psychotic symptoms in patients with dementia. These findings need to be replicated before citalopram or other serotonergic antidepressants can be recommended as alternatives to antipsychotics for the treatment of agitation or psychotic symptoms associated with dementia.