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 | Finding a cohort event | Finding a concept event | First, last, and censoring | Events occurring on the same date | Multiple windows and column names

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

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

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

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?

Introduction | Run achilles analysis | Differences with the Achilles R package | Execution Model | Small Cell Suppression | Observation Period Consistency

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

Introduction | Build observation_period table in GiBleed | First to extraction | First to last | Inpatient | Collapse 180 | Collapse+Persistence 180 | Collapse+Persistence 365 | Pediatric | Comparison of the different definitions | Final remarks

Introduction

Introduction | Load packages | Mock data

Introduction | Loading Necessary Libraries and Data | Subsetting the Data | Generating the Shiny App | Generated shiny app | Panels generated (panelDetails) | Understanding Panel Details (panelDetails) | Panel Structure (omopViewerPanels) | Customise panelStructure