Of the numerous available statistical methods, the list below presents a selection of procedures I am familiar with. It contains common and widely used methods (e.g., various tests or linear regression models), as well as advanced techniques (e.g., mixed-models) that I know in-depth. The grouping reflects the different data structures encountered in research.

Descriptive Statistics

Comprehensive tables of statistcs (location and spread of the data) for plausibility checks and reference

Tests

Hypothesis tests using parametric methods (e.g., Z test or t tests)

Hypothesis tests using nonparametric tests (e.g., Wilcoxon or Mann-Whitney test)

Permutation tests for exact p-values

Resampling methods

Analysis of Continuous Data

Correlation (Pearson, Spearman, Kendall)

Linear regression models, using “first aid transformations” (Tukey) and applying thorough model diagnostics

Group comparisons (ANOVA or Kruskal-Wallis test)

Nonlinear and robust methods

Nonparametric density or kernel estimation

Repeated Measurements

Analysis of Co-Variance (ANCOVA) models

Mixed-effects or hierarchical models (using maximum-likelihood, restricted ML, or robust methods)

Generalized Estimating Equations

Analysis of Categorical Data

Analysis of proportions/risks (chi-square or Fisher’s exact test)

Quantification of differences (risk difference, relative risk, odds ratio, number needed to treat)

Mantel-Haenszel methods

Logistic regression models

Exact-like logistic regression

Cumulative logit (or proportional-odds) models for ordinal categories

Analysis of Count Data

Analysis of counts or rates (counts per unit)

Quantification of differences (rate ratio)

Mantel-Haenszel methods

Poisson regression

Negativ-binomial regression

Survival or Failure or Reliability Analysis

Estimation of the survivor functions (Kaplan-Meier plot)

Log-rank (or Mantel-Haenszel or Mantel-Cox) and Gehan-Wilcoxon test of two survivor functions

Analysis of the hazard ratio (Mantel-Cox estimator)

Cox proportional hazards regression model

Weibull regression

Time-dependent covariates

Time-dependent coefficients

Competing risks and multistate models

Classification

Sensitivity and specificity of binary predictors

Predictive values and likelihood ratios

Analysis of ROC curves

Principal component analysis

Discriminant analysis

Trees and Forests

Reliability of Measurements and Raters

Measurements of reliability (Cohen’s kappa, Fleiss’s kappa, intra-class correlation)

Estimation of intra-rater, inter-rater, and test-retest reliability

Bland-Altman plots

Miscellaneous

Sample size calculations

Meta-analyses of results from the literature (plots, meta-regression)

Variable selection (lasso, elastic net)

Multivariate methods

High-dimensional analyses using copulas