A Perspective on Tachibana's Eta-Squared Analyses of the Results of the Nctr Collaborative Behavioral Teratology Study

1989 ◽  
Vol 65 (3) ◽  
pp. 819-823 ◽  
Author(s):  
Charles V. Vorhees
Keyword(s):  
Small Sample ◽  
Independent Experiment ◽  
Pregnant Rats ◽  
Treatment Groups ◽  
Large Numbers ◽  
Eta Squared ◽  
Behavioral Teratology ◽  
Small Sample Sizes ◽  
Strength Of Association ◽  
Insight Into

The NCTR Collaborative Behavioral Teratology Study (CBTS) data have been reanalyzed by Tachibana (1989) using the strength of association statistic η2 as a means of examining the reliability of the data. This is an important issue, because determining the reliability of behavioral teratological measures was one of the CBTS's principal aims. The η2 approach is a useful one, but its value is critically dependent on the assumption that it is applied to the data appropriately. The CBTS was a project which involved large numbers of pregnant rats and their offspring, tested postnatally on a large number of physical and behavioral tests. The project was conducted in six laboratories and involved two experiments with four treatment groups/experiment, 16 dams/group, and 8 offspring/dam. For practical more than theoretical reasons, each of these large experiments was conducted in balanced replicates of 4 dams/group. Since this had to be done, replicate was included in the statistical models used to evaluate the data. Tachibana (1989) has performed a series of η2 analyses on the CBTS data as if each replicate constituted an independent experiment and concluded that the reproducibility of the CBTS data was not particularly good. Tachibana's η2 analyses are discussed as problematic because the data should have been analyzed as intended, by experiment ( n = 16/group), not by replicates. There are well-known problems associated with the reliability of small sample sizes in any experiment and Tachibana's conclusion, based as it is on replicates, offers little insight into the reproducibility of behavioral teratogenicity data generally or from the CBTS project specifically.

scholarly journals Non-Invasive Monitoring of Adrenocortical Activity in Three Sympatric Desert Gerbil Species

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 75
Author(s):  
Álvaro Navarro-Castilla ◽  
Mario Garrido ◽  
Hadas Hawlena ◽  
Isabel Barja
Keyword(s):  
Small Sample ◽  
Post Injection ◽  
Control Groups ◽  
Treatment Groups ◽  
Adrenocortical Activity ◽  
Non Invasive ◽  
Endocrine Status ◽  
Corticosterone Metabolites ◽  
Small Sample Sizes ◽  
Fecal Corticosterone Metabolites

The study of the endocrine status can be useful to understand wildlife responses to the changing environment. Here, we validated an enzyme immunoassay (EIA) to non-invasively monitor adrenocortical activity by measuring fecal corticosterone metabolites (FCM) in three sympatric gerbil species (Gerbillus andersoni, G. gerbillus and G. pyramidum) from the Northwestern Negev Desert’s sands (Israel). Animals included into treatment groups were injected with adrenocorticotropic hormone (ACTH) to stimulate adrenocortical activity, while control groups received a saline solution. Feces were collected at different intervals and FCM were quantified by an EIA. Basal FCM levels were similar in the three species. The ACTH effect was evidenced, but the time of FCM peak concentrations appearance differed between the species (6–24 h post-injection). Furthermore, FCM peak values were observed sooner in G. andersoni females than in males (6 h and 18 h post-injection, respectively). G. andersoni and G. gerbillus males in control groups also increased FCM levels (18 h and 48 h post-injection, respectively). Despite the small sample sizes, our results confirmed the EIA suitability for analyzing FCM in these species as a reliable indicator of the adrenocortical activity. This study also revealed that close species, and individuals within a species, can respond differently to the same stressor.

The Predicament of Large Numbers of Observations and How We Got There: Critical Review

2020 ◽  
Author(s):  
Stanley S Levinson
Keyword(s):  
Statistical Significance ◽  
Roc Curves ◽  
Small Sample ◽  
Computational Techniques ◽  
Number Needed To Treat ◽  
Clinical Effects ◽  
Statistical Parameters ◽  
Large Numbers ◽  
Classical Statistics ◽  
Small Sample Sizes

Abstract Background Classical statistics were developed in a time when small sample sizes were the norm; thus, statistical significance typically ensured large clinical effects. Over the past 10–20 years, computational techniques have allowed studies with modest effects to reach statistical significance (usually P < 0.05) by analyzing very large numbers of patients. In this review, I discuss how this came about and provide an intuitive understanding of the strengths and weaknesses of various statistical parameters that provide insight into clinical effect sizes. Content In this review of the literature, a simple web-based program was used for calculations. Examples are shown. Odds and risk ratios are compared with ROC curves to allow better understanding of their predictive value. Summary In these complex times, an intuitive understanding of statistical procedures is increasingly important. This review will attempt to advance the reader’s knowledge so that one can calculate the number needed to treat and its confidence interval, understand the meaning of a modest association, and determine when a study is likely to be accurate but with questionable clinical utility.

scholarly journals Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions

Diagnostics ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 505
Author(s):  
Jorge D. Machicado ◽  
Eugene J. Koay ◽  
Somashekar G. Krishna
Keyword(s):  
Predictive Accuracy ◽  
Quantitative Imaging ◽  
Standard Of Care ◽  
Small Sample ◽  
Cystic Lesions ◽  
Current Standard ◽  
Cross Sectional ◽  
Small Sample Sizes ◽  
Insight Into

Radiomics, also known as quantitative imaging or texture analysis, involves extracting a large number of features traditionally unmeasured in conventional radiological cross-sectional images and converting them into mathematical models. This review describes this approach and its use in the evaluation of pancreatic cystic lesions (PCLs). This discipline has the potential of more accurately assessing, classifying, risk stratifying, and guiding the management of PCLs. Existing studies have provided important insight into the role of radiomics in managing PCLs. Although these studies are limited by the use of retrospective design, single center data, and small sample sizes, radiomic features in combination with clinical data appear to be superior to the current standard of care in differentiating cyst type and in identifying mucinous PCLs with high-grade dysplasia. Combining radiomic features with other novel endoscopic diagnostics, including cyst fluid molecular analysis and confocal endomicroscopy, can potentially optimize the predictive accuracy of these models. There is a need for multicenter prospective studies to elucidate the role of radiomics in the management of PCLs.

scholarly journals A Critical Review of Multimodal Interventions for Cachexia

2020 ◽  
Author(s):  
Clare McKeaveney ◽  
Peter Maxwell ◽  
Helen Noble ◽  
Joanne Reid
Keyword(s):  
Body Composition ◽  
Sample Size ◽  
Small Sample ◽  
Cochrane Library ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Treatment Groups ◽  
Small Sample Sizes

ABSTRACT Currently, there are no standardized treatments for cachexia or severe wasting. There is a growing consensus advocating multimodal interventions to address the complex pathogenesis and metabolic alterations in these conditions. This review examined multimodal treatments intended to alleviate and/or stabilize cachexia and severe wasting. The objectives of this review were to 1) identify multimodal interventions for the treatment of cachexia or associated wasting syndromes in patients with a chronic illness, 2) assess the quality of these studies, and 3) assess the effectiveness of multimodal interventions. Electronic databases including PubMed, MEDLINE, EMBASE, Scopus, Web of Science, Cochrane Library, CINAHL, PEDro, OpenGrey, and clinicaltrials.org were systematically searched using both text words and MeSH (medical subject heading) terms. The literature revealed a dearth of large, well-conducted trials in this area. Fourteen trials (n = 5 cancer, n = 5 chronic obstructive pulmonary disease, n = 4 chronic kidney disease) were included in this review. A total of 1026 patients were included across all studies; sample size ranged between 21 and 138 patients. Baseline and follow-up data were collected between 6 wk and 24 mo. All demonstrated some improvement in favor of the treatment groups, in relevant measures of body composition, nutrition, biomarkers, and functionality; however, caution should be applied due to the heterogenous nature of the interventions and small sample sizes. Overall, the evidence from this review supports the role of multimodal interventions in the treatment of severe wasting. However, randomized controlled trials with a powered sample size and sufficiently lengthy interaction period are necessary to assess if multimodal interventions are effective forms of therapy for improving body composition and nutritional and physical status in patients with cachexia and wasting. The protocol for this review is registered with Prospero (ID: CRD42019124374).

scholarly journals Seeing distinct groups where there are none: spurious patterns from between-group PCA

2019 ◽  
Author(s):  
Andrea Cardini ◽  
Paul O’Higgins ◽  
F. James Rohlf
Keyword(s):  
Dimensional Space ◽  
Small Sample ◽  
Small Samples ◽  
Sample Sizes ◽  
Large Samples ◽  
Large Numbers ◽  
Group Variation ◽  
Small Sample Sizes ◽  
Analysis Of Variation ◽  
Dimensions Of Variation

AbstractUsing sampling experiments, we found that, when there are fewer groups than variables, between-groups PCA (bgPCA) may suggest surprisingly distinct differences among groups for data in which none exist. While apparently not noticed before, the reasons for this problem are easy to understand. A bgPCA captures the g-1 dimensions of variation among the g group means, but only a fraction of the ∑ni − g dimensions of within-group variation (ni are the sample sizes), when the number of variables, p, is greater than g-1. This introduces a distortion in the appearance of the bgPCA plots because the within-group variation will be underrepresented, unless the variables are sufficiently correlated so that the total variation can be accounted for with just g-1 dimensions. The effect is most obvious when sample sizes are small relative to the number of variables, because smaller samples spread out less, but the distortion is present even for large samples. Strong covariance among variables largely reduces the magnitude of the problem, because it effectively reduces the dimensionality of the data and thus enables a larger proportion of the within-group variation to be accounted for within the g-1-dimensional space of a bgPCA. The distortion will still be relevant though its strength will vary from case to case depending on the structure of the data (p, g, covariances etc.). These are important problems for a method mainly designed for the analysis of variation among groups when there are very large numbers of variables and relatively small samples. In such cases, users are likely to conclude that the groups they are comparing are much more distinct than they really are. Having many variables but just small sample sizes is a common problem in fields ranging from morphometrics (as in our examples) to molecular analyses.

scholarly journals COVID-19 Infection-Related Coagulopathy and Viscoelastic Methods: A Paradigm for Their Clinical Utility in Critical Illness

Diagnostics ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 817 ◽  
Author(s):  
Argirios E. Tsantes ◽  
Andreas G. Tsantes ◽  
Styliani I. Kokoris ◽  
Stefanos Bonovas ◽  
Frantzeska Frantzeskaki ◽  
...  
Keyword(s):  
Clinical Utility ◽  
Small Sample ◽  
Close Monitoring ◽  
Rotational Thromboelastometry ◽  
Coagulation Tests ◽  
Attractive Option ◽  
Small Sample Sizes ◽  
Fibrinolysis Shutdown ◽  
Insight Into

Hypercoagulability and thrombosis remain a challenge to diagnose and treat in severe COVID-19 infection. The ability of conventional global coagulation tests to accurately reflect in vivo hypo- or hypercoagulability is questioned. The currently available evidence suggests that markedly increased D-dimers can be used in identifying COVID-19 patients who may need intensive care unit (ICU) admission and close monitoring or not. Viscoelastic methods (VMs), like thromboelastography (TEG) and rotational thromboelastometry (ROTEM), estimate the dynamics of blood coagulation. The evaluation of coagulopathy by VMs in severe COVID-19 infection seems an increasingly attractive option. Available evidence supports that COVID-19 patients with acute respiratory failure suffer from severe hypercoagulability rather than consumptive coagulopathy often associated with fibrinolysis shutdown. However, the variability in definitions of both the procoagulant profile and the clinical outcome assessment, in parallel with the small sample sizes in most of these studies, do not allow the establishment of a clear association between the hypercoagulable state and thrombotic events. VMs can effectively provide insight into the pathophysiology of coagulopathy, detecting the presence of hypercoagulability in critically ill COVID-19 patients. However, it remains unknown whether the degree of coagulopathy can be used in order to predict the outcome, establish a diagnosis or guide anticoagulant therapy.

scholarly journals ViSiElse: An innovative R-package to visualize raw behavioral data over time

2019 ◽  
Author(s):  
Elodie M Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Small Sample Sizes ◽  
Single Graph ◽  
Insight Into ◽  
Over Time

The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this paper, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.

scholarly journals ViSiElse: an innovative R-package to visualize raw behavioral data over time

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8341
Author(s):  
Elodie M. Garnier ◽  
Nastasia Fouret ◽  
Médéric Descoins
Keyword(s):  
R Package ◽  
Group Behavior ◽  
Small Sample ◽  
Time Data ◽  
Raw Data ◽  
New Approach ◽  
Small Sample Sizes ◽  
Single Graph ◽  
Insight Into ◽  
Over Time

The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this article, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed. Additional features allow users to further inspect their data by including summary statistics and time constraints.

Does Spending Money on Others Promote Happiness?: A Registered Replication Report

2020 ◽  
Author(s):  
Lara Beth Aknin ◽  
Elizabeth Warren Dunn ◽  
Jason Douglas Edward Proulx ◽  
Iris Lok ◽  
Michael I. Norton
Keyword(s):  
Prosocial Behavior ◽  
Small Sample ◽  
Task Engagement ◽  
Sample Sizes ◽  
Null Results ◽  
Prosocial Spending ◽  
Low Levels ◽  
Momentary Happiness ◽  
Small Sample Sizes ◽  
Insight Into

Research indicates that spending money on others—prosocial spending—leads to greater happiness than spending money on oneself (e.g., Dunn, Aknin, & Norton, 2008; 2014). These findings have received widespread attention because they offer insight into why people engage in costly prosocial behavior, and what constitutes happier spending more broadly. However, most studies on prosocial spending (like most research on the emotional benefits of generosity) utilized small sample sizes (n<100/cell). In light of new, improved standards for evidentiary value, we conducted high-powered registered replications of the central paradigms used in prosocial spending research. In Experiment 1, 712 students were randomly assigned to make a purchase for themselves or a stranger in need and then reported their happiness. As predicted, participants assigned to engage in prosocial (vs. personal) spending reported greater momentary happiness. In Experiment 2, 1950 adults recalled a time they spent money on themselves or someone else and then reported their current happiness; contrary to predictions, participants in the prosocial spending condition did not report greater happiness than those in the personal spending condition. Because low levels of task engagement may have produced these null results, we conducted a replication with minor changes designed to increase engagement; in this Experiment 3 (N = 5,199), participants who recalled a prosocial (vs. personal) spending memory reported greater happiness but differences were small. Taken together, these studies support the hypothesis that spending money on others does promote happiness, but demonstrate that the magnitude of the effect depends on several methodological features.

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