Plot of diagnostic categories

Histogram plot of diagnostic categories

plotDiagCat(
  groupedDataWide,
  idColName,
  groupColName = NULL,
  topN = 10,
  limitFreq = 0.01,
  pvalue = 0.05
)

Arguments

groupedDataWide: Wide table of data frame (generated from groupedDataLongToWide function).
topN: Numeric. Default is 10 (Top 10; 10 most common wrong ICD).
limitFreq: Numeric. minimum frequency shown (frequency below this threshold will not be shown in plot). Default is 0.01. In other words, the threshold is 1 percent patient among total patients been diagnosed in the same diagnostic category.
pvalue: Numeric. p value of chisq.test. Default is 0.05.

Value

A histogram plot and a data.table of summarized classified data.

Details

This function provides an overview of grouping category of the diagnostic code in histogram plot. User can observe the proportion of diagnostic categories in their dataset through this function. Also, Chi-square test and Fisher’s exact test are also included in this function. User can test if the proportion of each diagnostic category in case group and control group are statistical significantly different.

Examples

# sample file for example
head(sampleDxFile)
#>     ID  ICD       Date Version
#> 1:  A2 Z992 2020-05-22      10
#> 2:  A5 Z992 2020-01-24      10
#> 3:  A8 Z992 2015-10-27      10
#> 4: A13 Z992 2020-04-26      10
#> 5: A13 Z992 2025-02-02      10
#> 6: A15 Z992 2023-05-12      10

# Create a grouped data

ELIX <- icdDxToComorbid(dxDataFile = sampleDxFile,
                       idColName = ID,
                       icdColName = ICD,
                       dateColName = Date,
                       icd10usingDate = "2015/10/01",
                       comorbidMethod = elix)
#> Wrong ICD format: total 9 ICD codes (the number of occurrences is in brackets)
#> c("A0.11 (20)", "E114 (8)", "Z9.90 (6)", "F42 (6)", "001 (5)", "75.52 (4)", "755.2 (3)", "123.45 (3)", "7552 (2)")
#> 	
#> Wrong ICD version: total 7 ICD codes (the number of occurrences is in brackets)
#> c("V27.0 (18)", "A01.05 (8)", "42761 (7)", "V24.1 (6)", "A0105 (5)", "E03.0 (4)", "650 (4)")
#> 	
#> Warning: The ICD mentioned above matches to "NA" due to the format or other issues.
#> Warning: "Wrong ICD format" means the ICD has wrong format
#> Warning: "Wrong ICD version" means the ICD classify to wrong ICD version (cause the "icd10usingDate" or other issues)

head(ELIX$groupedDT)
#>    Short  ID  ICD       Date Comorbidity
#> 1:  Z992  A2 Z992 2020-05-22    RENLFAIL
#> 2:  Z992  A5 Z992 2020-01-24    RENLFAIL
#> 3:  Z992  A8 Z992 2015-10-27    RENLFAIL
#> 4:  Z992 A13 Z992 2020-04-26    RENLFAIL
#> 5:  Z992 A13 Z992 2025-02-02    RENLFAIL
#> 6:  Z992 A15 Z992 2023-05-12    RENLFAIL

# Convert long format of grouped data into wide binary format

groupedDataWide <- groupedDataLongToWide(ELIX$groupedDT,
                                         idColName = ID,
                                         categoryColName = Comorbidity,
                                         dateColName = Date)

# plot of top 10 common grouped categories and a list of the detail of grouped categories

plot1 <- plotDiagCat(groupedDataWide = groupedDataWide,
                          idColName = ID,
                          topN = 10,
                          limitFreq = 0.01)
plot1
#> $graph

#> 
#> $sigCate
#>     DiagnosticCategory  N Percentage
#>  1:           RENLFAIL 24      63.16
#>  2:              TUMOR  6      15.79
#>  3:               ARTH  5      13.16
#>  4:              LYMPH  4      10.53
#>  5:              PSYCH  4      10.53
#>  6:               DRUG  3       7.89
#>  7:              NEURO  3       7.89
#>  8:               PARA  2       5.26
#>  9:           PERIVASC  2       5.26
#> 10:              VALVE  2       5.26
#> 

# Select case with "Diseases of the urinary system" by level 2 of CCS classification

selectedCaseFile <- selectCases(dxDataFile = sampleDxFile,
                                idColName = ID,
                                icdColName = ICD,
                                dateColName = Date,
                                icdVerColName = NULL,
                                icd10usingDate = "2015/10/01",
                                groupDataType = ccslvl2,
                                caseCondition = "Diseases of the urinary system",
                                caseCount = 1)
#> Wrong ICD format: total 9 ICD codes (the number of occurrences is in brackets)
#> c("A0.11 (20)", "E114 (8)", "Z9.90 (6)", "F42 (6)", "001 (5)", "75.52 (4)", "755.2 (3)", "123.45 (3)", "7552 (2)")
#> 	
#> Wrong ICD version: total 7 ICD codes (the number of occurrences is in brackets)
#> c("V27.0 (18)", "A01.05 (8)", "42761 (7)", "V24.1 (6)", "A0105 (5)", "E03.0 (4)", "650 (4)")
#> 	
#> Warning: The ICD mentioned above matches to "NA" due to the format or other issues.
#> Warning: "Wrong ICD format" means the ICD has wrong format
#> Warning: "Wrong ICD version" means the ICD classify to wrong ICD version (cause the "icd10usingDate" or other issues)

# Convert the long format of grouped data into a wide binary format with selected case

groupedDataWide <- groupedDataLongToWide(ELIX$groupedDT,
                                         idColName = ID,
                                         categoryColName = Comorbidity,
                                         dateColName = Date,
                                         selectedCaseFile = selectedCaseFile)

# plot of top 10 common grouped categories and a list of the detail of grouped categories

plot2 <- plotDiagCat(groupedDataWide = groupedDataWide,
                          idColName = ID,
                          topN = 10,
                          limitFreq = 0.01,
                          pvalue = 0.05,
                          groupColName = selectedCase)
plot2
#> $graph

#> 
#> $sigCate
#>    DiagnosticCategory        Group  N Percentage
#> 1:           RENLFAIL non-Selected  0          0
#> 2:           RENLFAIL     Selected 24        100
#>

Arguments

Value

Details

See also

Examples