mediana-fundamentalslisted

# Mediana Fundamentals ## When to Use This Skill - Building Clinical Scenario Evaluation (CSE) frameworks - Defining data models with various endpoint distributions - Configuring analysis models with statistical tests - Setting up multiplicity adjustment procedures - Defining evaluation criteria (power metrics) - Running comprehensive trial simulations - Generating Word-based simulation reports ## Package Overview **Mediana** by Gautier Paux and Alex Dmitrienko provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation approach (Benda et al., 2010). ### CSE Framework Components 1. **Data Models** - Define data generation process 2. **Analysis Models** - Define statistical methods 3. **Evaluation Models** - Define success criteria ## Data Model ### Initialization ```r data.model <- DataModel() ``` ### OutcomeDist - Outcome Distribution Specifies the distribution of patient outcomes. ```r OutcomeDist( outcome.dist = "NormalDist", # Distribution type outcome.type = "standard" # "standard" or "event" ) ``` **Supported Distributions:** | Distribution | Parameters | Use Case | |--------------|------------|----------| | `UniformDist` | `max` | Uniform outcomes | | `NormalDist` | `mean`, `sd` | Continuous endpoints | | `BinomDist` | `prop` | Binary endpoints | | `BetaDist` | `a`, `b` | Proportions | | `ExpoDist` | `rate` | Time-to-event | | `WeibullDist` | `shape`, `scale` | Survival with shape | | `TruncatedExpoDis