Search results for "Sample Size"
showing 10 items of 219 documents
A Comparison of Formulae for Calculating Cost-Efficient Sample Sizes of Case-Control Studies with an Internal Validation Scheme
2000
When a case-control study is planned to include an internal validation study, the sample size of the study and the proportion of validated observations has to be calculated. There are a variety of alternative methods to accomplish this. In this article some possible procedures will be compared in order to clarify whether considerable differences in the suggested optimal designs occur, dependent on the used method.
Estimating person parameters via item response model and simple sum score in small samples with few polytomous items: A simulation study
2018
Background The Item Response Theory (IRT) is becoming increasingly popular for item analysis. Theoretical considerations and simulation studies suggest that parameter estimates will become precise only by utilizing many items in large samples. Method A simulation study focusing on a single scale was performed on data with (a) n = 40, 60, 80, 120, 200, 300, 500, and 900 cases utilizing (b) 4, 8, 16, or 32 items. The items were (c) symmetrically distributed vs. skew (skewness 0, 1, and 2). Item loadings were (d) homogeneous vs. heterogeneous. Item loadings were (e) low vs. high. Half of the items had (f) a correlated error or not. The number of answering categories (g) was four vs. five. A to…
Bayesian analysis and design for comparison of effect-sizes
2002
Comparison of effect-sizes, or more generally, of non-centrality parameters of non-central t distributions, is a common problem, especially in meta-analysis. The usual simplifying assumptions of either identical or non-related effect-sizes are often too restrictive to be appropriate. In this paper, the effect-sizes are modeled as random effects with t distributions. Bayesian hierarchical models are used both to design and analyze experiments. The main goal is to compare effect-sizes. Sample sizes are chosen so as to make accurate inferences about the difference of effect-sizes and also to convincingly solve the testing of equality of effect-sizes if such is the goal.
A Monte Carlo study comparing PIV, ULS and DWLS in the estimation of dichotomous confirmatory factor analysis
2012
We conducted a Monte Carlo study to investigate the performance of the polychoric instrumental variable estimator (PIV) in comparison to unweighted least squares (ULS) and diagonally weighted least squares (DWLS) in the estimation of a confirmatory factor analysis model with dichotomous indicators. The simulation involved 144 conditions (1,000 replications per condition) that were defined by a combination of (a) two types of latent factor models, (b) four sample sizes (100, 250, 500, 1,000), (c) three factor loadings (low, moderate, strong), (d) three levels of non-normality (normal, moderately, and extremely non-normal), and (e) whether the factor model was correctly specified or misspecif…
Multiple testing in candidate gene situations: a comparison of classical, discrete, and resampling-based procedures.
2011
In candidate gene association studies, usually several elementary hypotheses are tested simultaneously using one particular set of data. The data normally consist of partly correlated SNP information. Every SNP can be tested for association with the disease, e.g., using the Cochran-Armitage test for trend. To account for the multiplicity of the test situation, different types of multiple testing procedures have been proposed. The question arises whether procedures taking into account the discreteness of the situation show a benefit especially in case of correlated data. We empirically evaluate several different multiple testing procedures via simulation studies using simulated correlated SN…
Power of the Wilcoxon–Mann–Whitney test for non‐inferiority in the presence of death‐censored observations
2017
In clinical trials with patients in a critical state, death may preclude measurement of a quantitative endpoint of interest, and even early measurements, for example for intention-to-treat analysis, may not be available. For example, a non-negligible proportion of patients with acute pulmonary embolism will die before 30 day measurements on the efficacy of thrombolysis can be obtained. As excluding such patients may introduce bias, alternative analyses, and corresponding means for sample size calculation are needed. We specifically consider power analysis in a randomized clinical trial setting in which the goal is to demonstrate noninferiority of a new treatment as compared to a reference t…
Sample size planning for survival prediction with focus on high-dimensional data
2011
Sample size planning should reflect the primary objective of a trial. If the primary objective is prediction, the sample size determination should focus on prediction accuracy instead of power. We present formulas for the determination of training set sample size for survival prediction. Sample size is chosen to control the difference between optimal and expected prediction error. Prediction is carried out by Cox proportional hazards models. The general approach considers censoring as well as low-dimensional and high-dimensional explanatory variables. For dimension reduction in the high-dimensional setting, a variable selection step is inserted. If not all informative variables are included…
A computationally fast alternative to cross-validation in penalized Gaussian graphical models
2015
We study the problem of selection of regularization parameter in penalized Gaussian graphical models. When the goal is to obtain the model with good predicting power, cross validation is the gold standard. We present a new estimator of Kullback-Leibler loss in Gaussian Graphical model which provides a computationally fast alternative to cross-validation. The estimator is obtained by approximating leave-one-out-cross validation. Our approach is demonstrated on simulated data sets for various types of graphs. The proposed formula exhibits superior performance, especially in the typical small sample size scenario, compared to other available alternatives to cross validation, such as Akaike's i…
Sample-size calculation and reestimation for a semiparametric analysis of recurrent event data taking robust standard errors into account
2014
In some clinical trials, the repeated occurrence of the same type of event is of primary interest and the Andersen-Gill model has been proposed to analyze recurrent event data. Existing methods to determine the required sample size for an Andersen-Gill analysis rely on the strong assumption that all heterogeneity in the individuals' risk to experience events can be explained by known covariates. In practice, however, this assumption might be violated due to unknown or unmeasured covariates affecting the time to events. In these situations, the use of a robust variance estimate in calculating the test statistic is highly recommended to assure the type I error rate, but this will in turn decr…
Sample Size Requirements of a Mixture Analysis Method with Applications in Systematic Biology
1999
The available information on sample size requirements of mixture analysis methods is insufficient to permit a precise evaluation of the potential problems facing practical applications of mixture analysis. We use results from Monte Carlo simulation to assess the sample size requirements of a simple mixture analysis method under conditions relevant to biological applications of mixture analysis. The mixture model used includes two univariate normal components with equal variances but assumes that the researcher is ignorant as to the equality of the variances. The method used relies on the EM algorithm to compute the maximum likelihood estimates of the mixture parameters, and the likelihood r…