scholarly journals The need to include phylogeny in trait-based analyses of community composition

2016 ◽  
Author(s):  
Daijiang Li ◽  
Anthony R Ives
Keyword(s):  
Phylogenetic Relationships ◽  
Statistical Power ◽  
Type I Error ◽  
Phylogenetic Signal ◽  
Species Distributions ◽  
Error Rates ◽  
Estimation Accuracy ◽  
Type I ◽  
Type I Error Rates ◽  
Residual Variation

1. A growing number of studies incorporate functional trait information to analyse patterns and processes of community assembly. These studies of trait-environment relationships generally ignore phylogenetic relationships among species. When functional traits and the residual variation in species distributions among communities have phylogenetic signal, however, analyses ignoring phylogenetic relationships can decrease estimation accuracy and power, inflate type I error rates, and lead to potentially false conclusions. 2. Using simulations, we compared estimation accuracy, statistical power, and type I error rates of linear mixed models (LMM) and phylogenetic linear mixed models (PLMM) designed to test for trait-environment interactions in the distribution of species abundances among sites. We considered the consequences of both phylogenetic signal in traits and phylogenetic signal in the residual variation of species distributions generated by an unmeasured (latent) trait with phylogenetic signal. 3. When there was phylogenetic signal in the residual variation of species among sites, PLMM provided better estimates (closer to the true value) and greater statistical power for testing whether the trait-environment interaction regression coefficient differed from zero. LMM had unacceptably high type I error rates when there was phylogenetic signal in both traits and the residual variation in species distributions. When there was no phylogenetic signal in the residual variation in species distributions, LMM and PLMM had similar performances. 4. LMMs that ignore phylogenetic relationships can lead to poor statistical tests of trait-environment relationships when there is phylogenetic signal in the residual variation of species distributions among sites, such as caused by unmeasured traits. Therefore, phylogenies and PLMMs should be used when studying how functional traits affect species abundances among communities in response to environmental gradients.

scholarly journals A comparative analysis of cell-type adjustment methods for epigenome-wide association studies based on simulated and real data sets

2018 ◽  
Vol 20 (6) ◽  
pp. 2055-2065 ◽  
Author(s):  
Johannes Brägelmann ◽  
Justo Lorenzo Bermejo
Keyword(s):  
Statistical Power ◽  
Type I Error ◽  
Association Studies ◽  
Real Data ◽  
Error Rates ◽  
Data Sets ◽  
Type I ◽  
Cell Type ◽  
Type I Error Rates

Abstract Technological advances and reduced costs of high-density methylation arrays have led to an increasing number of association studies on the possible relationship between human disease and epigenetic variability. DNA samples from peripheral blood or other tissue types are analyzed in epigenome-wide association studies (EWAS) to detect methylation differences related to a particular phenotype. Since information on the cell-type composition of the sample is generally not available and methylation profiles are cell-type specific, statistical methods have been developed for adjustment of cell-type heterogeneity in EWAS. In this study we systematically compared five popular adjustment methods: the factored spectrally transformed linear mixed model (FaST-LMM-EWASher), the sparse principal component analysis algorithm ReFACTor, surrogate variable analysis (SVA), independent SVA (ISVA) and an optimized version of SVA (SmartSVA). We used real data and applied a multilayered simulation framework to assess the type I error rate, the statistical power and the quality of estimated methylation differences according to major study characteristics. While all five adjustment methods improved false-positive rates compared with unadjusted analyses, FaST-LMM-EWASher resulted in the lowest type I error rate at the expense of low statistical power. SVA efficiently corrected for cell-type heterogeneity in EWAS up to 200 cases and 200 controls, but did not control type I error rates in larger studies. Results based on real data sets confirmed simulation findings with the strongest control of type I error rates by FaST-LMM-EWASher and SmartSVA. Overall, ReFACTor, ISVA and SmartSVA showed the best comparable statistical power, quality of estimated methylation differences and runtime.

scholarly journals Bias, Type I Error Rates, and Statistical Power of a Latent Mediation Model in the Presence of Violations of Invariance

2017 ◽  
Vol 78 (3) ◽  
pp. 460-481 ◽  
Author(s):  
Margarita Olivera-Aguilar ◽  
Samuel H. Rikoon ◽  
Oscar Gonzalez ◽  
Yasemin Kisbu-Sakarya ◽  
David P. MacKinnon
Keyword(s):  
Measurement Invariance ◽  
Statistical Power ◽  
Type I Error ◽  
Error Rates ◽  
Parameter Estimates ◽  
Type I ◽  
Mediation Model ◽  
Type I Error Rates ◽  
Mediated Effects ◽  
The Impact

When testing a statistical mediation model, it is assumed that factorial measurement invariance holds for the mediating construct across levels of the independent variable X. The consequences of failing to address the violations of measurement invariance in mediation models are largely unknown. The purpose of the present study was to systematically examine the impact of mediator noninvariance on the Type I error rates, statistical power, and relative bias in parameter estimates of the mediated effect in the single mediator model. The results of a large simulation study indicated that, in general, the mediated effect was robust to violations of invariance in loadings. In contrast, most conditions with violations of intercept invariance exhibited severely positively biased mediated effects, Type I error rates above acceptable levels, and statistical power larger than in the invariant conditions. The implications of these results are discussed and recommendations are offered.

Comparison of Type I error rates and statistical power of different propensity score methods

2017 ◽  
Vol 88 (4) ◽  
pp. 769-784
Author(s):  
Falynn C. Turley ◽  
David Redden ◽  
Janice L. Case ◽  
Charles Katholi ◽  
Jeff Szychowski ◽  
...  
Keyword(s):  
Propensity Score ◽  
Statistical Power ◽  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rates ◽  
Propensity Score Methods

scholarly journals Capitalising on covariates in cluster-randomised experiments

2020 ◽  
Author(s):  
Jan Vanhove
Keyword(s):  
Statistical Power ◽  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rates ◽  
Individual Basis ◽  
Cluster Randomised

In cluster-randomised experiments, participants are randomly assigned to the conditions not on an individual basis but in entire groups. For instance, all pupils in a class are assigned to the same condition. This article reports on a series of simulations that were run to determine (1) how the clusters (e.g., classes) in such experiments should be assigned to the conditions if a relevant covariate is available at the outset of the study (e.g., a pretest) and (2) how the data the study produces should be analysed if researchers want to maximise their statistical power while retaining nominal Type-I error rates. The R code used for the simulation is freely accessible online, allowing researchers who need to plan and analyse a cluster-randomised experiment to tailor the simulation to the specifics of their study and determine which approach is likely to work best.

Are latent variable models preferable to composite score approaches when assessing risk factors of change? Evaluation of type-I error and statistical power in longitudinal cognitive studies

2017 ◽  
Vol 28 (7) ◽  
pp. 1942-1957 ◽  
Author(s):  
Cécile Proust-Lima ◽  
Viviane Philipps ◽  
Jean-François Dartigues ◽  
David A. Bennett ◽  
M Maria Glymour ◽  
...  
Keyword(s):  
Risk Factors ◽  
Latent Variable ◽  
Statistical Power ◽  
Type I Error ◽  
Latent Variable Models ◽  
Error Rates ◽  
Composite Score ◽  
Type I ◽  
Psychometric Tests ◽  
Type I Error Rates

As with many health constructs, cognition is difficult to measure accurately; it is assessed by multiple psychometric tests. Two approaches are commonly adopted to address this multivariate aspect in longitudinal analyses: the composite score approach summarizes the tests into a single outcome and subsequently analyzes its change; the multivariate approach relates the tests to the underlying cognitive level and simultaneously analyzes its change. We compared the quality of inference of these approaches in a simulation study based on three combinations of tests inspired by two population-based cohorts. In the absence of missing data and with relatively Gaussian psychometric tests, the composite score approach provided similar type-I error rates and statistical power as the multivariate latent process approach. In the more plausible scenario with departures from normality, transformations of each constituent test or of the composite score were required to avoid excess type-I error rates. When missing tests were more likely in cognitively impaired subjects, inference with the composite was not correct. In conclusion, composite scores can be used to assess risk factors for cognitive change provided they are correctly normalized, constituent tests are reliable and the amount of uninformative missing tests remains small. Otherwise, latent variable models are recommended.

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