scholarly journals Repeated Measures Analyses for Tree Fruit Experiments

HortScience ◽  
1995 ◽  
Vol 30 (4) ◽  
pp. 845C-845
Author(s):  
Richard P. Marini
Keyword(s):  
Repeated Measures ◽  
Type I Error ◽  
Univariate Analysis ◽  
Type I ◽  
Cross Sectional ◽  
Data Set ◽  
Repeated Measures Analysis ◽  
Tree Fruit ◽  
Split Plot ◽  
Main Effects

Experiments with perennial crops often span several years, and a response variable may be measured on the same plant at several points in time. Such data are often analyzed as a split-plot design, taking time as the split-plot factor. In other cases, separate analyses are performed for each time. The mathematical conditions required for validity of these types of analyses might not hold because measurements repeated on the same plant are not independent. Annual trunk cross-sectional-area (TCSA) measurements from a peach tree training experiment will be used to compare two methods of analyses. The 6-year experiment was a factorial of two heading heights at planting (low vs. high) and two tree forms (central leader vs. open vase). Univariate analysis of variance (ANOVA) and a multivariate repeated measures analysis (MANOVA) was performed. Main effects and interactions were more often significant with ANOVA than with MANOVA. ANOVA performed each year inflated the probability of falsely rejecting a true null hypothesis (Type I error), and was not appropriate for this data set.

Repeated-Measures Designs: Univariate or Multivariate Analysis of Variance?

1997 ◽  
Vol 85 (1) ◽  
pp. 193-194
Author(s):  
Peter Hassmén
Keyword(s):  
Multivariate Analysis ◽  
Analysis Of Variance ◽  
Repeated Measures ◽  
Type I Error ◽  
Univariate Analysis ◽  
Type I ◽  
Multivariate Analysis Of Variance ◽  
Repeated Measures Analysis ◽  
Alpha Level ◽  
Repeated Measures Designs

Violation of the sphericity assumption in repeated-measures analysis of variance can lead to positively biased tests, i.e., the likelihood of a Type I error exceeds the alpha level set by the user. Two widely applicable solutions exist, the use of an epsilon-corrected univariate analysis of variance or the use of a multivariate analysis of variance. It is argued that the latter method offers advantages over the former.

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.

scholarly journals REMAXINT: a two-mode clustering-based method for statistical inference on two-way interaction

2021 ◽  
Author(s):  
Zaheer Ahmed ◽  
Alberto Cassese ◽  
Gerard van Breukelen ◽  
Jan Schepers
Keyword(s):  
Null Hypothesis ◽  
Type I Error ◽  
Type I ◽  
The Novel ◽  
Simulation Studies ◽  
Test Statistic ◽  
Clustering Model ◽  
Novel Method ◽  
Main Effects ◽  
Empirical Case Studies

AbstractWe present a novel method, REMAXINT, that captures the gist of two-way interaction in row by column (i.e., two-mode) data, with one observation per cell. REMAXINT is a probabilistic two-mode clustering model that yields two-mode partitions with maximal interaction between row and column clusters. For estimation of the parameters of REMAXINT, we maximize a conditional classification likelihood in which the random row (or column) main effects are conditioned out. For testing the null hypothesis of no interaction between row and column clusters, we propose a $$max-F$$ m a x - F test statistic and discuss its properties. We develop a Monte Carlo approach to obtain its sampling distribution under the null hypothesis. We evaluate the performance of the method through simulation studies. Specifically, for selected values of data size and (true) numbers of clusters, we obtain critical values of the $$max-F$$ m a x - F statistic, determine empirical Type I error rate of the proposed inferential procedure and study its power to reject the null hypothesis. Next, we show that the novel method is useful in a variety of applications by presenting two empirical case studies and end with some concluding remarks.

Transdermal delivery of carnosine into equine skeletal muscle

2021 ◽  
pp. 1-6
Author(s):  
B.P. Dieter ◽  
C.J. Macias ◽  
T.J. Sharpe ◽  
B. Roberts ◽  
M. Wille ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Transdermal Delivery ◽  
Repeated Measures ◽  
Muscle Metabolism ◽  
Enzyme Linked Immunosorbent Assay ◽  
Time To Exhaustion ◽  
Thoroughbred Racehorses ◽  
Repeated Measures Analysis ◽  
Main Effect ◽  
Main Effects

The dipeptide carnosine consists of β-alanine and L-histidine. It plays a major role in skeletal muscle metabolism, especially as an intracellular buffer and antioxidant. Increasing intramuscular carnosine has been shown to improve recovery from exercise and increase anaerobic threshold and time-to-exhaustion. Dietary supplementation with carnosine does not effectively increase intramuscular carnosine due to the presence of carnosinase in the blood. However, an effective transdermal delivery process could expediently increase intramuscular concentrations of carnosine. This study’s objective was to examine the efficacy of a transdermal system for delivering carnosine into the skeletal muscle of horses, using a randomised, placebo controlled, crossover study. Carnosine plus a proprietary transdermal delivery agent or the agent alone (placebo) were applied to the middle gluteal muscles of 10 Thoroughbred racehorses, and muscle biopsies were taken before and 30, 60, and 120 min after application. Muscle carnosine concentration was measured using an enzyme-linked immunosorbent assay. A two-way repeated measures analysis of variance was used to test for the main effects of time and treatment (placebo or carnosine) as well as an interaction between time and treatment. Independent F-tests examined the change in intramuscular carnosine levels from baseline to each time point (30, 60, and 120 min). There was a significant main effect of treatment (P=0.004), no significant main effect for time (P=0.18), and a non-significant interaction of treatment with time (P=0.08). Mean intramuscular carnosine concentrations increased from baseline to 120 min. Compared to concentrations following placebo application, carnosine was greater by ~35% at 30 min (P=0.002) and ~46% after 60 min (P=0.044), but not at 120 min (P=0.20). The results indicated that intramuscular carnosine can be increased using a transdermal delivery system within 60 min of application which could have important implications for the health of horses, and their capacity to perform and recover from physical activity.

scholarly journals Improved Small Sample Inference Methods for a Mixed-Effects Model for Repeated Measures Approach in Incomplete Longitudinal Data Analysis

Stats ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 174-188
Author(s):  
Yoshifumi Ukyo ◽  
Hisashi Noma ◽  
Kazushi Maruo ◽  
Masahiko Gosho
Keyword(s):  
Clinical Trial ◽  
Repeated Measures ◽  
Type I Error ◽  
Mixed Effects ◽  
Error Rates ◽  
Small Sample ◽  
Mixed Effects Model ◽  
Model Complexity ◽  
Type I ◽  
Inference Methods

The mixed-effects model for repeated measures (MMRM) approach has been widely applied for longitudinal clinical trials. Many of the standard inference methods of MMRM could possibly lead to the inflation of type I error rates for the tests of treatment effect, when the longitudinal dataset is small and involves missing measurements. We propose two improved inference methods for the MMRM analyses, (1) the Bartlett correction with the adjustment term approximated by bootstrap, and (2) the Monte Carlo test using an estimated null distribution by bootstrap. These methods can be implemented regardless of model complexity and missing patterns via a unified computational framework. Through simulation studies, the proposed methods maintain the type I error rate properly, even for small and incomplete longitudinal clinical trial settings. Applications to a postnatal depression clinical trial are also presented.

scholarly journals Considering Both Statistical and Clinical Significance

2016 ◽  
Vol 5 (5) ◽  
pp. 16 ◽  
Author(s):  
Guolong Zhao
Keyword(s):  
Clinical Significance ◽  
Null Hypothesis ◽  
Type I Error ◽  
Statistical Significance ◽  
Error Rates ◽  
Type I ◽  
Data Set ◽  
Type I Error Rates ◽  
Empirical Coverage ◽  
Superiority Trials

To evaluate a drug, statistical significance alone is insufficient and clinical significance is also necessary. This paper explains how to analyze clinical data with considering both statistical and clinical significance. The analysis is practiced by combining a confidence interval under null hypothesis with that under non-null hypothesis. The combination conveys one of the four possible results: (i) both significant, (ii) only significant in the former, (iii) only significant in the latter or (iv) neither significant. The four results constitute a quadripartite procedure. Corresponding tests are mentioned for describing Type I error rates and power. The empirical coverage is exhibited by Monte Carlo simulations. In superiority trials, the four results are interpreted as clinical superiority, statistical superiority, non-superiority and indeterminate respectively. The interpretation is opposite in inferiority trials. The combination poses a deflated Type I error rate, a decreased power and an increased sample size. The four results may helpful for a meticulous evaluation of drugs. Of these, non-superiority is another profile of equivalence and so it can also be used to interpret equivalence. This approach may prepare a convenience for interpreting discordant cases. Nevertheless, a larger data set is usually needed. An example is taken from a real trial in naturally acquired influenza.

scholarly journals Effective Analysis of Interactive Effects with Non-Normal Data Using the Aligned Rank Transform, ARTool and SAS® University Edition

Horticulturae ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 57 ◽  
Author(s):  
Edward Durner
Keyword(s):  
Type I Error ◽  
Interactive Effects ◽  
Type I ◽  
Rank Transform ◽  
Parametric Testing ◽  
Aligned Rank ◽  
Departure From Normality ◽  
Main Effects ◽  
Horticultural Research ◽  
Non Parametric

Most statistical techniques commonly used in horticultural research are parametric tests that are valid only for normal data with homogeneous variances. While parametric tests are robust when the data ‘slightly’ deviate from normality, a significant departure from normality leads to reduced power and the probability of a type I error increases. Transformations often used to normalize non-normal data can be time consuming, cumbersome and confusing and common non-parametric tests are not appropriate for evaluating interactive effects common in horticultural research. The aligned rank transformation allows non-parametric testing for interactions and main effects using standard ANOVA techniques. This has not been widely adapted due to its rigorous mathematical nature, however, a downloadable (ARTool) is now available, which performs the math needed for the transformation. This study provides step-by-step instructions for integrating ARTool with the free edition of SAS (SAS University Edition) in an easily employed method for testing normality, transforming data with aligned ranks, and analysing data using standard ANOVAs.

scholarly journals A novel gene-set association test based on variance-gamma distribution

2018 ◽  
Vol 28 (9) ◽  
pp. 2868-2875
Author(s):  
Zhongxue Chen ◽  
Qingzhong Liu ◽  
Kai Wang
Keyword(s):  
Gamma Distribution ◽  
Type I Error ◽  
Null Distribution ◽  
Real Data ◽  
Association Test ◽  
P Value ◽  
Type I ◽  
Test Statistic ◽  
Data Set ◽  
Variance Gamma

Several gene- or set-based association tests have been proposed recently in the literature. Powerful statistical approaches are still highly desirable in this area. In this paper we propose a novel statistical association test, which uses information of the burden component and its complement from the genotypes. This new test statistic has a simple null distribution, which is a special and simplified variance-gamma distribution, and its p-value can be easily calculated. Through a comprehensive simulation study, we show that the new test can control type I error rate and has superior detecting power compared with some popular existing methods. We also apply the new approach to a real data set; the results demonstrate that this test is promising.

Testing equality of means in partially paired data with incompleteness in single response

2018 ◽  
Vol 28 (5) ◽  
pp. 1508-1522 ◽  
Author(s):  
Qianya Qi ◽  
Li Yan ◽  
Lili Tian
Keyword(s):  
Type I Error ◽  
Real Data ◽  
The Cancer Genome Atlas ◽  
P Value ◽  
Type I ◽  
Paired Data ◽  
Data Set ◽  
Equality Of Means ◽  
Breast Cancer Study ◽  
Single Response

In testing differentially expressed genes between tumor and healthy tissues, data are usually collected in paired form. However, incomplete paired data often occur. While extensive statistical researches exist for paired data with incompleteness in both arms, hardly any recent work can be found on paired data with incompleteness in single arm. This paper aims to fill this gap by proposing some new methods, namely, P-value pooling methods and a nonparametric combination test. Simulation studies are conducted to investigate the performance of the proposed methods in terms of type I error and power at small to moderate sample sizes. A real data set from The Cancer Genome Atlas (TCGA) breast cancer study is analyzed using the proposed methods.

Stepwise and Simultaneous Multiple Comparison Procedures of Repeated Measures’ Means

1994 ◽  
Vol 19 (2) ◽  
pp. 127-162 ◽  
Author(s):  
H. J. Keselman
Keyword(s):  
Repeated Measures ◽  
Type I Error ◽  
Multiple Comparison ◽  
Multivariate Normality ◽  
Type I ◽  
Stepwise Procedures ◽  
Multiple Comparison Procedures ◽  
Multisample Sphericity

Stepwise multiple comparison procedures (MCPs) for repeated measures’ means based on the methods of Hayter (1986) , Hochberg (1988) , Peritz (1970) , Ryan (1960) - Welsch (1977a) , Shaffer (1979 , 1986) , and Welsch (1977a) were compared for their overall familywise rates of Type I error when multisample sphericity and multivariate normality were not satisfied. Robust stepwise procedures were identified by Keselman, Keselman, and Shaffer (1991) with respect to three definitions of power. On average, Welsh’s (1977a) step-up procedure was found to be the most powerful MCP.

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