Titlebook: Statistical Analysis of Clinical Data on a Pocket Calculator, Part 2; Statistics on a Pock Ton J. Cleophas,Aeilko H. Zwinderman Book 2012 T

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Book 2012ry data, power, sample size, multiple testing, variability, confounding, interaction, and reliability. The current part 2 of this title reviews methods for handling missing data, manipulated data, multiple confounders, predictions beyond observation, uncertainty of diagnostic tests, and the problems

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2191-544X ologies for which there is little software are reviewed.Pock.The first part of this title contained all statistical tests relevant to starting clinical investigations, and included tests for continuous and binary data, power, sample size, multiple testing, variability, confounding, interaction, and

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Non-Linear Modeling on a Pocket Calculator,deling (see Chap. 24. In: Statistics Applied to Clinical Studies, Springer New York, 5th edition, 2012, and the . of “SPSS for Starters”, Part 2, Springer New York, 2012, both from the same authors). However, these methods generally require statistical.

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Superiority Testing Instead of Null Hypothesis Testing,the report. This may be a problem in practice, since lack of power indicates that the treatments are less efficacious than expected. Superiority testing assesses whether the eventual power of a study is in agreement with the power as stated in the sample size calculation of the study.

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Introduction,ts for continuous and binary data, as well as tests for power, sample size, multiple testing, variability, confounding, interaction, and reliability. Many more statistical methods can be carried out on a pocket calculator, and this small e book reviews the most important of them.

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Basic Logarithm for a Better Understanding of Statistical Methods,requency distributions and is used for the analysis of likelihood ratios. Basic knowledge of logarithms is, therefore, convenient for a better understanding of many statistical methods. Almost always natural logarithm (ln), otherwise called naperian logarithm, is used, i.e., logarithm to the base e.

DEBT

Missing Data Imputation,es if the data are complete. With only five values missing (one value missing per patient) five patients will not have complete data, and are rather useless for the analysis. This is not 5 % but 15 % of this small study population of 35 patients. An analysis of the remaining 85 % patients is likely

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Uncertainty in the Evaluation of Diagnostic Tests,they are estimates and come with certain amounts of uncertainty. The standards for reporting diagnostic accuracy (STARDS) working party recommends to include measures of uncertainty in any evaluation of a diagnostic test.