Proportion of sequences with inflated type I error probability.

<p>Calculations are based on <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0192065#pone.0192065.e047" target="_blank">Eq 12</a>. We set the significance level <i>α</i> = 0.05 and the selection effect <i>η</i> = <i>ρ</i> ⋅ <i>f</i>_{<i>m</i>,<i>K</i>}, where <i>f</i>_{<i>m</i>,<i>K</i>} denotes Cohen’s effect size, <i>K</i> the number of treatment groups and the number of subjects per group <i>m</i> = <i>N</i>/<i>K</i>.</p

Distribution of the type I error probability under selection bias for increasing selection effect, and different block and sample sizes.

<p>Each scenario is based on a sample of <i>r</i> = 10,000 sequences, assuming the selection effect <i>η</i> = <i>ρ</i> ⋅ <i>f</i>_{<i>m</i>,<i>K</i>} to be a proportion <i>ρ</i> of the Cohen’s size <i>f</i>_{<i>m</i>,<i>K</i>}, which depends on the group size <i>m</i> = <i>N</i>/<i>K</i> (small: <i>m</i> = 4, medium: <i>m</i> = 8, large: <i>m</i> = 32), and the number of treatment groups which are fixed at <i>K</i> = 3. The selection effect <i>η</i> increases as <i>ρ</i> ∈ {0, 1/4, 1/2, 1}. A red dot marks the mean type I error probability in each scenario. The red dashed line marks the 5% significance level. The axis range is (0, 0.25).</p

Proportion of sequences that inflate the type I error probability under selection bias for increasing selection effect, and different block and sample sizes.

<p>Each scenario is based on a sample of <i>r</i> = 10,000 sequences, assuming the selection effect <i>η</i> = <i>ρ</i> ⋅ <i>f</i>_{<i>m</i>,<i>K</i>} to be a proportion <i>ρ</i> of the Cohen’s size <i>f</i>_{<i>m</i>,<i>K</i>}, which depends on the group size <i>m</i> = <i>N</i>/<i>K</i> (small: <i>m</i> = 4, medium: <i>m</i> = 8, large: <i>m</i> = 32), and the number of treatment groups which are fixed at <i>K</i> = 3. The selection effect <i>η</i> increases as <i>ρ</i> ∈ {0, 1/4, 1/2, 1}.</p

Distribution of the type I error probability under selection bias for different biasing policies.

<p>Each scenario is based on a sample of <i>r</i> = 10,000 sequences, sample size <i>N</i> = 12 and number of treatment groups <i>K</i> = 3, assuming the selection effect <i>η</i> = <i>f</i>_4,3 = 1.07 for permuted block design (PBD). The red dashed line marks the 5% significance level.</p

Distribution of the type I error probability under selection bias for an increasing number of treatment groups, block and sample sizes.

<p>Each scenario is based on a sample of <i>r</i> = 10,000 sequences, assuming the selection effect <i>η</i> = <i>f</i>_{<i>m</i>,<i>K</i>} equal Cohen’s size <i>f</i>_{<i>m</i>,<i>K</i>}, which depends the group size <i>m</i> = <i>N</i>/<i>K</i> (small: <i>m</i> = 4, medium: <i>m</i> = 8, large: <i>m</i> = 32), and on the number of treatment groups <i>K</i> ∈ {3, 4, 6}. A red dot marks the mean type I error probability in each scenario. The red dashed line marks the 5% significance level. The axis range is (0, 0.25).</p

Example for computing the bias vector using biasing policy II in a trial with six patients and three treatment groups (<i>K</i> = 3) when the favoured treatments are .

<p>Example for computing the bias vector using biasing policy II in a trial with six patients and three treatment groups (<i>K</i> = 3) when the favoured treatments are .</p

Proportion of sequences that inflate the type I error probability under selection bias for an increasing number of treatment groups, and different block and sample sizes.

<p>Each scenario is based on a sample of <i>r</i> = 10,000 sequences, assuming the selection effect <i>η</i> = <i>f</i>_{<i>m</i>,<i>K</i>} equal Cohen’s size <i>f</i>_{<i>m</i>,<i>K</i>}, which depends the group size <i>m</i> = <i>N</i>/<i>K</i> (small: <i>m</i> = 4, medium: <i>m</i> = 8, large: <i>m</i> = 32), and on the number of treatment groups <i>K</i> ∈ {3, 4, 6}.</p

Additional file 2 of ERDO - a framework to select an appropriate randomization procedure for clinical trials

This document includes detailed tables of the sensitivity analysis. The amount of η and θ is varied from the estimate by “50% changes”, resulting in the values 0.04, 0.09 and 0.14 for ηand 0.13, 0.26 and 0.39 for θ. Further the upper limit of the 95%-CI for ηwith (-0.09; 0.26) and for θwith (-0.18; 0.71) are used as well as the point of no bias η=0, θ=0. All resulting combinations of η and θ are considered for the different randomization procedures. (PDF 74 kb

Randomization-Based Inference for Clinical Trials with Missing Outcome Data

Randomization-based inference is a natural way to analyze data from a clinical trial. But the presence of missing outcome data is problematic: if the data are removed, the randomization distribution is destroyed and randomization tests have no validity. In this paper we describe two approaches to imputing values for missing data that preserve the randomization distribution. We then compare these methods to population-based and parametric imputation approaches that are in standard use to compare error rates under both homogeneous and heterogeneous population models. We also describe randomization-based analogs of standard missing data mechanisms and describe a randomization-based procedure to determine if data are missing completely at random. We conclude that randomization-based methods are a reasonable approach to missing data that perform comparably to population-based methods.</p

Reduced astrocyte density underlying brain volume reduction in activity-based anorexia rats

<p><b>Objectives:</b> Severe grey and white matter volume reductions were found in patients with anorexia nervosa (AN) that were linked to neuropsychological deficits while their underlying pathophysiology remains unclear. For the first time, we analysed the cellular basis of brain volume changes in an animal model (activity-based anorexia, ABA).</p> <p><b>Methods:</b> Female rats had 24 h/day running wheel access and received reduced food intake until a 25% weight reduction was reached and maintained for 2 weeks.</p> <p><b>Results:</b> In ABA rats, the volumes of the cerebral cortex and corpus callosum were significantly reduced compared to controls by 6% and 9%, respectively. The number of GFAP-positive astrocytes in these regions decreased by 39% and 23%, total astrocyte-covered area by 83% and 63%. In neurons no changes were observed. The findings were complemented by a 60% and 49% reduction in astrocyte (GFAP) mRNA expression.</p> <p><b>Conclusions:</b> Volumetric brain changes in ABA animals mirror those in human AN patients. These alterations are associated with a reduction of GFAP-positive astrocytes as well as GFAP expression. Reduced astrocyte functioning could help explain neuronal dysfunctions leading to symptoms of rigidity and impaired learning. Astrocyte loss could constitute a new research target for understanding and treating semi-starvation and AN.</p