Set up | Estimating survival with competing risk | With stratification | Disconnect from the cdm database connection

Set up | Cohorts needed for survival | Target cohort design | Outcome cohort design | Competing outcome design | Adding strata | Multiple cohorts in one table | Pre-analysis checklist | Disconnect from the cdm database connection

Set up | Further analysis with single event survival | Further analysis with competing risk survival | Disconnect from the cdm database connection

Set up | Overall survival | Survival format | Changing estimation parameters | With stratification | Multiple target or outcome cohorts | Checking the assumption of proportionality between outcomes | Export and import a survival output | Disconnect from the cdm database connection

Drug(s) of interest | Running the diagnostics | Assessing the drug concepts (of interest) | Saving the diagnostic output | View results in the Shiny app & make it available for publishing

1) Standard OMOP CDM tables | 2) Cohort tables | 3) Achilles result tables | 4) Other tables | General rules for a cdm reference | Export metadata about the cdm reference

Cohort table | Cohort Set | Cohort Attrition | Cohort Codelist | Cohort Table | Combining generated cohort sets | Export metadata about a cohort table

Codelist | Concept set expression

Introduction | The source object | Methods | insertTable | listSourceTables | readSourceTable | dropSourceTable | insertCdmTo | summary | compute | The cdm reference object

Logging | Example | exportSummarisedResult

Introduction | Methods | CDM reference | Cohorts | Summarised result

Introduction | Results table | Settings | newSummarisedResult | Combining summarised results | Minimum cell count suppression | Export and import summarised results | Tidy a | Tidy method | 1. Split group, strata, and additional pairs into separate columns: | 2. Add settings of the object as columns: | 3. Pivot estimates as columns: | Example | Split | Pivot estimates | Add settings | Filter | Utility functions for | Column retrieval functions | Unite functions

Minimum cell count suppression | How suppression works | Suppressing a summarised_result object | Is a summarised_result object suppressed?

Plotting with a | Plotting principles for objects | Scatter plot | Bar plot | Box plot | Alluvial plot | Plotting style | Plotting with a | Custom plotting | Combine with ggplot2 | Combine with plotly

Quarto document settings | Characterisation results | Incidence results | Additional results | Captions and footers | In Markdown | In an R code chunk | Report example

Introduction | Branding styles using .yml | .yml structure | Style hierarchy | Applying styles to tables and plots | Use of _brand.yml | Alternative style customisation | Tables | Plots | Using non-registered font families in ggplot2 | 1. Install the font in the system | 2. Register the font | 3. Create plots with styles that use the font | Important notes | Final remarks

Create a Unit Test Definition | Load the Test Population into a CDM | Use the CDM in Unit Tests | Start from a Blank Excel Template | CSV Inputs | Clean Up

Create the workflow files | Add the expected test files | PostgreSQL and SQL Server actions | Databricks action | Suggested CI workflow

Use patientsCDM() with a remote database | Environment variables for direct use | A minimal backend test pattern

Overview | The One-at-a-Time Approach | The Optimized Batch DAG | Worked Example: Three Drug Exposure Cohorts | One-at-a-Time DAG | Optimized Batch DAG | How Equivalence Is Guaranteed | 1. Same Builder, Different Assembly | 2. Domain Filtering Includes Source Concepts | 3. QualifiedLimit Preservation | 4. Automated Equivalence Validation | Safety Mechanisms | Performance Characteristics | By Workload Type | Independent Cohorts (Different Domains) | Independent Cohorts (Overlapping Domains) | Subset Cohorts (Same Primary Criteria, Different Inclusion Rules) | Large Phenotype Libraries (100+ Cohorts) | Summary Table | Code Example | Single Cohort (No Optimization) | Batch (Optimized) | With CDMConnector | Validating Equivalence | Debugging | Appendix: How the DAG Is Enforced

Overview | How It Works | The Execution DAG | Merkle-Tree Hashing | The Cache Registry | Stable Table Naming | Cache-Aware Execution | Usage | Basic Usage | SQL-Only Usage | What Gets Cached vs. Not | Cached (persistent across runs) | Not Cached (rebuilt every run) | Why Concept Sets Aren't Individually Cached | Cache Management | Inspecting the Cache | Garbage Collection | Clearing the Cache | Correctness Guarantees | When to Use Caching | Example: Incremental Update

Overview | Performance improvements with the new approach | How to run the benchmark | Prerequisites | Single database | Multiple databases | Benchmark results CSV (timing) | Equivalence CSV (same results) | Summary

Postgres | Redshift | SQL Server | Snowflake | Databricks/Spark | Duckdb

Introduction | Database settings | CohortGenerator Module | TreatmentPatterns module | Execution

Publications | Presentations

Connect to an existing OMOP CDM database | Create a local vocabulary database | Download vocabularies from athena | Create a duckdb database

Vocabulary characteristics | Domains | Concept class | Relationships

Codelist | Create an empty codelist | Codelist with details | Create an empty codelist with details | Concept set expression | Create an empty concept set expression | Concept set expression from a JSON file | Change to different codelist types | Codelist/Concept set expression to codelist with details | Codelist/Codelist with details to concept set expression | Codelist with details/Concept set expression to codelist

Search for keyword match | Include non-standard concepts | Multiple search terms | Add descendants | With exclusions | Search using synonyms | Search via non-standard

Ingredient based codelists | Restrict to a specific dose form | Restrict to a specific dose unit | Restrict to a specific route | Search multiple ingredients | ATC based codelists

Importing concept sets

Introduction: Manipulate codelists | Subsetting a Codelist | Subset by Domain | Subset on vocabulary | Subset on Dose Unit | Subset on ingredient range | Subset on route category | Subset on dose forms | Stratify codelist | Stratify by Dose Unit | Stratify by Route Category | Stratify by Dose Form | Add or remove concepts from a codelist | Codelist construction | Compare codelists

Running diagnostics for a codelist | Summarise code use using ACHILLES tables | Summarise code use using patient-level data | Identify orphan codes | Run diagnostics within a cohort | Summarise code use at cohort entry | Cohort code use with a different codelist | Stratify cohort code use

Introduction | Set up | Mock data | Drug codes | Add drug utilisation information | addNumberExposures() | addNumberEras() | daysExposed | daysPrescribed | timeToExposure | initialExposureDuration | initialQuantity and cumulativeQuantity | initialDailyDose and cumulativeDose | addDrugUtilisation() | Use case | Summarise drug utilisation information | summariseDrugUtilisation() | tableDrugUtilisation()

Introduction | Creating a cdm_reference object | Create a drug users cohort | Creating a conceptSet | The gapEra parameter | Create your cohort | Analogous functions | Carry over days / Use days prescribed | Apply inclusion criteria to drug cohorts | requirePriorDrugWashout() | requireIsFirstDrugEntry() | requireObservationBeforeDrug() | requireDrugInDateRange() | The order matters | first and washout | first and minObs | first and 2011-2012 | washout and minObs | washout and 2011-2012 | minObs and 2011-2012 | Recommended order

Introduction | Summarising study cohorts | Stratified summaries | Summaries including presence in other cohorts | Summaries Using Concept Sets Directly | Summaries using clinical tables

Introduction | Overview of table functions | Main table functions | visTable() — format any data.frame | visOmopTable() — specialized for | Using .options | Formatting functions (the format*() family) | formatMinCellCount() — suppressed estimates | formatEstimateValue() — numeric formatting | formatEstimateName() — combine and order estimates | formatHeader() — multi-level headers | formatTable() — render the final table | Notes & recommendations

Introduction | Large scale characteristics of study cohorts | Stratified large scale characteristics | Plot large scale characteristics

Set up | Mock data | Shiny App: User Inteface | Example UI Code | Shiny App: Server | 1) Baseline characteristics | 2) Large Scale characteristics | 3) Incidence | Example Server Code | Run App

Introduction | Identifying cohort intersections | Options for identifying cohort intersection | Naming conventions for new variables

Introduction | Adding variables from concept sets | Adding multiple concept based variables | Cohort-based versus concept-based intersections

Introduction | Adding characteristics to OMOP CDM tables | Adding characteristics to a cohort tables | Getting multiple characteristics at once

Introduction | Variables type | Estimates names | Summarise our first table

Saving results | Evaluating Results | treatmentPathways | countsAge, countsSex, and countsYear | summaryStatsTherapyDuration | metadata | Sunburst Plot & Sankey Diagram

Introduction | Outcome definition | Using estimatePointPrevalence() and estimatePeriodPrevalence() | Using estimatePointPrevalence() | Using estimatePeriodPrevalence() | Stratified analyses | Other parameters | Attrition

Introduction | No washout, no repetitive events | Washout all history, no repetitive events | Some washout, no repetitive events | Some washout, repetitive events | Some washout, repetitive events, censoring event | Outcome definition | Using estimateIncidence() | Stratified analyses | Other parameters | Analysis attrition

Results from test databases | Results from real databases | Sharing your benchmarking results

Creating a reference to the OMOP CDM | Joining tables | Saving query results to the database | Selecting a subset of CDM tables | Subsetting a CDM | Flatten a CDM | Closing connections | Summary

Cohort Generation | Atlas cohort definitions | Subset a cohort | Custom Cohort Creation

Introduction | TLDR | Cohorts | Combinations | Settings

Target and Event cohorts | Database interface | CDMConnector | DatabaseConnector | Analysis | Events | Combinations | Non-Significant Overlap | Overlap Method | Acute and Therapy Splits | Event Windows | Anchoring | windowStart and windowEnd | Changing the anchoring | Pathways | Running the analysis

ATLAS | Cohort information | Viral Sinusitis Cohort parameters | Cohort Entry Events | Inclusion Criteria | Cohort Exit | Cohort Eras | CapR | Cohorts for TreatmentPatterns

Exporting result objects | minCellCount and censorType | ageWindow | archiveName | Patient-Level Export

Standardardised results format | Exporting and importing results | Validate minimum cell count suppression | Tidying results for further analysis

Introduction | No specific requirements | Specified study period | Specified study period and prior history requirement | Specified study period, prior history requirement, and age and sex criteria | Using generateDenominatorCohortSet() | Multiple options to return multiple denominator populations | Output

Using generateDenominatorCohortSet() with a target cohort | Applying cohort restrictions | Specifying time at risk

Set up | Creating the cdm reference | Putting it all together | Behind the scenes

Set up | CDM reference attributes | CDM name | CDM version | Database connection | Cohort attributes | Generated cohort set | Creating a bespoke cohort

Run the days supply check | Days supply Overall | Days supply by drug concept

Obtain a diagnostics summary

Run the Drug Dose check | Drug dose

Run duration check | Drug exposure duration Overall | Drug exposure duration by drug concept

Run the Sig checks | Sig | Sig by concept

Run checks | Drug Source by Concept

Run the drug type check | Drug Type Overall | Drug Type by drug concept

Run missingness check | Overall missingness | Missingness by drug concept

Run the quantity check | Quantity Overall | Quantity by concept

Run the route check | Route Overall | Route by drug concept

Run the Verbatim End Date check | Drug verbatim end date Overall | Drug verbatim end date by concept