Compare a numeric variable between groups

Tests whether a value differs between two or more groups of participants, and reports the group means and, for two groups, the difference in means with its interval.

Usage

Analyze_GroupDifference(
  dfData,
  strValueCol,
  strGroupCol,
  strMethod = "t",
  chrGroups = NULL,
  bPairwise = TRUE,
  strPAdjust = "holm",
  nConfLevel = 0.95,
  nMinGroup = nMinGroupDefault
)

Arguments

dfData

data.frame One row per participant.

strValueCol

character Name of the numeric column to compare.

strGroupCol

character Name of the column holding each participant's group.

strMethod

character The test: "t", "wilcoxon", "anova" or "kruskal". Default: "t".

chrGroups

character The groups to compare, in order; the difference in means is the first minus the second. Participants in any other group are dropped and counted. Default: NULL, every group present, in sorted order.

bPairwise

logical Compare every pair of groups when there are more than two. Default: TRUE.

strPAdjust

character The adjustment across the pairs, one of stats::p.adjust.methods. Default: "holm".

nConfLevel

numeric Confidence level of the intervals. Default: 0.95.

nMinGroup

numeric The smallest group the test is computed for. If any group has fewer participants with a value, the result has status "too_small" and no numbers. Default: nMinGroupDefault, which is 5. See StatisticsResult.

Value

The fixed result described in StatisticsResult. Here counts is a named list of group to participants used; estimates has a row named "Mean" per group and, for two groups, a row named "Difference in means"; and rows has one row per pair of groups when pairwise comparisons were made, with the columns group_1, group_2, n_1, n_2, counts (the two together), estimate (the difference in means, group_1 minus group_2), lower, upper, level, method, statistic, p_unadjusted, p_value (adjusted), adjustment, status, reason and warning. The intervals in rows are not adjusted.

Details

Each method is the base R function, called with R's defaults:

strMethod	Groups	R function
"t"	two	stats::t.test(), Welch: unequal variances.
"wilcoxon"	two	stats::wilcox.test(), with R's own switch between the exact and the approximate p-value.
"anova"	two or more	stats::aov(), one-way.
"kruskal"	two or more	stats::kruskal.test(), with its tie correction.

With exactly two groups, estimates includes the difference in means, the first group's mean minus the second's, with its interval. Both always come from t.test() (Welch), whichever test was asked for.

With more than two groups and bPairwise, every pair of groups is compared with the two-group test of the same family, t.test() for "anova" and wilcox.test() for "kruskal", and the p-values are adjusted across the pairs by stats::p.adjust(). The pairs are in rows.

See also

Other statistics: Analyze_Contingency(), Analyze_Correlation(), Analyze_CorrelationMatrix(), Analyze_Fit(), Analyze_Screen(), Analyze_Survival()

Examples

# Change in IL-6 from Baseline to Week 4, by arm
dfIL6 <- Synthetic_Results[Synthetic_Results$TEST == "IL-6", ]
dfBaseline <- dfIL6[dfIL6$VISIT == "Baseline", ]
dfWeek4 <- dfIL6[dfIL6$VISIT == "Week 4", ]
dfFrame <- Synthetic_Participants
dfFrame$Change <- dfWeek4$STRESN[match(dfFrame$USUBJID, dfWeek4$USUBJID)] -
  dfBaseline$STRESN[match(dfFrame$USUBJID, dfBaseline$USUBJID)]

lResult <- Analyze_GroupDifference(
  dfFrame, "Change", "ARM",
  chrGroups = c("Treatment", "Placebo")
)
lResult$method
#> [1] "Welch Two Sample t-test"
lResult$estimates
#>                  name               group    estimate     lower      upper
#> 1                Mean           Treatment -1.21031868        NA         NA
#> 2                Mean             Placebo  0.02472632        NA         NA
#> 3 Difference in means Treatment - Placebo -1.23504500 -1.626076 -0.8440141
#>   level
#> 1    NA
#> 2    NA
#> 3  0.95
lResult$p_value
#> [1] 3.229638e-09
lResult$counts
#> $Treatment
#> [1] 91
#> 
#> $Placebo
#> [1] 95
#>