Survival analysis for quantitative ordinal scale data
Arguments
- dat
Data frame containing the data to be processed.
- scale
A numeric vector indicating the scale or order of classes.
- grade
Logical. If TRUE, uses the class value. If FALSE, uses the NPE (Non-Parametric Estimate).
- ckData
Logical. If TRUE, returns the input data along with the results. If FALSE, returns only the results.
Value
A list containing the score statistic, hypothesis tests, adjusted significance level, and conclusion based on pairwise comparisons.
Details
This function supports analysis of quantitative ordinal scale data via interval-censored methods. The approach follows the workflow described by Chiang et al. (2023) and the reference implementation provided in the CompMuCens repository.
References
Chiang, K.S., Chang, Y.M., Liu, H.I., Lee, J.Y., El Jarroudi, M. and Bock, C. (2023). Survival Analysis as a Basis to Test Hypotheses When Using Quantitative Ordinal Scale Disease Severity Data. Phytopathology. https://apsjournals.apsnet.org/doi/abs/10.1094/PHYTO-02-23-0055-R
Examples
if (requireNamespace("interval", quietly = TRUE)) {
trAs <- c(5,4,2,5,5,4,4,2,5,2,2,3,4,3,2,2,6,2,2,4,2,4,2,4,5,3,4,2,2,3)
trBs <- c(5,3,2,4,4,5,4,5,4,4,6,4,5,5,5,2,6,2,3,5,2,6,4,3,2,5,3,5,4,5)
trCs <- c(2,3,1,4,1,1,4,1,1,3,2,1,4,1,1,2,5,2,1,3,1,4,2,2,2,4,2,3,2,2)
trDs <- c(5,5,4,5,5,6,6,4,6,4,3,5,5,6,4,6,5,6,5,4,5,5,5,3,5,6,5,5,5,6)
inputData <- data.frame(
treatment = c(rep("A",30), rep("B",30), rep("C",30), rep("D",30)),
x = c(trAs, trBs, trCs, trDs)
)
CompMuCens(dat = inputData,
scale = c(0,3,6,12,25,50,75,88,94,97,100,100),
ckData = TRUE)
}
#> $inputData
#> treatment ClassValue intervals
#> 1 A 5 [12, 25]
#> 2 A 4 [ 6, 12]
#> 3 A 2 [ 0, 3]
#> 4 A 5 [12, 25]
#> 5 A 5 [12, 25]
#> 6 A 4 [ 6, 12]
#> 7 A 4 [ 6, 12]
#> 8 A 2 [ 0, 3]
#> 9 A 5 [12, 25]
#> 10 A 2 [ 0, 3]
#> 11 A 2 [ 0, 3]
#> 12 A 3 [ 3, 6]
#> 13 A 4 [ 6, 12]
#> 14 A 3 [ 3, 6]
#> 15 A 2 [ 0, 3]
#> 16 A 2 [ 0, 3]
#> 17 A 6 [25, 50]
#> 18 A 2 [ 0, 3]
#> 19 A 2 [ 0, 3]
#> 20 A 4 [ 6, 12]
#> 21 A 2 [ 0, 3]
#> 22 A 4 [ 6, 12]
#> 23 A 2 [ 0, 3]
#> 24 A 4 [ 6, 12]
#> 25 A 5 [12, 25]
#> 26 A 3 [ 3, 6]
#> 27 A 4 [ 6, 12]
#> 28 A 2 [ 0, 3]
#> 29 A 2 [ 0, 3]
#> 30 A 3 [ 3, 6]
#> 31 B 5 [12, 25]
#> 32 B 3 [ 3, 6]
#> 33 B 2 [ 0, 3]
#> 34 B 4 [ 6, 12]
#> 35 B 4 [ 6, 12]
#> 36 B 5 [12, 25]
#> 37 B 4 [ 6, 12]
#> 38 B 5 [12, 25]
#> 39 B 4 [ 6, 12]
#> 40 B 4 [ 6, 12]
#> 41 B 6 [25, 50]
#> 42 B 4 [ 6, 12]
#> 43 B 5 [12, 25]
#> 44 B 5 [12, 25]
#> 45 B 5 [12, 25]
#> 46 B 2 [ 0, 3]
#> 47 B 6 [25, 50]
#> 48 B 2 [ 0, 3]
#> 49 B 3 [ 3, 6]
#> 50 B 5 [12, 25]
#> 51 B 2 [ 0, 3]
#> 52 B 6 [25, 50]
#> 53 B 4 [ 6, 12]
#> 54 B 3 [ 3, 6]
#> 55 B 2 [ 0, 3]
#> 56 B 5 [12, 25]
#> 57 B 3 [ 3, 6]
#> 58 B 5 [12, 25]
#> 59 B 4 [ 6, 12]
#> 60 B 5 [12, 25]
#> 61 C 2 [ 0, 3]
#> 62 C 3 [ 3, 6]
#> 63 C 1 0
#> 64 C 4 [ 6, 12]
#> 65 C 1 0
#> 66 C 1 0
#> 67 C 4 [ 6, 12]
#> 68 C 1 0
#> 69 C 1 0
#> 70 C 3 [ 3, 6]
#> 71 C 2 [ 0, 3]
#> 72 C 1 0
#> 73 C 4 [ 6, 12]
#> 74 C 1 0
#> 75 C 1 0
#> 76 C 2 [ 0, 3]
#> 77 C 5 [12, 25]
#> 78 C 2 [ 0, 3]
#> 79 C 1 0
#> 80 C 3 [ 3, 6]
#> 81 C 1 0
#> 82 C 4 [ 6, 12]
#> 83 C 2 [ 0, 3]
#> 84 C 2 [ 0, 3]
#> 85 C 2 [ 0, 3]
#> 86 C 4 [ 6, 12]
#> 87 C 2 [ 0, 3]
#> 88 C 3 [ 3, 6]
#> 89 C 2 [ 0, 3]
#> 90 C 2 [ 0, 3]
#> 91 D 5 [12, 25]
#> 92 D 5 [12, 25]
#> 93 D 4 [ 6, 12]
#> 94 D 5 [12, 25]
#> 95 D 5 [12, 25]
#> 96 D 6 [25, 50]
#> 97 D 6 [25, 50]
#> 98 D 4 [ 6, 12]
#> 99 D 6 [25, 50]
#> 100 D 4 [ 6, 12]
#> 101 D 3 [ 3, 6]
#> 102 D 5 [12, 25]
#> 103 D 5 [12, 25]
#> 104 D 6 [25, 50]
#> 105 D 4 [ 6, 12]
#> 106 D 6 [25, 50]
#> 107 D 5 [12, 25]
#> 108 D 6 [25, 50]
#> 109 D 5 [12, 25]
#> 110 D 4 [ 6, 12]
#> 111 D 5 [12, 25]
#> 112 D 5 [12, 25]
#> 113 D 5 [12, 25]
#> 114 D 3 [ 3, 6]
#> 115 D 5 [12, 25]
#> 116 D 6 [25, 50]
#> 117 D 5 [12, 25]
#> 118 D 5 [12, 25]
#> 119 D 5 [12, 25]
#> 120 D 6 [25, 50]
#>
#> $U.Score
#> # A tibble: 4 × 2
#> treatment score
#> <chr> <dbl>
#> 1 D -15.1
#> 2 B -4.54
#> 3 A 4.23
#> 4 C 15.4
#>
#> $Hypothesis.test
#> # A tibble: 3 × 4
#> treat1 treat2 `p-value for H0: treat1 <= treat2` p-value for H0: treat1 = tr…¹
#> <chr> <chr> <dbl> <lgl>
#> 1 D B 0.00188 NA
#> 2 B A 0.0113 NA
#> 3 A C 0.000746 NA
#> # ℹ abbreviated name: ¹`p-value for H0: treat1 = treat2`
#>
#> $adj.Signif
#> [1] 0.01666667
#>
#> $Conclusion
#> [1] "c(\"D>B\", \">A\", \">C\")"
#>