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 analysis of 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 d ata 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 binary predictor
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
Segmented Cox regression
Assessing seasonality
Probability assessment of survival with interval censored data analysis
Index.