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Author:   Ton J. Cleophas ,  Aeilko H. Zwinderman
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   Softcover reprint of the original 2nd ed. 2016
Dimensions:   Width: 15.50cm , Height: 2.10cm , Length: 23.50cm
Weight:   6.088kg
ISBN:  

9783319342504

ISBN 10:   3319342509
Pages:   375
Publication Date:   29 October 2016
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.
Language:   English

Table of Contents

Preface.- Introduction.- I Continuous outcome data.- One sample continuous data.-  Paired continuous outcome data normality assumed.- Paired continuous outcome data nonnormality accounted.- Paired continuous outcome data with predictors.- Unpaired continuous outcome data normality assumed.- Unpaired continuous outcome data nonnormality accounted.- Linear regression for continuous outcome data.- Recoding for categorical predictor data.- Repeated-measures-analysis of variance normality assumed.- Repeated-measures-analysis of variance nonnormality accounted.-  Doubly-repeated-measures-analysis of variance.- Multilevel modeling with mixed linear models. Random multilevel modeling with generalized mixed linear models.- One-way-analysis of variance normality assumed.- One-way-analysis of variance nonnormality accounted.- Trend tests of continuous outcome data.- Multistage regression.- Multivariate analysis with path statistics.- Multivariate analysisof variance.- Average-rank-testing for multiple outcome variables and categorical predictors.- Missing data imputation.- Meta-regression.- Poisson regression including a weight variable (time of observation) for rates.- Confounding.- Interaction.- Curvilinear analysis.- Loess and spline modeling for nonlinear data, where curvilinear models lack fit.- Monte Carlo analysis, the easy alternative for continuous outcome data.- Artificial intelligence as a distribution free alternative for nonlinear data.- Robust tests for data with large outliers.- Nonnegative outcome data using the gamma distribution.- Nonnegative outcome data with a big spike at zero using the Tweedie distribution.- Polynomial analysis for continuous outcome data with a sinusoidal pattern.- Validating quantitative diagnostic tests.- Reliability assessment of quantitative diagnostic tests.- II Binary outcome data.- One sample binary data.- Unpaired binary data.- Binary logistic regression with a binarypredictor.-  Binary logistic regression with categorical predictors.- Binary logistic regression with a continuous predictor.- Trend tests of binary data.- Paired binary outcome data without predictors.- Paired binary outcome data with predictors.- Repeated measures binary data.- Multinomial logistic regression for outcome categories.- Multinomial logistic regression with random intercepts for both categorical outcome and predictor data.- Comparing the performance of diagnostic tests.- Poisson regression for binary outcome data.- Loglinear models for the exploration of multidimensional contingency tables.- Probit regression for binary outcome data reported as response rates.- Monte Carlo analysis, the easy alternative for binary outcomes.- Validating qualitative diagnostic tests.- Reliability assessment of qualitative diagnostic tests. III Survival and longitudinal data.- Log rank tests.- Cox regression.- Cox regression with time-dependent variables.- SegmentedCox regression.- Assessing seasonality.- Probability assessment of survival with interval censored data analysis.- Index.

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