scholarly journals Region-of-interest analyses of one-dimensional biomechanical trajectories: bridging 0D and 1D theory, augmenting statistical power

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2652 ◽  
Author(s):  
Todd C. Pataky ◽  
Mark A. Robinson ◽  
Jos Vanrenterghem
Keyword(s):  
Statistical Power ◽  
Type I Error ◽  
A Priori ◽  
Region Of Interest ◽  
Type I ◽  
One Dimensional ◽  
Local Extrema ◽  
Special Cases ◽  
Theoretical Predictions ◽  
Public Datasets

One-dimensional (1D) kinematic, force, and EMG trajectories are often analyzed using zero-dimensional (0D) metrics like local extrema. Recently whole-trajectory 1D methods have emerged in the literature as alternatives. Since 0D and 1D methods can yield qualitatively different results, the two approaches may appear to be theoretically distinct. The purposes of this paper were (a) to clarify that 0D and 1D approaches are actually just special cases of a more general region-of-interest (ROI) analysis framework, and (b) to demonstrate how ROIs can augment statistical power. We first simulated millions of smooth, random 1D datasets to validate theoretical predictions of the 0D, 1D and ROI approaches and to emphasize how ROIs provide a continuous bridge between 0D and 1D results. We then analyzed a variety of public datasets to demonstrate potential effects of ROIs on biomechanical conclusions. Results showed, first, thata prioriROI particulars can qualitatively affect the biomechanical conclusions that emerge from analyses and, second, that ROIs derived from exploratory/pilot analyses can detect smaller biomechanical effects than are detectable using full 1D methods. We recommend regarding ROIs, like data filtering particulars and Type I error rate, as parameters which can affect hypothesis testing results, and thus as sensitivity analysis tools to ensure arbitrary decisions do not influence scientific interpretations. Last, we describe open-source Python and MATLAB implementations of 1D ROI analysis for arbitrary experimental designs ranging from one-samplettests to MANOVA.

scholarly journals A Multi-faceted Mess: A Review of Statistical Power Analysis in Psychology Journal Articles

2019 ◽  
Author(s):  
Rob Cribbie ◽  
Nataly Beribisky ◽  
Udi Alter
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Power Analysis ◽  
Statistical Power ◽  
Type I Error ◽  
A Priori ◽  
Type I ◽  
Specific Level ◽  
Maximum Sample Size ◽  
Power Analyses

Many bodies recommend that a sample planning procedure, such as traditional NHST a priori power analysis, is conducted during the planning stages of a study. Power analysis allows the researcher to estimate how many participants are required in order to detect a minimally meaningful effect size at a specific level of power and Type I error rate. However, there are several drawbacks to the procedure that render it “a mess.” Specifically, the identification of the minimally meaningful effect size is often difficult but unavoidable for conducting the procedure properly, the procedure is not precision oriented, and does not guide the researcher to collect as many participants as feasibly possible. In this study, we explore how these three theoretical issues are reflected in applied psychological research in order to better understand whether these issues are concerns in practice. To investigate how power analysis is currently used, this study reviewed the reporting of 443 power analyses in high impact psychology journals in 2016 and 2017. It was found that researchers rarely use the minimally meaningful effect size as a rationale for the chosen effect in a power analysis. Further, precision-based approaches and collecting the maximum sample size feasible are almost never used in tandem with power analyses. In light of these findings, we offer that researchers should focus on tools beyond traditional power analysis when sample planning, such as collecting the maximum sample size feasible.

One Size Fits All?

Methodology ◽  
2012 ◽  
Vol 8 (1) ◽  
pp. 23-38 ◽  
Author(s):  
Manuel C. Voelkle ◽  
Patrick E. McKnight
Keyword(s):  
Repeated Measures ◽  
Structural Equation ◽  
Type I Error ◽  
Equation Modeling ◽  
Type I ◽  
Finite Sample ◽  
Traditional Methods ◽  
Repeated Measures Anova ◽  
Special Cases ◽  
Latent Curve

The use of latent curve models (LCMs) has increased almost exponentially during the last decade. Oftentimes, researchers regard LCM as a “new” method to analyze change with little attention paid to the fact that the technique was originally introduced as an “alternative to standard repeated measures ANOVA and first-order auto-regressive methods” (Meredith & Tisak, 1990, p. 107). In the first part of the paper, this close relationship is reviewed, and it is demonstrated how “traditional” methods, such as the repeated measures ANOVA, and MANOVA, can be formulated as LCMs. Given that latent curve modeling is essentially a large-sample technique, compared to “traditional” finite-sample approaches, the second part of the paper addresses the question to what degree the more flexible LCMs can actually replace some of the older tests by means of a Monte-Carlo simulation. In addition, a structural equation modeling alternative to Mauchly’s (1940) test of sphericity is explored. Although “traditional” methods may be expressed as special cases of more general LCMs, we found the equivalence holds only asymptotically. For practical purposes, however, no approach always outperformed the other alternatives in terms of power and type I error, so the best method to be used depends on the situation. We provide detailed recommendations of when to use which method.

How to Detect Publication Bias in Psychological Research

2019 ◽  
Vol 227 (4) ◽  
pp. 261-279 ◽  
Author(s):  
Frank Renkewitz ◽  
Melanie Keiner
Keyword(s):  
Publication Bias ◽  
Effect Size ◽  
Statistical Power ◽  
Type I Error ◽  
Psychological Research ◽  
Type I ◽  
True Effect Size ◽  
Questionable Research Practices ◽  
True Effect ◽  
Meta Analyses

Abstract. Publication biases and questionable research practices are assumed to be two of the main causes of low replication rates. Both of these problems lead to severely inflated effect size estimates in meta-analyses. Methodologists have proposed a number of statistical tools to detect such bias in meta-analytic results. We present an evaluation of the performance of six of these tools. To assess the Type I error rate and the statistical power of these methods, we simulated a large variety of literatures that differed with regard to true effect size, heterogeneity, number of available primary studies, and sample sizes of these primary studies; furthermore, simulated studies were subjected to different degrees of publication bias. Our results show that across all simulated conditions, no method consistently outperformed the others. Additionally, all methods performed poorly when true effect sizes were heterogeneous or primary studies had a small chance of being published, irrespective of their results. This suggests that in many actual meta-analyses in psychology, bias will remain undiscovered no matter which detection method is used.

scholarly journals Cognitive tests used in chronic adult human randomised controlled trial micronutrient and phytochemical intervention studies

2010 ◽  
Vol 23 (2) ◽  
pp. 200-229 ◽  
Author(s):  
Anna L. Macready ◽  
Laurie T. Butler ◽  
Orla B. Kennedy ◽  
Judi A. Ellis ◽  
Claire M. Williams ◽  
...  
Keyword(s):  
Randomised Controlled Trial ◽  
Statistical Power ◽  
Type I Error ◽  
Spatial Working Memory ◽  
Controlled Trial ◽  
Type I ◽  
Cognitive Tests ◽  
Cognitive Domains ◽  
Positive Effects ◽  
Randomised Controlled

In recent years there has been a rapid growth of interest in exploring the relationship between nutritional therapies and the maintenance of cognitive function in adulthood. Emerging evidence reveals an increasingly complex picture with respect to the benefits of various food constituents on learning, memory and psychomotor function in adults. However, to date, there has been little consensus in human studies on the range of cognitive domains to be tested or the particular tests to be employed. To illustrate the potential difficulties that this poses, we conducted a systematic review of existing human adult randomised controlled trial (RCT) studies that have investigated the effects of 24 d to 36 months of supplementation with flavonoids and micronutrients on cognitive performance. There were thirty-nine studies employing a total of 121 different cognitive tasks that met the criteria for inclusion. Results showed that less than half of these studies reported positive effects of treatment, with some important cognitive domains either under-represented or not explored at all. Although there was some evidence of sensitivity to nutritional supplementation in a number of domains (for example, executive function, spatial working memory), interpretation is currently difficult given the prevailing ‘scattergun approach’ for selecting cognitive tests. Specifically, the practice means that it is often difficult to distinguish between a boundary condition for a particular nutrient and a lack of task sensitivity. We argue that for significant future progress to be made, researchers need to pay much closer attention to existing human RCT and animal data, as well as to more basic issues surrounding task sensitivity, statistical power and type I error.

scholarly journals Required sample size for comparing two independent means

2020 ◽  
Vol 6 (2) ◽  
pp. 106-113
Author(s):  
A. M. Grjibovski ◽  
M. A. Gorbatova ◽  
A. N. Narkevich ◽  
K. A. Vinogradov
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Type I Error ◽  
Sample Size Calculation ◽  
Biomedical Literature ◽  
Type I ◽  
Research Practice ◽  
False Null Hypothesis ◽  
Different Levels ◽  
Russian Research

Sample size calculation in a planning phase is still uncommon in Russian research practice. This situation threatens validity of the conclusions and may introduce Type I error when the false null hypothesis is accepted due to lack of statistical power to detect the existing difference between the means. Comparing two means using unpaired Students’ ttests is the most common statistical procedure in the Russian biomedical literature. However, calculations of the minimal required sample size or retrospective calculation of the statistical power were observed only in very few publications. In this paper we demonstrate how to calculate required sample size for comparing means in unpaired samples using WinPepi and Stata software. In addition, we produced tables for minimal required sample size for studies when two means have to be compared and body mass index and blood pressure are the variables of interest. The tables were constructed for unpaired samples for different levels of statistical power and standard deviations obtained from the literature.

scholarly journals Bayesian Two-Stage Adaptive Design in Bioequivalence

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Shengjie Liu ◽  
Jun Gao ◽  
Yuling Zheng ◽  
Lei Huang ◽  
Fangrong Yan
Keyword(s):  
Statistical Power ◽  
Adaptive Design ◽  
Type I Error ◽  
Probability Model ◽  
Type I ◽  
Two Stage ◽  
Stage Design ◽  
Estimation Strategy ◽  
Drug Products ◽  
Two Stage Design

AbstractBioequivalence (BE) studies are an integral component of new drug development process, and play an important role in approval and marketing of generic drug products. However, existing design and evaluation methods are basically under the framework of frequentist theory, while few implements Bayesian ideas. Based on the bioequivalence predictive probability model and sample re-estimation strategy, we propose a new Bayesian two-stage adaptive design and explore its application in bioequivalence testing. The new design differs from existing two-stage design (such as Potvin’s method B, C) in the following aspects. First, it not only incorporates historical information and expert information, but further combines experimental data flexibly to aid decision-making. Secondly, its sample re-estimation strategy is based on the ratio of the information in interim analysis to total information, which is simpler in calculation than the Potvin’s method. Simulation results manifested that the two-stage design can be combined with various stop boundary functions, and the results are different. Moreover, the proposed method saves sample size compared to the Potvin’s method under the conditions that type I error rate is below 0.05 and statistical power reaches 80 %.

scholarly journals Data-driven region-of-interest selection without inflating Type I error rate

2016 ◽  
Vol 54 (1) ◽  
pp. 100-113 ◽  
Author(s):  
Joseph L. Brooks ◽  
Alexia Zoumpoulaki ◽  
Howard Bowman
Keyword(s):  
Error Rate ◽  
Type I Error ◽  
Region Of Interest ◽  
Data Driven ◽  
Type I ◽  
Type I Error Rate

Optimal selection of genetic variants for adjustment of population stratification in European association studies

2019 ◽  
Vol 21 (3) ◽  
pp. 753-761 ◽  
Author(s):  
Regina Brinster ◽  
Dominique Scherer ◽  
Justo Lorenzo Bermejo
Keyword(s):  
Genetic Variants ◽  
Population Stratification ◽  
Statistical Power ◽  
Type I Error ◽  
Association Studies ◽  
Reference Sample ◽  
Error Rates ◽  
The Cancer Genome Atlas ◽  
Type I ◽  
Genotype Data

Abstract Population stratification is usually corrected relying on principal component analysis (PCA) of genome-wide genotype data, even in populations considered genetically homogeneous, such as Europeans. The need to genotype only a small number of genetic variants that show large differences in allele frequency among subpopulations—so-called ancestry-informative markers (AIMs)—instead of the whole genome for stratification adjustment could represent an advantage for replication studies and candidate gene/pathway studies. Here we compare the correction performance of classical and robust principal components (PCs) with the use of AIMs selected according to four different methods: the informativeness for assignment measure ($IN$-AIMs), the combination of PCA and F-statistics, PCA-correlated measurement and the PCA weighted loadings for each genetic variant. We used real genotype data from the Population Reference Sample and The Cancer Genome Atlas to simulate European genetic association studies and to quantify type I error rate and statistical power in different case–control settings. In studies with the same numbers of cases and controls per country and control-to-case ratios reflecting actual rates of disease prevalence, no adjustment for population stratification was required. The unnecessary inclusion of the country of origin, PCs or AIMs as covariates in the regression models translated into increasing type I error rates. In studies with cases and controls from separate countries, no investigated method was able to adequately correct for population stratification. The first classical and the first two robust PCs achieved the lowest (although inflated) type I error, followed at some distance by the first eight $IN$-AIMs.

scholarly journals The asymptotic distribution of modularity in weighted signed networks

Biometrika ◽  
2020 ◽  
Author(s):  
Rong Ma ◽  
Ian Barnett
Keyword(s):  
Community Structure ◽  
Asymptotic Distribution ◽  
Statistical Power ◽  
Type I Error ◽  
Real Data ◽  
Asymptotic Distributions ◽  
Edge Weight ◽  
Type I ◽  
Largest Eigenvalue ◽  
The Largest Eigenvalue

Summary Modularity is a popular metric for quantifying the degree of community structure within a network. The distribution of the largest eigenvalue of a network’s edge weight or adjacency matrix is well studied and is frequently used as a substitute for modularity when performing statistical inference. However, we show that the largest eigenvalue and modularity are asymptotically uncorrelated, which suggests the need for inference directly on modularity itself when the network is large. To this end, we derive the asymptotic distribution of modularity in the case where the network’s edge weight matrix belongs to the Gaussian orthogonal ensemble, and study the statistical power of the corresponding test for community structure under some alternative models. We empirically explore universality extensions of the limiting distribution and demonstrate the accuracy of these asymptotic distributions through Type I error simulations. We also compare the empirical powers of the modularity-based tests and some existing methods. Our method is then used to test for the presence of community structure in two real data applications.

scholarly journals Calibrate your confidence in research findings: A tutorial on improving research methods and practices

2020 ◽  
Vol 14 ◽  
Author(s):  
Aline da Silva Frost ◽  
Alison Ledgerwood
Keyword(s):  
Research Methods ◽  
Statistical Power ◽  
Type I Error ◽  
Effect Sizes ◽  
Online Media ◽  
Type I ◽  
Journal Articles ◽  
Psychological Science ◽  
Different Types ◽  
Research Findings

Abstract This article provides an accessible tutorial with concrete guidance for how to start improving research methods and practices in your lab. Following recent calls to improve research methods and practices within and beyond the borders of psychological science, resources have proliferated across book chapters, journal articles, and online media. Many researchers are interested in learning more about cutting-edge methods and practices but are unsure where to begin. In this tutorial, we describe specific tools that help researchers calibrate their confidence in a given set of findings. In Part I, we describe strategies for assessing the likely statistical power of a study, including when and how to conduct different types of power calculations, how to estimate effect sizes, and how to think about power for detecting interactions. In Part II, we provide strategies for assessing the likely type I error rate of a study, including distinguishing clearly between data-independent (“confirmatory”) and data-dependent (“exploratory”) analyses and thinking carefully about different forms and functions of preregistration.

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