Changelog
Source:NEWS.md
Characterization 3.0.0
- Splitting the aggregateCovariates into: riskFactor, targetBaseline and caseSeries to make the inputs clearer.
- Adding a new function for cohort generation for the riskFactor, targetBaseline and caseSeries settings to ensure cohort generation is efficient and remove redundancies.
- Updating the results tables for the partitioning of aggregateCovariates into riskFactor, targetBaseline and caseSeries
- riskFactor analysis now has three modes: efficient (target without case vs case), CohortIncidence (target without case who have some TAR vs case) and PatientLevelPrediction (target without case and without people with outcome during washout period before TAR vs case).
- riskFactor analysis calculates SMD during execution and min SMD values can be specified to reduce result size.
- User can specify the number of target jobs (how many groups of targetIds to partition the jobs into) and the number of threads (how many threads to run the jobs in parallel).
- Intermediate results are stored as Andromeda files rather than csv files
- minCellCount censoring is done when exporting the intermediate results so it can be changed after execution without rerunning whole analysis.
- When running in incremental model results are checked to ensure all jobs executed successful before exporting to csv otherwise an error is returned to show some jobs were incomplete.
- riskFactor and caseSeries use the new FeatureExtarction option of exporting the features into a database table rather than downloading this enables additional processing to be done efficiently in SQL instead of R.
- The cohorts used by riskFactor, targetBaseline and caseSeries are saved into a user specified database and table
- The attritions for riskFactor, targetBaseline and caseSeries cohort creation are saved to enable users to see where patients were removed.
Characterization 2.2.0
CRAN release: 2025-09-10
- fixed csv spec: made mean_exposure_time a float and specified that min_characterization_mean in covariate table must be non-null and is in the pk.
- changed Line 284 in AggregateCovariates.R to cast exposure_time summary values to bigint due to integer overflow in some dbms.
- added dummy sql code to prevent warnings about missing variables
- added code to save empty csv files when there are no rows as that way it is easier to see there are no results vs an error saving.
- removed progress bar from custom during features
- added option includedFiles in insertResultsToDatabase() where you can specify the csv files to upload to prevent warnings of missing csv files.
- made sure all connections are disconnected after use
- fixed counts to use count_big (thanks Anthony Sena) to fix an issue where the number was bigger than an integer.
- added code to copy csv files in batches this is needed when the csv files are very large.
Characterization 2.1.3
CRAN release: 2025-03-04
- prepared for CRAN by adding examples, removing getwd(), replacing T/F with TRUE/FALSE and added example data inside package so no download required.
Characterization 2.1.2
- added input ignoreWhenEmpty to cleanIncremental() that does not run incrementalClean if there are no incremental files
Characterization 2.1.0
- risk factors and case series now restrict to first outcome only.
- added documentation describing the different analyses with examples.
Characterization 2.0.1
- edited cohort_type in results to varchar(12)
- fixed setting id being messed up by readr loading
Characterization 2.0.0
- added tests for all HADES supported dbms
- updated minCellCount censoring
- fixed issues with incremental
- made the code more modular to enable new characterizations to be added
- added job optimization code to optimize the distributuion of jobs
- fixed tests and made minor bug updates
Characterization 1.0.0
- Added parallelization in the aggregate covariates analysis
- Extact all results as csv files
- Removed sqlite result creation
- now using ResultModelManager to upload results into database
Characterization 0.3.1
- Removed DatabaseConnector from Remotes in DESCRIPTION. Fixes GitHb issue 38.
- Added check to covariateSettings input in createAggregateCovariateSettings to error if temporal is T
- adding during cohort covariate settings
- added a case covariate settings inputs to aggregate covariates
- added casePreTargetDuration and casePostTreatmentDuration integer inputs to aggregate covariates
Characterization 0.3.0
- Added new outcomeWashoutDays parameter to createAggregateCovariateSettings to remove outcome occurances that are continuations of a prior outcome occurrence
- Changed the way cohort definition ids are created in createAggregateCovariateSettings to use hash of target id, outcome id and type. This lets users combine different studies into a single result database.
- Added database migration capability and created new migrations for the recent updates.
Characterization 0.2.0
Updated dependency to FeatureExtraction (>= 3.5.0) to support minCharacterizationMean paramater.
Characterization 0.1.5
Changed export to csv approach to use batch export from SQLite (#41)
Characterization 0.1.3
Optimized aggregate features to remove T and not Os (as these can be calculated using T and T and Os) - requires latest shiny app though Optimized database extraction to csv
Characterization 0.1.2
Fixing bug where first outcome was still all outcomes Updating shiny app to work with old and new ShinyAppBuilder
Characterization 0.1.0
- added support to enable target cohorts with multiple cohort entries for the aggregate covariate analysis by restricting to first cohort entry and ensuring the subject has a user specified minPriorObservation days observation in the database at first entry and also perform analysis on first outcomes and any outcome that is recorded during TAR.
- added shiny app