Operation groups | Example

The YAML Specification Format | Working with a Single Domain | Loading and Inspecting | Modifying Columns | Schema Validation | Modifying Parameters | Rows | Saving Changes | Working with a Study | Loading a Study | Populating Core Variables | Populating Predecessor Metadata | The Complete Study Pipeline | Saving a Study | Conditional Metadata | Creating Flat Metadata Tables | Next Steps

Overview | General structure | Domain identifier | Required Field | Example | Primary keys | Label | Class | Common values | Structure | Population | Filter execution order | Base domains | Domain-specific filtering | Global filters | Using external domains in global filters | Simple example | Complex example | Key Points | Columns | Fields | The method field | Component object | Examples | Simple column operations | Derived columns | Examples (continued): | Summary of column rules | Component reuse rules | One component, multiple output columns | Same component, different parameters | Overlapping outputs cause an error | Cross-domain reuse | No intermediate columns | Row operations | Parameters | Row vs. parameter operations

What is a component? | Anatomy of a component template | Retrieve and inspect a component | Render a component | Evaluate rendered code | Writing a custom component | Component repos | Automatic code validation | Testing components

Overview | Component types | Component Structure | Metadata Header | Metadata Tags | @title | @description | @param | @type | @origin | @method | @depends | @outputs | @code | R Script Components | When to Use | Code Structure | Example: Simple Column Derivation | Example: Dependent Derivation | Example: Multiple Dependencies | Mustache Template Components | Template Syntax | Example: Parameterized Column Derivation | Example: Reusing Templates with Different Parameters | Handling external dependencies | Standard vs. custom components | How mighty finds a component | In-built components

Overview | External data | Example: | Component repositories | Component IDs

Your first mighty ADaM program | Study directory guidelines | Set up your framework configuration file | Set up your ADaM specification | Define your custom component | Render the ADaM program

Three stages of code generation | Template syntax | The keep_vars pipeline | mighty_read_data | Example | Parameters | connector_path_expr | domains | How data_type is determined | When is_current_domain is TRUE | mighty_init_domain | self | keep_vars | source_domain_rbind | src_mutations | mighty_filter_domain | joins | domain_filter | global_filter | mighty_col_mutate | rename_var | source_var | mighty_col_rename | mighty_col_echo | join_dataset | select_expr | by_vars | needs_rename | output_var / var_to_add | mighty_write_data | file_ext | row_order_vars

Standards and terminology

Types of nodes

Overview | Types of missing data | Workflow

Introduction | Configuration and schema | Work with a configuration

Introduction | Define a schema | Create a child class | Use the child class | Construction | Accessing values | Class hierarchy | Validation | Method dispatch

Overview | Framework configuration (_mighty.yml) | ADaM Specification | Code Components | Study properties (_study.yml) | Data Model Metadata (optional)

Introduction | Ways to call the run() function | Single and multiple files, and wild cards | Using list() as input | Using a configuration file as input | Adjusting the log directory | How to use the log_dir argument to specify where to store the logs

Generating some data to run examples | Controlling verbosity | Specifying the estimand | Built-in estimands - average treatment effect and rate ratio | Specifying any estimand | Variance estimation using cross validation | Prognostic covariate adjustment | Specifying learners | Default learners | Creating own list of learners | Inspecting the prognostic model

Plug-in estimation of marginal effects and variance estimation using influence functions | Simulating data for exploratory analyses | Fitting rctglm() without prognostic covariate adjustment | Using prognostic covariate adjustment | Investigating the prognostic model | Prospective power approximation | For marginal effects | For linear models | Creating a plot of prospective power curves | Using repeat_power_marginaleffect() and plotting the results

Estimating the power for marginal effects | Simulating some data | Controlling assumptions

Introduction | Connecting to a SharePoint Site | Without connector package | With connector package (recommended) | Reading and writing data | Conclusion

Creating a new connector | Create custom generic method | Use extra class for simple customization | Special handling of files | Add new file format

Introduction | Connector configuration | Working example

Introduction | Methods and Return Values | Detailed Explanations | read_cnt() | write_cnt() | list_content_cnt() | remove_cnt() | download_cnt() | upload_cnt() | create_directory_cnt() | remove_directory_cnt() | upload_directory_cnt() | download_directory_cnt() | tbl_cnt()

Creating Individual Connectors | File System Connector | Database Connector | Using Individual Connectors | Creating Multiple Connectors with connectors() | Working with Multiple Connectors | Mixed Storage Types | Summary

Introduction | Connecting to a Databricks | Without connector package | With connector package (recommended) | Listing data | Reading and writing data | Conclusion