72 research outputs found

    Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials

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    Abstract Background Recommendations for statistical methods in rare disease trials are scarce, especially for cross-over designs. As a result various state-of-the-art methodologies were compared as neutrally as possible using an illustrative data set from epidermolysis bullosa research to build recommendations for count, binary, and ordinal outcome variables. For this purpose, parametric (model averaging), semiparametric (generalized estimating equations type [GEE-like]) and nonparametric (generalized pairwise comparisons [GPC] and a marginal model implemented in the R package nparLD) methods were chosen by an international consortium of statisticians. Results It was found that there is no uniformly best method for the aforementioned types of outcome variables, but in particular situations, there are methods that perform better than others. Especially if maximizing power is the primary goal, the prioritized unmatched GPC method was able to achieve particularly good results, besides being appropriate for prioritizing clinically relevant time points. Model averaging led to favorable results in some scenarios especially within the binary outcome setting and, like the GEE-like semiparametric method, also allows for considering period and carry-over effects properly. Inference based on the nonparametric marginal model was able to achieve high power, especially in the ordinal outcome scenario, despite small sample sizes due to separate testing of treatment periods, and is suitable when longitudinal and interaction effects have to be considered. Conclusion Overall, a balance has to be found between achieving high power, accounting for cross-over, period, or carry-over effects, and prioritizing clinically relevant time points

    The need for robust critique of arts and health research: Dance‚Äźmovement therapy, girls, and depression

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    We examine a highly cited randomized controlled trial on dance-movement therapy with adolescent girls with mild depression and examine its treatment in 14 evidence reviews and meta-analyses of dance research. We demonstrate substantial limitations in the trial which seriously undermine the conclusions reached regarding the effectiveness of dance movement therapy in reducing depression. We also show that the dance research reviews vary substantially in their treatment of the study. Some reviews provide a positive assessment of the study and take its findings at face value without critical commentary. Others are critical of the study, identifying significant limitations, but showing marked differences in Cochrane Risk of Bias assessments. Drawing on recent criticisms of systematic reviewing and meta-analysis, we consider how reviews can be so variable and discuss what is needed to improve the quality of primary studies, systematic reviews, and meta-analyses in the field of creative arts and health

    Predictors of pre-European deforestation on Pacific islands:a re-analysis using modern multivariate non-parametric statistical methods

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    The many different islands of the Pacific provide a natural experiment that is ideal for studying predictors affecting plant cover and habitat-type. Previous research into pre-European deforestation and forest replacement across the Pacific islands detected multiple significant environmental and cultural variables. Here we re-analyse data for 67 islands using modern multivariate non-parametric statistical methods. For this analysis, neither parametric assumptions nor transformations are needed. According to our results there are congruities, but also differences from previous work. Although our analysis controls the familywise error rate, we found more relevant variables. Regarding deforestation, rainfall is the most important variable, but tephra and absolute latitude are also highly significant. Rainfall and tephra are negatively correlated with deforestation, whereas absolute latitude and deforestation have a positive correlation. Regarding forest replacement, area, dust and tephra are highly significant. These three variables are negatively correlated with the extent of forest replacement. In summary, we confirm the strong influence of environmental predictors

    Sample Size Calculation and Blinded Recalculation for Analysis of Covariance Models with Multiple Random Covariates

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    When testing for superiority in a parallel-group setting with a continuous outcome, adjusting for covariates is usually recommended. For this purpose, the analysis of covariance is frequently used, and recently several exact and approximate sample size calculation procedures have been proposed. However, in case of multiple covariates, the planning might pose some practical challenges and pitfalls. Therefore, we propose a method, which allows for blinded re-estimation of the sample size during the course of the trial. Simulations confirm that the proposed method provides reliable results in many practically relevant situations, and applicability is illustrated by a real-life data example

    Journal of Biopharmaceutical Statistics / Sample size calculation and blinded recalculation for analysis of covariance models with multiple random covariates

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    When testing for superiority in a parallel-group setting with a continuous outcome, adjusting for covariates is usually recommended. For this purpose, the analysis of covariance is frequently used, and recently several exact and approximate sample size calculation procedures have been proposed. However, in case of multiple covariates, the planning might pose some practical challenges and pitfalls. Therefore, we propose a method, which allows for blinded re-estimation of the sample size during the course of the trial. Simulations confirm that the proposed method provides reliable results in many practically relevant situations, and applicability is illustrated by a real-life data example.(VLID)471778

    Pseudo-Ranks: How to Calculate Them Efficiently in R

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    Many popular nonparametric inferential methods are based on ranks. Among the most commonly used and most famous tests are for example the Wilcoxon-Mann-Whitney test for two independent samples, and the Kruskal-Wallis test for multiple independent groups. However, recently, it has become clear that the use of ranks may lead to paradoxical results in case of more than two groups. Luckily, these problems can be avoided simply by using pseudo-ranks instead of ranks. These pseudo-ranks, however, suffer from being (a) at first less intuitive and not as straightforward in their interpretation, (b) computationally much more expensive to calculate. The computational cost has been prohibitive, for example, for large-scale simulative evaluations or application of resampling-based pseudorank procedures. In this paper, we provide different algorithms to calculate pseudo-ranks efficiently in order to solve problem (b) and thus render it possible to overcome the current limitations of procedures based on pseudo-ranks

    Prediction of Cognitive Decline in Temporal Lobe Epilepsy and Mild Cognitive Impairment by EEG, MRI, and Neuropsychology

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    Cognitive decline is a severe concern of patients with mild cognitive impairment. Also, in patients with temporal lobe epilepsy, memory problems are a frequently encountered problem with potential progression. On the background of a unifying hypothesis for cognitive decline, we merged knowledge from dementia and epilepsy research in order to identify biomarkers with a high predictive value for cognitive decline across and beyond these groups that can be fed into intelligent systems. We prospectively assessed patients with temporal lobe epilepsy (N‚ÄČ=‚ÄČ9), mild cognitive impairment (N‚ÄČ=‚ÄČ19), and subjective cognitive complaints (N‚ÄČ=‚ÄČ4) and healthy controls (N‚ÄČ=‚ÄČ18). All had structural cerebral MRI, EEG at rest and during declarative verbal memory performance, and a neuropsychological assessment which was repeated after 18 months. Cognitive decline was defined as significant change on neuropsychological subscales. We extracted volumetric and shape features from MRI and brain network measures from EEG and fed these features alongside a baseline testing in neuropsychology into a machine learning framework with feature subset selection and 5-fold cross validation. Out of 50 patients, 27 had a decline over time in executive functions, 23 in visual-verbal memory, 23 in divided attention, and 7 patients had an increase in depression scores. The best sensitivity/specificity for decline was 72%/82% for executive functions based on a feature combination from MRI volumetry and EEG partial coherence during recall of memories; 95%/74% for visual-verbal memory by combination of MRI-wavelet features and neuropsychology; 84%/76% for divided attention by combination of MRI-wavelet features and neuropsychology; and 81%/90% for increase of depression by combination of EEG partial directed coherence factor at rest and neuropsychology. Combining information from EEG, MRI, and neuropsychology in order to predict neuropsychological changes in a heterogeneous population could create a more general model of cognitive performance decline
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