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Biomarker Screen Table

Source: R/Table_BiomarkerScreen.R
Table_BiomarkerScreen.Rd

The rows of the screen the biomarker screen opens on, as a table: one row per biomarker, with R's estimate and its interval, the counts, the unadjusted p-value and the p-value adjusted across the rows, written as the chart prints them. The numbers are Analyze_Screen()'s on the frame the chart hands R.

Usage

Table_BiomarkerScreen(
  dfResults,
  dfParticipants = NULL,
  lSettings = list(),
  dfOutcomes = NULL
)

Arguments

dfResults

data.frame Long-format results, one row per participant, biomarker and visit. Column names are supplied by lSettings; the defaults expect USUBJID/TEST/STRESN/VISIT/VISITNUM/STRESU, the columns of Synthetic_Results.

dfParticipants

data.frame One row per participant, or NULL. With it the chart has filters, and the columns of groups and the numbers it offers are read from it. Default: NULL.

lSettings

list bio.viz biomarker screen settings, as Widget_BiomarkerScreen() takes them, and title, subtitle and footnotes. Default: list().

dfOutcomes

data.frame An outcomes table, one row per participant and endpoint with a time and a flag, as Widget_StratifiedSurvival() takes it, or NULL. With it the screen offers a hazard ratio. It comes after lSettings, so a call written for v0.1.0 works as it did. Default: NULL.

Value

A data.frame of text: Biomarker, then Statistic, Method, Estimate, Counts, p-value, Adjusted p-value and Note, with the attributes of Table_GroupComparison().

Titles and footnotes

As for Visualize_BiomarkerScreen(): {heading}, {comparison}, {visit}, {endpoint}, {biomarkers}, {n}, {filters}, {date} and {version}.

Display rules

A p-value is written to three decimals, p < 0.001 below that and p > 0.999 above, never with stars. Every row is labelled exploratory, with its adjustment named when it has one. An estimate is written to four significant digits with its confidence interval. A statistic R did not compute has no p-value, and its note is R's reason.

See also

Visualize_BiomarkerScreen() and Widget_BiomarkerScreen().

Other tables: Table_AssociationScatter(), Table_CorrelationMatrix(), Table_CrossTab(), Table_GroupComparison(), Table_StratifiedSurvival(), Write_RTF()

Examples

Table_BiomarkerScreen(
  Synthetic_Results,
  Synthetic_Participants,
  lSettings = list(visit = "Week 4", value_type = "change", group_by = "ARM")
)
#>    Biomarker                           Statistic                  Method
#> 1        CRP Standardised difference (Hedges’ g) Welch Two Sample t-test
#> 2    D-dimer Standardised difference (Hedges’ g) Welch Two Sample t-test
#> 3   Ferritin Standardised difference (Hedges’ g) Welch Two Sample t-test
#> 4  IFN-gamma Standardised difference (Hedges’ g) Welch Two Sample t-test
#> 5   IL-1beta Standardised difference (Hedges’ g) Welch Two Sample t-test
#> 6       IL-2 Standardised difference (Hedges’ g) Welch Two Sample t-test
#> 7       IL-6 Standardised difference (Hedges’ g) Welch Two Sample t-test
#> 8       IL-8 Standardised difference (Hedges’ g) Welch Two Sample t-test
#> 9      IL-10 Standardised difference (Hedges’ g) Welch Two Sample t-test
#> 10       LDH Standardised difference (Hedges’ g) Welch Two Sample t-test
#> 11 TNF-alpha Standardised difference (Hedges’ g) Welch Two Sample t-test
#> 12      VEGF Standardised difference (Hedges’ g) Welch Two Sample t-test
#>                                               Estimate
#> 1   -0.09193, 95% confidence interval -0.379 to 0.1954
#> 2     0.1746, 95% confidence interval -0.118 to 0.4667
#> 3   0.06825, 95% confidence interval -0.2197 to 0.3561
#> 4     0.1288, 95% confidence interval -0.1587 to 0.416
#> 5   0.2589, 95% confidence interval -0.03126 to 0.5484
#> 6  0.005833, 95% confidence interval -0.2805 to 0.2921
#> 7      0.9133, 95% confidence interval 0.6111 to 1.213
#> 8     0.1298, 95% confidence interval -0.1577 to 0.417
#> 9  -0.09502, 95% confidence interval -0.3836 to 0.1939
#> 10   0.1342, 95% confidence interval -0.1542 to 0.4222
#> 11   -0.1621, 95% confidence interval -0.451 to 0.1272
#> 12   0.1544, 95% confidence interval -0.1356 to 0.4441
#>                              Counts   p-value Adjusted p-value
#> 1  Placebo n = 94, Treatment n = 91 p = 0.530        p = 0.636
#> 2  Placebo n = 90, Treatment n = 89 p = 0.243        p = 0.575
#> 3  Placebo n = 93, Treatment n = 91 p = 0.642        p = 0.700
#> 4  Placebo n = 94, Treatment n = 91 p = 0.383        p = 0.575
#> 5  Placebo n = 93, Treatment n = 90 p = 0.080        p = 0.481
#> 6  Placebo n = 95, Treatment n = 91 p = 0.968        p = 0.968
#> 7  Placebo n = 95, Treatment n = 91 p < 0.001        p < 0.001
#> 8  Placebo n = 94, Treatment n = 91 p = 0.378        p = 0.575
#> 9  Placebo n = 91, Treatment n = 92 p = 0.520        p = 0.636
#> 10 Placebo n = 94, Treatment n = 90 p = 0.362        p = 0.575
#> 11 Placebo n = 93, Treatment n = 90 p = 0.272        p = 0.575
#> 12 Placebo n = 93, Treatment n = 89 p = 0.297        p = 0.575
#>                                           Note
#> 1  Exploratory, adjusted (Benjamini-Hochberg).
#> 2  Exploratory, adjusted (Benjamini-Hochberg).
#> 3  Exploratory, adjusted (Benjamini-Hochberg).
#> 4  Exploratory, adjusted (Benjamini-Hochberg).
#> 5  Exploratory, adjusted (Benjamini-Hochberg).
#> 6  Exploratory, adjusted (Benjamini-Hochberg).
#> 7  Exploratory, adjusted (Benjamini-Hochberg).
#> 8  Exploratory, adjusted (Benjamini-Hochberg).
#> 9  Exploratory, adjusted (Benjamini-Hochberg).
#> 10 Exploratory, adjusted (Benjamini-Hochberg).
#> 11 Exploratory, adjusted (Benjamini-Hochberg).
#> 12 Exploratory, adjusted (Benjamini-Hochberg).