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Dunnett's Many-to-One Comparisons Test

Source: R/dunnett_test.R
dunnett_test.Rd

Performs Dunnett's test for comparing each of several treatment groups against a single control (reference) group. Unlike all-pairwise post-hoc tests, Dunnett's procedure controls the family-wise error rate over only the k - 1 treatment-vs-control comparisons, using the exact multivariate-t distribution (which accounts for the correlation between the comparisons that share the control group).

This is a pipe-friendly wrapper around emmeans::emmeans() + emmeans::contrast() (with adjust = "mvt"), so the emmeans package must be installed. The results match DescTools::DunnettTest() and multcomp::glht(..., mcp(... = "Dunnett")).

Usage

dunnett_test(
  data,
  formula,
  ref.group = NULL,
  conf.level = 0.95,
  detailed = FALSE
)

Arguments

data

a data.frame containing the variables in the formula.

formula

a formula of the form x ~ group where x is a numeric variable giving the data values and group is a factor with one or multiple levels giving the corresponding groups. For example, formula = TP53 ~ cancer_group.

ref.group

a character string specifying the reference (control) group. Each remaining group level is compared against this group. If NULL (default), the first level of the grouping variable is used as the control.

conf.level

confidence level of the (simultaneous) confidence intervals.

detailed

logical value. Default is FALSE. If TRUE, a detailed result is shown.

Value

a data frame with some of the following columns:

  • .y.: the outcome variable used in the test.

  • group1,group2: the compared groups; group1 is the control (reference) and group2 is the treatment, consistent with the ref.group convention of t_test()/wilcox_test()/ dunn_test()/emmeans_test().

  • n1,n2: sample sizes of the control and treatment groups.

  • estimate: the estimated mean difference group1 - group2 (control minus treatment).

  • conf.low,conf.high: simultaneous confidence interval for the difference.

  • statistic: the t-statistic.

  • df: degrees of freedom.

  • p.adj: the Dunnett-adjusted p-value.

  • method: the statistical test used.

  • p.adj.signif: the significance level of the adjusted p-value.

The estimate, confidence interval, se and method columns are returned only when detailed = TRUE.

The returned object has an attribute called args, which is a list holding the test arguments.

References

Dunnett, C. W. (1955) A multiple comparison procedure for comparing several treatments with a control. Journal of the American Statistical Association, 50, 1096-1121.

See also

tukey_hsd(), games_howell_test(), emmeans_test()

Examples

# Compare each dose to the control dose ("0.5")
ToothGrowth %>% dunnett_test(len ~ dose)
#> # A tibble: 2 × 9
#>   .y.   group1 group2    n1    n2 statistic    df    p.adj p.adj.signif
#> * <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>    <dbl> <chr>       
#> 1 len   0.5    1         20    20     -6.81    57 1.34e- 8 ****        
#> 2 len   0.5    2         20    20    -11.6     57 2.22e-16 ****        

# Detailed output (estimate + simultaneous confidence interval)
ToothGrowth %>% dunnett_test(len ~ dose, detailed = TRUE)
#> # A tibble: 2 × 14
#>   .y.   group1 group2    n1    n2 estimate conf.low conf.high    se statistic
#> * <chr> <chr>  <chr>  <int> <int>    <dbl>    <dbl>     <dbl> <dbl>     <dbl>
#> 1 len   0.5    1         20    20    -9.13    -12.2     -6.09  1.34     -6.81
#> 2 len   0.5    2         20    20   -15.5     -18.5    -12.5   1.34    -11.6 
#> # ℹ 4 more variables: df <dbl>, p.adj <dbl>, p.adj.signif <chr>, method <chr>

# Grouped data
ToothGrowth %>%
  group_by(supp) %>%
  dunnett_test(len ~ dose)
#> # A tibble: 4 × 10
#>   supp  .y.   group1 group2    n1    n2 statistic    df    p.adj p.adj.signif
#> * <fct> <chr> <chr>  <chr>  <int> <int>     <dbl> <dbl>    <dbl> <chr>       
#> 1 OJ    len   0.5    1         10    10     -5.64    27 1.07e- 5 ****        
#> 2 OJ    len   0.5    2         10    10     -7.65    27 6.31e- 8 ****        
#> 3 VC    len   0.5    1         10    10     -5.61    27 1.19e- 5 ****        
#> 4 VC    len   0.5    2         10    10    -11.6     27 1.11e-11 ****