Introduction | The simulation framework | Specifying scenarios | Example: power analysis for a cluster-randomized trial

Prevalence | Finding the intercept | Risk ratios | Risk differences | AUC

Overview | Defining the Data | Generating the data | Assigning treatment/exposure | More details on data definitions | Formulas | Distributions | beta | binary | binomial | categorical | clusterSize | custom | exponential | gamma | mixture | negBinomial | nonrandom | normal | noZeroPoisson | poisson | trtAssign | uniform | uniformInt | Generating multiple variables with a single definition | Adding data to an existing data table | defDataAdd/defRepeatAdd/readDataAdd and addColumns | defCondition and addCondition

Example 1 | Example 2

Setting cluster sizes

Correlated data: additional distributions

Simple correlation matrix generation | Specifying a structure | Cluster-specific correlation matrices | More elaborate example | Block matrices for temporal data | Cross-sectional data | Exchangeable | Decay | Closed cohort | Generating block matrices and simulating data | Cross-sectional data with exchangeable correlation | Cross-sectional data with correlation decay | Cohort data with exchangeable correlation | Cohort data with correlation decay | Varying correlation matrices by cluster

Updating existing definition tables | Double-dot external variable reference | Using non-scalar double-dot variable reference

Longitudinal data with varying observation and interval times

Longitudinal data with missingness

Comparing response distributions of different populations | The cumulative proportional odds model | Simulation | Non-proportional odds | Correlated multivariate ordinal data

Weibull distribution | Generating standard survival data with censoring | Competing risks | Introducing non-proportional hazards | Generating parameters for survival distribution

Assigned treatment | Assigned treatment using trtAssign distribution in defData | Observed treatment | Stepped-wedge design