The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Samuel B. Green - One of the best experts on this subject based on the ideXlab platform.
-
An adjusted Bonferroni Method for elimination of parameters in specification addition searches
Structural Equation Modeling: A Multidisciplinary Journal, 2001Co-Authors: Samuel B. Green, Marilyn S. Thompson, Jennifer PoirerAbstract:An adjusted Bonferroni Method for controlling familywise error rate when deleting parameters in specification addition searches was presented and evaluated in a 2-part study. First, data were generated based on a factor model and then analyzed using backward selection, with and without applying the adjusted Bonferroni Method. Three factors were manipulated: sample size, magnitude of weights, and number of parameters in a search. Under most of the 30 explored conditions, the empirical familywise error rates were relatively close to the nominal alpha level of. 05 when the adjusted Bonferroni Method was applied. Error rates that exceeded the. 05 level appeared to be a function of the multivariate Wald test and not of the adjusted Bonferroni Method. Second, data were generated based on a path model and analyzed using 2-stage and backward selection Methods when the initial model was misspecified. Controlling stringently for Type I errors in the initial addition stage of a 2-stage search created selection error...
-
A Monte Carlo Investigation of Methods for Controlling Type I Errors with Specification Searches in Structural Equation Modeling.
Multivariate behavioral research, 1998Co-Authors: Samuel B. Green, Marilyn S. Thompson, Michael A. BabyakAbstract:A standard strategy in structural equation modeling is to conduct multiple Lagrange multiplier (LM) tests after rejection of an initial model. Controlling for Type 1 error across these tests minimizes the likelihood of including unnecessary additional parameters in the model. Three Methods for controlling Type I errors are evaluated using simulated data for factor analytic models: the standard approach which involves testing each parameter at the .05 level, a Bonferroni approach, and a simultaneous test procedure (STP). In the first part of the study, all samples were generated from a population in which all null hypotheses associated with the LM tests were correct. Three factors were manipu1,~ted: factor weights, sample size, and number of parameters in the specification search. The standard and the STP approaches yielded overly liberal and overly conservative familywise error rates, respectively, while the Bonferroni approach yielded error rates closer to the nominal level. In the second part of the study, data were generated in which one or more null hypotheses associated with the LM test were incorrect, and the number of parameters in the search was manipulated. Again the Bonferroni Method was the best approach in controlling familywise: error rate, particularly when the alpha level was adjusted for the number of parameters evaluated at each step.
Zachary N. Stowe - One of the best experts on this subject based on the ideXlab platform.
-
Pet therapy program for antepartum high-risk pregnancies: a pilot study
Journal of Perinatology, 2014Co-Authors: Christian E. Lynch, Everett F. Magann, S N Barringer, Song Ounpraseuth, D G Eastham, S D Lewis, Zachary N. StoweAbstract:Objective: This pilot study evaluated the potential benefits of pet therapy on symptoms of anxiety and depression in antepartum hospitalized women with high-risk pregnancies. Study design: Eighty-two women in a hospital-based setting completed the State-Trait Anxiety Inventory and the Beck Depression Inventory before and after the pet therapy visit. For both questionnaires, paired t -test was used and adjusted P -values were obtained using the Hochberg step-up Bonferroni Method. Result: The mean scores for depressive symptoms significantly improved from the pre-pet therapy (10.1±6.3) compared with the post-pet therapy (6.3±5.9) ( P
-
Pet therapy program for antepartum high-risk pregnancies: a pilot study
Journal of perinatology : official journal of the California Perinatal Association, 2014Co-Authors: Christian E. Lynch, Everett F. Magann, S N Barringer, Song Ounpraseuth, D G Eastham, S D Lewis, Zachary N. StoweAbstract:This pilot study evaluated the potential benefits of pet therapy on symptoms of anxiety and depression in antepartum hospitalized women with high-risk pregnancies. Eighty-two women in a hospital-based setting completed the State-Trait Anxiety Inventory and the Beck Depression Inventory before and after the pet therapy visit. For both questionnaires, paired t-test was used and adjusted P-values were obtained using the Hochberg step-up Bonferroni Method. The mean scores for depressive symptoms significantly improved from the pre-pet therapy (10.1±6.3) compared with the post-pet therapy (6.3±5.9) (P
Sin-ho Jung - One of the best experts on this subject based on the ideXlab platform.
-
Sample Size Calculation for Simulation-Based Multiple-Testing Procedures
Journal of biopharmaceutical statistics, 2005Co-Authors: Heejung Bang, Sin-ho Jung, Stephen L. GeorgeAbstract:In this article, we present a simple Method to calculate sample size and power for a simulation-based multiple testing procedure which gives a sharper critical value than the standard Bonferroni Method. The Method is especially useful when several highly correlated test statistics are involved in a multiple-testing procedure. The formula for sample size calculation will be useful in designing clinical trials with multiple endpoints or correlated outcomes. We illustrate our Method with a quality-of-life study for patients with early stage prostate cancer. Our Method can also be used for comparing multiple independent groups.
-
sample size calculation for multiple testing in microarray data analysis
Biostatistics, 2005Co-Authors: Sin-ho Jung, Heejung Bang, Stanley YoungAbstract:Microarray technology is rapidly emerging for genome-wide screening of differentially expressed genes between clinical subtypes or different conditions of human diseases. Traditional statistical testing approaches, such as the two-sample t-test or Wilcoxon test, are frequently used for evaluating statistical significance of informative expressions but require adjustment for large-scale multiplicity. Due to its simplicity, Bonferroni adjustment has been widely used to circumvent this problem. It is well known, however, that the standard Bonferroni test is often very conservative. In the present paper, we compare three multiple testing procedures in the microarray context: the original Bonferroni Method, a Bonferroni-type improved single-step Method and a step-down Method. The latter two Methods are based on nonparametric resampling, by which the null distribution can be derived with the dependency structure among gene expressions preserved and the family-wise error rate accurately controlled at the desired level. We also present a sample size calculation Method for designing microarray studies. Through simulations and data analyses, we find that the proposed Methods for testing and sample size calculation are computationally fast and control error and power precisely.
Jennifer Poirer - One of the best experts on this subject based on the ideXlab platform.
-
An adjusted Bonferroni Method for elimination of parameters in specification addition searches
Structural Equation Modeling: A Multidisciplinary Journal, 2001Co-Authors: Samuel B. Green, Marilyn S. Thompson, Jennifer PoirerAbstract:An adjusted Bonferroni Method for controlling familywise error rate when deleting parameters in specification addition searches was presented and evaluated in a 2-part study. First, data were generated based on a factor model and then analyzed using backward selection, with and without applying the adjusted Bonferroni Method. Three factors were manipulated: sample size, magnitude of weights, and number of parameters in a search. Under most of the 30 explored conditions, the empirical familywise error rates were relatively close to the nominal alpha level of. 05 when the adjusted Bonferroni Method was applied. Error rates that exceeded the. 05 level appeared to be a function of the multivariate Wald test and not of the adjusted Bonferroni Method. Second, data were generated based on a path model and analyzed using 2-stage and backward selection Methods when the initial model was misspecified. Controlling stringently for Type I errors in the initial addition stage of a 2-stage search created selection error...
Heejung Bang - One of the best experts on this subject based on the ideXlab platform.
-
Sample Size Calculation for Simulation-Based Multiple-Testing Procedures
Journal of biopharmaceutical statistics, 2005Co-Authors: Heejung Bang, Sin-ho Jung, Stephen L. GeorgeAbstract:In this article, we present a simple Method to calculate sample size and power for a simulation-based multiple testing procedure which gives a sharper critical value than the standard Bonferroni Method. The Method is especially useful when several highly correlated test statistics are involved in a multiple-testing procedure. The formula for sample size calculation will be useful in designing clinical trials with multiple endpoints or correlated outcomes. We illustrate our Method with a quality-of-life study for patients with early stage prostate cancer. Our Method can also be used for comparing multiple independent groups.
-
sample size calculation for multiple testing in microarray data analysis
Biostatistics, 2005Co-Authors: Sin-ho Jung, Heejung Bang, Stanley YoungAbstract:Microarray technology is rapidly emerging for genome-wide screening of differentially expressed genes between clinical subtypes or different conditions of human diseases. Traditional statistical testing approaches, such as the two-sample t-test or Wilcoxon test, are frequently used for evaluating statistical significance of informative expressions but require adjustment for large-scale multiplicity. Due to its simplicity, Bonferroni adjustment has been widely used to circumvent this problem. It is well known, however, that the standard Bonferroni test is often very conservative. In the present paper, we compare three multiple testing procedures in the microarray context: the original Bonferroni Method, a Bonferroni-type improved single-step Method and a step-down Method. The latter two Methods are based on nonparametric resampling, by which the null distribution can be derived with the dependency structure among gene expressions preserved and the family-wise error rate accurately controlled at the desired level. We also present a sample size calculation Method for designing microarray studies. Through simulations and data analyses, we find that the proposed Methods for testing and sample size calculation are computationally fast and control error and power precisely.