Newcombe | Confidence Intervals for Proportions and Related Measures of Effect Size | Buch | sack.de

Newcombe Confidence Intervals for Proportions and Related Measures of Effect Size



1. Auflage 2012, 468 Seiten, Gebunden, Format (B × H): 161 mm x 238 mm, Gewicht: 808 g Reihe: Chapman & Hall/CRC Biostatistics Series
ISBN: 978-1-4398-1278-5
Verlag: Taylor & Francis Inc


Newcombe Confidence Intervals for Proportions and Related Measures of Effect Size

Confidence Intervals for Proportions and Related Measures of Effect Size illustrates the use of effect size measures and corresponding confidence intervals as more informative alternatives to the most basic and widely used significance tests. The book provides you with a deep understanding of what happens when these statistical methods are applied in situations far removed from the familiar Gaussian case.

Drawing on his extensive work as a statistician and professor at Cardiff University School of Medicine, the author brings together methods for calculating confidence intervals for proportions and several other important measures, including differences, ratios, and nonparametric effect size measures generalizing Mann-Whitney and Wilcoxon tests. He also explains three important approaches to obtaining intervals for related measures. Many examples illustrate the application of the methods in the health and social sciences. Requiring little computational skills, the book offers user-friendly Excel spreadsheets for download at www.crcpress.com, enabling you to easily apply the methods to your own empirical data.

Zielgruppe


Researchers, students, and applied statisticians involved in quantitative research in the health and social sciences.


Autoren/Hrsg.


Weitere Infos & Material


Hypothesis Tests and Confidence IntervalsSample and PopulationHypothesis Testing and Confidence Intervals: The FundamentalsWhy Confidence Intervals Are Generally More Informative Than p-ValuesMeasures of Effect SizeWhen Are Point and Interval Estimates Less Helpful?Frequentist, Bayesian and Likelihood IntervalsJust What Is Meant by the Population?The Unit of DataSample Size Planning

Means and Their DifferencesConfidence Interval for a MeanConfidence Interval for the Difference between Means of Independent SamplesConfidence Interval for the Difference between Two Means Based on Individually Paired SamplesScale TransformationNon-Parametric MethodsThe Effect of Dichotomising Continuous Variables

Confidence Intervals for a Simple Binomial ProportionIntroductionThe Wald IntervalBoundary AnomaliesAlternative IntervalsAlgebraic Definitions for Several Confidence Intervals for the Binomial ProportionImplementation of Wilson Score Interval in MS ExcelSample Size for Estimating a Proportion

Criteria for OptimalityHow Can We Say Which Methods Are Good Ones?CoverageExpected WidthInterval LocationComputational Ease and Transparency

Evaluation of Performance of Confidence Interval MethodsIntroductionAn Example of EvaluationApproaches Used in Evaluations for the Binomial ProportionThe Need for Illustrative Examples

Intervals for the Poisson Parameter and the Substitution ApproachThe Poisson Distribution and Its ApplicationsConfidence Intervals for the Poisson Parameter and Related QuantitiesWidening the Applicability of Confidence Interval Methods: The Substitution Approach

Difference between Independent Proportions and the Square-and-Add ApproachThe Ordinary 2 x 2 Table for Unpaired DataThe Wald IntervalThe Square-and-Add or MOVER ApproachOther Well-Behaved Intervals for the Difference between Independent ProportionsEvaluation of PerformanceNumber Needed to TreatBayesian IntervalsInterpreting Overlapping IntervalsSample Size Planning

Difference between Proportions Based on Individually Paired DataThe 2 x 2 Table for Paired Binary DataWald and Conditional IntervalsIntervals Based on Profile LikelihoodsScore-Based IntervalsEvaluation of Performance

Methods for Triads of ProportionsIntroductionTrinomial Variables on Equally Spaced ScalesUnordered Trinomial Data: Generalising the Tail-Based p-Value to Characterise Conformity to Prescribed NormsA Ternary Plot for Unordered Trinomial Data

Relative Risk and Rate RatioA Ratio of Independent ProportionsThree Effect Size Measures Comparing ProportionsRatio Measures Behave Best on a Log ScaleIntervals Corresponding to the Empirical EstimateInfinite Bias in Ratio EstimatesIntervals Based on Mesially Shrunk Estimated RisksA Ratio of Proportions Based on Paired DataA Ratio of Sizes of Overlapping GroupsA Ratio of Two RatesImplementation in MS Excel

The Odds Ratio and Logistic RegressionThe Rationale for the Odds RatioDisadvantages of the Odds RatioIntervals Corresponding to the Empirical EstimateDeterministic Bootstrap Intervals Based on Median Unbiased EstimatesLogistic RegressionAn Odds Ratio Based on Paired DataImplementation

Screening and Diagnostic TestsBackgroundSensitivity and SpecificityPositive and Negative Predictive ValuesTrade-Off between Sensitivity and Specificity: The ROC CurveSimultaneous Comparison of Sensitivity and Specificity between Two Tests

Widening the Applicability of Confidence Interval Methods: The Propagating Imprecision ApproachBackgroundThe Origin of the PropImp ApproachThe PropImp Method DefinedPropImp and MOVER Wilson Intervals for Measures Comparing Two ProportionsImplementation of the PropImp MethodEvaluationThe Thorny Issue of MonotonicitySome Issues Relating to MOVER and PropImp Approaches

Several Applications of the MOVER and PropImp ApproachesIntroductionAdditive-Scale Interaction for ProportionsRadiation Dose RatioLevin’s Attributable Risk Population Risk Difference and Population Impact Number Quantification of Copy Number Variations Standardised Mortality Ratio Adjusted for Incomplete Data on Cause of Death RD and NNT from Baseline Risk and Rela


Newcombe, Robert Gordon
Robert G. Newcombe is a professor in the Institute of Primary Care and Public Health at Cardiff University School of Medicine, where he teaches medical statistics and epidemiology and is involved in medical and dental research. Dr. Newcombe is a member of the editorial board of Statistical Methods in Medical Research and serves on the Cardiff & Vale Research Review Service and Wales Ambulance Service Trust Research & Development panels.


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