Measurement bias (MB) has been described in causal structures but is still not entirely clear. In practice, the correctness of substitution estimate (SE) of effect is a prerequisite for causal ...inference, usually based on a bidirectionally non-differential misclassification between the measured exposure and the measured outcome. Based on a directed acyclic graph (DAG), this paper proposes a structure for the single-variable measure, where its MB is derived from the choice of an imperfect, "input/output device-like" measurement system. The MB of the SE is influenced both by the measurement system itself and by factors outside the measurement system: while the independence or dependence mechanism of the measurement system still ensures that the MB of the SE is bidirectionally non-differential; however, the misclassification can be bidirectionally non-differential, unidirectionally differential, or bidirectionally differential resulted from the factors outside the measurement system. In addition, reverse causalit
Most reported associations in observational clinical research are false, and the minority of associations that are true are often exaggerated. This credibility problem has many causes, including the ...failure of authors, reviewers, and editors to recognize the inherent limitations of these studies. This issue is especially problematic for weak associations, variably defined as relative risks (RRs) or odds ratios (ORs) less than 4. Such associations, commonly reported in the medical literature, are more likely to be attributable to bias than to causal association. All observational research has bias (which can include selection, information, and confounding bias). Hence, detection of small associations falls below the discriminatory ability of observational studies. In general, unless RRs in cohort studies exceed 2 to 3 or ORs in case-control studies exceed 3 or 4, associations in observational research findings should not be considered credible. However, these guidelines are not foolproofstrong (yet spurious) associations can result when large amounts of bias are present. Only in a properly performed randomized controlled trial, free of bias, should small associations merit attention. Better training and more circumspection on the part of investigators, tougher editorial standards on the part of journals, and hefty skepticism on the part of referees and readers are necessary to avoid the dangers of false alarms, pseudo-epidemics, and their unfortunate consequences.
Despite growing recognition that attention fluctuates from moment-to-moment during sustained performance, prevailing analysis strategies involve averaging data across multiple trials or time points, ...treating these fluctuations as noise. Here, using alternative approaches, we clarify the relationship between ongoing brain activity and performance fluctuations during sustained attention. We introduce a novel task (the gradual onset continuous performance task), along with innovative analysis procedures that probe the relationships between reaction time (RT) variability, attention lapses, and intrinsic brain activity. Our results highlight 2 attentional states-a stable, less error-prone state ("in the zone"), characterized by higher default mode network (DMN) activity but during which subjects are at risk of erring if DMN activity rises beyond intermediate levels, and a more effortful mode of processing ("out of the zone"), that is less optimal for sustained performance and relies on activity in dorsal attention network (DAN) regions. These findings motivate a new view of DMN and DAN functioning capable of integrating seemingly disparate reports of their role in goal-directed behavior. Further, they hold potential to reconcile conflicting theories of sustained attention, and represent an important step forward in linking intrinsic brain activity to behavioral phenomena.
Flow cytometry (FCM) is widely used in both clinical and basic research to characterize cell phenotypes and functions. The latest FCM instruments analyze up to 20 markers of individual cells, ...producing high-dimensional data. This requires the use of the latest clustering and dimensionality reduction techniques to automatically segregate cell sub-populations in an unbiased manner. However, automated analyses may lead to false discoveries due to inter-sample differences in quality and properties.
We present an R package, flowAI, containing two methods to clean FCM files from unwanted events: (i) an automatic method that adopts algorithms for the detection of anomalies and (ii) an interactive method with a graphical user interface implemented into an R shiny application. The general approach behind the two methods consists of three key steps to check and remove suspected anomalies that derive from (i) abrupt changes in the flow rate, (ii) instability of signal acquisition and (iii) outliers in the lower limit and margin events in the upper limit of the dynamic range. For each file analyzed our software generates a summary of the quality assessment from the aforementioned steps. The software presented is an intuitive solution seeking to improve the results not only of manual but also and in particular of automatic analysis on FCM data.
R source code available through Bioconductor: http://bioconductor.org/packages/flowAI/ CONTACTS: mongianni1@gmail.com or Anis_Larbi@immunol.a-star.edu.sg
Supplementary data are available at Bioinformatics online.
AbstractMATRICS Consensus Cognitive Battery (MCCB), packaging 10 tests selected from more than 90 nominated tests, is a method developed by the Measurement and Treatment Research to Improve Cognition ...in Schizophrenia (MATRICS) group to evaluate the efficacy of treatments targeting cognitive impairments in schizophrenia. MCCB had been translated into a number of languages, but only the US and Spain had normative data reported. Inconsistency in translation and cultural differences make direct application of MCCB in China problematic. In this study, we administered the battery to a representative community sample based on Chinese population census in 2005 and obtained normative data. The effects of age, gender, education level, and scale of residence area on test performance were examined. The sample included 656 healthy volunteers from six sites in China. At each site, sample was stratified according to age, gender, and educational level, and scale of the area one was born in, grew up in and currently living in was recorded. We found age, gender, and education had significant effects on the normative data for MCCB in China, which are comparable to those found for the original standardized English version in the U.S. and the Spanish version in Spain. Remarkably, the residence scale effects on neuropsychological performance were significant, which should be taking into account when calculating the standardized T score for each subject. The practice effects were minor and test-retest reliability of MCCB was good, which suggests MCCB as an appropriate measure for clinical and research usage in China.
In this review I discuss the appropriateness of various statistical methods for use with small sample sizes. I review the assumptions and limitations of these methods and provide recommendations for ...figures and statistical tests.
ObjectiveMany published meta-analyses are underpowered. We explored the role of trial sequential analysis (TSA) in assessing the reliability of conclusions in underpowered meta-analyses.MethodsWe ...screened The Cochrane Database of Systematic Reviews and selected 100 meta-analyses with a binary outcome, a negative result and sufficient power. We defined a negative result as one where the 95% CI for the effect included 1.00, a positive result as one where the 95% CI did not include 1.00, and sufficient power as the required information size for 80% power, 5% type 1 error, relative risk reduction of 10% or number needed to treat of 100, and control event proportion and heterogeneity taken from the included studies. We re-conducted the meta-analyses, using conventional cumulative techniques, to measure how many false positives would have occurred if these meta-analyses had been updated after each new trial. For each false positive, we performed TSA, using three different approaches.ResultsWe screened 4736 systematic reviews to find 100 meta-analyses that fulfilled our inclusion criteria. Using conventional cumulative meta-analysis, false positives were present in seven of the meta-analyses (7%, 95% CI 3% to 14%), occurring more than once in three. The total number of false positives was 14 and TSA prevented 13 of these (93%, 95% CI 68% to 98%). In a post hoc analysis, we found that Cochrane meta-analyses that are negative are 1.67 times more likely to be updated (95% CI 0.92 to 2.68) than those that are positive.ConclusionsWe found false positives in 7% (95% CI 3% to 14%) of the included meta-analyses. Owing to limitations of external validity and to the decreased likelihood of updating positive meta-analyses, the true proportion of false positives in meta-analysis is probably higher. TSA prevented 93% of the false positives (95% CI 68% to 98%).
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