Set up | Estimating survival with competing risk | Same-day event hierarchy | With stratification | 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

Introduction | Running PhenotypeDiagnostics | Database diagnostics | Codelist diagnostics | Cohort diagnostics | Modify windows, event in windows, and episodes in window | Population diagnostics | Save the results | Visualisation of the results

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

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

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

Introduction: Run ShinyDiagnostics | Shiny App Overview | Special cases

Comparing phenotype diagnostic results against phenotype expectations | Creating phenotype expectations | Using an LLM to draft your phenotype expectations

Introduction | Concept based cohort creation | Demographic based cohort creation | Measurement Cohort | Death cohort

Keep only the first record per person | Keep only the last record per person | Keep only a range of records per person | Keep only records within a date range | Keep only if entry lasts a given duration | Applying multiple cohort requirements | Keep only records from cohorts with a minimum number of individuals

Restrict cohort by age | Restrict cohort by sex | Restrict cohort by number of prior observations | Applying multiple demographic requirements to a cohort

Introduction | Exit at Specific Date | exitAtObservationEnd() | exitAtDeath() | Cohort Entry or Exit Based on Other Date Columns | entryAtFirstDate() | entryAtLastDate() | exitAtFirstDate() | exitAtLastDate() | Trim Dates Functions | trimDemographics() | trimToDateRange() | Pad Dates Functions | padCohortStart() | padCohortEnd() | padCohortDate() | Cohort ID argument

Vocabulary characteristics | Domains | Concept class | Relationships

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

Restrictions on cohort presence | Restrictions on concept presence | Restrictions on presence in clinical tables

Introduction | stratifyCohorts | yearCohorts

Introduction | Collaboration | Cohorts | Cohort counts and overlap | Performance | By definition | By domain | Cohort stratification

Introduction | Create mock data | Use matchCohorts() to create an age-sex matched cohort | matchSex parameter | matchYear parameter | ratio parameter | Generate matched cohorts simultaneously across multiple cohorts

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

Building a cohort set by domain/ clinical table | Deriving study cohorts from base cohorts | Considerations when building cohorts

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