====== EZR ======

Although there are many commercially available statistical software packages, only a few implement a competing risk analysis or a proportional hazards regression model with time-dependent covariates.

In addition, most packages are not clinician friendly, as they require that commands be written based on statistical languages. 

A report describes the statistical software 'EZR' (Easy R), which is based on R and R commander. EZR enables the application of statistical functions that are frequently used in clinical studies, such as survival analyses, including competing risk analyses and the use of time-dependent covariates, receiver operating characteristics analyses, meta-analyses, sample size calculation and so on, by point-and-click access. EZR is freely available on (http://www.jichi.ac.jp/saitama-sct/SaitamaHP.files/statmed.html) and runs on both Windows (Microsoft Corporation, USA) and Mac OS X (Apple, USA). A report provides instructions for the installation and operation of EZR
((Kanda Y. Investigation of the freely available easy-to-use software 'EZR' for 
medical statistics. Bone Marrow Transplant. 2013 Mar;48(3):452-8. doi:
10.1038/bmt.2012.244. Epub 2012 Dec 3. PubMed PMID: 23208313; PubMed Central
PMCID: PMC3590441.
)).
----


Statistical functions of EZR
For discrete variables

Frequency distributions/cr Confidence interval for a proportion

One sample proportion test

Confidence interval for a difference between two proportions

Confidence interval for a ratio of two proportions

Compare two proportions ([[Fisher's exact test]] and Chi-square test)

Compare proportions of two paired samples (McNemar test)

Compare proportions of more than two paired samples (Cochran Q test)

Cochran-Armitage test for trend in proportions

Logistic regression

For [[continuous variable]]s

Numerical summaries

Smirnov-Grubbs test for outliers

Kolmogorov-Smimov test for normal distribution

Confidence interval for a mean

Single-sample t-test

Two-variances F-test

Two-sample t-test

Paired t-test

Bartlett's test

One-way ANOVA

Repeated-measures ANOVA

Multi-way ANOVA

ANCOVA

Test for Pearson's correlation

Linear regression

For nonparametric tests for continuous variables

[[Mann Whitney U test]]

Wilcoxon's signed rank test

Kruskal-Wallis test

Friedman test

Jonckheere-Terpstra test

Spearman's rank correlation test

For survival analysis

Kaplan-Meier survival curve and logrank test
Logrank trend test
Cox proportional hazard regression
Cox proportional hazard regression with time-dependent covariate
Cumulative incidence of competing events and Gray test
Fine-Gray proportional hazard regression for competing events

For diagnostic test analysis

Accuracy of qualitative test
Kappa statistics for agreement of two tests
Compute positive and negative predictive values
ROC curve analysis for quantitative test
Compare two ROC curves
Cronbach's alpha coefficient for reliability

For matched-pair analysis

Extract matched controls (This function relys on optmatch package and is limietd to academic use.)
Mantel-Haenzel test for matched proportions
Conditional logistic regression for matched-pair analysis
Stratified Cox proportional hazard regression for matched-pair analysis

For meta-analysis and meta-regression test

Meta-analysis and meta-regression test for proportions
Meta-analysis and meta-regression test for means
Meta-analysis and meta-regression test for hazard ratios

For smaple size and power calculation

Calculate sample size from control and desired response rates
Calculate sample size from proportion and confidence interval
Calculate sample size or power for comparison with specified proportion
Calculate sample size or power for comparison between two proportions
Calculate sample size for non-inferiority trial of two proportions
Calculate sample size from standard deviation and confidence interval
Calculate sample size or power for comparison between two means
Calculate sample size or power for comparison between two paired means
Calculate sample size or power for comparison between two survival curves

For drawing graphs

Bar graph(Frequencies)
Pie chart(Frequencies)
Stem-and-leaf display
Histogram
QQ plot
Bar graph(Means)
Line graph(Means)
Line graph(Repeated measures)
Boxplot
Dot chart
Ordered chart
Scatterplot
Scatterplot matrix
Adjusted survival curve
Stacked cumulative incidences

Statistical functions from original R commander

Principal-components analysis
Factor analysis
k-means cluster analysis
Hierarchical cluster analysis
Summarize hierarchical clustering
Add hierarchical clustering to data set
Linear hypothesis
Variance-inflation factor
Breusch-Pagan test for heteroscedasticity
Durbin-Watson test for autocorrelation
RESET test for nonlinearity
Bonferroni outlier test
Basic diagnostic plots
Residual quantile-comparison plot
Component+residual plots
Added-variable plots
Influence plot
Effect plots