Top 10 EZR Functions Every Researcher Should KnowEZR (Easy R) is a free, user-friendly graphical interface for R tailored to clinicians and researchers who need reliable statistical tools without deep programming. Built on R and R Commander, EZR simplifies common biostatistical tasks with point-and-click menus while still exposing the power of R. This article walks through the top 10 EZR functions every researcher should know, explaining what they do, when to use them, and practical tips to avoid common pitfalls.
1. Data Import and Management
Why it matters: Clean, well-structured data are the foundation of reproducible analysis.
What it does: EZR supports importing data from CSV, Excel, SPSS, and direct R data frames. Once imported, you can rename variables, recode categories, handle missing values, and create factor variables via menus.
When to use: At the start of every project — before any analysis.
Practical tips:
- Always check variable types (numeric vs. factor) before analysis.
- Use “Recode variables” to combine sparse categories or correct miscoded responses.
- Keep a copy of the raw dataset untouched; operate on a duplicate for cleaning.
2. Descriptive Statistics and Tables
Why it matters: Descriptive statistics summarize your sample and guide choice of further analyses.
What it does: EZR produces summary tables (means, medians, SDs, ranges) and frequency tables, with options to stratify by groups and include p-values for simple comparisons.
When to use: For initial data exploration and to report baseline characteristics in manuscripts.
Practical tips:
- For skewed data, report medians and interquartile ranges instead of means.
- Use stratified tables to detect baseline imbalances between groups.
3. t-Tests and Nonparametric Alternatives
Why it matters: Comparing two groups is one of the most common inferential tasks.
What it does: EZR runs independent and paired t-tests via menus, and offers nonparametric alternatives like the Wilcoxon rank-sum and signed-rank tests when assumptions are violated.
When to use: Comparing means (or distributions) between two groups.
Practical tips:
- Check normality visually (histogram/QQ plot) and with tests before choosing t-test vs. nonparametric tests.
- For unequal variances, use Welch’s t-test (available in EZR).
4. ANOVA and Kruskal-Wallis Tests
Why it matters: ANOVA extends two-group comparisons to multiple groups.
What it does: EZR performs one-way and factorial ANOVA, with post-hoc comparisons (Tukey, Bonferroni). When assumptions fail, use Kruskal-Wallis for nonparametric comparisons.
When to use: Comparing a continuous outcome across three or more groups.
Practical tips:
- Inspect residuals to check homoscedasticity and normality.
- For repeated measures, choose the appropriate repeated-measures ANOVA menu or use linear mixed models.
5. Linear Regression (Simple and Multiple)
Why it matters: Regression quantifies relationships, adjusts for confounders, and provides effect estimates with confidence intervals.
What it does: EZR performs simple and multiple linear regression, displays coefficients, standard errors, p-values, R-squared, and diagnostics (residual plots, influence measures).
When to use: Modeling continuous outcomes with predictors.
Practical tips:
- Check multicollinearity (variance inflation factors) and consider centering variables if needed.
- Use residual and leverage plots to identify influential observations.
6. Logistic Regression
Why it matters: Logistic regression models binary outcomes, common in clinical research (e.g., disease vs. no disease).
What it does: EZR fits univariable and multivariable logistic regression models, provides odds ratios (ORs) with 95% CIs, and offers model diagnostics like ROC curves and Hosmer-Lemeshow goodness-of-fit tests.
When to use: When the dependent variable is binary.
Practical tips:
- Ensure adequate events-per-variable (EPV) — a common rule is at least 10 events per predictor.
- For rare outcomes, consider penalized regression techniques (not directly available in basic EZR menus).
7. Survival Analysis (Kaplan–Meier and Cox Proportional Hazards)
Why it matters: Time-to-event data require specialized methods to account for censoring.
What it does: EZR produces Kaplan–Meier survival curves with log-rank tests, and fits Cox proportional hazards models with hazard ratios (HRs). It also provides tests and plots to check proportional hazards assumptions.
When to use: Analyzing time until an event (death, relapse, failure).
Practical tips:
- Plot survival curves stratified by key covariates.
- Check proportional hazards with Schoenfeld residuals; consider time-varying covariates if violated.
8. Sample Size and Power Calculations
Why it matters: Proper sample size planning prevents underpowered studies and wasted resources.
What it does: EZR includes sample size calculators for means, proportions, and survival analyses, and computes power for given sample sizes and effect sizes.
When to use: During study design and grant planning.
Practical tips:
- Use realistic effect sizes drawn from pilot data or literature.
- Consider dropouts and missing data by inflating sample size.
9. Propensity Score Methods
Why it matters: Observational studies often need methods to reduce confounding; propensity scores are a common approach.
What it does: EZR offers propensity score estimation, matching, stratification, and inverse probability weighting. It provides balance diagnostics to assess covariate balance after adjustment.
When to use: When comparing treatment groups in nonrandomized studies.
Practical tips:
- Examine covariate balance before and after matching using standardized differences.
- Avoid overfitting the propensity score model; include variables related to both treatment and outcome.
10. ROC Curves and Diagnostic Test Evaluation
Why it matters: When evaluating biomarkers or diagnostic tests, sensitivity, specificity, and area under the ROC curve (AUC) are essential.
What it does: EZR plots ROC curves, calculates AUC with confidence intervals, and can compare ROC curves between tests or models.
When to use: Assessing diagnostic performance or predictive models.
Practical tips:
- Report threshold-specific sensitivity and specificity along with AUC.
- Use bootstrapping for more robust confidence intervals if sample size is limited.
Common Pitfalls and Best Practices
- Document every data-cleaning step and analysis decision for reproducibility.
- Don’t rely solely on default settings; inspect diagnostic plots and assumption checks.
- When in doubt, complement EZR output with R code (EZR allows users to view underlying R commands), which helps for customization and reproducibility.
Example Workflow (concise)
- Import data and check variable types.
- Run descriptive statistics and visualize key variables.
- Choose appropriate tests (t-test/ANOVA/regression) guided by variable types and assumptions.
- Fit multivariable models with careful variable selection and diagnostics.
- Report estimates with CIs and check sensitivity analyses (e.g., excluding influential observations).
EZR brings accessible, reproducible statistical analysis to clinicians and researchers who prefer graphical interfaces without sacrificing the flexibility of R. Mastering the functions above will cover the majority of standard analyses in clinical and epidemiological research.