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#Cost tables

CDM version 5.0 had cost tables that were deleted in version 5.0.1. To avoid errors, use a parameter when calling the achilles function that specifies runCostAnalysis = FALSE

#How to run Achilles Heel only:

Execution of all analyses computations is not necessary if all you want to do is to run new data quality measures in a revised version of Heel. Instead of 10+ hours, you can be done in few minutes with running just heel

#initialize your connectionDetails as usual
#set your schema names for where is data and where to store results
  myCdm='cdm5_inst';resDb='results'

#run heel only like this
  heel<-achillesHeel(connectionDetails,cdmDatabaseSchema = myCdm, resultsDatabaseSchema = resDb,cdmVersion = "5")

#optionally - get heel errors and warnings as a small CSV file
  heelRes<-fetchAchillesHeelResults(connectionDetails,resDb)
  write.csv(heelRes,paste0(myCdm,'-01-heel-res.csv'),row.names = F,na = '')

#How to save time on running full Achilles - make it finish much earlier If you are willing to skip cost analyses (not used very often), this smaller set of analyses will finish much earlier

#get all possible analyses first
allAnalyses=getAnalysisDetails()$ANALYSIS_ID

#cost analyses may take 15+ hours and may not be always necessary
longAnalyses1=c(1500:1699)

#exclude them
subSet1=setdiff(allAnalyses,longAnalyses1)


#create connection details (modify for your server)
connectionDetails <- createConnectionDetails(dbms="redshift", server="server.com", user="secret",
                            password='secret', schema="cdm5_inst", port="5439")

#run Achilles (and Heel)  with this smaller subSet1 only (this will save several hours (or days) of your execution time)
achillesResults <- achilles(connectionDetails, cdmDatabaseSchema="cdm5_inst", 
                            resultsDatabaseSchema="results", sourceName="My Source Name", 
                            vocabDatabaseSchema="vocabulary",cdmVersion = "5",analysisIds = subSet1)

#Execute only few new analyses Achilles can take a long time to execute. To see new analyses, it is possible to only execute those new analyses. E.g., newly integrated Iris analyses. Use the following code that specifies a set of analysis_id's. The key is to specify which anlayses to run, and to specify createTables to FALSE so that this execution will preserve results previously executed.

cdmDatabaseSchema='ccae_v5'    #change to yours
resultsDatabaseSchema='nih'    #change to yours
vocabDatabaseSchema='ccae_v5'  #change to yours
achillesResults <- achilles(connectionDetails,cdmDatabaseSchema=cdmDatabaseSchema,
                            resultsDatabaseSchema=resultsDatabaseSchema,
                            sourceName="My Source Name", 
                            vocabDatabaseSchema=vocabDatabaseSchema,
                            cdmVersion = "5",
                            createTable = F,analysisIds = c(2000,2001))

#Small maintenance tasks for the package ##update CSV overview file for analyses

connectionDetails$schema=resultsDatabaseSchema
conn<-connect(connectionDetails)
achilles_analysis<-querySql(conn,'select * from achilles_analysis')
#this line caused issue 151: names(achilles_analysis) <- tolower(names(achilles_analysis))
write.csv(achilles_analysis,file = 'inst/csv/analysisDetails.csv',na = '',row.names = F)

#optionaly insert rule overview into the database
#or rewrite this as  package file
read.csv(system.file("csv","derived_analysis_details",package="Achilles"),as.is=T)

#achilles_derived_measures<-read.csv(file = 'inst/csv/derived_analysis_details.csv',as.is=T)
achilles_derived_measures<-read.csv(system.file("csv","derived_analysis_details.csv",package="Achilles"),as.is=T)
insertTable(conn,'achilles_derived_measures',achilles_derived_measures)

achilles_rule<-read.csv(system.file("csv","achilles_rule.csv",package="Achilles"),as.is=T)
insertTable(conn,'achilles_rule',achilles_rule)

##overview html files The code below updates html files that show content overview. Use rawgit.com/OHDSI/... to view it nicely.

tempf<-tempfile(pattern = 'temp', fileext = '.Rmd')
writeLines('---\ntitle: "Rules"\n---\n```{r, echo=FALSE}\n rules<-read.csv(system.file("csv","achilles_rule.csv",package="Achilles"),as.is=T);knitr::kable(rules)\n```',tempf)
rmarkdown::render(tempf,output_file = 'c:/temp/Heel-Rules.html',rmarkdown::html_document(toc = F, fig_caption = TRUE))


tempf<-tempfile(pattern = 'temp', fileext = '.Rmd')
writeLines('---\ntitle: "Overview"\n---\n```{r, echo=FALSE}\n rules<-read.csv(system.file("csv","derived_analysis_details.csv",package="Achilles"),as.is=T);knitr::kable(rules)\n```',tempf)
rmarkdown::render(tempf,output_file = 'c:/temp/Derived-Analyses.html',rmarkdown::html_document(toc = F, fig_caption = TRUE))

tempf<-tempfile(pattern = 'temp', fileext = '.Rmd')
writeLines('---\ntitle: "Overview"\n---\n```{r, echo=FALSE}\n rules<-read.csv(system.file("csv","rule_drill_down.csv",package="Achilles"),as.is=T);knitr::kable(rules)\n```',tempf)
rmarkdown::render(tempf,output_file = 'c:/temp/Rule-Drill-Down.html',rmarkdown::html_document(toc = F, fig_caption = TRUE))

#Data Quality CDM These notes relate Achilles and Achilles Heel to Data Quality CDM (DQ CDM)

DQM terminology is slightly different

Achilles term = DQM term
analysis = measure
stratum = dimension
rule = check

##Classification of measures ###by PURPOSE

  • general purpose measure (% of males)
  • measure specific for DQ (count of rows with invalid provider_id) ###by OUTPUT
  • single row measure (count of providers)
  • multiple rows measure (medium, large, very large) (depends on stratification) ###by TERMINOLOGY/MODEL
  • terminology dependent measure/rule (hysterectomy (using SNOMED (SCT0013513) (Athena CIDs)) (ICD9CM, 10PCS, CPT)
  • terminology independent measure/rule (eg, at least 1 numerical lab result value in 1000 person sample)
  • model independent measure/rule (eg, zombie events, prior conception events)

##By outputed results ###Stratified analyses

These anlyses use table ACHILLES_results

###distributions Such analyses use table ACHILLES_results_dist e.g., 103,104,105,106,107,203,206,211,403,406,506,511,512,513,514,515,603,606,704,706,715,716,717,803,806,815

##By nature

###general Some analyses are checking data size (and useful in general)
###conformance to data model Other analyses have only while others are s ###data quality specific analyses e.g., analysis_id 7,8,9,207

#Analyzing Heel Results ###Simple rules: There are simple rules that generate a single error or warning.

###Complex rules However, some rules (e.g., rule_id 6) can generate multiple rows. The true primary key for output is combination of rule_id and analysis_id