Cohort covariates #167

schuemie · 2022-06-03T12:52:31Z

Adds covariates based on other cohorts. These can be binary or 'count', meaning the count of cohort occurrences within the time window. Both types are available per-person and aggregated. It is also possible to construct temporal covariates, which are always binary. The temporal covariates can also be fetched as aggregated statistics.

Included are unit tests and a vignette.

I believe cohort-based covariates are an important step in moving HADES away from custom coding per study (specifically as part of Strategus).

codecov · 2022-06-03T14:18:38Z

Codecov Report

Merging #167 (087a745) into develop (1e46b67) will increase coverage by 0.10%.
The diff coverage is 94.24%.

❗ Current head 087a745 differs from pull request most recent head 6aa0a4c. Consider uploading reports for the commit 6aa0a4c to get more accurate results

@@             Coverage Diff             @@
##           develop     #167      +/-   ##
===========================================
+ Coverage    93.30%   93.40%   +0.10%     
===========================================
  Files           15       16       +1     
  Lines         1165     1304     +139     
===========================================
+ Hits          1087     1218     +131     
- Misses          78       86       +8

Impacted Files	Coverage Δ
R/FeatureExtraction.R	`60.00% <ø> (ø)`
R/HelperFunctions.R	`88.88% <40.00%> (-6.12%)`	⬇️
R/GetCovariatesFromOtherCohorts.R	`96.26% <96.26%> (ø)`

... and 10 files with indirect coverage changes

anthonysena

After reviewing these changes, I just wanted to confirm that the desired behavior here. From my review, my understanding is that we aim to detect if a target cohort table entry has a matching cohort_covariate for the subject_id and for the #covariate_cohort_ref.cohort_definition_id specified. To do this, the cohort.cohort_start_date is adjusted to see if covariate_cohort.cohort_start_date the that falls in a specified time window. For temporal, this time window will use the #time_ref to adjust the cohort_start_date of the target cohort to see if the cohort_covariate.cohort_start_date falls in that range and if it is "non-temporal" it will use the @start_day and @end_day respectively.

Just wanted to confirm this behavior and add a few more questions/comments before approving.

anthonysena · 2022-06-06T13:42:40Z

R/GetCovariatesFromOtherCohorts.R

+                                                       temporalEndDays = -365:-1,
+                                                       includedCovariateIds = c()) {
+  errorMessages <- checkmate::makeAssertCollection()
+  checkmate::assertInt(analysisId, lower = 1, upper = 999, add = errorMessages)


Should we also consider ensuring that the analysisId specified does not collide with pre-specified analysis IDs? This would apply for creating the temporal & non-temporal settings objects.

That would be nice, but I'm not sure at what point we can do that. Right now, multiple covariate settings are simply combined in a generic list, like here. That means the first place we can look for collisions is in the getDbCovariateData() function (for example here. It would definitely be a good idea to add a check there, because throwing an error is better than generating different covariates with the same covariate ID, which would lead to strange but perhaps undetectable behavior. Ideally we'd be able to warn the user earlier, but I don't see when.

I've updated the PR so the createSettings functions throw a warning when the analysis ID is also used for one of the pre-specified analyses.

In addition, I recommend somewhere in the future we make it so getDbCovariateData() throws an error if there are overlapping covariate IDs between covariate builders. I've created an issue for this.

vignettes/CreatingCovariatesBasedOnOtherCohorts.Rmd

schuemie · 2022-06-07T04:43:39Z

Yes, I believe your summary is correct.

gowthamrao · 2022-06-30T01:22:27Z

java/org/ohdsi/featureExtraction/FeatureExtraction.java

@@ -76,20 +75,20 @@ public class FeatureExtraction {
 	private static String									ADD_DESCENDANTS_SQL				= "SELECT descendant_concept_id AS id\nINTO @target_temp\nFROM @cdm_database_schema.concept_ancestor\nINNER JOIN @source_temp\n\tON ancestor_concept_id = id;\n\n";

 	public static void main(String[] args) {
-		//init("C:/Users/mschuemi/git/FeatureExtraction/inst");
+		init("C:/Users/mschuemi/git/FeatureExtraction/inst");


does that look right @schuemie

This is a function that is used by the Java developer for testing. It is not used by the package. So yes, seems right.

gowthamrao · 2022-12-02T12:29:05Z

@anthonysena is this mature for use?

anthonysena · 2022-12-02T15:24:46Z

You can use it at your own risk - I reviewed this some time ago but I need to find some time to do a deeper dive and merge it into develop.

gowthamrao · 2022-12-06T17:16:02Z

OK - i am going to give it a try to generate some content for these two forum posts

https://forums.ohdsi.org/t/phenotype-submission-transverse-myelitis/17769/6
https://forums.ohdsi.org/t/phenotype-submission-symptoms-and-signs/17895

gowthamrao · 2022-12-08T11:09:11Z

I am curious to know, why valueType = "binary", parameter is supported for createCohortBasedCovariateSettings but not createCohortBasedTemporalCovariateSettings

FeatureExtraction/R/GetCovariatesFromOtherCohorts.R

Line 161 in 967025d

valueType = "binary",

schuemie · 2022-12-08T13:51:54Z

No strong reason. I think at the time I imagined most people would use 1-day windows for temporal covariates, and you can only be in a cohort once per day. But of course you could have bigger windows.

Allowing count type temporal covariates would require some additions to the SQL.

schuemie · 2022-12-08T15:51:11Z

Now added: 4aa04e4

gowthamrao · 2022-12-09T12:25:17Z

@schuemie i ran the script and this Pull Request I think is going to be very valuable.

Example: I ran the branch on the cohorts in OHDSI Phenotype library. I selected cohortId = 19 which is 'Typical Pneumonia' and a set of Cohorts that maybe symptoms

cohortDefinitionSet <- PhenotypeLibrary::getPlCohortDefinitionSet(cohortIds = c(19,
                                                                                3, 5, 6, 10, 11, 13, 14, 20, 23))

cohortId cohortName

1 3 [P] Cough or Sputum (3Ps, 30Era)
2 5 [P] Dyspnea (7Pe, 90Era)
3 6 [P] Fever (3Pe, 30Era)
4 10 [P] Nausea or Vomiting (3Pe, 30Era)
5 19 [P] Acute Typical Pneumonia (14Pe, 30Era)
6 20 [P] Bronchitis (14Pe, 30Era)

I then created a composite cohort using CohortAlgebra

CohortAlgebra::unionCohorts(
  connectionDetails = connectionDetails,
  cohortDatabaseSchema = cdmSource$cohortDatabaseSchemaFinal,
  cohortTable = paste0(
    stringr::str_squish("pl_"),
    stringr::str_squish(cdmSource$sourceKey)
  ),
  oldToNewCohortId = dplyr::tibble(
    oldCohortId = c(3, 5, 6, 10, 20),
    newCohortId = 9999999
  ), 
  purgeConflicts = TRUE
)

and then used all the symptom cohorts and the composite cohort as a covariate

covariateSettings <-
FeatureExtraction::createCohortBasedTemporalCovariateSettings(
analysisId = 999,
covariateCohorts = cohortDefinitionSet %>%
dplyr::filter(cohortId != 19),
temporalStartDays = c(-365,-30, 0, 1, 31,-9999,-7),
temporalEndDays = c(-31,-1, 0, 30, 365, 9999,-1)
)

covariateData <-
FeatureExtraction::getDbCovariateData(
connection = DatabaseConnector::connect(connectionDetails),
cdmDatabaseSchema = cdmDatabaseSchema,
cohortDatabaseSchema = cohortDatabaseSchema,
cohortTable = cohortTable,
cohortId = 19,
rowIdField = "subject_id",
covariateSettings = covariateSettings,
aggregated = TRUE
)

I executed this on a US claims based data source

and got these results

This passes my "sniff test". i.e. the cohorts of dyspnea, cough, bronchitis are ranked higher in the typical pneumonia cohort. The composite of all these relevant symptoms and signs was found in 58% of the cohort.

gowthamrao · 2022-12-09T12:31:56Z

Now, i want to understand how we can represent the relationship between the two cohorts (target cohort and covariate cohort) in terms of Allen's interval algebra.

https://en.wikipedia.org/wiki/Allen%27s_interval_algebra

Based on my review of this SQL

FeatureExtraction/inst/sql/sql_server/CohortBasedBinaryCovariates.sql

Lines 35 to 43 in 4aa04e4

    
           {@temporal} ? { 
        
           	INNER JOIN #time_period time_period 
        
           		ON covariate_cohort.cohort_start_date <= DATEADD(DAY, time_period.end_day, cohort.cohort_start_date) 
        
           	WHERE CASE WHEN covariate_cohort.cohort_end_date IS NULL THEN covariate_cohort.cohort_start_date ELSE covariate_cohort.cohort_end_date END  >= DATEADD(DAY, time_period.start_day, cohort.cohort_start_date) 
        
           } : { 
        
           	WHERE covariate_cohort.cohort_start_date <= DATEADD(DAY, {@temporal_sequence} ? {@sequence_end_day} : {@end_day}, cohort.cohort_start_date) 
        
           {@start_day != 'anyTimePrior'} ? {		 
        
           		AND CASE WHEN covariate_cohort.cohort_end_date IS NULL THEN covariate_cohort.cohort_start_date ELSE covariate_cohort.cohort_end_date END >= DATEADD(DAY, {@temporal_sequence} ? {@sequence_start_day} : {@start_day}, cohort.cohort_start_date) 
        
           }

is logically equivalent to (i got this using SqlRender)

INNER JOIN #time_period time_period
ON covariate_cohort.cohort_start_date <= DATEADD(DAY, time_period.end_day, cohort.cohort_start_date)
WHERE covariate_cohort.cohort_end_date >= DATEADD(DAY, time_period.start_day, cohort.cohort_start_date)

which becomes
cov_cohort_start_date <= target_cohort_start_date AND cov_cohort_end_date >= target_cohort_start_date

gowthamrao · 2022-12-09T12:34:24Z

Which in Allen's interval algebra form would be ANY of the four relationships between x and y

x meets y (or inverse - y is met by x)
x overlaps with y (or inverse - y is overlapped by x)
x starts y (or inverse - y is started by x)
x equals y (no inverse)

is that interpretation correct?

schuemie · 2022-12-09T13:07:53Z

@gowthamrao: you're leaving out the window part (time_period.start_day and time_period.end_day). That SQL can be interpreted as:

covariate_cohort_start <= window_end AND covariate_cohort_end >= window_start

(remember that both the window start and end are defined relative to the target cohort start)

This means that my current implementation sets the feature to value 1 for a window if there is at least 1 day in the window when someone was in the covariate cohort.

gowthamrao · 2022-12-09T16:05:01Z

you're leaving out the window part (time_period.start_day and time_period.end_day).

So may be lets go thru scenarios: Lets start with time_period.start_day = 0 and time_period.end_day = 0, given this

is this accuraate

temporal relationship is

cov_cohort_start_date <= target_cohort_start_date AND cov_cohort_end_date >= target_cohort_start_date

which in Allen's interval algebra ontology becomes

would be ANY of the four relationships between x and y

x meets y (or inverse - y is met by x)
x overlaps with y (or inverse - y is overlapped by x)
x starts y (or inverse - y is started by x)
x equals y (no inverse)

schuemie · 2022-12-09T16:15:27Z

I think that is correct, except 'x meets y' seems to imply there's no overlap?

gowthamrao · 2022-12-09T16:18:59Z

I think that is correct, except 'x meets y' seems to imply there's no overlap?

here cov_cohort_end_date = target_cohort_start_date i think

that would satisfy cov_cohort_start_date <= target_cohort_start_date AND cov_cohort_end_date >= target_cohort_start_date ?

gowthamrao · 2023-03-04T11:28:17Z

@schuemie in my testing, i wanted to compute features using both cohort covariates and feature covariates for the same set of cohorts. These are my temporal covariate settings. My question is - can i combine these temporalCovariateSettings into one - so that I get one result set with results from both featureCovariates and cohortCovariates, or should i execute them serially one after the other, and then try to combine the results outside FeatureExtraction? I think i would prefer to be able to combine them - so that i get one result set.

# this is from CohortDiagnostics - its a temporalCovariateSetting
cohortDiagnosticsCovariateSettings <-
  CohortDiagnostics::getDefaultCovariateSettings()

this is new, using createCohortBasedTemporalCovariateSettings

cohortBasedCovariateSettings <-
  FeatureExtraction::cohortBasedCovariateSettings <-
  FeatureExtraction::createCohortBasedTemporalCovariateSettings(
    analysisId = 150,
    covariateCohortDatabaseSchema = covariateCohortDatabaseSchema,
    covariateCohortTable = covariateCohortTable,
    covariateCohorts = PhenotypeLibrary::getPhenotypeLog(cohortIds = c(1:10)) |>
      dplyr::select(cohortId, cohortName),
    valueType = "binary",
    temporalStartDays = cohortDiagnosticsCovariateSettings$temporalStartDays,
    temporalEndDays = cohortDiagnosticsCovariateSettings$temporalEndDays
  )(
    analysisId = 150,
    covariateCohortDatabaseSchema = covariateCohortDatabaseSchema,
    covariateCohortTable = covariateCohortTable,
    covariateCohorts = PhenotypeLibrary::getPhenotypeLog(cohortIds = c(1:10)) |>
      dplyr::select(cohortId, cohortName),
    valueType = "binary",
    temporalStartDays = cohortDiagnosticsCovariateSettings$temporalStartDays,
    temporalEndDays = cohortDiagnosticsCovariateSettings$temporalEndDays
  )

schuemie · 2023-03-06T08:53:43Z

@gowthamrao , as always, you can combine cohort builders by putting their settings in a list. You just have to be careful they don't have overlapping analysis IDs, or else you could get colliding covariate IDs. So in your example, you'd do:

# this is from CohortDiagnostics - its a temporalCovariateSetting
covariateSettings1 <- CohortDiagnostics::getDefaultCovariateSettings()

covariateSettings2 <- FeatureExtraction::createCohortBasedTemporalCovariateSettings(
    analysisId = 150,
    covariateCohortDatabaseSchema = covariateCohortDatabaseSchema,
    covariateCohortTable = covariateCohortTable,
    covariateCohorts = PhenotypeLibrary::getPhenotypeLog(cohortIds = c(1:10)) |>
      dplyr::select(cohortId, cohortName),
    valueType = "binary",
    temporalStartDays = covariateSettings1$temporalStartDays,
    temporalEndDays = covariateSettings1$temporalEndDays
  )
covariateSettings <- list(covariateSettings1, covariateSettings2)

gowthamrao · 2023-03-21T19:06:07Z

Documentation of function FeatureExtraction::getDbCohortBasedCovariatesData and assertCheck here

FeatureExtraction/R/GetCovariatesFromOtherCohorts.R

Line 43 in 967025d

checkmate::assertInt(cohortId, add = errorMessages)

seems to imply that cohortId should only support 1 cohort per execution. However, the -1 suggests the function is able to support more than one cohortId at a time

cohortId For which cohort ID should covariates be constructed? If set to -1, covariates will be constructed for all cohorts in the specified cohort table.

This behavior is different from FeatureExtraction::getDbCovariateData where there is no assertCheck and is able to accept more than one cohortId

FeatureExtraction/R/GetCovariates.R

Line 72 in 4aa04e4

cohortId = -1,

Maybe the checkmate::assertInt(cohortId, add = errorMessages) should be changed to support an array of cohortIds?

See reproducible code

This seems to work fine


remotes::install_github("OHDSI/FeatureExtraction", ref = "cohortCovariates")
remotes::install_github("OHDSI/SkeletonCohortDiagnostics")

cohortDefinitionSet <-
  CohortGenerator::getCohortDefinitionSet(
    settingsFileName = "settings/CohortsToCreate.csv",
    jsonFolder = "cohorts",
    sqlFolder = "sql/sql_server",
    packageName = "SkeletonCohortDiagnosticsStudy",
    cohortFileNameValue = "cohortId"
  ) %>%  dplyr::tibble()

connectionDetails <- Eunomia::getEunomiaConnectionDetails()
connection <-
  DatabaseConnector::connect(connectionDetails = connectionDetails)


cohortTableNames = CohortGenerator::getCohortTableNames(cohortTable = 'cohort')

# Execution ----
## Create cohort tables on remote ----
CohortGenerator::createCohortTables(
  connectionDetails = connectionDetails,
  cohortDatabaseSchema = 'main',
  cohortTableNames = cohortTableNames,
  incremental = FALSE
)
## Generate cohort on remote ----
cohortGenerated <- CohortGenerator::generateCohortSet(
  connectionDetails = connectionDetails,
  cdmDatabaseSchema = 'main',
  cohortTableNames = cohortTableNames,
  cohortDefinitionSet = cohortDefinitionSet,
  cohortDatabaseSchema = 'main',
  incremental = FALSE
)

FeatureExtractionSettings <- FeatureExtraction::createDefaultTemporalCovariateSettings()
FeatureExtractionSettingsCohortBasedCovariateSettings <-
  FeatureExtraction::createCohortBasedTemporalCovariateSettings(
    analysisId = 150,
    covariateCohortDatabaseSchema = 'main',
    covariateCohortTable = cohortTableNames$cohortTable,
    covariateCohorts = cohortDefinitionSet |>
      dplyr::filter(cohortId %in% c(14907)) |>
      dplyr::select(cohortId,
                    cohortName),
    valueType = "binary",
    temporalStartDays = FeatureExtractionSettings$temporalStartDays,
    temporalEndDays = FeatureExtractionSettings$temporalEndDays
  )
FeatureExtractionCovariateSettings <-
  list(
    FeatureExtractionSettingsCohortBasedCovariateSettings,
    FeatureExtraction::createDefaultTemporalCovariateSettings()
  )


featureExtractionOutput <-
  FeatureExtraction::getDbCovariateData(
    connection = connection,
    cdmDatabaseSchema = 'main',
    cohortDatabaseSchema = 'main',
    cohortTable = cohortTableNames$cohortTable,
    covariateSettings = FeatureExtractionCovariateSettings,
    aggregated = TRUE
  )

however, if in the last step, i subset to only selected cohortIds as

featureExtractionOutput <-
  FeatureExtraction::getDbCovariateData(
    connection = connection,
    cdmDatabaseSchema = 'main',
    cohortDatabaseSchema = 'main',
    cohortTable = cohortTableNames$cohortTable,
    cohortId = cohortDefinitionSet[1:2, ]$cohortId,
    covariateSettings = FeatureExtractionCovariateSettings,
    aggregated = TRUE
  )

then i get this error

Error in FeatureExtraction::getDbCovariateData(connection = connection, :
1 assertions failed:

Variable 'cohortId': Must have length 1.
2: checkmate::assertIntegerish(temporalStartDays, add = errorMessages)
1: FeatureExtraction::createCohortBasedTemporalCovariateSettings(analysisId = 150,
covariateCohortDatabaseSchema = "main", covariateCohortTable = cohortTableNames$cohortTable,
covariateCohorts = dplyr::select(dplyr::filter(cohortDefinitionSet,
cohortId %in% c(14907)), cohortId, cohortName), valueType = "binary",
temporalStartDays = FeatureExtractionSettingsCohortDiagnostics$temporalStartDays,
temporalEndDays = FeatureExtractionSettingsCohortDiagnostics$temporalEndDays)

The difference being

cohortId = cohortDefinitionSet[1:2, ]$cohortId,

schuemie · 2023-03-22T05:07:27Z

Yes @gowthamrao , I think you're right. The cohortId field in any covariate builder (including getDbCovariateData()) allows a vector of cohort IDs, but is still called cohortId (singular) for historical reasons. I just forgot when creating the input checks for getDbCohortBasedCovariatesData(). I'll modify the check, and add a unit test for multiple cohort IDs.

@anthonysena : perhaps it is time to introduce a cohortIds argument, and deprecate cohortId?

ginberg · 2023-05-11T08:58:35Z

@schuemie @anthonysena this is looking good to me. I am just wondering 2 things:

can we make the CohortBasedCovariatesVignetteDataFetch such that it uses a public database? We recently updated vignetteDataFetch so it uses Eunomia and the data can be generated easily by anyone
should we update the feature extraction jar version to 3.3.0? Since that is the package version in the develop branch

Schuemie added 6 commits May 26, 2022 05:15

Adding covariate builders based on other cohorts

fddbdd6

Fixing build

adc05c2

Regenerating jar using maven

06b3b82

Implementing runTestsOnEunomia switch for new tests

794a274

Some fixes to cohort-based covariates

1ba9303

Adding a vignette for cohort-based covariates.

c35cca4

schuemie requested a review from anthonysena June 3, 2022 12:52

anthonysena reviewed Jun 6, 2022

View reviewed changes

Update CreatingCovariatesBasedOnOtherCohorts.Rmd

7df5b34

Throw warning if using pre-spec analysis ID for cohort-based covariate.

967025d

gowthamrao reviewed Jun 30, 2022

View reviewed changes

Adding count type temporal covariates based on other cohorts

4aa04e4

anthonysena linked an issue Jan 10, 2023 that may be closed by this pull request

Add covariate builder that uses cohorts to build (binary) features #96

Closed

anthonysena mentioned this pull request Jan 10, 2023

Create cohort-based standard table 1 #169

Open

Allowing cohordId to be vector in cohort-based covariate builder

f8a82ee

anthonysena mentioned this pull request Mar 22, 2023

Deprecate cohortId and use cohortIds #188

Closed

anthonysena and others added 2 commits April 12, 2023 09:24

Merge branch 'develop' into cohortCovariates

eabf137

Merge branch 'develop' into cohortCovariates

9c2a691

anthonysena added 2 commits June 8, 2023 16:43

Update vignette

cb2cb10

Merge branch 'develop' into cohortCovariates

ef3fafa

anthonysena mentioned this pull request Jun 27, 2023

Cohort covariates unit tests #204

Merged

Expand cohort covariates unit tests (#204)

6aa0a4c

anthonysena merged commit cf4a169 into develop Jun 28, 2023

anthonysena deleted the cohortCovariates branch June 28, 2023 12:03

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Cohort covariates #167

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Cohort covariates #167

Cohort covariates #167

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