# Pre-processing of clinical data for clinical data review report

#### Laure Cougnaud, Michela Pasetto

#### April 24, 2024

This vignette shows functionalities used for annotating and filtering the data within the `clinDataReview` package.

Utility functions to automate standard pre-processing steps of the data are available in the package.

Note that these functions are mainly useful in combination with the specification of the parameters in ‘config’ file in the clinical data reports (see the dedicated reporting vignette).

For this vignette, we will use example data available in the `clinUtils` package.

    library(clinDataReview)

# 1 Data format

The input dataset for the clinical data review should be a `data.frame` with clinical data. Such data is typically imported from SAS data file or xpt data file.
Such dataset can be imported for multiple files at once via the `clinUtils::loadDataADaMSDTM` function.

The label of the variables stored in the `SAS` datasets is also used for title/captions.

A few `ADaM` datasets are included in the `clinUtils` package for the demonstration, via the dataset `dataADaMCDISCP01` and corresponding variable labels.

    library(clinUtils)

    data(dataADaMCDISCP01)
    labelVars <- attr(dataADaMCDISCP01, "labelVars")

    dataLB <- dataADaMCDISCP01$ADLBC
    dataDM <- dataADaMCDISCP01$ADSL
    dataAE <- dataADaMCDISCP01$ADAE

# 2 Annotate data

The `annotateData` enables to add metadata for a specific domain/dataset.

    dataLBAnnot <- annotateData(
        data = dataLB,
        annotations = list(data = dataDM, vars = c("ETHNIC", "ARM")),
        verbose = TRUE
    )

    ## Data annotated with variable(s): ETHNIC ('ETHNIC'), ARM ('ARM') from the 'custom' dataset based on the variable(s):  USUBJID ('USUBJID').

    knitr::kable(
        head(dataLBAnnot),
        caption = paste("Laboratory parameters annotated with",
            "demographics information with the `annotatedData` function"
        )
    )

Laboratory parameters annotated with demographics information with the `annotatedData` function

STUDYID

SUBJID

USUBJID

TRTP

TRTPN

TRTA

TRTAN

TRTSDT

TRTEDT

AGE

AGEGR1

AGEGR1N

RACE

RACEN

SEX

COMP24FL

DSRAEFL

SAFFL

AVISIT

AVISITN

ADY

ADT

VISIT

VISITNUM

PARAM

PARAMCD

PARAMN

PARCAT1

AVAL

BASE

CHG

A1LO

A1HI

R2A1LO

R2A1HI

BR2A1LO

BR2A1HI

ANL01FL

ALBTRVAL

ANRIND

BNRIND

ABLFL

AENTMTFL

LBSEQ

LBNRIND

LBSTRESN

DATASET

ETHNIC

ARM

CDISCPILOT01

1148

01-701-1148

Xanomeline High Dose

81

Xanomeline High Dose

81

2013-08-23

2014-02-20

57

<65

1

WHITE

1

M

Y

Y

Baseline

0

-9

2013-08-14

SCREENING 1

1

Sodium (mmol/L)

SODIUM

18

CHEM

139.00

139.00

NA

132.0

147.0

1.053030

0.9455782

1.053030

0.9455782

81.50

N

N

Y

26

NORMAL

139.00

ADLBC

NOT HISPANIC OR LATINO

Xanomeline High Dose

CDISCPILOT01

1148

01-701-1148

Xanomeline High Dose

81

Xanomeline High Dose

81

2013-08-23

2014-02-20

57

<65

1

WHITE

1

M

Y

Y

Baseline

0

-9

2013-08-14

SCREENING 1

1

Potassium (mmol/L)

K

19

CHEM

4.00

4.00

NA

3.4

5.4

1.176471

0.7407407

1.176471

0.7407407

4.10

N

N

Y

19

NORMAL

4.00

ADLBC

NOT HISPANIC OR LATINO

Xanomeline High Dose

CDISCPILOT01

1148

01-701-1148

Xanomeline High Dose

81

Xanomeline High Dose

81

2013-08-23

2014-02-20

57

<65

1

WHITE

1

M

Y

Y

Baseline

0

-9

2013-08-14

SCREENING 1

1

Chloride (mmol/L)

CL

20

CHEM

109.00

109.00

NA

94.0

112.0

1.159574

0.9732143

1.159574

0.9732143

62.00

N

N

Y

11

NORMAL

109.00

ADLBC

NOT HISPANIC OR LATINO

Xanomeline High Dose

CDISCPILOT01

1148

01-701-1148

Xanomeline High Dose

81

Xanomeline High Dose

81

2013-08-23

2014-02-20

57

<65

1

WHITE

1

M

Y

Y

Baseline

0

-9

2013-08-14

SCREENING 1

1

Bilirubin (umol/L)

BILI

21

CHEM

8.55

8.55

NA

3.0

21.0

2.850000

0.4071429

2.850000

0.4071429

22.95

N

N

Y

6

NORMAL

8.55

ADLBC

NOT HISPANIC OR LATINO

Xanomeline High Dose

CDISCPILOT01

1148

01-701-1148

Xanomeline High Dose

81

Xanomeline High Dose

81

2013-08-23

2014-02-20

57

<65

1

WHITE

1

M

Y

Y

Baseline

0

-9

2013-08-14

SCREENING 1

1

Alkaline Phosphatase (U/L)

ALP

22

CHEM

88.00

88.00

NA

31.0

110.0

2.838710

0.8000000

2.838710

0.8000000

77.00

N

N

Y

2

NORMAL

88.00

ADLBC

NOT HISPANIC OR LATINO

Xanomeline High Dose

CDISCPILOT01

1148

01-701-1148

Xanomeline High Dose

81

Xanomeline High Dose

81

2013-08-23

2014-02-20

57

<65

1

WHITE

1

M

Y

Y

Baseline

0

-9

2013-08-14

SCREENING 1

1

Gamma Glutamyl Transferase (U/L)

GGT

23

CHEM

43.00

43.00

NA

10.0

61.0

4.300000

0.7049180

4.300000

0.7049180

48.50

N

N

Y

15

NORMAL

43.00

ADLBC

NOT HISPANIC OR LATINO

Xanomeline High Dose

# 3 Filter data

The `filterData` enables to filter a dataset.

    dataLBAnnotTreatment <- filterData(
        data = dataLBAnnot,
        filters = list(var = "ARM", value = "Placebo", rev = TRUE),
        verbose = TRUE
    )

    ## 354 records with ARM ('ARM') %in% 'Placebo' are filtered in the data.

    knitr::kable(
        unique(dataLBAnnotTreatment[, c("USUBJID", "ARM")]),
        caption = paste("Subset of laboratory parameters filtered",
            "with placebo patients"
        )
    )

Subset of laboratory parameters filtered with placebo patients
| USUBJID | ARM
--- | --- | ---
1 | 01-701-1148 | Xanomeline High Dose
397 | 01-701-1192 | Xanomeline Low Dose
793 | 01-701-1211 | Xanomeline Low Dose
1363 | 01-718-1371 | Xanomeline High Dose
1615 | 01-718-1427 | Xanomeline High Dose

# 4 Transform data

The `transformData` enables to convert data to a different format.

For example, the laboratory data is converted from a long format, containing one record per endpoint * visit * subject to a wide format containing one record per visit * subject. The endpoints are included in different columns.

    eDishData <- transformData(
        data = subset(dataLB, PARAMCD %in% c("ALT", "BILI")),
        transformations = list(
            type = "pivot_wider",
            varsID = c("USUBJID", "VISIT"),
            varsValue = c("LBSTRESN", "LBNRIND"),
            varPivot = "PARAMCD"
        ),
        verbose = TRUE,
        labelVars = labelVars
    )

    ## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying = varying, : some constant variables
    ## (AVISIT,AVISITN,PARAM,PARAMN,AVAL,BASE,CHG,A1LO,A1HI,R2A1LO,R2A1HI,BR2A1LO,BR2A1HI,ANL01FL,ALBTRVAL,LBSEQ) are really varying

    ## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying = varying, : multiple rows match for PARAMCD=BILI: first taken

    ## Warning in reshapeWide(data, idvar = idvar, timevar = timevar, varying = varying, : multiple rows match for PARAMCD=ALT: first taken

    ## Data is converted to a wide format with variables: 'LBSTRESN', 'LBNRIND' for different: 'PARAMCD' by 'Unique Subject Identifier', 'Visit Name' pivoted to different columns.

    knitr::kable(head(eDishData))

STUDYID

SUBJID

USUBJID

TRTP

TRTPN

TRTA

TRTAN

TRTSDT

TRTEDT

AGE

AGEGR1

AGEGR1N

RACE

RACEN

SEX

COMP24FL

DSRAEFL

SAFFL

AVISIT

AVISITN

ADY

ADT

VISIT

VISITNUM

PARAM

PARAMN

PARCAT1

AVAL

BASE

CHG

A1LO

A1HI

R2A1LO

R2A1HI

BR2A1LO

BR2A1HI

ANL01FL

ALBTRVAL

ANRIND

BNRIND

ABLFL

AENTMTFL

LBSEQ

DATASET

LBSTRESN.BILI

LBNRIND.BILI

LBSTRESN.ALT

LBNRIND.ALT

4 | CDISCPILOT01 | 1148 | 01-701-1148 | Xanomeline High Dose | 81 | Xanomeline High Dose | 81 | 2013-08-23 | 2014-02-20 | 57 | <65 | 1 | WHITE | 1 | M | Y |  | Y | Baseline | 0 | -9 | 2013-08-14 | SCREENING 1 | 1 | Bilirubin (umol/L) | 21 | CHEM | 8.55 | 8.55 | NA | 3 | 21 | 2.85 | 0.4071429 | 2.85 | 0.4071429 |  | 22.95 | N | N | Y |  | 6 | ADLBC | 8.55 | NORMAL | 34 | NORMAL
40 | CDISCPILOT01 | 1148 | 01-701-1148 | Xanomeline High Dose | 81 | Xanomeline High Dose | 81 | 2013-08-23 | 2014-02-20 | 57 | <65 | 1 | WHITE | 1 | M | Y |  | Y | Week 2 | 2 | 14 | 2013-09-05 | WEEK 2 | 4 | Bilirubin (umol/L) | 21 | CHEM | 8.55 | 8.55 | 0.00 | 3 | 21 | 2.85 | 0.4071429 | 2.85 | 0.4071429 |  | 22.95 | N | N |  |  | 43 | ADLBC | 8.55 | NORMAL | 41 | NORMAL
76 | CDISCPILOT01 | 1148 | 01-701-1148 | Xanomeline High Dose | 81 | Xanomeline High Dose | 81 | 2013-08-23 | 2014-02-20 | 57 | <65 | 1 | WHITE | 1 | M | Y |  | Y | Week 4 | 4 | 28 | 2013-09-19 | WEEK 4 | 5 | Bilirubin (umol/L) | 21 | CHEM | 8.55 | 8.55 | 0.00 | 3 | 21 | 2.85 | 0.4071429 | 2.85 | 0.4071429 |  | 22.95 | N | N |  |  | 78 | ADLBC | 8.55 | NORMAL | 35 | NORMAL
112 | CDISCPILOT01 | 1148 | 01-701-1148 | Xanomeline High Dose | 81 | Xanomeline High Dose | 81 | 2013-08-23 | 2014-02-20 | 57 | <65 | 1 | WHITE | 1 | M | Y |  | Y | Week 6 | 6 | 42 | 2013-10-03 | WEEK 6 | 7 | Bilirubin (umol/L) | 21 | CHEM | 8.55 | 8.55 | 0.00 | 3 | 21 | 2.85 | 0.4071429 | 2.85 | 0.4071429 |  | 22.95 | N | N |  |  | 108 | ADLBC | 8.55 | NORMAL | 31 | NORMAL
148 | CDISCPILOT01 | 1148 | 01-701-1148 | Xanomeline High Dose | 81 | Xanomeline High Dose | 81 | 2013-08-23 | 2014-02-20 | 57 | <65 | 1 | WHITE | 1 | M | Y |  | Y | Week 8 | 8 | 57 | 2013-10-18 | WEEK 8 | 8 | Bilirubin (umol/L) | 21 | CHEM | 8.55 | 8.55 | 0.00 | 3 | 21 | 2.85 | 0.4071429 | 2.85 | 0.4071429 |  | 22.95 | N | N |  |  | 138 | ADLBC | 8.55 | NORMAL | 31 | NORMAL
184 | CDISCPILOT01 | 1148 | 01-701-1148 | Xanomeline High Dose | 81 | Xanomeline High Dose | 81 | 2013-08-23 | 2014-02-20 | 57 | <65 | 1 | WHITE | 1 | M | Y |  | Y | Week 12 | 12 | 87 | 2013-11-17 | WEEK 12 | 9 | Bilirubin (umol/L) | 21 | CHEM | 6.84 | 8.55 | -1.71 | 3 | 21 | 2.28 | 0.3257143 | 2.85 | 0.4071429 | Y | 24.66 | N | N |  |  | 168 | ADLBC | 6.84 | NORMAL | 39 | NORMAL

# 5 Process data

The `processData` function executes all the pre-processing steps described in the previous section at once.

    dataLBAnnotTreatment2 <- processData(
        data = dataLB,
        processing = list(
            list(annotate = list(data = dataDM, vars = c("ETHNIC", "ARM"))),
            list(filter = list(var = "ARM", value = "Placebo", rev = TRUE))
        ),
        verbose = TRUE
    )

    identical(dataLBAnnotTreatment, dataLBAnnotTreatment2)

[1] TRUE

# 6 Appendix

## 6.1 Session info

R version 4.3.3 (2024-02-29) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 22.04.4 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0

locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=C LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

time zone: Etc/UTC tzcode source: system (glibc)

attached base packages: [1] stats graphics grDevices utils datasets methods base

other attached packages: [1] clinUtils_0.1.5 clinDataReview_1.5.1 knitr_1.46

loaded via a namespace (and not attached): [1] plotly_4.10.4 sass_0.4.9 utf8_1.2.4 generics_0.1.3 tidyr_1.3.1 xml2_1.3.6 stringi_1.8.3 jsonvalidate_1.3.2 [9] hms_1.1.3 digest_0.6.35 magrittr_2.0.3 evaluate_0.23 grid_4.3.3 bookdown_0.39 fastmap_1.1.1 plyr_1.8.9
[17] jsonlite_1.8.8 httr_1.4.7 purrr_1.0.2 fansi_1.0.6 crosstalk_1.2.1 viridisLite_0.4.2 scales_1.3.0 lazyeval_0.2.2
[25] jquerylib_0.1.4 cli_3.6.2 rlang_1.1.3 munsell_0.5.1 cachem_1.0.8 yaml_2.3.8 tools_4.3.3 parallel_4.3.3
[33] dplyr_1.1.4 colorspace_2.1-0 ggplot2_3.5.0 DT_0.33 forcats_1.0.0 vctrs_0.6.5 R6_2.5.1 lifecycle_1.0.4
[41] stringr_1.5.1 htmlwidgets_1.6.4 pkgconfig_2.0.3 pillar_1.9.0 bslib_0.7.0 gtable_0.3.4 data.table_1.15.4 glue_1.7.0
[49] Rcpp_1.0.12 haven_2.5.4 xfun_0.43 tibble_3.2.1 tidyselect_1.2.1 htmltools_0.5.8.1 rmarkdown_2.26 compiler_4.3.3