Distinguishing autism from co-existing conditions: a behavioural profiling investigation

2016 ◽  
Vol 2 (1) ◽  
pp. 41-54
Author(s):  
Ashleigh Saunders ◽  
Karen E. Waldie
Keyword(s):  
Statistical Power ◽  
Psychiatric Comorbidity ◽  
Adaptive Functioning ◽  
Autism Spectrum ◽  
Small Sample ◽  
Comorbid Conditions ◽  
Sample Sizes ◽  
Content Type ◽  
Small Sample Sizes ◽  
Comorbid Adhd

Purpose – Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition for which there is no known cure. The rate of psychiatric comorbidity in autism is extremely high, which raises questions about the nature of the co-occurring symptoms. It is unclear whether these additional conditions are true comorbid conditions, or can simply be accounted for through the ASD diagnosis. The paper aims to discuss this issue. Design/methodology/approach – A number of questionnaires and a computer-based task were used in the current study. The authors asked the participants about symptoms of ASD, attention deficit hyperactivity disorder (ADHD) and anxiety, as well as overall adaptive functioning. Findings – The results demonstrate that each condition, in its pure form, can be clearly differentiated from one another (and from neurotypical controls). Further analyses revealed that when ASD occurs together with anxiety, anxiety appears to be a separate condition. In contrast, there is no clear behavioural profile for when ASD and ADHD co-occur. Research limitations/implications – First, due to small sample sizes, some analyses performed were targeted to specific groups (i.e. comparing ADHD, ASD to comorbid ADHD+ASD). Larger sample sizes would have given the statistical power to perform a full scale comparative analysis of all experimental groups when split by their comorbid conditions. Second, males were over-represented in the ASD group and females were over-represented in the anxiety group, due to the uneven gender balance in the prevalence of these conditions. Lastly, the main profiling techniques used were questionnaires. Clinical interviews would have been preferable, as they give a more objective account of behavioural difficulties. Practical implications – The rate of psychiatric comorbidity in autism is extremely high, which raises questions about the nature of the co-occurring symptoms. It is unclear whether these additional conditions are true comorbid conditions, or can simply be accounted for through the ASD diagnosis. Social implications – This information will be important, not only to healthcare practitioners when administering a diagnosis, but also to therapists who need to apply evidence-based treatment to comorbid and stand-alone conditions. Originality/value – This study is the first to investigate the nature of co-existing conditions in ASD in a New Zealand population.

scholarly journals G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Florent Le Borgne ◽  
Arthur Chatton ◽  
Maxime Léger ◽  
Rémi Lenain ◽  
Yohann Foucher
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Statistical Power ◽  
Small Sample ◽  
Causal Effects ◽  
Small Samples ◽  
Support Vector ◽  
Sample Sizes ◽  
Super Learner ◽  
Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

Small sample discrete reliability growth modeling using a grey systems model

2018 ◽  
Vol 8 (3) ◽  
pp. 246-271 ◽  
Author(s):  
Thomas Paul Talafuse ◽  
Edward A. Pohl
Keyword(s):  
Growth Model ◽  
Small Sample ◽  
System Level ◽  
Parameter Estimates ◽  
Reliability Growth ◽  
Sample Sizes ◽  
Content Type ◽  
Failure Data ◽  
Amsaa Model ◽  
Small Sample Sizes

PurposeWhen performing system-level developmental testing, time and expenses generally warrant a small sample size for failure data. Upon failure discovery, redesigns and/or corrective actions can be implemented to improve system reliability. Current methods for estimating discrete (one-shot) reliability growth, namely the Crow (AMSAA) growth model, stipulate that parameter estimates have a great level of uncertainty when dealing with small sample sizes. The purpose of this paper is to present an application of a modified GM(1,1) model for handling system-level testing constrained by small sample sizes.Design/methodology/approachThe paper presents a methodology for incorporating failure data into a modified GM(1,1) model for systems with failures following a poly-Weibull distribution. Notional failure data are generated for complex systems and characterization of reliability growth parameters is performed via both the traditional AMSAA model and the GM(1,1) model for purposes of comparing and assessing performance.FindingsThe modified GM(1,1) model requires less complex computational effort and provides a more accurate prediction of reliability growth model parameters for small sample sizes and multiple failure modes when compared to the AMSAA model. It is especially superior to the AMSAA model in later stages of testing.Originality/valueThis research identifies cost-effective methods for developing more accurate reliability growth parameter estimates than those currently used.

scholarly journals Indices of Rank Histogram Flatness and Their Sampling Properties

2019 ◽  
Vol 147 (2) ◽  
pp. 763-769 ◽  
Author(s):  
D. S. Wilks
Keyword(s):  
Null Hypothesis ◽  
Statistical Power ◽  
Small Sample ◽  
Sample Sizes ◽  
Sampling Distributions ◽  
Power Of Tests ◽  
Rank Histogram ◽  
Small Sample Sizes ◽  
Formal Hypothesis Testing ◽  
Two Alternatives

Abstract Quantitative evaluation of the flatness of the verification rank histogram can be approached through formal hypothesis testing. Traditionally, the familiar χ2 test has been used for this purpose. Recently, two alternatives—the reliability index (RI) and an entropy statistic (Ω)—have been suggested in the literature. This paper presents approximations to the sampling distributions of these latter two rank histogram flatness metrics, and compares the statistical power of tests based on the three statistics, in a controlled setting. The χ2 test is generally most powerful (i.e., most sensitive to violations of the null hypothesis of rank uniformity), although for overdispersed ensembles and small sample sizes, the test based on the entropy statistic Ω is more powerful. The RI-based test is preferred only for unbiased forecasts with small ensembles and very small sample sizes.

scholarly journals Evaluation of TagSeq, a reliable low-cost alternative for RNAseq

2016 ◽  
Author(s):  
Brian Keith Lohman ◽  
Jesse N Weber ◽  
Daniel I Bolnick
Keyword(s):  
Gene Expression ◽  
Statistical Power ◽  
Low Cost ◽  
Ecological Genetics ◽  
Small Sample ◽  
Sample Sizes ◽  
Experimental Conditions ◽  
Efficient Alternative ◽  
Highly Correlated ◽  
Small Sample Sizes

RNAseq is a relatively new tool for ecological genetics that offers researchers insight into changes in gene expression in response to a myriad of natural or experimental conditions. However, standard RNAseq methods (e.g., Illumina TruSeq® or NEBNext®) can be cost prohibitive, especially when study designs require large sample sizes. Consequently, RNAseq is often underused as a method, or is applied to small sample sizes that confer poor statistical power. Low cost RNAseq methods could therefore enable far greater and more powerful applications of transcriptomics in ecological genetics and beyond. Standard mRNAseq is costly partly because one sequences portions of the full length of all transcripts. Such whole-mRNA data is redundant for estimates of relative gene expression. TagSeq is an alternative method that focuses sequencing effort on mRNAs 3-prime end, thereby reducing the necessary sequencing depth per sample, and thus cost. Here we present a revised TagSeq protocol, and compare its performance against NEBNext®, the gold-standard whole mRNAseq method. We built both TagSeq and NEBNext® libraries from the same biological samples, each spiked with control RNAs. We found that TagSeq measured the control RNA distribution more accurately than NEBNext®, for a fraction of the cost per sample (~10%). The higher accuracy of TagSeq was particularly apparent for transcripts of moderate to low abundance. Technical replicates of TagSeq libraries are highly correlated, and were correlated with NEBNext® results. Overall, we show that our modified TagSeq protocol is an efficient alternative to traditional whole mRNAseq, offering researchers comparable data at greatly reduced cost.

Comparing Alternative Corrections for Bias in the Bias-Corrected Bootstrap Test of Mediation

2021 ◽  
pp. 016327872110243
Author(s):  
Donna Chen ◽  
Matthew S. Fritz
Keyword(s):  
Effect Size ◽  
Statistical Power ◽  
Type I Error ◽  
Small Sample ◽  
Medium Effect ◽  
Type I ◽  
Sample Sizes ◽  
Bootstrap Test ◽  
Small Effect Size ◽  
Small Sample Sizes

Although the bias-corrected (BC) bootstrap is an often-recommended method for testing mediation due to its higher statistical power relative to other tests, it has also been found to have elevated Type I error rates with small sample sizes. Under limitations for participant recruitment, obtaining a larger sample size is not always feasible. Thus, this study examines whether using alternative corrections for bias in the BC bootstrap test of mediation for small sample sizes can achieve equal levels of statistical power without the associated increase in Type I error. A simulation study was conducted to compare Efron and Tibshirani’s original correction for bias, z 0, to six alternative corrections for bias: (a) mean, (b–e) Winsorized mean with 10%, 20%, 30%, and 40% trimming in each tail, and (f) medcouple (robust skewness measure). Most variation in Type I error (given a medium effect size of one regression slope and zero for the other slope) and power (small effect size in both regression slopes) was found with small sample sizes. Recommendations for applied researchers are made based on the results. An empirical example using data from the ATLAS drug prevention intervention study is presented to illustrate these results. Limitations and future directions are discussed.

scholarly journals Big Correlations in Little Studies: Inflated fMRI Correlations Reflect Low Statistical Power—Commentary on Vul et al. (2009)

2009 ◽  
Vol 4 (3) ◽  
pp. 294-298 ◽  
Author(s):  
Tal Yarkoni
Keyword(s):  
Empirical Analysis ◽  
Cognitive Neuroscience ◽  
Statistical Power ◽  
Small Sample ◽  
Sample Sizes ◽  
Whole Brain ◽  
Small Sample Sizes ◽  
Brain Fmri

Vul, Harris, Winkielman, and Pashler (2009) , (this issue) argue that correlations in many cognitive neuroscience studies are grossly inflated due to a widespread tendency to use nonindependent analyses. In this article, I argue that Vul et al.'s primary conclusion is correct, but for different reasons than they suggest. I demonstrate that the primary cause of grossly inflated correlations in whole-brain fMRI analyses is not nonindependence, but the pernicious combination of small sample sizes and stringent alpha-correction levels. Far from defusing Vul et al.'s conclusions, the simulations presented suggest that the level of inflation may be even worse than Vul et al.'s empirical analysis would suggest.

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