patientProfilesVis
Visualization of Patient Profiles

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Introduction to the `patientProfilesVis` package"

Introduction to the `patientProfilesVis` package"
In patientProfilesVis: Visualization of Patient Profiles

Introduction

    library(knitr)
    opts_chunk$set(
        error = FALSE, 
        fig.width = 14, fig.height = 7,
        dev = "png",
        out.width = "100%",
        fig.path = "./figures_vignette/",
        fig.align = 'center'
    )
    # include warnings where they occur
    options(warn = 1)

    heightLineIn  <- 0.2

This package patientProfilesVis enables to create subject profile reports of patients/subjects in a clinical trial.

Such visualization can be used to obtain a global view of the subject metadata information, combined with its treatment exposure and concomitant medications, in relation with the adverse events occurring during the trial, and any measurements conducted during a clinical trial (e.g. laboratory, vital signs or ECG).

    library(patientProfilesVis)
    library(pander)

Input data

Data format

The input dataset for the creation of patient profiles should be a data.frame, typically CDISC 'Study Data Tabulation Model' (a.k.a SDTM) or 'Analysis Data Model' (a.k.a. ADaM) datasets.

The package also support tibble datasets as imported by the read_sas/read_xpt functions from the haven.

Alternatively, datasets can be imported at once with the loadDataADaMSDTM function from the clinUtils package.

Furthermore, the input dataset should contain a variable containing subject identifier. This variable is set to USUBJID by default, but can be overwritten via the subjectVar parameter.

Example SDTM dataset

The package is demonstrated with a subset of the SDTM datasets from the CDISC Pilot 01 dataset, available in the clinUtils package.

library(clinUtils)

# import example data:
data(dataSDTMCDISCP01)
# formatted as a list of data.frame (one per domain)
dataSDTM <- dataSDTMCDISCP01
names(dataSDTM)

# and corresponding labels
labelVarsSDTM <- attr(dataSDTM, "labelVars")
head(labelVarsSDTM)

Example ADaM dataset

A subset of the ADaM datasets from the CDISC Pilot 01 dataset, available in the clinUtils package, is also imported for the example in section [ADaM dataset].

# import example data:
data(dataADaMCDISCP01)
# formatted as a list of data.frame (one per domain)
dataADaM <- dataADaMCDISCP01
names(dataADaM)

# and corresponding labels
labelVarsADaM <- attr(dataADaM, "labelVars")
head(labelVarsADaM)

# example subjects for the vignette:
subjectAE <- "01-718-1427"
subjectMH <- "01-718-1371"
subjectCM <- "01-701-1148"
subjectLB <- "01-704-1445"

Creation of the plot modules

General

Different types of visualization (a.k.a 'modules') are available via dedicated R function. Each function creates a separate visualization for each subject available in the dataset.

Four plot types/modules are currently available in the package:

Each of this function returns a nested list of plots (ggplot object).

Each element of the list contains the plots for a specific subject. The subject profile plot for a specific subject/module is possibly split into multiple plots to fit in the final report (formatReport parameter).

Text module

The 'text' module enables to specify meta-information for each subject. There are two ways to specify such information, either by specifying a set of variables/columns of the data (paramValueVar only), or by a variable/column containing the parameter name (paramNameVar) and variable(s)/column(s) containing the parameter value (paramValueVar).

Wide format

    # annotate subject demographics meta-data
    # by specifying a set of variables to include
    dmPlots <- subjectProfileTextPlot(
        data = dataSDTM$DM,
        paramValueVar = c("SEX|AGE", "RACE|COUNTRY", "ARM"),
        labelVars = labelVarsSDTM
    )

    print(dmPlots[[1]][[1]])

Long format

General

It is possible to specify multiple variable to represent in the plot for a certain variable name.

    # annotate subject medical history
    # by specifying a combination of parameter value/name
    mhPlots <- subjectProfileTextPlot(
        data = dataSDTM$MH,
        paramNameVar = c("MHDECOD"),
        paramValueVar = c("MHSTDTC", "MHSEV"),
        paramGroupVar = "MHCAT",
        title = "Medical History: status",
        labelVars = labelVarsSDTM
    )

    print(mhPlots[[subjectMH]][[1]])

Table format (listing)

Information is displayed as a listing, by setting the table parameter to TRUE.

    aeListingPlots <- subjectProfileTextPlot(
        data = dataSDTM$AE,
        paramValueVar = c(
            "AEBODSYS", "AESOC", "AEHLT", 
            "AELLT", "AEDECOD", "AESTDTC", 
            "AEENDTC", "AESER", "AEACN"
        ),
        paramGroupVar = "AESTDTC",
        labelVars = labelVarsSDTM,
        table = TRUE
    )

    print(aeListingPlots[[subjectAE]][[1]])

By default, the widths of the columns of the table are optimized based on the column content, but custom widths can be specified via the colWidth parameter.

For example, the column for the system organ class is enlarged.

    aeListingPlots <- subjectProfileTextPlot(
        data = dataSDTM$AE,
        paramValueVar = c(
            "AEBODSYS", "AESOC", "AEHLT", 
            "AELLT", "AEDECOD", "AESTDTC", 
            "AEENDTC", "AESER", "AEACN"
        ),
        paramGroupVar = "AESTDTC",
        labelVars = labelVarsSDTM,
        table = TRUE,
        colWidth = c(
            0.2, 0.2, 0.05, 
            0.1, 0.1, 0.05, 
            0.05, 0.05, 0.05
        )
    )

    print(aeListingPlots[[subjectAE]][[1]])

Customization for multiple variables

In case multiple variable are used as paramValueVar and they should be concatenated with a specific format, a function can be specified via the parameter: paramValueVar.

    # annotate subject medical history
    # by specifying a combination of parameter value/name
    paramValueVarFct <- function(data)
        with(data, paste0(
            ifelse(MHSEV != "", paste("severity:", MHSEV, ""), ""),
            "(start = ", ifelse(MHSTDTC != "", MHSTDTC, "undefined"), ")"
        ))
    mhPlotsMultipleVars <- subjectProfileTextPlot(
        data = dataSDTM$MH,
        paramNameVar = "MHDECOD",
        paramValueVar = paramValueVarFct,
        title = "Medical History: status with dates",
        labelVars = labelVarsSDTM
    )

    print(mhPlotsMultipleVars[[subjectMH]][[1]])

With grouping

    # annotate subject medical history
    # by specifying a combination of parameter value/name
    mhPlotsGroup <- subjectProfileTextPlot(
        data = dataSDTM$MH,
        paramNameVar = "MHDECOD",
        paramValueVar = c("MHDECOD", "MHSTDTC"),
        paramGroupVar = "MHCAT",
        title = "Medical History: grouped by category",
        labelVars = labelVarsSDTM
    )

    print(mhPlotsGroup[[subjectMH]][[1]])

Interval/Range module

Event with a fixed start/end time are displayed as time interval via the 'interval' module.

Adverse events

This module is used to represent the start/end date of the adverse events.

Please check section Missing starting/end time for further information on how records with missing start/end date are represented.

    dataAE <- dataSDTM$AE

    # sort severities
    dataAE[, "AESEV"] <- factor(dataAE[, "AESEV"], levels = c("MILD", "MODERATE", "SEVERE"))

    aePlots <- subjectProfileIntervalPlot(
        data = dataAE,
        paramVar = "AETERM",
        timeStartVar = "AESTDY",
        timeEndVar = "AEENDY",
        colorVar = "AESEV",
        labelVars = labelVarsSDTM,
        title = "Adverse events"
    )

    print(aePlots[[subjectAE]][[1]])

Exposure

The exposure of the patients to certain treatment(s) is also represented in this time interval visualization

    exPlots <- subjectProfileIntervalPlot(
        data = dataSDTM$EX,
        paramVar = c("EXTRT", "EXDOSE", "EXDOSU"),
        timeStartVar = "EXSTDY",
        timeEndVar = "EXENDY",
        colorVar = "EXDOSFRM",
        labelVars = labelVarsSDTM,
        title = "Treatment exposure"
    )

    print(exPlots[[1]][[1]])

Concomitant medications

    cmPlots <- subjectProfileIntervalPlot(
        data = dataSDTM$CM,
        paramVar = c(
            "CMTRT", 
            "CMDOSE", "CMDOSU", "CMROUTE", 
            "CMDOSFRQ"
        ),
        timeStartVar = "CMSTDY",
        timeEndVar = "CMENDY",
        paramGroupVar = "CMCLAS",
        colorVar = "CMCLAS",
        labelVars = labelVarsSDTM,
        title = "Concomitant medications"
    )

    print(cmPlots[[subjectCM]][[1]])

Missing starting/end time {#intervalMissingStartEnd}

The interval visualization requires specified start/end time for each record.

However, it is frequent that the start or the end time of an event/record is missing in clinical data, especially if the data is being collected.

Different types of missing values can occur during a clinical study:

It might be important to still display these records in the visualization, so different types of imputation for missing start/end date for the interval visualization are available in the package.

Please have a look at the section 'Details' of the documentation of the subjectProfileIntervalPlot function for the most up-to-date information on this imputation.

Default imputation

By default, minimal imputation is used (specified via the parameter timeImpType). Specific symbols are used to represent missing starting/end time.

Records with:

    aePlots <- subjectProfileIntervalPlot(
        data = dataAE,
        paramVar = "AETERM",
        timeStartVar = "AESTDY",
        timeEndVar = "AEENDY",
        colorVar = "AESEV",
        labelVars = labelVarsSDTM,
        title = "Adverse events"
    )

    print(aePlots[[subjectAE]][[1]])

Imputation based on an external dataset

To set the values represented for records with missing start/end dates, the time limits can be extracted from a specified dataset containing the start/end date for each subject via the timeLimData/timeLimStartVar/timeLimEndVar parameters.

This option is used below to impute missing starting/end time with the first/last visit for each subject based on the 'Subject Visit' dataset.

As the start and end of the subject visit dates are not available as relative day in the example data, these are first computed based on the subject reference start date/time available in the demography dataset.

dataSV <- dataSDTM$SV
dataSV$RFSTDTC <- dataSDTM$DM[match(dataSV$USUBJID, dataSDTM$DM$USUBJID), "RFSTDTC"]
dataSV$SVSTDY <- with(dataSV, as.numeric(as.Date(SVSTDTC)-as.Date(RFSTDTC)+1))
dataSV$SVENDY <- with(dataSV, as.numeric(as.Date(SVENDTC)-as.Date(RFSTDTC)+1))

    aePlotsTimLimFromSV <- subjectProfileIntervalPlot(
        data = dataAE,
        paramVar = "AETERM",
        timeStartVar = "AESTDY",
        timeEndVar = "AEENDY",
        colorVar = "AESEV",
        labelVars = labelVarsSDTM,
        title = "Adverse events",
        timeLimData = dataSV,
        timeLimStartVar = "SVSTDY", timeLimStartLab = "First subject visit", 
        timeLimEndVar = "SVENDY", timeLimEndLab = "Last subject visit", 
    )

    print(aePlotsTimLimFromSV[[subjectAE]][[1]])

    svSubjectAE <- subset(dataSV, USUBJID == subjectAE)[, c("VISIT", "SVSTDY", "SVENDY")]
    pander(svSubjectAE)

This is also used to restrict the time limits of the plots.

As the modules will be combined with the same time limits, it might be advisable to restrict the time limits for this module via the timeLimData, timeLimStartVar and timeLimEndVar parameter.
In this example the time limits are restricted to the minimum/maximum time range of the subject visits.

    cmPlotsTimeSV <- subjectProfileIntervalPlot(
        data = dataSDTM$CM,
        paramVar = c(
            "CMTRT", 
            "CMDOSE", "CMDOSU", "CMROUTE", 
            "CMDOSFRQ"
        ),
        timeStartVar = "CMSTDY",
        timeEndVar = "CMENDY",
        paramGroupVar = "CMCLAS",
        colorVar = "CMCLAS",
        labelVars = labelVarsSDTM,
        title = "Concomitant medications",
        timeLimData = dataSV,
        timeLimStartVar = "SVSTDY",
        timeLimEndVar = "SVENDY",
        timeAlign = FALSE
    )

    print(cmPlotsTimeSV[[subjectCM]][[1]])

Custom specification for missing and partial dates

Missing start/end dates, partial dates or custom date status can be specified by creating two extra variables in the input data containing the status of the start/end time (timeStartShapeVar/timeEndShapeVar).

This status is represented as different symbols in the plot.

Please note that because the default ggplot2 symbol palette doesn't contain the left and right triangle symbols; these are specified in Unicode format in hexadecimal (see List of unicode symbols).

    # add status for dates:
    dataAE$AESTDYST <- with(dataAE, 
        ifelse(is.na(AESTDY) & !is.na(AESTDY), "Missing start", "")
    )

    shapePalette <- c(
        `Missing start`= "\u25C4", # left-pointing arrow
        'NOT RECOVERED/NOT RESOLVED' = "\u25BA", # right-pointing arrow
        'RECOVERED/RESOLVED' = "\u25A0", # small square
        'FATAL' = "\u2666", # diamond
        UNKNOWN = "+"
    )

    aePlotsShape <- subjectProfileIntervalPlot(
        data = dataAE,
        paramVar = "AETERM",
        timeStartVar = "AESTDY", timeEndVar = "AEENDY",
        timeStartShapeVar = "AESTDYST", timeEndShapeVar = "AEOUT",
        shapePalette = shapePalette,
        shapeLab = "Study date status", 
        colorVar = "AESEV",
        labelVars = labelVarsSDTM,
        title = "Adverse events"
    )

    print(aePlotsShape[[subjectAE]][[1]])

Specification of time limits

To restrict the time range in the visualization, the time limits can be set via the timeLim parameter.

The visualization are restricted to the timr range from baseline to the last visit (Week 26).

    timeLim <- c(0, 182)
    cmPlotsTimeSpec <- subjectProfileIntervalPlot(
        data = dataSDTM$CM,
        paramVar = c(
            "CMTRT", 
            "CMDOSE", "CMDOSU", "CMROUTE", 
            "CMDOSFRQ"
        ),
        timeStartVar = "CMSTDY",
        timeEndVar = "CMENDY",
        paramGroupVar = "CMCLAS",
        colorVar = "CMCLAS",
        labelVars = labelVarsSDTM,
        title = "Concomitant medications",
        timeLim = timeLim
    )

    print(cmPlotsTimeSpec[[subjectCM]][[1]])

Non-alignment across subjects {#subjectIntervalTimeAlign}

By default, the visualizations created with the subjectProfileIntervalPlot are aligned in the time-axis across subjects.

To obtain visualization which don't align, the parameter: timeAlign is set to FALSE.

    cmPlotsNotAligned <- subjectProfileIntervalPlot(
        data = dataSDTM$CM,
        paramVar = c(
            "CMTRT", 
            "CMDOSE", "CMDOSU", "CMROUTE", 
            "CMDOSFRQ"
        ),
        timeStartVar = "CMSTDY",
        timeEndVar = "CMENDY",
        paramGroupVar = "CMCLAS",
        colorVar = "CMCLAS",
        labelVars = labelVarsSDTM,
        title = "Concomitant medications",
        timeAlign = FALSE
    )

In this case, each visualization contains specific time-limits.

    print(cmPlotsNotAligned[[subjectCM]][[1]])

When building the report, the same parameter should be used (see section Report creation).

Event module

General

The 'event' module enables to represent event data.

This is used to represent the presence/absence of a certain laboratory measurement (and corresponding time).

# consider a subset of the laboratory data for example:
lbTests <- c("CHOL", "PHOS", "ANISO", "MCHC", "PLAT", "KETONES")
dataLB <- subset(dataSDTM$LB, LBTESTCD %in% lbTests)
# sort the categories (empty values '', if any, becomes NA)
dataLB$LBNRIND <- factor(dataLB$LBNRIND, levels = c("LOW", "NORMAL", "HIGH", "ABNORMAL"))

    # create plot
    lbPlots <- subjectProfileEventPlot(
        data = dataLB,
        paramVar = c("LBCAT", "LBTEST"),
        paramGroupVar = "LBCAT",
        timeVar = "LBDY",
        labelVars = labelVarsSDTM,
        title = "Laboratory test measurements"
    )

    print(lbPlots[[subjectLB]][[1]])

Color/symbol

The laboratory events are colored based on the category of the laboratory parameter, with the colorVar parameter.

The reference range indicator is used to set different symbols via the shapeVar. Symbols specific of this categorization are used via the shapePalette parameter: bottom/top arrow for low/high measurements, dot for measurements in normal range and star for abnormal measurements.

    # create plot
    lbPlotsColorShape <- subjectProfileEventPlot(
        data = dataLB,
        paramVar = "LBTEST",
        paramGroupVar = "LBCAT",
        timeVar = "LBDY",
        colorVar = "LBCAT",
        labelVars = labelVarsSDTM,
        shapeVar = "LBNRIND",
        shapePalette = c(
            'LOW' = 25, 'NORMAL' = 19, 'HIGH' = 24, 
            'ABNORMAL' = 11
        ),
        title = "Laboratory test measurements: reference range indicator"
    )

    print(lbPlotsColorShape[[subjectLB]][[1]])

Line module

General

The 'line' module enables to represent value of a variable across time.

This is used to represent the evolution of the lab parameters.

    # create plot
    lbLinePlots <- subjectProfileLinePlot(
        data = dataLB,
        paramNameVar = "LBTEST", 
        paramValueVar = "LBSTRESN",
        paramGroupVar = "LBCAT",
        timeVar = "LBDY",
        title = "Laboratory test measurements: actual value",
        labelVars = labelVarsSDTM
    )

    print(lbLinePlots[[subjectLB]][[1]])

Color/symbols of each observation

The color and the shape of the points can be specified via the colorVar and shapeVar parameters, similarly as for the subjectProfileEventPlot function. The reference range measurement is represented via these parameters.

    # create plot
    lbLinePlotsColorShape <- subjectProfileLinePlot(
        data = dataLB,
        paramNameVar = "LBTEST", 
        paramValueVar = "LBSTRESN",
        colorVar = "LBCAT",
        shapeVar = "LBNRIND",
        shapePalette = c(
            'LOW' = 25, 'NORMAL' = 19, 'HIGH' = 24, 
            'ABNORMAL' = 11
        ),
        paramGroupVar = "LBCAT",
        timeVar = "LBDY",
        title = "Laboratory test measurements: actual value",
        labelVars = labelVarsSDTM
    )

    print(lbLinePlotsColorShape[[subjectLB]][[1]])

Reference range

Display reference range indicators

A reference range for each parameter can be visualized if the variables containing the low and upper limit of the range are specified via paramValueRangeVar:

    # create plot
    lbLineRefRangePlots <- subjectProfileLinePlot(
        data = dataLB,
        paramNameVar = "LBTEST", 
        paramValueVar = "LBSTRESN",
        paramGroupVar = "LBCAT",
        paramValueRangeVar = c("LBSTNRLO", "LBSTNRHI"),
        shapeVar = "LBNRIND",
        shapePalette = c(
            'LOW' = 25, 'NORMAL' = 19, 'HIGH' = 24, 
            'ABNORMAL' = 11
        ),
        timeVar = "LBDY",
        title = "Laboratory test measurements: actual value",
        labelVars = labelVarsSDTM
    )

    print(lbLineRefRangePlots[[subjectLB]][[1]])

Range of the y-axis based on the observations or the reference range

By default, for each parameter, the range of the y-axis is extended to the reference range in case the range of the associated observations is smaller than the specified reference range.

If the range of the y-axis should only contain the range of the actual measurements, (so shouldn't be extended to cover the reference range), the yLimFrom parameter should be set on: 'value'.

    # create plot
    lbLineYLimFromValuePlots <- subjectProfileLinePlot(
        data = dataLB,
        paramNameVar = "LBTEST", 
        paramValueVar = "LBSTRESN",
        paramGroupVar = "LBCAT",
        paramValueRangeVar = c("LBSTNRLO", "LBSTNRHI"),
        shapeVar = "LBNRIND",
        shapePalette = c(
            'LOW' = 25, 'NORMAL' = 19, 'HIGH' = 24, 
            'ABNORMAL' = 11
        ),
        yLimFrom = "value",
        timeVar = "LBDY",
        title = "Laboratory test measurements: actual value",
        labelVars = labelVarsSDTM
    )

    print(lbLineYLimFromValuePlots[[subjectLB]][[1]])

Subset of interest

A subset of interest can be specified via:

These parameters are also available for all other module types.

Subset based on extra variable

If only a subset of parameters are of interest subsetVar and subsetValue can be used.

By default, the subset is extracted from the current data, but can also be extracted from a different dataset specified via subsetData.

The patient laboratory profile is only created for the patients with severe adverse events:

    # create plot
    lbPlotsSubset <- subjectProfileEventPlot(
        data = dataLB,
        paramVar = "LBTEST",
        # select subjects of interest:
        subsetData = dataSDTM$AE,
        subsetVar = "AESEV", subsetValue = "SEVERE",
        timeVar = "LBDY",
        colorVar = "LBNRIND",
        shapeVar = "LBNRIND",
        shapePalette = c(
            'LOW' = 25, 'NORMAL' = 19, 'HIGH' = 24, 
            'ABNORMAL' = 11
        ),
        title = "Hematology test measurements",
        labelVars = labelVarsSDTM
    )
    cat("Only the", length(lbPlotsSubset), "patients with severe adverse events:", toString(names(lbPlotsSubset)), "are considered.\n")

Set of subjects of interest

A set of subjects of interest from the input data can be specified via the subjectSubset parameter (by default extracted from the subjectVar parameter):

    # create plot
    lbPlotsSubjectSubset <- subjectProfileEventPlot(
        data = dataLB,
        paramVar = "LBTEST",
        subsetVar = "LBCAT", subsetValue = "HEMATOLOGY",
        subjectSubset = subjectLB,
        timeVar = "LBDY",
        colorVar = "LBNRIND",
        shapeVar = "LBNRIND",
        shapePalette = c(
            'LOW' = 25, 'NORMAL' = 19, 'HIGH' = 24, 
            'ABNORMAL' = 11
        ),
        title = "Laboratory test measurements for subject of interest",
        labelVars = labelVarsSDTM
    )
    cat("Only the patient:", toString(names(lbPlotsSubjectSubset)), "is considered.\n")

Specify colors/shapes

Missing values

Missing values in the specified color/shape variables are always displayed in the legend and associated palette.

If the variable is specified as character (by default when the dataset is loaded into R), the variable is converted to a factor and empty values ('', if any) in the variable are converted to missing (NA).

If the variable is specified as factor, the missing values are included in the levels of the factor (via exclude = NULL in factor).

Order of the categories

By default, if a character vector is specified, the categories are sorted in alphabetical order when the variable is converted to a factor in R.

    dataLB <- subset(dataSDTM$LB, LBTESTCD %in% lbTests)

    # LBRIND is a character: elements sorted in alphabetical order
    lbPlotsColor <- subjectProfileEventPlot(
        data = dataLB,
        paramVar = "LBTEST",
        paramGroupVar = "LBCAT",
        timeVar = "LBDY",
        colorVar = "LBNRIND",
        title = "Laboratory test measurements: actual value",
        labelVars = labelVarsSDTM
    )

print(lbPlotsColor[[subjectLB]][[1]])

To specify the elements of the variable in a specific order (e.g. ordered categories), the variable should be converted to a factor with its levels sorted in the order of interest (as by default in ggplot2).

For example, the reference ranges for the laboratory measurements are sorted from low to high in the legend: 

    dataLB <- subset(dataSDTM$LB, LBTESTCD %in% lbTests)
    # sort LBRIND
    dataLB$LBNRIND <- with(dataLB, 
        factor(LBNRIND, levels = c("LOW", "NORMAL", "HIGH", "ABNORMAL"))
    )

    # create plot
    lbPlotsColor <- subjectProfileEventPlot(
        data = dataLB,
        paramVar = "LBTEST",
        paramGroupVar = "LBCAT",
        timeVar = "LBDY",
        colorVar = "LBNRIND",
        title = "Laboratory test measurements: actual value",
        labelVars = labelVarsSDTM
    )

print(lbPlotsColor[[subjectLB]][[1]])

Sometimes, the variable are also available their numeric form in the CDISC datasets.

In this case, corresponding numeric variable can be used for sorting:

    dataLB <- subset(dataSDTM$LB, LBTESTCD %in% lbTests)

    # for the demo, creates numeric variable associated to reference range
    # (often already available)
    dataLB$LBNRINDN <- c(LOW = 1, NORMAL = 2, HIGH = 3, ABNORMAL = 10)[dataLB$LBNRIND]

    dataLB$LBNRIND <- with(dataLB, reorder(LBNRIND, LBNRINDN))

    lbPlotsColor <- subjectProfileEventPlot(
        data = dataLB,
        paramVar = "LBTEST",
        paramGroupVar = "LBCAT",
        timeVar = "LBDY",
        colorVar = "LBNRIND", shapeVar = "LBNRIND",
        title = "Laboratory test measurements: actual value",
        labelVars = labelVarsSDTM
    )

print(lbPlotsColor[[subjectLB]][[1]])

Palettes

Set palette for the entire session

Palette for the colors and shapes associated with specific variables can be set for all patient profile visualizations at once by setting the patientProfilesVis.colors and patientProfilesVis.shapes options at the start of the R session.

The default palette for colors is the viridis colorblind palette and a custom palette for shapes has been created in the package.

# display default palettes
colorsDefault <- getOption("patientProfilesVis.colors")
str(colorsDefault)
shapesDefault <- getOption("patientProfilesVis.shapes")
shapesDefault

# create plot
lbPlots <- subjectProfileEventPlot(
    data = dataLB,
    paramVar = "LBTEST",
    paramGroupVar = "LBCAT",
    timeVar = "LBDY",
    colorVar = "LBNRIND", 
    shapeVar = "LBNRIND", 
    title = "Laboratory test measurements: actual value",
    labelVars = labelVarsSDTM
)

print(lbPlots[[subjectLB]][[1]])

The palettes can be set for all patient profile visualization, e.g. at the start of the R session, with:

# change palettes for the entire R session
options(patientProfilesVis.colors = c("gold", "pink", "cyan"))
options(patientProfilesVis.shapes = c("cross", "diamond", "circle", "square"))

In case the palette contains less elements than available in the data, these are replicated.

# create plot
lbPlots <- subjectProfileEventPlot(
    data = dataLB,
    paramVar = "LBTEST",
    paramGroupVar = "LBCAT",
    timeVar = "LBDY",
    colorVar = "LBNRIND", 
    shapeVar = "LBNRIND", 
    title = "Laboratory test measurements: actual value",
    labelVars = labelVarsSDTM
)

    print(lbPlots[[subjectLB]][[1]])

Palettes are reset to the default patient profiles palettes at the start of a new R session, or by setting:

# change palettes for the entire R session
options(patientProfilesVis.colors = colorsDefault)
options(patientProfilesVis.shapes = shapesDefault)

Palette for standard CDISC variables

Custom palettes for standard reference indicator variable are available in the clinUtils package, via the function getPaletteCDISC.

# sort LBNRIND
dataLB$LBNRIND <- with(dataLB, 
    factor(LBNRIND, levels = c("LOW", "NORMAL", "HIGH", "ABNORMAL"))
)

colorPaletteLBNRIND <- getPaletteCDISC(dataLB$LBNRIND, var = "NRIND", type = "color")
print(colorPaletteLBNRIND)

shapePaletteLBNRIND <- getPaletteCDISC(dataLB$LBNRIND, var = "NRIND", type = "shape")
print(shapePaletteLBNRIND)

# create plot
lbPlots <- subjectProfileEventPlot(
    data = dataLB,
    paramVar = "LBTEST",
    paramGroupVar = "LBCAT",
    timeVar = "LBDY",
    colorVar = "LBNRIND", colorPalette = colorPaletteLBNRIND,
    shapeVar = "LBNRIND", shapePalette = shapePaletteLBNRIND,
    title = "Laboratory test measurements: actual value",
    labelVars = labelVarsSDTM
)

print(lbPlots[[subjectLB]][[1]])

Time transformation

For certain module, it might be of interest to transform the time axis to e.g. 'zoom' in one part of the the study timeframe. The timeTrans parameter is used to specify a custom transformation of the time-axis.

The getTimeTrans provides convenient transformations:

This is typically of interest for domains including events occurring/recorded long before the start of the study (e.g. concomitant medications).

For example, the following subject has a concomitant medication starting long before the start of the study. This results into the positive part of the time axis being 'squeezed'.

    cmPlots <- subjectProfileIntervalPlot(
        data = dataSDTM$CM,
        paramVar = c(
            "CMTRT", 
            "CMDOSE", "CMDOSU", "CMROUTE", 
            "CMDOSFRQ"
        ),
        timeStartVar = "CMSTDY",
        timeEndVar = "CMENDY",
        paramGroupVar = "CMCLAS",
        colorVar = "CMCLAS",
        title = "Concomitant medications",
        labelVars = labelVarsSDTM
    )

    subjectCMTimeTrans <- "01-701-1192"

    print(cmPlots[[subjectCMTimeTrans]][[1]])

A hyperbolic arc-sine transformation is applied on the time axis, only for the negative times, to focus mainly on the medications taken after the start of the treatment exposure (after time 0).

    timeTrans <- getTimeTrans("asinh-neg")

    cmPlotsTimeTrans <- subjectProfileIntervalPlot(
        data = dataSDTM$CM,
        paramVar = c(
            "CMTRT", 
            "CMDOSE", "CMDOSU", "CMROUTE", 
            "CMDOSFRQ"
        ),
        timeStartVar = "CMSTDY",
        timeEndVar = "CMENDY",
        paramGroupVar = "CMCLAS",
        colorVar = "CMCLAS",
        timeTrans = timeTrans,
        title = "Concomitant medications",
        labelVars = labelVarsSDTM
    )

    print(cmPlotsTimeTrans[[subjectCMTimeTrans]][[1]])

Creation of subject report

A report, combining all subject profile visualizations is created via the function createSubjectProfileReport.

The function:

Please note that the example report(s) in the section are not created by default in the vignette, for time constraints.

Feel free to run yourself the code, and check the resulting pdf report!

Example report

Example code to create patient profiles for SDTM or ADaM datasets is described below.

SDTM dataset

# demography
dmPlots <- subjectProfileTextPlot(
    data = dataSDTM$DM,
    paramValueVar = c("SEX|AGE", "RACE|COUNTRY", "ARM"),
    labelVars = labelVarsSDTM
)

# medical history
mhPlots <- subjectProfileTextPlot(
    data = dataSDTM$MH,
    paramNameVar = c("MHDECOD"),
    paramValueVar = c("MHCAT", "MHTERM", "MHSTDTC"),
    title = "Medical History: status",
    labelVars = labelVarsSDTM
)

# concomitant medications
cmPlots <- subjectProfileIntervalPlot(
    data = dataSDTM$CM,
    paramVar = c(
        "CMTRT", 
        "CMDOSE", "CMDOSU", "CMROUTE", 
        "CMDOSFRQ"
    ),
    timeStartVar = "CMSTDY",
    timeEndVar = "CMENDY",
    paramGroupVar = "CMCLAS",
    colorVar = "CMCLAS",
    timeTrans = timeTrans,
    title = "Concomitant medications",
    labelVars = labelVarsSDTM
)

# treatment exposure
exPlots <- subjectProfileIntervalPlot(
    data = dataSDTM$EX,
    paramVar = c("EXTRT", "EXDOSE", "EXDOSU"),
    timeStartVar = "EXSTDY",
    timeEndVar = "EXENDY",
    colorVar = "EXDOSFRM",
    labelVars = labelVarsSDTM,
    title = "Treatment exposure"
)

# adverse events:
dataAE <- dataSDTM$AE
# sort severities
dataAE[, "AESEV"] <- factor(dataAE[, "AESEV"], levels = c("MILD", "MODERATE", "SEVERE"))
aePlots <- subjectProfileIntervalPlot(
    data = dataAE,
    paramVar = "AETERM",
    timeStartVar = "AESTDY",
    timeEndVar = "AEENDY",
    colorVar = "AESEV",
    labelVars = labelVarsSDTM,
    title = "Adverse events"
)

# laboratory parameter
lbLinePlots <- subjectProfileLinePlot(
    data = dataSDTM$LB,
    paramNameVar = "LBTEST", 
    paramValueVar = "LBSTRESN",
    paramValueRangeVar = c("LBSTNRLO", "LBSTNRHI"),
    paramGroupVar = "LBCAT",
    timeVar = "LBDY",
    title = "Laboratory test measurements: actual value",
    labelVars = labelVarsSDTM
)

# create report
pathReport <- "subjectProfile_SDTM.pdf"
createSubjectProfileReport(
    listPlots = list(
        dmPlots, 
        mhPlots, 
        cmPlots, 
        exPlots, 
        aePlots, 
        lbLinePlots
    ),
    outputFile = pathReport
)

ADaM dataset

# demography
adslPlots <- subjectProfileTextPlot(
    data = dataADaM$ADSL,
    paramValueVar = c("SEX|AGE", "RACE", "TRT01P"),
    labelVars = labelVarsADaM
)

# adverse events:
dataADAE <- dataADaM$ADAE
# sort severities
dataADAE[, "AESEV"] <- factor(dataAE[, "AESEV"], levels = c("MILD", "MODERATE", "SEVERE"))
adaePlots <- subjectProfileIntervalPlot(
    data = dataADAE,
    paramVar = "AEDECOD",
    timeStartVar = "ASTDY",
    timeEndVar = "AENDY",
    colorVar = "AESEV",
    labelVars = labelVarsADaM,
    timeTrans = getTimeTrans("asinh-neg"),
    title = "Adverse events"
)

# laboratory parameter
adlbcPlots <- subjectProfileLinePlot(
    data = dataADaM$ADLBC,
    paramNameVar = "PARAM", 
    paramValueVar = "AVAL",
    paramValueRangeVar = c("A1LO", "A1HI"),
    paramGroupVar = "PARCAT1",
    timeVar = "ADY",
    title = "Laboratory test measurements: actual value",
    labelVars = labelVarsADaM
)

# create report
pathReport <- "subjectProfile_ADaM.pdf"
createSubjectProfileReport(
    listPlots = list(
        adslPlots, 
        adaePlots, 
        adlbcPlots
    ),
    outputFile = pathReport
)

Reference lines

Specify custom reference lines

Reference lines can be displayed as vertical lines spanning all visualizations.

Custom reference lines to indicated the two screening visits and the baseline are displayed for a example subject:

# reference lines input parameter
refLinesParam <- list(
    list(
        time = -7, 
        label = "Screening 1",
        color = "purple"
    ),
    list(
        time = -7, 
        label = "Screening 2",
        color = "purple"
    ),
    list(
        time = 1, 
        label = "Baseline",
        color = "darkblue"
    )
)

# create report
pathReport <- "subjectProfile_SDTM_referenceLines_custom.pdf"
createSubjectProfileReport(
    listPlots = list(
        dmPlots, 
        mhPlots, 
        cmPlots, 
        exPlots, 
        aePlots, 
        lbLinePlots
    ),
    refLines = refLinesParam,
    outputFile = pathReport
)

Reference lines from subject visits

In the following example: the reference lines are extracted from the subject visits: SV dataset. 

# create report
pathReport <- "subjectProfile_SDTM_referenceLines_subjectVisit.pdf"

# only retain screening, baseline and planned visits
dataSV <- subset(dataSDTM$SV, grepl("SCREENING|WEEK|BASELINE", VISIT))

createSubjectProfileReport(
    listPlots = list(
        dmPlots, 
        mhPlots, 
        cmPlots, 
        exPlots, 
        aePlots, 
        lbLinePlots
    ),
    # reference line(s)
    refLinesData = dataSV,
    refLinesTimeVar = "VISITDY",
    refLinesLabelVar = "VISIT",
    outputFile = pathReport
)

Bookmarks

A simple index by sex and arm of each subject is created via the bookmark parameter.

# create report
pathReport <- "subjectProfile_SDTM_bookmarks.pdf"

dataDM <- dataSDTM$DM
# sort arm categories
dataDM$ARM <- factor(dataDM$ARM, 
    levels = c("Placebo", "Xanomeline Low Dose", "Xanomeline High Dose"))

createSubjectProfileReport(
    listPlots = list(
        dmPlots, 
        mhPlots, 
        cmPlots, 
        exPlots, 
        aePlots, 
        lbLinePlots
    ),
    subset = c("01-718-1427", "01-704-1445", "01-701-1211"),
    # bookmark(s)
    bookmarkData = dataDM,
    bookmarkVar = c("SEX", "ARM"),
    # sort subjects in the report based on:
    subjectSortData = dataDM,
    subjectSortVar = "ARM",
    outputFile = pathReport
)

Time alignment {#createSubjectProfileReportTimeAlign}

In order that the different visualizations are not aligned in the time axis, the modules to be aligned can be specified to the timeAlign parameter.

This can be of interest when combining a visualization displaying concomitant medications with historical data with a high time range and visualization of events occuring only during the study timeframe; or for modules with different time units.

Please note that the corresponding interval module(s) should also be created with the parameter: timeAlign = FALSE in the function subjectProfileIntervalPlot call (see section Interval module).

Please find an example below of subject profiles displaying the adverse events occurring from baseline associated with the laboratory measurements before and after baseline.

    # create the list of visualizations
    # The list is named in order that the names are used
    # to reference the module for the alignment parameters
    listPlots <- list(AE = aePlots, LB = lbLinePlots)
    subsetPatients <- c(subjectAE, subjectLB)

Visualizations aligned across domains and subjects

By default, the visualizations are aligned across domains (timeAlign is 'all') and subjects (timeAlignPerSubject is "none").

Please note that because all domains are aligned, the adverse event domain is extended to also contain the times for laboratory measurements (and not only from baseline on as specified during the creation of the AE visualizations). 

    pathReport <- "subjectProfile_timeAlign-all_timeAlignPerSubject-none.pdf"
    createSubjectProfileReport(
        listPlots = listPlots,
        outputFile = pathReport,
        subset = subsetPatients
    )

Visualizations aligned across subjects only for a specific domain

The visualizations are aligned only for the adverse events domain (timeAlign set to: 'AE') and across subjects (timeAlignPerSubject is "none").

    pathReport <- "subjectProfile_timeAlign-AE_timeAlignPerSubject-none.pdf"
    createSubjectProfileReport(
        listPlots = listPlots,
        outputFile = pathReport,
        subset = subsetPatients,
        timeAlign = "AE"
    )

Visualizations not aligned across domains

The visualizations are not aligned across domain (timeAlign set to: 'none') neither subjects (timeAlignPerSubject is "none").

    pathReport <- "subjectProfile_timeAlign-none_timeAlignPerSubject-none.pdf"
    createSubjectProfileReport(
        listPlots = listPlots,
        outputFile = pathReport,
        subset = subsetPatients,
        timeAlign = "none"
    )

Visualizations aligned per subject

The visualizations are aligned (timeAlign set to: 'all') per subject (timeAlignPerSubject is "all").

    pathReport <- "subjectProfile_timeAlign-all_timeAlignPerSubject-all.pdf"
    createSubjectProfileReport(
        listPlots = listPlots,
        outputFile = pathReport,
        subset = subsetPatients,
        timeAlignPerSubject = "all"
    )

Optimization of patient profiles creation

For clinical trial with high number of patients (e.g. phase 3), the creation of the subject profile report can be time-consuming.

Please find below a few advices:

Appendix

Session information

    pander(sessionInfo())


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patientProfilesVis documentation built on Nov. 18, 2022, 5:12 p.m.

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