library(SelfControlledCaseSeries) library(EmpiricalCalibration) outputFolder <- "d:/temp/sccsVignette2" diclofenac <- 1124300 giBleed <- 77
In this vignette we focus on running several different analyses on several exposure-outcome pairs This can be useful when we want to explore the sensitivity to analyses choices, include controls, or run an experiment similar to the OMOP experiment to empirically identify the optimal analysis choices for a particular research question.
This vignette assumes you are already familiar with the SelfControlledCaseSeries
package and are able to perform single studies. We will walk through all the steps needed to perform an exemplar set of analyses, and we have selected the well-studied topic of the effect of nonsteroidal anti-inflammatory drugs (NSAIDs) on gastrointestinal (GI) bleeding-related hospitalization. For simplicity, we focus on one NSAID: diclofenac. We will execute various variations of an analysis for the primary exposure pair and a large set of negative control exposures.
The general approach to running a set of analyses is that you specify all the function arguments of the functions you would normally call, and create sets of these function arguments. The final outcome models as well as intermediate data objects will all be saved to disk for later extraction.
An analysis will be executed by calling these functions in sequence:
getDbSccsData()
createStudyPopulation()
createSccsIntervalData()
fitSccsModel()
When you provide several analyses to the SelfControlledCaseSeries
package, it will determine whether any of the analyses and exposure-outcome pairs have anything in common, and will take advantage of this fact. For example, if we specify several exposure-outcome pairs with the same outcome, the data for the outcome will be extracted only once.
The function arguments you need to define have been divided into four groups:
SelfControlledCaseSeries
package, such as the SccsIntervalData
argument of the createSccsIntervalData
function. These cannot be specified by the user.We need to tell R how to connect to the server where the data are. SelfControlledCaseSeries
uses the DatabaseConnector
package, which provides the createConnectionDetails
function. Type ?createConnectionDetails
for the specific settings required for the various database management systems (DBMS). For example, one might connect to a PostgreSQL database using this code:
connectionDetails <- createConnectionDetails( dbms = "postgresql", server = "localhost/ohdsi", user = "joe", password = "supersecret" ) outputFolder <- "s:/temp/sccsVignette2" cdmDatabaseSchema <- "my_cdm_data" cohortDatabaseSchema <- "my_cohorts" options(sqlRenderTempEmulationSchema = NULL) cdmVersion <- "5"
The last three lines define the cdmDatabaseSchema
and cohortDatabaseSchema
variables, as well as the CDM version. We'll use these later to tell R where the data in CDM format live, where we want to store the (outcome) cohorts, and what version CDM is used. Note that for Microsoft SQL Server, databaseschemas need to specify both the database and the schema, so for example cdmDatabaseSchema <- "my_cdm_data.dbo"
.
We also need to prepare our exposures and outcomes of interest. The drug_era table in the OMOP Common Data Model already contains pre-specified cohorts of users at the ingredient level, so we will use that for the exposures. For the outcome we can use the OHDSI PhenotypeLibrary
to retrieve a community-approved definition of GI bleeding:
giBleed <- 77 cohortDefinitionSet <- PhenotypeLibrary::getPlCohortDefinitionSet(giBleed)
We can use the CohortGenerator
package to instantiate this cohort:
connection <- DatabaseConnector::connect(connectionDetails) cohortTableNames <- CohortGenerator::getCohortTableNames(cohortTable) CohortGenerator::createCohortTables(connection = connection, cohortDatabaseSchema = cohortDatabaseSchema, cohortTableNames = cohortTableNames) counts <- CohortGenerator::generateCohortSet(connection = connection, cdmDatabaseSchema = cdmDatabaseSchema, cohortDatabaseSchema = cohortDatabaseSchema, cohortTableNames = cohortTableNames, cohortDefinitionSet = cohortDefinitionSet) DatabaseConnector::disconnect(connection)
The first group of arguments define the exposures and outcome. Here we demonstrate how to create an exposures-outcome set:
diclofenac <- 1124300 negativeControls <- c( 705178, 705944, 710650, 714785, 719174, 719311, 735340, 742185, 780369, 781182, 924724, 990760, 1110942, 1111706, 1136601, 1317967, 1501309, 1505346, 1551673, 1560278, 1584910, 19010309, 40163731 ) giBleed <- 77 exposuresOutcomeList <- list() exposuresOutcomeList[[1]] <- createExposuresOutcome( outcomeId = giBleed, exposures = list(createExposure(exposureId = diclofenac)) ) for (exposureId in c(negativeControls)) { exposuresOutcome <- createExposuresOutcome( outcomeId = giBleed, exposures = list(createExposure(exposureId = exposureId, trueEffectSize = 1)) ) exposuresOutcomeList[[length(exposuresOutcomeList) + 1]] <- exposuresOutcome }
We defined the outcome of interest to be the cohort with the ID stored in giBleed
. The exposures include diclofenac (concept ID 1124300) and a large number of negative control exposures. Note that for the negative controls we specify trueEffectSize = 1
since we assume negative controls have no causal effect on the outcome.
A convenient way to save exposuresOutcomeList
to file is by using the saveExposuresOutcomeList
function, and we can load it again using the loadExposuresOutcomeList
function.
The second group of arguments are not specific to a hypothesis of interest, and comprise the majority of arguments. For each function that will be called during the execution of the analyses, a companion function is available that has (almost) the same arguments. For example, for the fitSccsModel()
function there is the createFitSccsModelArgs()
function. These companion functions can be used to create the arguments to be used during execution:
getDbSccsDataArgs <- createGetDbSccsDataArgs( useCustomCovariates = FALSE, deleteCovariatesSmallCount = 100, exposureIds = c(), maxCasesPerOutcome = 100000 ) createStudyPopulationArgs <- createCreateStudyPopulationArgs( naivePeriod = 180, firstOutcomeOnly = FALSE ) covarExposureOfInt <- createEraCovariateSettings( label = "Exposure of interest", includeEraIds = "exposureId", start = 1, end = 0, endAnchor = "era end", profileLikelihood = TRUE, exposureOfInterest = TRUE ) createSccsIntervalDataArgs1 <- createCreateSccsIntervalDataArgs( eraCovariateSettings = covarExposureOfInt ) fitSccsModelArgs <- createFitSccsModelArgs()
Any argument that is not explicitly specified by the user will assume the default value specified in the function. Note that for several arguments for concept or cohort definition IDs we can use the exposureIdRef
(default = "exposureId"
) in the Exposure
objects that we used in createExposuresOutcome()
. In this case, we defined the argument includeEraIds
to get the value of the exposureId
variable, meaning it will take the value of the diclofenac concept ID, or any of the negative control IDs. Also note that we set exposureOfInterest = TRUE
, which will cause the estimate for this covariate to be included in the result summary later on.
Important: at least one era covariate in your analysis must have exposureOfInterest = TRUE
.
We can now combine the arguments for the various functions into a single analysis:
sccsAnalysis1 <- createSccsAnalysis( analysisId = 1, description = "Simplest model", getDbSccsDataArgs = getDbSccsDataArgs, createStudyPopulationArgs = createStudyPopulationArgs, createIntervalDataArgs = createSccsIntervalDataArgs1, fitSccsModelArgs = fitSccsModelArgs )
Note that we have assigned an analysis ID (1) to this set of arguments. We can use this later to link the results back to this specific set of choices. We also include a short description of the analysis.
We can easily create more analyses, for example by including adjustments for age and seasonality, or for including other drugs in the model:
ppis <- c(911735, 929887, 923645, 904453, 948078, 19039926) covarPreExp <- createEraCovariateSettings( label = "Pre-exposure", includeEraIds = "exposureId", start = -30, end = -1, endAnchor = "era start" ) covarProphylactics <- createEraCovariateSettings( label = "Prophylactics", includeEraIds = ppis, start = 1, end = 0, endAnchor = "era end" ) createSccsIntervalDataArgs2 <- createCreateSccsIntervalDataArgs( eraCovariateSettings = list( covarExposureOfInt, covarPreExp, covarProphylactics ) ) sccsAnalysis2 <- createSccsAnalysis( analysisId = 2, description = "Including prophylactics and pre-exposure", getDbSccsDataArgs = getDbSccsDataArgs, createStudyPopulationArgs = createStudyPopulationArgs, createIntervalDataArgs = createSccsIntervalDataArgs2, fitSccsModelArgs = fitSccsModelArgs ) seasonalitySettings <- createSeasonalityCovariateSettings(seasonKnots = 5) calendarTimeSettings <- createCalendarTimeCovariateSettings(calendarTimeKnots = 5) createSccsIntervalDataArgs3 <- createCreateSccsIntervalDataArgs( eraCovariateSettings = list( covarExposureOfInt, covarPreExp, covarProphylactics ), seasonalityCovariateSettings = seasonalitySettings, calendarTimeCovariateSettings = calendarTimeSettings ) sccsAnalysis3 <- createSccsAnalysis( analysisId = 3, description = "Including prophylactics, season, calendar time, and pre-exposure", getDbSccsDataArgs = getDbSccsDataArgs, createStudyPopulationArgs = createStudyPopulationArgs, createIntervalDataArgs = createSccsIntervalDataArgs3, fitSccsModelArgs = fitSccsModelArgs ) covarAllDrugs <- createEraCovariateSettings( label = "Other exposures", includeEraIds = c(), excludeEraIds = "exposureId", stratifyById = TRUE, start = 1, end = 0, endAnchor = "era end", allowRegularization = TRUE ) createSccsIntervalDataArgs4 <- createCreateSccsIntervalDataArgs( eraCovariateSettings = list( covarExposureOfInt, covarPreExp, covarAllDrugs ), seasonalityCovariateSettings = seasonalitySettings, calendarTimeCovariateSettings = calendarTimeSettings ) sccsAnalysis4 <- createSccsAnalysis( analysisId = 4, description = "Including all other drugs", getDbSccsDataArgs = getDbSccsDataArgs, createStudyPopulationArgs = createStudyPopulationArgs, createIntervalDataArgs = createSccsIntervalDataArgs4, fitSccsModelArgs = fitSccsModelArgs ) createSccsIntervalDataArgs5 <- createCreateSccsIntervalDataArgs( eraCovariateSettings = list( covarExposureOfInt, covarPreExp, covarProphylactics ), eventDependentObservation = TRUE ) sccsAnalysis5 <- createSccsAnalysis( analysisId = 5, description = "Adjusting for event-dependent obs. end", getDbSccsDataArgs = getDbSccsDataArgs, createStudyPopulationArgs = createStudyPopulationArgs, createIntervalDataArgs = createSccsIntervalDataArgs5, fitSccsModelArgs = fitSccsModelArgs )
These analyses can be combined in a list:
sccsAnalysisList <- list( sccsAnalysis1, sccsAnalysis2, sccsAnalysis3, sccsAnalysis4, sccsAnalysis5 )
A convenient way to save sccsAnalysisList
to file is by using the saveSccsAnalysisList
function, and we can load it again using the loadSccsAnalysisList
function.
We can now run the analyses against the hypotheses of interest using the runSccsAnalyses()
function. This function will run all specified analyses against all hypotheses of interest, meaning that the total number of outcome models is length(sccsAnalysisList) * length(exposuresOutcomeList)
. (If we want, we can skip some of these combinations using the analysesToExclude
argument.)
multiThreadingSettings <- createDefaultSccsMultiThreadingSettings( parallel::detectCores() - 1 ) referenceTable <- runSccsAnalyses( connectionDetails = connectionDetails, cdmDatabaseSchema = cdmDatabaseSchema, exposureDatabaseSchema = cdmDatabaseSchema, exposureTable = "drug_era", outcomeDatabaseSchema = cohortDatabaseSchema, outcomeTable = cohortTable, cdmVersion = cdmVersion, outputFolder = outputFolder, combineDataFetchAcrossOutcomes = TRUE, exposuresOutcomeList = exposuresOutcomeList, sccsAnalysisList = sccsAnalysisList, sccsMultiThreadingSettings = multiThreadingSettings, controlType = "exposure" )
In the code above, we first specify how many parallel threads SelfControlledCaseSeries
can use. Many of the computations can be computed in parallel, and providing more than one CPU core can greatly speed up the computation. Here we specify SelfControlledCaseSeries
can use all but one of the CPU cores detected in the system (using the parallel::detectCores()
function).
We call runSccsAnalyses
, providing the arguments for connecting to the database, which schemas and tables to use, as well as the analyses and hypotheses of interest. The outputFolder
specifies where the outcome models and intermediate files will be written. Because in this example we use negative control exposures, we must explicitly specify controlType = "exposure"
. This will cause the different negative control exposure-outcome pairs to be used for the same outcome.
If for some reason the execution was interrupted, you can restart by re-issuing the runSccsAnalyses()
command. Any intermediate and final products that have already been completed and written to disk will be skipped.
The result of the runSccsAnalyses()
is a data frame with one row per exposures-outcome-analysis combination, so one row per fitted SCCS model. It provides the file names of the intermediate and end-result files that were constructed. For example, we can retrieve the fitted model for the combination of our drug of interest, outcome, and first analysis:
sccsModelFile <- referenceTable$sccsModelFile[result$exposureId == diclofenac & referenceTable$outcomeId == giBleed & referenceTable$analysisId == 1] sccsModel <- readRDS(file.path(outputFolder, sccsModelFile)) sccsModel
if (file.exists(file.path(outputFolder, "outcomeModelReference.rds"))) { referenceTable <- getFileReference(outputFolder) sccsModelFile <- referenceTable$sccsModelFile[referenceTable$exposureId == diclofenac & referenceTable$outcomeId == giBleed & referenceTable$analysisId == 1] sccsModel <- readRDS(file.path(outputFolder, sccsModelFile)) sccsModel }
Note that some of the file names will appear several times in the table. In our example all analysis share the same sccsData
object.
We can always retrieve the file reference table again using the getFileReference()
function:
referenceTable <- getFileReference(outputFolder)
We can get a summary of the results using getResultsSummary()
:
resultsSum <- getResultsSummary(outputFolder) head(resultsSum)
if (file.exists(file.path(outputFolder, "outcomeModelReference.rds"))) { resultsSum <- getResultsSummary(outputFolder) head(resultsSum) }
This tells us, per exposure-outcome-analysis combination (so possible multiple rows per SCCS model) the estimated relative risk and 95% confidence interval, as well as the number of subjects (cases) and the number of events observed for those subjects. The only covariates included in this summary are those we marked with exposureOfInterest = TRUE
when calling createEraCovariateSettings()
earlier.
Now that we have produced estimates for all outcomes including our negative controls, we can look at the distrubtion of negative controls. In each plot, the blue dots represent our negative control exposures, and the yellow diamond represents our exposure of interest: diclofenac. An unbiased, well-calibrated analysis should have 95% of the negative controls between the dashed lines (ie. 95% should have p > .05). Note that empirical calibration was already automatically performed, and calibrated confidence intervals and p-values are included separately in the analyses summary.
install.packages("EmpiricalCalibration") library(EmpiricalCalibration) # Analysis 1: Simplest model negCons <- resultsSum[resultsSum$analysisId == 1 & resultsSum$eraId != diclofenac, ] ei <- resultsSum[resultsSum$analysisId == 1 & resultsSum$eraId == diclofenac, ] null <- fitNull( negCons$logRr, negCons$seLogRr ) plotCalibrationEffect( logRrNegatives = negCons$logRr, seLogRrNegatives = negCons$seLogRr, logRrPositives = ei$logRr, seLogRrPositives = ei$seLogRr, null )
if (file.exists(file.path(outputFolder, "outcomeModelReference.rds"))) { negCons <- resultsSum[resultsSum$analysisId == 1 & resultsSum$eraId != diclofenac, ] ei <- resultsSum[resultsSum$analysisId == 1 & resultsSum$eraId == diclofenac, ] null <- fitNull( negCons$logRr, negCons$seLogRr ) plotCalibrationEffect( logRrNegatives = negCons$logRr, seLogRrNegatives = negCons$seLogRr, logRrPositives = ei$logRr, seLogRrPositives = ei$seLogRr, null ) }
# Analysis 2: Including prophylactics and pre-exposure negCons <- resultsSum[resultsSum$analysisId == 2 & resultsSum$eraId != diclofenac, ] ei <- resultsSum[resultsSum$analysisId == 2 & resultsSum$eraId == diclofenac, ] null <- fitNull( negCons$logRr, negCons$seLogRr ) plotCalibrationEffect( logRrNegatives = negCons$logRr, seLogRrNegatives = negCons$seLogRr, logRrPositives = ei$logRr, seLogRrPositives = ei$seLogRr, null )
if (file.exists(file.path(outputFolder, "outcomeModelReference.rds"))) { negCons <- resultsSum[resultsSum$analysisId == 2 & resultsSum$eraId != diclofenac, ] ei <- resultsSum[resultsSum$analysisId == 2 & resultsSum$eraId == diclofenac, ] null <- fitNull( negCons$logRr, negCons$seLogRr ) plotCalibrationEffect( logRrNegatives = negCons$logRr, seLogRrNegatives = negCons$seLogRr, logRrPositives = ei$logRr, seLogRrPositives = ei$seLogRr, null ) }
# Analysis 3: Including prophylactics, season, calendar time, and pre-exposure negCons <- resultsSum[resultsSum$analysisId == 3 & resultsSum$eraId != diclofenac, ] ei <- resultsSum[resultsSum$analysisId == 3 & resultsSum$eraId == diclofenac, ] null <- fitNull( negCons$logRr, negCons$seLogRr ) plotCalibrationEffect( logRrNegatives = negCons$logRr, seLogRrNegatives = negCons$seLogRr, logRrPositives = ei$logRr, seLogRrPositives = ei$seLogRr, null )
if (file.exists(file.path(outputFolder, "outcomeModelReference.rds"))) { negCons <- resultsSum[resultsSum$analysisId == 3 & resultsSum$eraId != diclofenac, ] ei <- resultsSum[resultsSum$analysisId == 3 & resultsSum$eraId == diclofenac, ] null <- fitNull( negCons$logRr, negCons$seLogRr ) plotCalibrationEffect( logRrNegatives = negCons$logRr, seLogRrNegatives = negCons$seLogRr, logRrPositives = ei$logRr, seLogRrPositives = ei$seLogRr, null ) }
# Analysis 4: Including all other drugs negCons <- resultsSum[resultsSum$analysisId == 4 & resultsSum$eraId != diclofenac, ] ei <- resultsSum[resultsSum$analysisId == 4 & resultsSum$eraId == diclofenac, ] null <- fitNull( negCons$logRr, negCons$seLogRr ) plotCalibrationEffect( logRrNegatives = negCons$logRr, seLogRrNegatives = negCons$seLogRr, logRrPositives = ei$logRr, seLogRrPositives = ei$seLogRr, null )
if (file.exists(file.path(outputFolder, "outcomeModelReference.rds"))) { negCons <- resultsSum[resultsSum$analysisId == 4 & resultsSum$eraId != diclofenac, ] ei <- resultsSum[resultsSum$analysisId == 4 & resultsSum$eraId == diclofenac, ] null <- fitNull( negCons$logRr, negCons$seLogRr ) plotCalibrationEffect( logRrNegatives = negCons$logRr, seLogRrNegatives = negCons$seLogRr, logRrPositives = ei$logRr, seLogRrPositives = ei$seLogRr, null ) }
# Analysis 5: Adjusting for event-dependent obs. end negCons <- resultsSum[resultsSum$analysisId == 4 & resultsSum$eraId != diclofenac, ] ei <- resultsSum[resultsSum$analysisId == 4 & resultsSum$eraId == diclofenac, ] null <- fitNull( negCons$logRr, negCons$seLogRr ) plotCalibrationEffect( logRrNegatives = negCons$logRr, seLogRrNegatives = negCons$seLogRr, logRrPositives = ei$logRr, seLogRrPositives = ei$seLogRr, null )
if (file.exists(file.path(outputFolder, "outcomeModelReference.rds"))) { negCons <- resultsSum[resultsSum$analysisId == 4 & resultsSum$eraId != diclofenac, ] ei <- resultsSum[resultsSum$analysisId == 4 & resultsSum$eraId == diclofenac, ] null <- fitNull( negCons$logRr, negCons$seLogRr ) plotCalibrationEffect( logRrNegatives = negCons$logRr, seLogRrNegatives = negCons$seLogRr, logRrPositives = ei$logRr, seLogRrPositives = ei$seLogRr, null ) }
Considerable work has been dedicated to provide the SelfControlledCaseSeries
package.
citation("SelfControlledCaseSeries")
Further, SelfControlledCaseSeries
makes extensive use of the Cyclops
package.
citation("Cyclops")
This work is supported in part through the National Science Foundation grant IIS 1251151.
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