Scatterplot module • dv.explorer.parameter

This guide provides a detailed overview of the scatterplot module and its features. It is meant to provide guidance to App Creators on creating Apps in DaVinci using the scatterplot module. Walk-throughs for sample app creation using the module are also included to demonstrate the various module specific features.

The scatterplot module makes it possible to visualize a scatterplot of two biomarkers with different levels of grouping.

Pre-requisite:

“Parameter” Term Disambiguation

The guide uses the term “parameter” at several places. This term in the guide represents clinical analysis parameters and values such as laboratory values, safety values, etc as used in the clinical dataset context. This can be confused with the word parameter as used in a programming context - “parameters of a function”. Therefore, to fully disambiguate the usage in this guide:

Parameter is used exclusively in the clinical dataset context
Argument is used to represent parameter of a function in the programming context

Features

scatterplot features the following plot and tables:

A scatterplot with different custom groupings showing a regression line.
A table showing the population listing of the selected points in the chart.
A table showing summary data about the regression and correlation between the parameters

It supports bookmarking.

Arguments for the module

dv.explorer.parameter::mod_scatterplot() module uses several arguments with the following being mandatory and the rest optional. As part of app creation, the app creator should specify the values for these arguments as applicable.

Mandatory Arguments

module_id : A unique identifier of type character for the module in the app.
subjid_var: A common column across all datasets that uniquely identify subjects. By default: “SUBJID”
bm_dataset_name: The dataset that contains the continuous parameters. It expects a dataset similar to https://www.cdisc.org/kb/examples/adam-basic-data-structure-bds-using-paramcd-80288192 , 1 record per subject per parameter per analysis visit

It expects, at least, the columns passed in the arguments, subjid_var, cat_var, par_var, visit_var and value_vars.
group_dataset_name:

It expects a dataset with an structure similar to https://www.cdisc.org/kb/examples/adam-subject-level-analysis-adsl-dataset-80283806 , one record per subject It expects to contain, at least, subjid_var

Refer to dv.explorer.parameter::mod_scatterplot() for the complete list of arguments and their description.

Input menus

A set of menus allows to select a set of parameters, groupings and settings of the visualization.

Visualizations

Scatterplot

This visualization consists of a scatterplott based on the set of parameters and grouping variable. The chart includes an orientative regression line with 95% C.I.

Tables

Population listing

A table containing a listing of the population selected by brushing in the scatterplot.

Estimates table

A table containing a set of statistics regarding the regression and correlation.

Creating a boxplot application

adbm_dataset <- dv.explorer.parameter:::safety_data()[["bm"]] %>%
  dplyr::mutate(
    USUBJID = factor(USUBJID),
    PARCAT1 = factor(PARCAT1),
    PARAM = factor(PARAM),
    AVISIT = factor(AVISIT)
  )

adsl_dataset <- dv.explorer.parameter:::safety_data()[["sl"]] %>%
  dplyr::mutate(USUBJID = factor(USUBJID))

dv.manager::run_app(
  data = list(dummy = list(adbm = adbm_dataset, adsl = adsl_dataset)),
  module_list = list(
    Scatterplot = dv.explorer.parameter::mod_scatterplot(
      "scatterplot",
      bm_dataset_name = "adbm",
      group_dataset_name = "adsl",
      cat_var = "PARCAT1",
      par_var = "PARAM",
      value_vars = "AVAL",
      visit_var = "AVISIT",
      subjid_var = "USUBJID"
    )
  ),
  filter_data = "adsl",
  filter_key = "USUBJID"
)