The Experts below are selected from a list of 41085 Experts worldwide ranked by ideXlab platform
John Eng - One of the best experts on this subject based on the ideXlab platform.
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Sample Size Estimation: A Glimpse beyond Simple
2004Co-Authors: John EngAbstract:Small increments in the complexity of clinical studies can readily take Sample Size Estimation and statistical power analysis beyond the capabilities of simple mathematic formulas. In this article, the method of simulation is presented as a general technique with which Sample Size may be calculated for complex study designs. Applications of simulation for determining Sample Size requirements in studies involving correlated data and comparisons of receiver operating characteristic curves are discussed. © RSNA, 2004
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Sample Size Estimation: A Glimpse beyond Simple Formulas
Radiology, 2004Co-Authors: John EngAbstract:Small increments in the complexity of clinical studies can readily take Sample Size Estimation and statistical power analysis beyond the capabilities of simple mathematic formulas. In this article, the method of simulation is presented as a general technique with which Sample Size may be calculated for complex study designs. Applications of simulation for determining Sample Size requirements in studies involving correlated data and comparisons of receiver operating characteristic curves are discussed.
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Sample Size Estimation: How Many Individuals Should Be Studied?
Radiology, 2003Co-Authors: John EngAbstract:The number of individuals to include in a research study, the Sample Size of the study, is an important consideration in the design of many clinical studies. This article reviews the basic factors that determine an appropriate Sample Size and provides methods for its calculation in some simple, yet common, cases. Sample Size is closely tied to statistical power, which is the ability of a study to enable detection of a statistically significant difference when there truly is one. A trade-off exists between a feasible Sample Size and adequate statistical power. Strategies for reducing the necessary Sample Size while maintaining a reasonable power will also be discussed. © RSNA, 2003
Pierre Hadaya - One of the best experts on this subject based on the ideXlab platform.
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minimum Sample Size Estimation in pls sem the inverse square root and gamma exponential methods
Information Systems Journal, 2018Co-Authors: Ned Kock, Pierre HadayaAbstract:Partial least squares-based structural equation modelling (PLS-SEM) is extensively used in the field of information systems, as well as in many other fields where multivariate statistical methods are used. One of the most fundamental issues in PLS-SEM is that of minimum Sample Size Estimation. The ‘10-times rule’ has been a favourite because of its simplicity of application, even though it tends to yield imprecise estimates. We propose two related methods, based on mathematical equations, as alternatives for minimum Sample Size Estimation in PLS-SEM: the inverse square root method, and the gamma-exponential method. Based on three Monte Carlo experiments, we demonstrate that both methods are fairly accurate. The inverse square root method is particularly attractive in terms of its simplicity of application. © 2016 John Wiley & Sons Ltd
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Minimum Sample Size Estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods
Information Systems Journal, 2016Co-Authors: Ned Kock, Pierre HadayaAbstract:Partial least squares-based structural equation modelling (PLS-SEM) is extensively used in the field of information systems, as well as in many other fields where multivariate statistical methods are used. One of the most fundamental issues in PLS-SEM is that of minimum Sample Size Estimation. The ‘10-times rule’ has been a favourite because of its simplicity of application, even though it tends to yield imprecise estimates. We propose two related methods, based on mathematical equations, as alternatives for minimum Sample Size Estimation in PLS-SEM: the inverse square root method, and the gamma-exponential method. Based on three Monte Carlo experiments, we demonstrate that both methods are fairly accurate. The inverse square root method is particularly attractive in terms of its simplicity of application. © 2016 John Wiley & Sons Ltd
Ned Kock - One of the best experts on this subject based on the ideXlab platform.
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minimum Sample Size Estimation in pls sem the inverse square root and gamma exponential methods
Information Systems Journal, 2018Co-Authors: Ned Kock, Pierre HadayaAbstract:Partial least squares-based structural equation modelling (PLS-SEM) is extensively used in the field of information systems, as well as in many other fields where multivariate statistical methods are used. One of the most fundamental issues in PLS-SEM is that of minimum Sample Size Estimation. The ‘10-times rule’ has been a favourite because of its simplicity of application, even though it tends to yield imprecise estimates. We propose two related methods, based on mathematical equations, as alternatives for minimum Sample Size Estimation in PLS-SEM: the inverse square root method, and the gamma-exponential method. Based on three Monte Carlo experiments, we demonstrate that both methods are fairly accurate. The inverse square root method is particularly attractive in terms of its simplicity of application. © 2016 John Wiley & Sons Ltd
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Minimum Sample Size Estimation in PLS‐SEM: The inverse square root and gamma‐exponential methods
Information Systems Journal, 2016Co-Authors: Ned Kock, Pierre HadayaAbstract:Partial least squares-based structural equation modelling (PLS-SEM) is extensively used in the field of information systems, as well as in many other fields where multivariate statistical methods are used. One of the most fundamental issues in PLS-SEM is that of minimum Sample Size Estimation. The ‘10-times rule’ has been a favourite because of its simplicity of application, even though it tends to yield imprecise estimates. We propose two related methods, based on mathematical equations, as alternatives for minimum Sample Size Estimation in PLS-SEM: the inverse square root method, and the gamma-exponential method. Based on three Monte Carlo experiments, we demonstrate that both methods are fairly accurate. The inverse square root method is particularly attractive in terms of its simplicity of application. © 2016 John Wiley & Sons Ltd
Diane Kelly - One of the best experts on this subject based on the ideXlab platform.
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statistical power analysis for Sample Size Estimation in information retrieval experiments with users
European Conference on Information Retrieval, 2015Co-Authors: Diane KellyAbstract:One critical decision researchers must make when designing laboratory experiments with users is how many participants to study. In interactive information retrieval (IR), the determination of Sample Size is often based on heuristics and limited by practical constraints such as time and finances. As a result, many studies are underpowered and it is common to see researchers make statements like “With more participants significance might have been detected,” but what does this mean? What does it mean for a study to be underpowered? How does this effect what we are able to discover, how we interpret study results and how we make choices about what to study next? How does one determine an appropriate Sample Size? What does it even mean for a Sample Size to be appropriate? This tutorial addressed these uestions by introducing participants to the use of statistical power analysis for Sample Size Estimation in laboratory experiments with users. In discussing this topic, the issues of effect Size, Type I and Type II errors and experimental design, including choice of statistical procedures, were also addressed.
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ECIR - Statistical Power Analysis for Sample Size Estimation in Information Retrieval Experiments with Users
Lecture Notes in Computer Science, 2015Co-Authors: Diane KellyAbstract:One critical decision researchers must make when designing laboratory experiments with users is how many participants to study. In interactive information retrieval (IR), the determination of Sample Size is often based on heuristics and limited by practical constraints such as time and finances. As a result, many studies are underpowered and it is common to see researchers make statements like “With more participants significance might have been detected,” but what does this mean? What does it mean for a study to be underpowered? How does this effect what we are able to discover, how we interpret study results and how we make choices about what to study next? How does one determine an appropriate Sample Size? What does it even mean for a Sample Size to be appropriate? This tutorial addressed these uestions by introducing participants to the use of statistical power analysis for Sample Size Estimation in laboratory experiments with users. In discussing this topic, the issues of effect Size, Type I and Type II errors and experimental design, including choice of statistical procedures, were also addressed.
Constantine Gatsonis - One of the best experts on this subject based on the ideXlab platform.
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Sample Size Estimation for time-dependent receiver operating characteristic.
Statistics in medicine, 2013Co-Authors: Constantine GatsonisAbstract:In contrast to the usual ROC analysis with a contemporaneous reference standard, the time-dependent setting introduces the possibility that the reference standard refers to an event at a future time and may not be known for every patient due to censoring. The goal of this research is to determine the Sample Size required for a study design to address the question of the accuracy of a diagnostic test using the area under the curve in time-dependent ROC analysis. We adapt a previously published estimator of the time-dependent area under the ROC curve, which is a function of the expected conditional survival functions. This estimator accommodates censored data. The Estimation of the required Sample Size is based on approximations of the expected conditional survival functions and their variances, derived under parametric assumptions of an exponential failure time and an exponential censoring time. We also consider different patient enrollment strategies. The proposed method can provide an adequate Sample Size to ensure that the test's accuracy is estimated to a prespecified precision. We present results of a simulation study to assess the accuracy of the method and its robustness to departures from the parametric assumptions. We apply the proposed method to design of a study of positron emission tomography as predictor of disease free survival in women undergoing therapy for cervical cancer. Copyright © 2013 John Wiley & Sons, Ltd.
