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In this case, it is typically used in estimating sufficient sample sizes to achieve adequate power. It can also be conducted before the research study (a priori). Statistical power analysis can be performed after data analysis (post hoc) to assess what the power was in the study. This is the ability of a test to detect an effect, if the effect exists. Type II error, which probability of occurring is usually denoted β, is nevertheless essential, as it is related to the statistical power of the test (1-β). Type I error, which probability of occurring is usually denoted α, may seem more important because it corresponds to the incorrect identification of statistically significant features. In statistical test theory, type I and type II errors refer, respectively, to the rejection of the null hypothesis when it is true (false positive) and the failure to reject the null hypothesis when it is false (false negative). Sample size determination, chemometrics, metabolic phenotyping Introduction DSD toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) for Kernel and log-normal estimates, and in GNU Octave for log-normal estimates (Kernel density estimates are not robust enough in GNU octave). Optimal sample size determination is achieved in a context of biomarker discovery (at least one statistically significant variation) or metabolic exploration (a maximum of statistically significant variations). Statistically significant metabolic variations are evaluated using the Benjamini–Yekutieli correction and processed for data sets of various sizes. The statistical recoupling of variables procedure is used to identify metabolic variables, and their intensity distributions are estimated by Kernel smoothing or log-normal density fitting.
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The procedure uses analytical data only from a small pilot cohort to generate an expanded data set. DSD allows the determination of an optimized sample size in metabolic phenotyping studies. Original research concerning DSD was published elsewhere.
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We release an automated implementation of the Data-driven Sample size Determination (DSD) algorithm for MATLAB and GNU Octave. In this review, we discuss sample size estimation procedures for metabolic phenotyping studies. Until now, there was no standard procedure available to address this purpose. This is due particularly to the multiple hypothesis-testing framework and the top-down hypothesis-free approach, with no a priori known metabolic target. In metabolic phenotyping studies, sample size determination remains a complex step. The number of samples needed to identify significant effects is a key question in biomedical studies, with consequences on experimental designs, costs and potential discoveries.